correlational research topics for stem students

ORIGINAL RESEARCH article

Potential factors to enhance students' stem college learning and career orientation.

• 1 The Department of Educational Psychology, Texas A&M University, College Station, TX, United States
• 2 The Department of Individual, Family, & Community Education, University of New Mexico, Albuquerque, NM, United States

In this study, we highlight the importance of high school students having a college-attending and career-ready mindset in STEM fields. With this purpose, we adopted a stepwise multiple regression analysis to determine which variables are significant predictors of students' STEM college learning and career orientation. The participants were 1,105 high school students from nine randomly selected high schools across greater Houston Texas. Forty-two percent of the variance on STEM college learning and career orientation as an outcome variable can be explained by six predictor variables: (a) parental involvement; (b) STEM related activities engagement; (c) academic experience; (d) teacher effective pedagogy; (e) technology/facilities; and (f) self-esteem. The results indicate that when students received support from teachers and parents, they could develop more positive attitudes toward future post-secondary education and career pathways in STEM fields.

Introduction

In 1986, the idea of Science, Technology, Engineering, and Math (STEM) education was first brought up to the public in a report named “Neal Report: Undergraduate Education Statement” by the National Science Board ( Prados, 1998 ). The National Science Foundation further suggested STEM education policy reform within K-12 education ( Fortenberry, 2005 ). In 2009, former President Barack Obama re-emphasized the importance of STEM education and invested more money in STEM teachers' professional development ( Johnson, 2012 ). In 2015, STEM education was incorporated into Every Student Succeeds Act (ESSA) signed by former President Obama ( Every Student Succeeds Act, 2015 ). The ESSA is the latest reauthorization of the Elementary and Secondary Education Act ( Every Student Succeeds Act, 2015 ). This reauthorization aims to enhance students' performance and interests in STEM education, to discover students' potential to be scientists, computer programmers, engineers, and mathematicians, as well as to enhance STEM teachers' teaching skills. For this reason, high school education emphasizes STEM curriculum and teacher professional development in STEM education, which will hopefully help enhance high school students' academic and career interests in STEM fields.

In this study, we highlighted the importance of high school students having a college-attending and career-ready mindset in STEM fields ( Conley, 2010 ; Radcliffe and Bos, 2013 ). According to the Center on Education Policy (2011) and the College Board (2011) , they suggested that developing the college-attending and career-ready mindset can enhance high school students' knowledge about their future-to-be (occupations) and their willingness to pursue a college degree. In addition, according to the Center on Education the Workforce (2013) , between 2013 and 2020 there will be 55 million job openings; 76% of these jobs will require the applicants to have post-secondary education attainment and achievement (e.g., vocational certificate, associate's degree, or bachelor's degree).

To enhance high school students' STEM college learning and career orientation, we have to think from their perspective so as to better understand what they need. Then we can address what their schools can do for these students. With this purpose, we wanted to discover what factors influence high school students' STEM college learning and career orientation.

Literature Review

Career decision is the biggest challenge for high school students in the process of college and career readiness. This decision will force students to choose what they will study in college and what practical trainings they want to take. However, career decision is an ongoing process, and this decision is influenced by individuals' ecologies such as school and home according to Lent et al.'s social cognitive career theory (1994). Social cognitive career theory emphasizes that individuals' self-efficacy influences their formation of educational and vocational interests, decision making in education and career, persistence in academic and occupational endeavors, as well as performance attainment ( Lent et al., 1994 ). Individuals' learning experiences influence their self-efficacy while individuals' learning experiences are influenced by person factors (e.g., gender and ethnicity) and background contextual factors (e.g., support system from school, home, or community). Social cognitive career theory was developed based on Bandura's social learning theory. Social learning theory emphasizes that an individual's beliefs, emotions, and thoughts are influencers of their behaviors ( Bandura, 1977 ). These behaviors in turn help predict patterns of an individual's beliefs, emotions, and thoughts. Environment influences an individual's beliefs and behaviors, while those beliefs and behaviors help predict in what environment an individual may choose to stay.

For high school students, they need to make their first career decision regarding educational and career plans before they graduate. Therefore, helping high school students to understand what their academic and vocational interests are and enhancing their interests are important aspects. The research literature indicates that positive awareness and aspiration toward education and career among high school students can be fostered and developed through improvements in the multiple learning environments in which students reside (e.g., home and school), as well as through the development of protective factors within those environments (e.g., parents in the home environment and teachers or mentors in the school environment) ( Wang and Staver, 2001 ; Gushue and Whitson, 2006 ; Kirdök, 2018 ).

The Role of Parents

Parents play a critical role in their children's educational and career paths and socialization ( Ginevra et al., 2015 ; Heddy and Sinatra, 2017 ; Niles and Harris-Bowlsbey, 2017 ). According to Sharf (2006) , children's relationship with their parents will influence what educational and career paths the children will take. When children make their educational and career decisions, they respect their parents' feedback as well as rely on emotional and financial support from their parents. Research indicates that parents' positive support such as encouragement and guidance would enhance children's self-determination on achieving educational goals ( Urdan et al., 2007 ; Ramsdal et al., 2015 ; Zhang et al., 2019 ) and career goals ( Urdan et al., 2007 ; Zhang et al., 2019 ). In addition, research indicates that if parents maintain positive attitudes about their children's educational and career endeavors, then children are more likely to actively continue their educational and career paths ( Zhang et al., 2019 ).

Regarding increasing students' STEM learning interests and career orientation, parents' constant involvement in their children's learning has been shown to be an effective factor ( Gottfried et al., 2016 ). According to Heddy and Sinatra (2017) , students' learning interest in science can be better maintained when their parents get more involved in the learning process. Furthermore, research corroborated that parental involvement is associated with students' learning performance in math ( Sheldon and Epstein, 2005 ).

The Role of Schools and Teachers

Students' academic and career paths can be affected or enhanced by schools and teachers. When high school students consider which academic or career path they would like to take, they rely on resources the school provides such as learning facilities ( Xie and Reider, 2014 ), college and career guidance ( Schwartz et al., 2016 ), as well as counseling service ( Schwartz et al., 2016 ). In addition, students get to know their academic and/or vocational interests better when schools provide educational activities such as college and career day, and learning exposition ( Zeng et al., 2018 ). Nugent et al. (2015) discovered that when students participate in STEM-related activities in informal learning environments, such as STEM summer camps, their STEM learning interests and career orientation are enhanced. These out-of-school STEM learning experiences could support and enhance students' STEM learning in classroom ( Nugent et al., 2015 ).

Research indicates that students develop more positive awareness and aspiration toward education and career when they receive teachers' support in the classroom learning environment ( Hurtado et al., 1996 ; Kao and Thompson, 2003 ; Lazarides and Watt, 2015 ) and parental involvement in their learning ( Chavira et al., 2016 ; Holmes et al., 2018 ). Dalgety and Coll (2006) investigated first-year college students' learning attitudes and self-efficacy regarding chemistry learning; they found that these students' previous learning experience and achievement in high school may be critical to their self-efficacy in college-level chemistry learning. Lee et al. (2008) further argued that teachers play an important role in the process by which students make educational or career decisions, as students' positive learning attitudes and achievements are affected by teachers' instructional contents, tools, and skills.

With the aforementioned purpose of this study and review of literature focusing contextual factors on high school students' educational and career paths, one research question is addressed in this study: from high school students' perspectives, what factors (e.g., parental engagement, academic experience, and teachers' effective pedagogy) will influence their STEM college learning and career orientation.

Our study adopted a mixed-method design. We collected quantitative data through a survey and qualitative data was collected through two focus group interviews. In this study, we primarily focused on the quantitative results; the qualitative results were used for supporting evidence through data triangulation.

Participants

The study was carried out in nine high schools across greater Houston, Texas. These nine schools were randomly selected to participate in the study (e.g., survey and focus group interviews) based on a list of high schools provided by one school district. The total of student participants was 1,540. Students who did not answer the survey completely were removed from the analysis. As a result, there were only 1,105 student participants in our study. Participants' distribution by grade level was 413 ninth grade, 324 tenth grade, 206 eleventh grade, and 162 twelfth grade students. There were 529 male students and 576 female students. The age range for participants was 14 years old to 17 years old (mean = 15.2). Regarding the focus group interviews, three students from each grade were randomly chosen for a total of 12 students (two focus group interviews).

Survey Instrument

A bilingual survey (Spanish/English) was developed for students. The survey was mainly designed to gather (a) basic background information, (b) systematic information on classroom/home teaching/learning environments, (c) systematic information on resources in the home learning environment, and (d) beliefs and attitudes toward STEM education and STEM careers and degrees. The survey contained nine constructs. These constructs were: (a) STEM related activity engagement; (b) STEM college learning and career orientation; (c) teacher support; (d) school support; (e) self-esteem; (f) parental involvement; (g) teachers' effective pedagogy; (h) safety and behavior at school; and (i) technology-assisted learning. There were 47 closed items with a six-point Likert scale. Each survey item offered one of two types of answer choices for the students. The first type of choice was the disagree-agree type (strongly disagree = 1; disagree = 2; slightly disagree = 3; slightly agree = 4; agree = 5; and strongly agree = 6). The second type of choice was the frequency type (never = 1; seldom = 2; sometimes = 3; frequently = 4; usually = 5; always = 6).

Examples of survey items and the Cronbach's Alpha values for each construct are provided below:

a. STEM Related Activity Engagement (Cronbach Alpha =0.7/5 items):

In my STEM classes, I work with other students on projects during class and after school (disagree-agree choice).

b. STEM College Learning and Career Orientation (Cronbach Alpha =0.75/5 items):

If I perform well in the STEM subjects, it will lead me to a great college or a great job in STEM fields (disagree-agree choice).

c. Teacher Support (Cronbach Alpha =0.86/5 items):

My STEM teachers mentor me effectively in preparation for my STEM projects (frequency choice).

d. School Support (Cronbach Alpha =0.75/5 items):

A guidance counselor at school has given me advice on how to get into a college or career in STEM fields after graduation (disagree-agree choice).

e. Self-efficacy (Cronbach Alpha =0.82/5 items):

I am confident I can produce high quality work in my STEM classes (disagree-agree choice).

f. Parental Involvement (Cronbach Alpha =0.73/5 items):

My parents support my attending STEM related activities at school (frequency choice).

g. Teachers' Effective Pedagogy (Cronbach Alpha =0.9/7 items):

My STEM teacher uses open-ended or guided questions to help us deeply understand the idea behind the STEM curriculum (frequency choice).

h. Safety and Behavior at School (Cronbach Alpha =0.81/5 items):

Discipline is fairly enforced at school (disagree-agree choice).

i. Technology-Assisted Learning (Cronbach Alpha =0.88/5 items):

The computers and equipment available to students for STEM projects and labs are up to date (disagree-agree choice).

Survey Implementation

The procedure for survey implementation involved three steps including (1) survey development, (2) survey piloting, and (3) survey implementation.

Step 1: For the development of the survey, we examined literature on: (a) home learning environment research (e.g., Peterson et al., 2005 ; Urdan et al., 2007 ; Sad and Gurbuzturk, 2013 ; Ramsdal et al., 2015 ); (b) parental involvement (e.g., Chavira et al., 2016 ; Holmes et al., 2018 ); (c) effective teaching practices in STEM programs (e.g., National Research Council., 2011 ; Bruce-Davis et al., 2014 ); and (d) STEM classroom learning environment research (e.g., Smith et al., 2009 ; Denson et al., 2015 ). Examining these studies helped us better understand areas of focus for the survey. In addition, we examined literature on College and Career Readiness Standards (e.g., American Institutes for Research, 2014 ; Neri et al., 2016 ), as well as literature on STEM Program Development ( Lara-Alecio et al., 2012 ; Kim, 2016 ; Mupira and Ramnarain, 2018 ). By further examining these studies, we could develop items addressing educational experiences in home and classroom environments as viewed and experienced by the students during home and/or classroom activities.

Step 2: This step involved the piloting of the survey with two focus groups, one in Spanish and one in English, in an effort to do the final calibration of the instrument with high school students from ninth through twelfth grades. These focus groups assisted us by addressing any language ambiguity and/or revising poorly written items across all surveys.

Step 3: Upon obtaining all signed consent forms from the students and permission forms from their parents, the online survey was implemented. Students could choose an English or Spanish survey to answer. Students were led by teachers to a computer lab where they took the online survey. Regarding the implementation of the survey, a survey protocol designed by the researchers was given to the teachers. The average time for survey completion by participants ranged between 12 and 15 min.

Survey Analysis

A stepwise multiple regression analysis was used to determine which variables are significant predictors of an outcome variable. In our analysis, we used STEM college learning and career orientation as an outcome variable with the other eight constructs as predictor variables: (a) STEM related activity engagement; (b) teacher support; (c) school support; (d) self-esteem; (e) parental involvement; (f) teachers' effective pedagogy; (g) safety and behavior at school; and (h) technology-assisted learning. The variables that were selected in our multiple regression model were potent factors to predict the outcome variable (STEM college learning and career orientation). According to Larson-Hall (2016) , significant factors included in the model have independent power to affect the outcome variable. In a stepwise multiple regression, “the choice of which factor is entered first is based on the strength of the correlation” ( Larson-Hall, 2016 , p. 240). In addition, a series of moderator analysis was conducted to determine if a relationship between two variables is moderated by a third variable. Figures 1 – 4 show the moderator analyses that we conducted. For example, Figure 1 illustrates if a relationship between students' “STEM related activity engagement” and “STEM college learning and career orientation” could be moderated by parental involvement.

Figure 1 . The relationships between parental involvement, STEM activity engagement, and STEM college learning and career orientation by using a moderator analysis. * p < 0.05.

Interview Questions for the Focus Group

With the preliminary results, six topics were developed to align to six significant predictors: (a) parental involvement; (b) STEM related activity engagement; (c) teacher support; (d) STEM teacher effective pedagogy; (e) technology-assisted learning; and (f) self-efficacy. There were one or two open-ended questions under each predictor, with a total of 10 questions. For example, under the topic of parental involvement, one of the questions was “In your view, what are the ways in which your school and teachers can get your parents involved in your STEM education and career readiness?” Under the topic of teacher support, one of the questions was “In your view, what are some of the key steps that STEM teachers need to take if they want students to become resilience (or persevere) in STEM? What do they need to do to get you college ready?”

Interview Implementation

Each of the interview sessions lasted 1.5 h. Each session included an explanatory introduction, interview questions, and a closing statement. During the session, all students were required to give their most considerate answer to all of the 10 interview questions. The 12 students in this focus group all agreed to audio recording of the sessions; they consented to allow that their quotes could be included in this study anonymously.

Several quotes by students were provided in the discussion section to support our survey findings. These quotes represented the overall thinking of the students in the focus group. To increase the reliability of findings from the interview, we invited one researcher to review the results and quotes. This researcher has worked in the field of education for over 5 years; her research expertise is mixed methods research and parental involvement. An additional researcher would “arrive at similar findings from the data” ( Rafuls and Moon, 1996 , p. 77).

SPSS Version 20 was used to examine the survey data. As stated in the method section, there were nine constructs on our survey, with a combined total of 47 items. These constructs were found to be highly reliable, with reliability coefficients ranging from 0.7 to 0.9 (mean = 0.8). As mentioned above, a stepwise multiple regression analysis was used to examine eight predictor variables with students' STEM college learning and career orientation specified as an outcome variable. These eight predictor variables considered in the equation were: (a) STEM related activity engagement; (b) teacher support; (c) school support; (d) self-efficacy; (e) parental involvement; (f) teachers' effective pedagogy; (g) safety and behavior at school; and (h) technology-assisted learning. Six significant predictor variables (factors) were identified in a stepwise multiple regression model: (a) parental involvement; (b) STEM related activities engagement; (c) academic experience; (d) teacher effective pedagogy; (e) technology-assisted learning; and (f) self-efficacy. A multiple R of 0.65 was obtained, accounting for 42% (adjusted) of the variance (See Table 1 ), suggesting that these six factors helped explained 42% of variance in students' STEM learning and career orientation. Table 1 shows that these six identified predictors independently affect students' STEM college learning and career orientation; parental involvement has the strongest correlation with students' STEM college learning and career orientation.

Table 1 . Multiple regression analysis of STEM college learning and career orientation as an outcome variable.

The purpose of this study was to discover from students' perspectives what factors may influence their STEM college learning and career orientation. The results showed that 42% of the variance on STEM college learning and career orientation can be explained by six predictors that include: (a) parental involvement; (b) STEM related activity engagement; (c) teacher support; (d) STEM teacher effective pedagogy; (e) technology-assisted learning; and (f) self-efficacy. This overall finding indicates that students' physical and psychosocial learning environments should elevate their beliefs and behaviors in STEM learning, which would later help predict their future STEM college and career orientation. This indication is supported by Lent et al.'s social cognitive career theory (1994). The overall finding also indicates that students' physical and psychosocial learning environments should leverage their self-efficacy, which would help enhance their educational and career interests in STEM and persistence in academic and occupational endeavors. This indication is supported by Bandura's social learning theory (1977).

The results first revealed that parental involvement accounted for 28% of the variance in students' STEM college learning and career orientation, and that parental involvement had a significantly positive and moderate correlation with STEM college learning and career orientation. These findings indicate that if parents get more involved in their children's STEM learning, their children would be more determined and positive about their post-secondary education and career orientation in STEM fields. When parents get involved in their children's learning activities, they should be supportive and provide positive feedback to their children. When parents give encouragement, share expectation, and present positive attitudes, their children's academic and vocational interests can be enhanced ( Urdan et al., 2007 ; Zhang et al., 2019 ). When communicating with their children about academic and career decisions, parents are suggested to maintain a reciprocal conversation with their children, to help the children understand their strengths, and to work with the children to help them analyze potential pros and cons of their decisions about their future. Meanwhile, in the conversation, parents should look at their children's behaviors, emotions, and cognitions (e.g., thinking process) from the view of the children instead of from the view of the parents alone. According to Lent et al. (2000) , parents' disapproval can draw children away from their original career choice and may hinder their career progress. We further analyzed: (a) the relationship between parental involvement, students' STEM related activity engagement, and students' STEM college learning and career orientation (see Figure 1 ); and (b) the relationship between teacher support, parental involvement, and students' STEM related activity engagement (see Figure 2 ). With the results in Figure 1 , we found that from students' perspectives, parental involvement could positively moderate the relationship between their STEM related activity engagement and STEM careers and degrees. With the results in Figure 2 , we found that to enhance the relationship between parental involvement and students' STEM related activity engagement, teacher support plays a significantly critical role.

Figure 2 . The relationships between teacher support, parental involvement, and STEM college learning and career orientation by using a moderator analysis. * p < 0.05.

Second, the results revealed that by engaging in more STEM related activities , students would feel more positive about their future post-secondary education and career orientation in STEM fields. To enhance students' STEM college learning and career orientation, STEM teachers are strongly suggested to provide their students with activities aligned to the students' academic interests and learning needs. Schools are suggested to develop and offer STEM-related activities or practicum to students for enhancing the students' educational and vocational interests in STEM. The practicum aims to give students opportunities to apply STEM theories and knowledge into real-life practice. Through participation in the practicum, students' STEM knowledge, skills and abilities can be enhanced in a sustained way. In the practicum, students will be able to communicate with teachers, peers, and professionals. Through educational communication and hands-on experience, students can integrate their theoretical knowledge and real-world practice, and their academic and vocational interests in STEM fields can be enhanced ( Malin and Hackmann, 2017 ). With the results in Figure 1 , to enhance students' STEM related activity engagement which could further enhance their STEM careers and degrees, we suggest that teachers help parents increase their level of involvement in their children's STEM learning. Additionally, teachers should work with schools to provide parents with capacity building activity so that parents can learn how to effectively engage in the education of their children. The goals of these activities are to enhance communication and collaboration between parents, students, and teachers, to optimize positive impacts on students' STEM college learning and career orientation.

Third, the results revealed that STEM teachers' support in students' STEM learning accounted for enhancing students' future STEM college learning and career orientation. We further analyzed the relationship between teachers' support, students' STEM related activity engagement, and STEM college learning and career orientation (see Figure 3 ). We found that from students' perspective, teachers' support could positively moderate the relationship between students' STEM related activity engagement and STEM college learning and career orientation. Teachers' support could also enhance students' STEM related activity engagement, which could further enhance their STEM college learning and career orientation. These findings are consistent with previous studies which found that when students received support from their teachers ( Walker et al., 2004 ), the students could develop more positive attitudes, which later may influence their perspectives about future STEM activity engagement and post-secondary education pathways. To develop or enhance students' educational and vocational interests in STEM fields, teachers are encouraged to maintain a mentoring/apprenticeship program to give students guidance and assistance in STEM learning. More specifically, this program is to assist students with understanding real-world practices in STEM fields and effective ways to interact with professionals. Teachers should consider providing a 2-h window in their weekly schedule for their students to walk in for discussion and consultation; the aims of this discussion would be (a) to help the students solve their challenges in learning and life, (b) to enhance the students' learning interests, and (c) to assist the students with monitoring their learning growth and finishing their study in high school. Teachers are encouraged to help students develop future educational and career paths, and help the students get involved in community service. For example, teachers can to develop and participate in activities involving all their students (e.g., field trips and career talks by professionals).

Figure 3 . The relationships between Teacher Support, STEM activity engagement, and STEM college learning, and career orientation by using a moderator analysis. * p < 0.05.

Fourth, the results revealed that STEM teachers' teaching effective pedagogy could affect students' STEM college learning and career orientation. Regarding how teachers can enhance students' post-secondary education and career in STEM fields, teachers can modify their lesson plan by incorporating Trowbridge and Bybee's 5E model ( Trowbridge and Bybee, 1996 ; Bybee et al., 2006 ): Engagement, Exploration, Explanation, Elaboration, and Evaluation. Ample evidence has shown the effect of 5E model on enhancing students' STEM academic performance ( Lara-Alecio et al., 2012 ; Kim, 2016 ; Mupira and Ramnarain, 2018 ). To help strengthen students' STEM interests, Burke (2014) suggested to add “Enrichment” to the model. To pay attention to each individual's learning background and progress, teachers are encouraged to use differentiated instruction ( LaForce et al., 2016 ). According to Tomlinson (2001) , teachers can focus on adjusting lesson content, lesson process, and lesson product.

Fifth, the results revealed that students' perceptions about classroom technology and facilities could influence their STEM college learning and career orientation. To enhance students' STEM college learning and career orientation, STEM teachers are advised to maintain a technology-assisted learning environment by working with school administrators ( Hawkins et al., 2017 ). Students' learning is enhanced due to the multiple learning functions and interactive learning environments provided by using technology in the classroom. Some researchers (e.g., Hsu et al., 2015 ; Kaniawati et al., 2016 ) found computer-assisted or multimedia-assisted learning is more effective to facilitate students' STEM content knowledge learning when compared with traditional classroom learning. This is because the computer-assisted learning environment creates an opportunity for students to easily monitor their learning process and adjust their learning when they make mistakes ( Hsu et al., 2015 ). In addition, a computer-assisted learning environment helps students gain some additional skills such as learning autonomy and computer literacy ( Cerezo et al., 2014 ).

Sixth, the results showed that students' self-efficacy would help enhance their STEM college learning and career orientation. A student's self-efficacy is developed based on his/her previous learning experience, performance, and attitudes that can be directly influenced by teachers ( Dalgety and Coll, 2006 ). To enhance high school students' self-efficacy, teachers are suggested to assist their students with goal-setting and goal achievement. Students with higher efficacy have higher goal commitment, and they are more likely to achieve their goals ( Wilson and Narayan, 2016 ). According to Gist and Mitchell (1992) and Peterson (1993) , self-efficacy manifests itself in successful completion of designated tasks. Our results further showed that students with higher self-efficacy believed more strongly that they could successfully finish STEM-related hands-on tasks and assignments ( r = 0.90). We further analyzed the relationship between students' self-efficacy, STEM related activity engagement, and STEM college learning and career orientation (see Figure 4 ). We found that from students' perspectives, their STEM related activity engagement could positively moderate the relationship between their self-efficacy and STEM college learning and career orientation. These students' engagement in STEM related activities could enhance their self-efficacy, which could further enhance their STEM college learning and career orientation. With these findings, we suggest that to enhance students' self-efficacy, teachers should provide their students with more resources and opportunities to engage in STEM-related hands-on activities.

Figure 4 . The relationships between STEM activity engagement, students' self-efficacy, and STEM college learning and career orientation by using a moderator analysis. * p < 0.05.

Finally, in order to continue building resilience in students, schools are strongly encouraged to continue increasing efforts that are clearly connected to teacher professional development and parental capacity building; these are key protective factors that can build and support students' resilience. From our qualitative results, we found that students valued how teachers can inspire them to try and attain a college degree or career in STEM fields.

“ I feel like the STEM program gives us opportunities……and it's like one on one, the teacher and the student, and it really gives us more opportunities to put our learned knowledge into practice.”

“ The STEM program allows us to explore different aspects of different fields……and to have us immersed into real-life situations.”

Students were cognizant of the fact that some teachers should not only bring real-life situations to STEM classrooms, but should also help identify how students can use different strategies to solve real-world problems. Additionally, teachers should invite a guest speaker to share with students how they can solve these problems in practical ways.

“ I do think that teachers can help us focus more on real-world problems and guide us how we can solve these problems in different ways….STEM teachers should have someone……someone who's really like an expert in the field……if we can seek this kind of person in the field, it can help us understand and solve the real-world problems in a more practical way.”

“ Some of STEM teachers……like math and science……just teach us content knowledge……we need to know some practical skills to cope with real-world problems……we expect teachers to give us not only the knowledge but also practical skills……give us some examples of how these skills are, what these skills look like.”

Regarding parents, the students wanted their parents to get more involved in their learning and to work with teachers to help enhance their learning performance and interests.

“ I feel like my parents do not pay attention to my learning process, but my grades instead……focusing on my grades is fine, but not the way they sit down with me and help my school work. I hope my parents could get more involved in school activities……it's important and they should be involved in the school, because they can get to know our teachers and understand how they can help us meet teachers' and school's expectations……teachers can also know how my parents think about my……STEM education.”

“ I feel like parents should always encourage us on our learning performance, not criticize. They should not give us too much instructional criticism……but should help us be more focused on our learning process.”

To follow up on other studies emanating from the social cognitive career theory framework (e.g., Lent et al., 2008 ; Nugent et al., 2015 ; Gottfried et al., 2016 ; Zhang et al., 2019 ), we operationalized relevant variables focusing on high school students as our target population. The results of our study helped us to better understand that the interplay of socio-contextual, motivational, and instructional factors operating within learning environments can impact high school students' future STEM college learning and career orientation.

Our results revealed that to develop or enhance high school students' STEM college learning and career orientation, we should pay attention to their parental involvement, STEM related activity engagement, teacher support, STEM teacher effective pedagogy, technology-assisted learning, and self-efficacy. To develop and enhance high-school-aged children's STEM college learning and career orientation, parents are suggested get actively involved in their children's STEM learning. To sustain their STEM college learning and career orientation, parents should provide constant support and encouragement to their children in STEM learning. When developing and enhancing high school students' STEM college learning and career orientation, teachers should understand: (a) how each individual student may have different learning needs; (b) how to adapt instructional strategies and lesson materials to align to students' needs; (c) how to create interactive lessons using electronic learning materials; and (d) what learning resources to provide for enhancing their students' learning interests in STEM. Schools should provide students more educational and vocational STEM-related activities to further develop their STEM college learning and career orientation, as well as to put learned STEM knowledge into real-life practice. We encourage that parents, teachers, and schools work together to hopefully have a more positive impact on high school students' educational and career decisions in STEM fields.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Ozgur Ozer (Harmony Public Schools). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

HR contributions are school connection, data collection, and manuscript revising. J-TL contributions are data collection, data analysis, manuscript drafting, and manuscript revising.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: high school, STEM—science technology engineering mathematics, college readiness, career decision, parent involvement

Citation: Rivera H and Li J-T (2020) Potential Factors to Enhance Students' STEM College Learning and Career Orientation. Front. Educ. 5:25. doi: 10.3389/feduc.2020.00025

Received: 04 October 2019; Accepted: 09 March 2020; Published: 16 April 2020.

Reviewed by:

Copyright © 2020 Rivera and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jui-Teng Li, juitengli@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Interest and Confidence in Mathematics and Science: Precursors in Choosing the STEM Strand

81 Pages Posted: 25 Apr 2019

Aiza Dumapias

Victor t. tabuzo, independent.

Date Written: March 19, 2018

The Philippines has just recently shifted from a 10-year basic education to 12 years of basic education known as the K to 12 Program. In this new curriculum, students get to choose a track of their interest in the Senior High School (SHS) and one of these is the Academic Track with the Science, Technology, Engineering and Mathematics (STEM) strand. In the first two years of implementation of the said program, STEM has recorded a significantly low enrollment. This was the main problem of this study. This study looked at the interest of the students in Mathematics and Science and correlated with their interest in pursuing the STEM strand. The descriptive correlational research was employed with the use of survey questionnaire. Data obtained was interpreted using the weighted mean, sum of ranks, and Pearson-r correlation coefficient. Results revealed that the respondents were interested in the subjects Mathematics and Science. They were confident in their scientific ability but only slightly confident in mathematical abilities. There was a significant, moderately high relationship between the interest of the respondents in Mathematics and Science and in their confidence level in Mathematics and Science. The very high relationships between the interest and confidence in Mathematics, and interest and confidence in Science were also significant. Teacher influence registered to be the most important factor affecting their interest in Mathematics and Science while Family influence affects their confidence. The respondents also showed interest in the STEM strand. Lastly, the relationship between their interest and confidence in Mathematics and Science and their interest in pursuing the STEM strand was moderately high and significant.

Keywords: interest, confidence in Math and Science

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• Published: 10 September 2024

Depression, anxiety, and student satisfaction with university life among college students: a cross-lagged study

• Xinqiao Liu   ORCID: orcid.org/0000-0001-6620-4119 1 &
• Jingxuan Wang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1172 ( 2024 ) Cite this article

Metrics details

Previous studies have shown that a high prevalence of depression and anxiety is a key factor leading to a decrease in student satisfaction with university life. Therefore, this study used two waves of longitudinal data to investigate the longitudinal relationships among depression, anxiety, and student satisfaction with university life among college students. We employed correlation analysis and cross-lagged models to analyze the correlation and cross-lagged relationships among depression, anxiety, and student satisfaction with university life. The results indicate a significant negative correlation between depression and student satisfaction with university life. The cross-lagged models indicate that depression (Time 1) negatively predicts student satisfaction with university life (Time 2). Anxiety (Time 1) does not have a significant predictive effect on student satisfaction with university life (Time 2). Moreover, student satisfaction with university life negatively predicts both depression (Time 2) and anxiety (Time 2). Improving student satisfaction with university life has a significant impact on reducing levels of depression and anxiety among college students. The research results can provide valuable information for mental health professionals, school administrators, and policymakers, enabling them to take more targeted measures to reduce depression and anxiety symptoms among university students and enhance student satisfaction with university life.

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Introduction.

According to a survey report by the World Health Organization (WHO), one out of every eight individuals worldwide suffers from mental health problems. Addressing mental health problems promptly is imperative. Depression and anxiety are the most common mental health problems (Ghahramani et al., 2023 ; Hall et al., 2023 ; Ten Have et al., 2023 ), with over 280 million people diagnosed with depression and 301 million people suffering from anxiety.

College students in the transitional stage of life are more likely to experience mental health problems such as depression and anxiety (Denovan & Macaskill, 2017 ; Basri et al., 2022 ; Ooi et al., 2022 ). Research from different countries shows that the number of students suffering from depression and anxiety is increasing (Grineski et al., 2024 ; Xiao et al., 2022 ). As crucial pillars for future economic and social development, depression and anxiety among university students severely affect their early adulthood development during their college years, disrupting their daily lives and leading to poor emotional experiences, academic performance, insomnia, dropping out, and even suicidal tendencies (Floyd et al., 2007 ; Buchanan, 2012 ; Deng & Zhang, 2023 ). Therefore, researching the predictive factors of depression and anxiety among university students is of significant practical importance for the prevention and intervention of their mental health.

School is an important place for students’ mental health development. Currently, due to the high prevalence of depression and anxiety, there has been a significant reduction in student satisfaction with university life (Renshaw & Cohen, 2014 ; Lukaschek et al., 2017 ). Student satisfaction with university life is also an important indicator of the physical and mental well-being of university students (Headey et al., 1993 ; Irie & Yokomitsu, 2019 ). It refers to students’ perceptions and evaluations of the overall campus environment during their university experience (Astin, 1997 ). A supportive and positive campus environment not only enhances university students’ satisfaction with university life but also benefits their psychological well-being (Tan et al., 2020 ; Wang & Liu, 2024 ). It helps students develop a positive self-perception, contributing to their overall success (Ahn & Davis, 2020 ). Additionally, for universities, student satisfaction with university life impacts the educational quality and reputation of the institution, which are closely related to overall university development (Elsharnouby, 2015 ). Focusing on student needs, establishing a positive campus environment, and using student satisfaction assessments to guide future research directions are fundamental factors for the success of institutions. (Kanwar & Sanjeeva, 2022 ). Therefore, studying the relationship between depression, anxiety, and student satisfaction with university life not only helps universities maintain students’ psychological well-being but also contributes to the high-quality development of the institutions themselves.

Based on large-sample longitudinal data, this study employs a cross-lagged model to explore the longitudinal correlation among depression, anxiety, and student satisfaction with university life. It provides research evidence in the context of Chinese education to reduce negative emotions such as depression and anxiety among university students and enhance their life satisfaction during their academic study period. This is highly important for maintaining the psychological well-being of Chinese university students and promoting the high-quality development of higher education in China.

This study is based on existing literature and proposes the following three contributions: First, this study is one of the few that explores the relationship between depression, anxiety, and student satisfaction with university life among Chinese university students using large-sample longitudinal data. Second, this research innovatively uses a customer satisfaction model to explain the relationship between depression, anxiety, and student satisfaction with university life. Third, the findings not only supplement the academic understanding of the complex relationship between anxiety and student satisfaction with university life but also deepen the understanding of the predictive relationships among depression, anxiety, and student satisfaction with university life.

Literature review

The relationship between depression, anxiety, and student satisfaction with university life.

Depression is a mood disorder characterized by persistent feelings of sadness, hopelessness, and a loss of interest or pleasure in most activities (Daros et al., 2021 ). Anxiety is an emotional state characterized by heightened worry in response to ambiguous or perceived threats. It is divided into state anxiety and trait anxiety (Leal et al., 2017 ; Shamir & Shamir Balderman, 2024 ). However, in this study, we adopt a unified conceptualization of anxiety and no longer distinguish between the two types. Both depression and anxiety significantly impact students’ perceptions of their own quality of life and well-being. Existing studies have shown a close relationship among depression, anxiety, and student satisfaction with university life. Many scholars believe that depression and anxiety are negatively correlated with student satisfaction with university life (Paschali & Tsitsas, 2010 ; Hajduk et al., 2019 ; Li et al., 2021 ; Liu et al., 2023b ). The greater the levels of depression and anxiety are, the lower the evaluation of student satisfaction with university life. Low student satisfaction with university life can also have negative impacts on students’ well-being, with high levels of negativity being key symptoms of depression and indicative of anxiety (Garber & Weersing, 2010 ; King & dela Rosa, 2019 ). However, the relationship between anxiety and student satisfaction with university life has yielded contrasting conclusions in recent research. Some scholars argue that there is a strong negative correlation between student satisfaction with university life and anxiety, especially during the COVID-19 period, when students experience high levels of anxiety that lower their satisfaction with university life (Duong, 2021 ; Sahin & Tuna, 2022 ). On the other hand, Esteban’s research suggested a positive correlation between anxiety and student satisfaction with university life. Some students exhibit high levels of positive emotions and constructive thinking and display positive self-perception, interpersonal relationships, and life goals. These students can regulate the alertness emotions generated by anxiety through positive psychological functions (Esteban et al., 2022 ). Further investigation is needed to explore the negative correlation between anxiety and student satisfaction with university life. Additionally, factors such as age (Khesht-Masjedi et al., 2019 ), gender (Gigantesco et al., 2019 ), personality (Hong & Giannakopoulos, 1994 ), family status (Shao et al., 2020 ), and other factors can also affect the relationships among depression, anxiety, and student satisfaction with university life. To date, there have been numerous studies on the relationships among depression, anxiety, and student satisfaction with university life, but the findings have been contradictory, necessitating further research to better understand these conflicting findings and the relationships among depression, anxiety, and student satisfaction with university life. Based on the aforementioned research, we propose Hypothesis I.

Hypothesis I: Depression and anxiety among college students are negatively correlated with their satisfaction with university life.

The predictive relationship of depression and anxiety on college satisfaction

Research on the longitudinal relationships among depression, anxiety, and student satisfaction with university life is limited. Regarding the predictive relationship between depression and student satisfaction with university life, a cross-sectional study involving Malaysian university students revealed that depression negatively predicted student satisfaction with university life (Ooi et al., 2022 ). Studies conducted on Australian adults have shown that depression is a significant predictor of life satisfaction, with its influence even surpassing that of variables such as religious beliefs, psychological reactions, and age (Headey et al., 1993 ). When exploring the predictive relationship between anxiety and student satisfaction with university life, scholars believe that anxiety is an emotional consequence of persistent negative thoughts (LeDoux, 2000 ). Individuals with anxiety disorders tend to engage in negative persistent thinking, which negatively predicts life satisfaction (Skalski‐Bednarz et al., 2024 ). Research has shown that depression and anxiety are negative predictors of student satisfaction with university life (Almeida et al., 2021 ), with individuals experiencing depression and anxiety more likely to have issues with lower satisfaction with university life (Tang et al., 2023 ). This is because depression and anxiety can influence individuals’ attitudes and coping mechanisms, leading them to engage in self-blame, denial, and self-distraction behaviors, thereby triggering maladaptive emotional regulatory mechanisms. Particularly for lower-level students, the depressive and anxious emotions experienced upon entering university may have long-term effects on changes in student satisfaction with university life in the future (Denovan & Macaskill, 2017 ). However, some studies have shown that anxiety is not a significant predictor of student satisfaction with university life, indicating that anxious individuals may overcome their anxiety and still experience meaningful and satisfying experiences in their life (Oladipo et al., 2013 ). Similarly, a study focusing on teachers found that anxiety is not a statistically significant predictor of job satisfaction among teachers (Ferguson et al., 2012 ). The longitudinal relationship between anxiety and student satisfaction with university life remains worthy of discussion. Notably, external factors such as interpersonal relationships and parental and peer support play a moderating role in the longitudinal relationships among depression, anxiety, and student satisfaction with university life (Liem et al., 2010 ; Ooi et al., 2022 ). Positive relationships and peer support can mitigate the impact of depression and anxiety on student satisfaction with university life. Based on previous research, we propose Hypothesis II.

Hypothesis II: Depression and anxiety among college students can negatively predict their satisfaction with university life.

The predictive relationship of college satisfaction on depression and anxiety

Some studies have also explored the predictive effect of student satisfaction with university life on depression and anxiety. Research from different countries has shown that in Jordanian university students, student satisfaction with university life is the best predictor of depressive symptoms (Zawawi & Hamaideh, 2009 ). A cross-sectional study involving South Korean university students revealed that increasing student life satisfaction can prevent depression (Seo et al., 2018 ). Additionally, through SEMs, scholars studying the mental health status of Peruvian university students during the pandemic found that satisfaction negatively predicts depression (Esteban et al., 2022 ). Previous research has already established that satisfaction with university life negatively predicts depression. This suggests that when students’ expectations do not align with reality at the college level, leading to lower student satisfaction with university life, students are more likely to adopt negative coping mechanisms in daily life, which may exacerbate the onset of depression. Thus, enhancing student satisfaction with university life is crucial for preventing depression. Despite the abundance of research on the predictive relationship between student satisfaction with university life and depression, only a few studies have examined the predictive relationship between student satisfaction with university life and anxiety. A cross-sectional study on the mental health of health science students showed that student satisfaction with university life strongly predicted depression and anxiety (Franzen et al., 2021 ). From a social psychological, behavioral, and cognitive perspective, students with low satisfaction with university life are more prone to negative thinking, which is the strongest predictor of anxiety (Mahmoud et al., 2015 ). Therefore, increasing satisfaction with university life to reduce negative thinking is vital in helping students manage anxiety. Based on previous research, we propose Hypothesis III.

Hypothesis III: College student satisfaction with university life can negatively predict depression and anxiety.

Theoretical framework and research objectives

Theoretical framework.

The customer satisfaction model has been widely used in research on satisfaction with university life (Naidoo & Whitty, 2014 ; Calma & Dickson-Deane, 2020 ; Khatri & Duggal, 2022 ). With the complexity and marketization of higher education, students are not only learners but also consumers (Nixon et al., 2018 ), known as “students as consumers” or “students as customers” (Tight, 2013 ). The customer satisfaction model (Cardozo, 1965 ) is a theoretical model used to measure consumers’ satisfaction with products or services, emphasizing the difference between customer expectations and actual experiences, leading to changes in customer satisfaction with products or services. Indeed, relatively few studies have incorporated depression and anxiety into customer satisfaction models. During their university years, students are consumers of educational services, and under the influence of depression and anxiety, they may feel dissatisfied with their daily academic life. They may have lower satisfaction with the services provided by the school, such as the teaching environment, learning facilities, and faculty strength, as the actual learning experience does not meet their expectations. This results in lower satisfaction with university life, which, as a form of negative thinking, can further exacerbate the severity of depression and anxiety (Franzen et al., 2021 ; Mahmoud et al., 2015 ). This not only affects the mental and physical health of college students but also impacts the quality of higher education and hinders societal development. Therefore, studies on the relationships among depression, anxiety, and student satisfaction with university life are urgently needed.

Research objectives

Although the literature has explored the relationships among depression, anxiety, and student satisfaction with university life, there are still several research gaps. First, there is a lack of large-scale studies with Chinese college students as samples, leading to insufficient long-term investigations into the relationships among depression, anxiety, and student satisfaction with university life among Chinese students. Second, most existing studies have utilized cross-sectional research designs, focusing solely on the relationships between variables within specific time frames, without adequately capturing the longitudinal dynamics of these variables. Additionally, research on the longitudinal relationships among these variables has mostly remained at the theoretical or conceptual level, with limited empirical studies examining these relationships. Third, although the customer satisfaction model has been widely applied in studies on student satisfaction with university life, there has been limited research incorporating depression and anxiety into the model to analyze satisfaction. Fourth, conflicting findings exist regarding the relationship between anxiety and student satisfaction with university life, necessitating a more systematic exploration of this relationship.

This study utilizes large-scale longitudinal survey data and employs a cross-lagged model to capture the dynamic changes in depression, anxiety, and student satisfaction with university life over time. This study innovatively adopts the customer satisfaction model as the theoretical basis to further elucidate the underlying logic of the relationships among these variables (see Fig. 1 ). Compared to previous cross-sectional studies, employing a cross-lagged model to investigate the longitudinal relationships between variables is more persuasive and helps address the limitations of past research. To ensure the robustness of the research findings, gender, age, extroversion, and family social status are included as control variables based on the literature, enhancing the credibility of the results. This study not only enriches the literature on the longitudinal relationships among depression, anxiety, and student satisfaction with university life but also contributes to understanding the sample characteristics in China. Moreover, it holds significance for shaping the healthy personality and social psychological development of college students, reducing the prevalence of mental health issues among students, enhancing student satisfaction with university life, optimizing student management practices, and improving the educational management system in higher education institutions.

The negative sign in parentheses indicates a negative correlation or predictive relationship.

Participants

This study selected Chinese college students as the research participants and used a self-report questionnaire for data collection. The data of a total of 2298 participants were collected at T1. Follow-up data were collected after one year, and 2070 participants were tracked at T2. The sample grade at T1 was junior, and the sample grade at T2 was senior. The age of the participants was 18–28 years (M = 21.550, SD = 0.895). We used the t test to test the key characteristic variables of the sample (gender, age, depression score, anxiety score, student satisfaction with university life score, etc.), and missing scores were identified as missing completely at random. Multiple studies using the same dataset have indicated high data reliability (Cao & Liu, 2024 ; Liu et al., 2024a ; Liu et al., 2024b ; Liu et al., 2024c ; Liu et al., 2024d ). All students voluntarily participated in this study and signed an informed consent form before the study.

Depression was measured using the DASS-42 scale, which contains 14 items (Lovibond & Lovibond, 1995 ). Each topic was evaluated on a 4-point scale ranging from 0 (“not applicable at all”) to 3 (“very applicable or most applicable”). The score was calculated by adding the scores of related items. Students evaluated the 14 questions according to their personal feelings. According to the definition of the DASS-42, a degree of depression between 0 and 9 points was rated as “normal”. A degree of depression between 10 and 13 points was considered mild, a degree of depression between 14 and 20 points was considered moderate, a degree of depression between 21 and 27 points was considered severe, and a degree of depression between 28 points was considered extremely severe. A high score indicates a high degree of depression. In this study, the Cronbach’s alpha values of the depression scale at time 1 and time 2 were 0.9004 and 0.9141, respectively.

Anxiety was measured using the DASS-42 scale, which consists of 14 items (Lovibond & Lovibond, 1995 ). Each item was assessed on a 4-point scale ranging from 0 (“not applicable at all”) to 3 (“extremely applicable” or “most applicable”). Students evaluated the 14 items based on their personal feelings. The anxiety score was primarily calculated by summing the scores of relevant items. According to the definition of the DASS-42, anxiety levels were categorized as follows: a score of 0–7 indicated “normal” anxiety, 8–9 indicated “mild” anxiety, 10–14 corresponded to “moderate” anxiety, 15–19 indicated “severe” anxiety, and a score of 20 or higher represented “extremely severe” anxiety. A higher score indicated a greater level of anxiety. In this study, the Cronbach’s alpha values of the anxiety scale at time 1 and time 2 were 0.8477 and 0.8746, respectively.

Student satisfaction with university life

Satisfaction with university life was measured using 9 items, including “Teaching facilities,” “Teachers’ research capabilities,” “Teachers’ teaching abilities,” “Academic status in the country,” “Systematic nature of the courses,” “Usefulness of the courses,” “Extracurricular activities,” “Student-teacher relationships,” and “Learning atmosphere.” Each item was rated on a scale of 1 (“very poor”) to 10 (“excellent”). The scores for satisfaction with university life were primarily calculated by summing the scores of relevant items based on students’ perceptions. A higher score indicates greater satisfaction with university life. In this study, the Cronbach’s alphas for student satisfaction with the university life scale at time 1 and time 2 were 0.9234 and 0.9239, respectively.

Control variables

Extroversion refers to the assessment of individual personality traits and is measured by the following question: “Overall, do you consider yourself more introverted or extroverted? Please select a number from 1 to 9 in the following picture to represent the degree of your personality trait.” In this question, students rate their personality trait based on their self-perceived introversion or extroversion using a scale from 1 (introverted) to 9 (extroverted).

Family social status is the individual’s assessment of his or her family’s position in the social hierarchy. It is measured by the following question: “In our society, some people are in the upper social strata, and some are in the lower social strata. In which stratum do you think your family (referring to your parents, yourself, and your siblings) currently belongs?” This question is rated on a scale from 1 to 5, representing lower class, lower-middle class, middle class, upper-middle class, and upper class. Students rate their family’s social status based on their own assessment.

Data analysis

First, this study used Stata 15.0 to analyze the means, standard deviations, and correlations among the variables of depression, anxiety, and student satisfaction among Chinese university students. Subsequently, the cross-lagged panel model (CLPM) was constructed using Mplus 8.3 to further explore the cross-effects and predictive relationships among depression, academic self-efficacy, and academic performance of university students through the autoregressive model (M1), the leading model (M2), the outcome model (M3), the interaction model (M4), and the control model (M5). The specific design of the model is as follows (see Figs. 2 and 3 ). The comparative fit indices (CFI), Tucker‒Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR) were used to evaluate the model fit. The critical values for CFI and TLI were greater than 0.90, RMSEA was less than 0.10, and SRMR was less than 0.10 (Hu & Bentler, 1999 ). Importantly, due to the large sample size ( N  = 2298), the chi-square/degrees of freedom ratio was not applicable for assessing model fit.

Note: This study utilizes five models: M1 as an autoregressive model, M2 as a preceding model, and M3 as an outcome model. All paths from M1 to M3 are encompassed within M4, which is an interactive model. M5 is the control model that includes additional control variables such as gender, age, extroversion, and family social status.

Descriptive statistics and correlation analysis

Table 1 presents the descriptive statistical variables, such as correlation, mean, and standard deviation, among depression, anxiety, and student satisfaction of Chinese university students. The correlation analysis results indicate that depression, anxiety, and student satisfaction with university life are significantly correlated at different time points, suggesting a stable relationship among all variables. In terms of significance, depression was significantly negatively correlated with college life satisfaction within two years ( p  < 0.05), and anxiety at time 1 was significantly negatively correlated with student satisfaction with university life at time 1 ( p  < 0.05). There was no correlation between anxiety at time 2 and student satisfaction with university life at time 2 ( p  > 0.05). Within two years, depression and anxiety were significantly positively correlated ( p  < 0.05). According to Cohen’s guidelines, a correlation coefficient of r equal to 0.1 is considered a small effect size, 0.3 is considered a medium effect size, and 0.5 is considered a large effect size (Cohen, 1992 ). In the first year, depression at time 1 and student satisfaction with university life at time 1 had small effect sizes ( r  = −0.146), anxiety at time 1 and student satisfaction with university life at time 1 also had small effect sizes ( r  = −0.114), while depression and anxiety had large effect sizes ( r  = 0.684). In the second year, depression at time 2 and student satisfaction with university life at time 2 had small effect sizes ( r  = −0.088); anxiety at time 2 and student satisfaction with university life at time 2 also had small effect sizes ( r  = −0.030); and depression at time 2 and anxiety at time 2 had large effect sizes ( r  = 0.756). In t tests, we found significant differences in the mean scores of student satisfaction with university life at time 1 and time 2 ( p  < 0.05), with the mean score of student satisfaction with university life at time 2 (M = 63.673) being greater than that at time 1 (M = 61.270).

Cross-lagged relationship between depression and student satisfaction with university life

Table 2 presents the fit indices of the cross-lagged model between depression and student satisfaction with university life. First, an autoregressive model (M1) was established, and the model fit was good (CFI = 0.967, TLI = 0.956, RMSEA = 0.076, SRMR = 0.032), indicating that the variables were stable at both time points. Next, we added the cross-lagged path from depression (T1) to student satisfaction with university life (T2) based on M1 and created a preliminary model (M2) to examine the predictive effect of depression (T1) on student satisfaction with university life (T2). The model fit well (CFI = 0.967, TLI = 0.956, RMSEA = 0.076, SRMR = 0.026), and compared to M1, the difference was significant (∆ χ 2  = 8.755, p < 0.05), indicating that M2 had a better fit than M1. Then, we added the cross-lagged path from student satisfaction with university life (T1) to depression (T2) based on M1 and built the final model (M3) to test the predictive effect of student satisfaction with university life (T1) on depression (T2). The model fit well (CFI = 0.967, TLI = 0.956, RMSEA = 0.076, SRMR = 0.023), and compared to M1, the difference was significant (∆ χ 2  = 10.254, p  < 0.05), indicating that M3 had a better fit than M1. Subsequently, an interaction model (M4) was constructed by incorporating both cross-lagged paths between depression and student satisfaction based on M1. The model fit well (CFI = 0.968, TLI = 0.955, RMSEA = 0.076, SRMR = 0.017), and the chi-square comparison suggested that compared to M1, the difference was significant (∆ χ 2  = 18.840, p  < 0.05), indicating that M4 had a better fit than M1. This suggests a bidirectional relationship between depression and student satisfaction with university life among Chinese university students. Based on M4, we developed Model M5 by adding control variables such as gender, age, extroversion, and family social status (CFI = 0.964, TLI = 0.949, RMSEA = 0.063, SRMR = 0.017). Compared to M1, the fitting result of M5 was better (∆ χ 2  = 92.269, p  < 0.05). M5 can further enhance the reliability and robustness of the research results.

The results in Table 3 indicate that the autoregressive paths of all five models are significant. In the prior model (M2), depression (T1) negatively predicts student satisfaction with university life (T2), with a value of \({\beta }_{DS}\)  = −0.061, p  = 0.003. According to the outcome model (M3), student satisfaction with university life (T1) negatively predicts depression (T2), with a value of \({\beta }_{SD}\)  = −0.067, p  = 0.001. In the interaction model (M4), the values are \({\beta }_{DS}\)  = −0.060, p  = 0.003 and \({\beta }_{SD}\)  = −0.067, p  = 0.001, consistent with the conclusions from M2 and M3, indicating that depression (T1) negatively predicts student satisfaction with university life (T2) and that student satisfaction with university life (T1) negatively predicts depression (T2). There is a bidirectional relationship between depression and student satisfaction with university life among Chinese university students. In M5, which includes control variables such as gender, age, extroversion, and family social status, the values are \({\beta }_{DS}\)  = −0.064, p  = 0.002 and \({\beta }_{SD}\)  = −0.069, p  = 0.001, respectively. These results confirm the previous conclusions, indicating a stable relationship between depression and satisfaction with university life.

Cross-lagged relationship between anxiety and student satisfaction with university life

Table 4 shows the fit indices of the cross-model between anxiety and student satisfaction with university life. First, an autoregressive model (M1) was established, and the model fit was good (CFI = 0.969, TLI = 0.959, RMSEA = 0.071, SRMR = 0.022), indicating that the variables were stable at both time points. Next, we added the cross-lagged path from anxiety (T1) to student satisfaction with university life (T2) based on M1 and established a preliminary model (M2) to test the predictive effect of anxiety (T1) on student satisfaction with university life (T2). The model fit well (CFI = 0.969, TLI = 0.958, RMSEA = 0.071, SRMR = 0.020), but compared to M1, the difference was not significant (∆ χ 2  = 1.041, p  > 0.05), suggesting that M2 had a relatively poorer fit than M1. Then, we added the cross-lagged path from student satisfaction with university life (T1) to anxiety (T2) based on M1 and established the final model (M3) to test the predictive effect of student satisfaction with university life (T1) on anxiety (T2). The model fit well (CFI = 0.969, TLI = 0.958, RMSEA = 0.071, SRMR = 0.020), and compared to M1, the difference was significant (∆ χ 2  = 3.954, p  < 0.05), indicating that M3 had a better fit than M1. Furthermore, an interaction model (M4) was established by adding both cross-lagged paths between anxiety and student satisfaction with university life based on M1. The model fit well (CFI = 0.969, TLI = 0.957, RMSEA = 0.072, SRMR = 0.017), but compared to M1, the difference was not significant (∆χ 2  = 5.003, p > 0.05), suggesting that M4 had a relatively poorer fit than M1. Building on M4, we constructed Model M5 by adding control variables such as gender, age, extroversion, and family social status (CFI = 0.966, TLI = 0.951, RMSEA = 0.059, SRMR = 0.017). Compared to M1, the fitting result of M5 was better (∆ χ 2  = 94.501, p  < 0.05). M5 can further enhance the reliability and robustness of the research results.

Table 5 indicates that the autoregressive paths of all five models are significant. In the prior model (M2), the predictive relationship between anxiety (T1) and student satisfaction with university life (T2) is not significant, with a value of \({\beta }_{AS}\)  = −0.021, p  = 0.307. In the outcome model (M3), student satisfaction with university life (T1) negatively predicts anxiety (T2), with a value of \({\beta }_{SA}\)  = −0.042, p  = 0.047. In the interaction model (M4), the values are \({\beta }_{AS}\)  = −0.021, p = 0.306 and \({\beta }_{SA}\)  = −0.042, p  = 0.046, respectively, consistent with the conclusions from M3, indicating that student satisfaction with university life (T1) negatively predicts anxiety (T2). In M5, which includes control variables such as gender, age, extroversion status, and family social status, the values are \({\beta }_{AS}\) =−0.024, p  = 0.266 and \({\beta }_{SA}\)  = −0.046, p  = 0.030, respectively. These results confirm the previous conclusions, demonstrating that the negative predictive relationship between student satisfaction with university life (T1) and anxiety (T2) remains robust even after accounting for these variables.

This study focuses on Chinese university students as research subjects and investigates the longitudinal relationships among depression, anxiety, and student satisfaction with university life during their third year (T1) and fourth year (T2) through a one-year follow-up survey using a dual-wave cross-lagged model.

First, this study used correlation analysis to reveal a negative relationship between depression and student satisfaction with university life among college students, which is consistent with previous research findings (Paschali & Tsitsas, 2010 ; Hajduk et al., 2019 ; Li et al., 2021 ). When students have lower levels of satisfaction with their university life, they are more likely to experience negative emotions. These negative emotions can hinder students’ perception of their surrounding environment, thus reducing their overall student satisfaction with university life. According to the study, we found that anxiety and student satisfaction with university life were significantly negatively correlated only during the junior year. For junior students, the causes of anxiety may be related to uncertainties about postgraduate studies and employment, as well as the pressure of peer competition (Peng et al., 2010 ; Posselt & Lipson, 2016 ), which are often closely related to factors such as students’ academic major, learning environment, and teaching quality (Sojkin et al., 2012 ; Alqurashi, 2019 ). When students are in high-paying employment fields, collaborative learning environments, and environments with high-quality teaching, where their expectations align with reality, their anxiety levels are lower, and their college life satisfaction is greater. However, in the senior year, there was no significant negative correlation between anxiety and student satisfaction with university life, which contradicts findings from most previous studies (Duong, 2021 ; Sahin & Tuna, 2022 ). This discrepancy may be due to the measurement methods used. Our study also revealed that senior students typically exhibit greater adaptability than junior students do, with their average satisfaction with university life levels being greater. This conclusion is consistent with the finding that students in higher grades generally report higher levels of student satisfaction with university life than do students in lower grades (El Ansari, 2002 ).

Second, this study utilized a cross-lagged model to analyze the longitudinal relationships among depression, anxiety, and student satisfaction with university life. We found that depression among college students negatively predicts their satisfaction with university life; that is, lower levels of depression in the junior year correspond to higher levels of student satisfaction with university life in the senior year, while higher levels of depression in the junior year are associated with lower levels of student satisfaction with university life in the senior year, which is consistent with previous research findings (Tang et al., 2023 ; Ooi et al., 2022 ; Almeida et al., 2021 ; Denovan & Macaskill, 2017 ; Headey et al., 1993 ). When students experience depression, it can lead to poor emotional experiences, lower academic performance, and even negative behaviors such as dropping out or suicidal tendencies (Floyd et al., 2007 ; Buchanan, 2012 ; Deng & Zhang, 2023 ). According to the customer satisfaction model, for students, as consumers of services provided by universities, due to the series of negative behaviors and impacts caused by depression, students’ perceptions and assessments of daily academic life are reduced. This leads to their actual experiences of academic life falling short of their expectations, and this negative behavior typically has a certain degree of persistence, thereby decreasing students’ college life satisfaction in the next stage. However, anxiety does not significantly predict student satisfaction with university life, which aligns with some research results (Oladipo et al., 2013 ; Ferguson et al., 2012 ). This may be due to the measurement methods used in our study. For Chinese university students, depression may be a relatively more serious psychological issue that can alter students’ satisfaction with university life. On the other hand, anxiety might be a relatively milder psychological issue, as the current level of anxiety among university students does not reach a point where it significantly impacts their satisfaction with university life. This further underscores the importance of paying more attention to depressed college students as a vulnerable group in terms of mental health among university students. Providing them with more mental health resources to prevent and intervene in their depressive emotions is crucial. To alleviate the negative predictive relationship between depression and student satisfaction with university life, students should strive to cultivate a positive mindset, foster good interpersonal relationships, and maintain a healthy lifestyle (Hames et al., 2013 ; Seo et al., 2018 ; Cao, 2023 ; Cao et al., 2023 ) to reduce the negative effects of depression on their physical and mental well-being. University administrators and teachers should enhance communication and interaction with students, prioritize their mental health problems, and promptly offer intervention measures. This is essential for safeguarding students’ psychological well-being and ensuring the high-quality development of universities. Subsequent research will delve deeper into the pathways between high school students’ depression and anxiety and their impact on student satisfaction with university life to propose more effective intervention strategies.

Third, we found that student satisfaction with university life negatively predicts both depression and anxiety. Specifically, higher levels of student satisfaction in the junior year correspond to lower levels of depression and anxiety in the senior year, and vice versa. This is because university students are considered more susceptible to the external conditions of their current life circumstances, and student satisfaction with university life is often closely intertwined with the educational environment in which they are situated (Tan et al., 2020 ; Wang & Liu, 2024 ). Universities should not only provide a conducive campus environment to help students successfully obtain their degrees in a positive learning atmosphere but also assist them in achieving their academic goals and preparing for future careers (Calma & Dickson-Deane, 2020 ; Ahn & Davis, 2020 ). This can effectively enhance student satisfaction with university life and promote students’ psychological health and growth (Ahn & Davis, 2020 ). A low level of student satisfaction not only negatively impacts students’ psychological well-being but also may lead to the loss of talented students, damage the reputation of the institution, and hinder its long-term development. Universities should realize that students are not only “consumers” but also partners. Universities should prioritize increasing student engagement. Student involvement in higher education can enhance the development of the teaching and learning environment within institutions (Liu et al., 2023a ; Howson & Weller, 2016 ). This relationship-building approach is essential for improving student satisfaction (Kandiko Howson & Matos, 2021 ) and reducing student mental health problems.

Limitations

This study is subject to five limitations. First, the measurements of depression, anxiety, student satisfaction with university life, extroversion, and family social status in this study rely on self-reported measures, thus potentially introducing measurement errors. Second, although the results of the correlation analysis indicate a significant negative correlation between depression and student satisfaction with university life, the effect size is small. Third, the college education level of the students may influence the outcomes. The sample of this study consisted of university students at higher grade levels, which may differ from the experiences encountered by students at lower grade levels. Fourth, the instrument used to measure student satisfaction was not previously validated and remains untested in terms of retest reliability or referenced in previously published literature. Fifth, the mean of measuring extroversion and social status did not involve validated instruments.

Implications for educational practice and conclusions

Implications for educational practice.

This study provides preliminary evidence of the longitudinal relationships among depression, anxiety, and student satisfaction with university life. This study offers theoretical support for further exploration of how psychological health education can help students better adapt to university life. It also provides new insights for the administrative departments of higher education institutions in terms of student development and education, with important guiding significance.

Given the important role of student satisfaction with university life in students’ mental health, schools should prioritize educational philosophies, curriculum design, teacher‒student relationships, and student support services. Strengthening humanistic and cultural construction and aligning student management with student development around the goal of cultivating high-level talent are essential. More humanized management and services should be provided. Schools should enhance teaching reforms, improve teaching quality, increase the societal recognition of universities, and enhance students’ recognition of self-worth to improve student satisfaction with university life and reduce levels of depression and anxiety. Additionally, schools should pay attention to the psychological health of university students, especially those who are already experiencing depression and anxiety, and take timely and effective measures for intervention and treatment to prevent these negative emotions from impacting their academics and lives.

On the one hand, educators need to pay attention to students’ emotional states and identify and address potential psychological issues in a timely manner. On the other hand, they should provide appropriate support and counseling to help students establish healthy psychological defense mechanisms and enhance psychological resilience.

Although this study focused on the relationships among depression, anxiety, and student satisfaction with university life among Chinese college students, future research could broaden the scope to include students from other countries and regions. This study contributes to exploring the relationship between college students’ psychological health and student satisfaction in cross-cultural contexts and provides a theoretical basis for international education practices. Future research should continue to make efforts to overcome the limitations of existing studies and further explore the complex relationships among these variables. Through these studies, we can better understand the impact of students’ life experiences in college on their mental health, thereby providing targeted support and services for colleges.

Conclusions

First, there is a negative correlation between depression and student satisfaction with university life among college students, whereas no such negative correlation is observed between anxiety and student satisfaction with university life.

Second, depression among college students is found to have a negative predictive influence on student satisfaction with university life, while anxiety does not have a significant prospective impact on student satisfaction with university life.

Third, student satisfaction with university life is negatively predictive of both anxiety and depression.

Data availability

The data ownership belongs to the National Survey Research Center, Renmin University of China. Since the dataset has not been publicly released, the authors only obtained the right to use the dataset and do not have the authority to publicly distribute it. Therefore, a download link for the dataset cannot be provided. However, descriptive statistical analysis results regarding this dataset have been published in the appendix of the author’s previously published paper. You can refer to the following paper for more information: https://doi.org/10.1057/s41599-023-02252-2 . The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Liu, X., Wang, J. Depression, anxiety, and student satisfaction with university life among college students: a cross-lagged study. Humanit Soc Sci Commun 11 , 1172 (2024). https://doi.org/10.1057/s41599-024-03686-y

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Active funding opportunity

Nsf 24-578: hispanic serving institutions: equitable transformation in stem education (etse), program solicitation, document information, document history.

• Posted: May 24, 2024

Program Solicitation NSF 24-578

Full Proposal Deadline(s) (due by 5 p.m. submitting organization’s local time):

     September 11, 2024

     August 27, 2025

     Last Wednesday in August, Annually Thereafter

Important Information And Revision Notes

The Hispanic Serving Institutions: Equitable Transformation in STEM Education (HSI: ETSE) solicitation is a part of the larger Improving Undergraduate STEM Education (IUSE): Hispanic Serving Institutions (HSI) program at NSF. The IUSE: HSI program funds a breadth of projects across HSIs. Prospective Principal Investigators (PIs) are encouraged to carefully review this solicitation and NSF Hispanic-Serving Institutions: Enriching Learning, Programs and Student Experiences (ELPSE) to determine which opportunity fits a particular proposal.

With this new Equitable Transformation in STEM Education (ETSE) competition, the HSI program is introducing two new tracks, (1) Departmental/Division Transformation Track which centers on the transformation of a single department or division within an institution; and (2) Emerging Faculty Research is a new track that invites proposals from individual investigators at 2- and 4-year Primarily Undergraduate Institutions (PUIs), including community colleges, to engage in STEM research, including undergraduate STEM education or STEM broadening participation research.

The HSI program team will host webinars in which key features and expectations of the HSI program will be discussed. Information regarding the webinars will be posted to the HSI program webpage for this solicitation.

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

Summary Of Program Requirements

General information.

Program Title:

Hispanic Serving Institutions: Equitable Transformation in STEM Education (ETSE)

Hispanic Serving Institutions (HSI) are an important component of the nation’s higher education ecosystem and play a critical role in realizing the National Science Board Vision Report for a more diverse and capable science and engineering workforce. Aligned with this vision and the NSF Strategic Plan 2022 -2026 the goals of the NSF HSI Program are to: Enhance the quality of undergraduate science, technology, engineering, and mathematics (STEM) education at HSIs. Increase the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM at HSIs. Meeting these goals requires institutions to understand and embrace their students’ strengths, challenges, identities and lived experiences. This can happen in many ways and across many areas of an institution. As such, the IUSE: HSI program provides multiple opportunities to support an institution’s goal to become more student centered, including the Equitable Transformation in STEM Education (ETSE ) competition. This competition includes the following tracks: Departmental/Division Transformation Track (DDTT) - New Institutional Transformation Track (ITT) Emerging Faculty Research Track (EFRT) - New HSI Program Resource Hubs (Hubs) This solicitation will also accept conference proposals and planning proposals, as defined by the PAPPG . The ETSE competition focuses on (1) institutional transformation projects that support HSIs in their effort to achieve equity in STEM education, and (2) the infrastructure—the HSI-Net network of resource hubs—which supports the overall program goals. Institutions are encouraged to consider how their HSI designation, and their organizational mission align to better support STEM success of all students. The ETSE competition welcomes proposals that look to implement and evaluate promising practices and/or conduct research related to broadening participation or improving recruitment, retention, graduation, and other successful outcomes in STEM undergraduate education. The ETSE solicitation supports projects designed to catalyze change and help HSIs meet students where they are, accounting for their assets and the challenges they may face. Identities and experiences are not determined solely by membership in a single monolithic population of students (e.g., Hispanic, first-generation, commuter, etc.). Consequently, institutions are expected to use institutional data to identify equity gaps, identify areas of need, and unpack the factors that shape students’ individual identities and shared experiences. The perspectives gained from this data should be central to the design of the proposed project. Please see below for specific information about each track. While proposals are focused on mechanisms for transforming undergraduate STEM education, projects should also consider student voices and include mechanisms to aggregate and analyze existing student feedback and collect quantitative and qualitative student data throughout the life of the proposed project.

Cognizant Program Officer(s):

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

Sonja Montas-Hunter, telephone: (703) 292-7404, email: [email protected]

Michael J. Ferrara, telephone: (703) 292-2635, email: [email protected]

James Alvarez, telephone: (703) 292-2323, email: [email protected]

Sonal S. Dekhane, telephone: (703) 405-8977, email: [email protected]

Elsa Gonzalez, telephone: (703) 292-4690, email: [email protected]

Julio G. Soto, telephone: (703) 292-2973, email: [email protected]

• 47.076 --- STEM Education

Award Information

Anticipated Type of Award: Standard Grant or Continuing Grant

This Program anticipates making:

• Award Size: Up to $1,000,000
• Award Length: For up to five-year-long projects
• Award Size: Up to $3,000,000
• Award Length: For five-year-long projects
• Award Size: Up to $200,000
• Award Length: For up to three-year-long projects

Anticipated Funding Amount: $20,000,000

The number of new awards is subject to the availability of funds.

Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following: With the exception of conference proposals, proposals may only be submitted by the following: To be eligible for funding an institution must meet the following criteria: Be an accredited institution of higher education. Offer Undergraduate STEM educational programs that result in certificates or degrees. Satisfy the definition of an HSI as specified in section 502 of the Higher Education Act of 1965 (20 U.S.C. 1101a) and meet the eligibility of an HSI by the U.S. Department of Education definition. Documentation (eligibility letter) from the Department of Education confirming HSI designation must be submitted as a supplemental document. Additional requirements to be eligible for funding in the Emerging Faculty Research Track (EFRT), the institution must meet the four criteria listed above at the time of submission and: Be an eligible Primarily Undergraduate Institution (PUI) [ 1 ]. Eligible PUIs are accredited colleges and universities (including two-year community colleges) that award Associate's degrees, Bachelor's degrees, and/or Master's degrees in NSF-supported fields, but have awarded 20 or fewer Ph.D./D.Sc.. degrees in all NSF-supported fields during the combined previous two academic years.

Who May Serve as PI:

ITT proposals require an upper-level administrator with decision-making authority (i.e. Dean or higher) as PI or co-PI. For DDTT proposals, the unit head, chair, or equivalent should be a PI or co-PI for the duration of the project. No restrictions for Hub and EFRT proposals.

Limit on Number of Proposals per Organization:

DDTT proposals: Eligible institutions with an active Track 3: Institutional Transformation project (ITP) award from NSF 22-611 , NSF 22-545 , or NSF 20-599 or an active ITT award from this solicitation must describe how the proposed DDTT project is compatible with the efforts being undertaken by the active award. ITT proposals: Eligible institutions may submit one proposal and may not have an active Track 3 Institutional Transformation Project (ITP) award from, NSF 22-611 , NSF 22-545 , or NSF 20-599 . Institutions with an active DDTT award from this solicitation must describe how the proposed ITT project is compatible with the departmental/divisional transformation effort being undertaken by the active award. EFRT and Hub proposals: No Restrictions

Limit on Number of Proposals per PI or co-PI:

For DDTT, ITT and EFRT, an individual may be listed as PI or co-PI on only one proposal. An individual may only serve as a PI or co-PI on one Hub proposal or active Hub project at any time.

Proposal Preparation and Submission Instructions

A. proposal preparation instructions.

• Letters of Intent: Not required
• Preliminary Proposal Submission: Not required

Full Proposals:

• Full Proposals submitted via Research.gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .
• Full Proposals submitted via Grants.gov: NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov guidelines apply (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ).

B. Budgetary Information

Cost Sharing Requirements:

Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations:

Not Applicable

Other Budgetary Limitations:

Other budgetary limitations apply. Please see the full text of this solicitation for further information.

C. Due Dates

Proposal review information criteria.

Merit Review Criteria:

National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information

Award Conditions:

Additional award conditions apply. Please see the full text of this solicitation for further information.

Reporting Requirements:

Standard NSF reporting requirements apply.

I. Introduction

The National Science Foundation’s Improving Undergraduate STEM Education: Hispanic Serving Institutions (HSI) Program is part of a Foundation-wide effort to accelerate improvements in the quality and effectiveness of undergraduate education in all STEM fields including the learning, social, behavioral, and economic sciences. As its name implies, the HSI program specifically supports initiatives to (1) enhance the quality of undergraduate science, technology, engineering, and mathematics (STEM) education and (2) increase the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM at colleges that have been designated as Hispanic Serving Institutions (HSIs). To achieve these goals and with Congressional support , the HSI program aims to build capacity at Hispanic-serving institutions. Building organizational capacity, as encouraged by the explanatory statement of the Consolidated Appropriations Act, 2017 Public Law 115-31, is concerned with creating and implementing flexible systems that support new and old ideas. Building capacity should involve developing structures that foster student and/or faculty growth while meeting the students where they are in their college careers academically, financially, and socially. Institutional structures may also include sociocultural supports and collaborative processes that promote effective learning environments and inclusiveness as well as mechanisms to support students’ personal development and professional learning.

To accomplish these goals, the IUSE HSI program runs multiple competitions annually. One of these is a competition for the Equitable Transformation in STEM Education (ETSE) . Recognizing the diverse nature and context of HSIs, ETSE is designed to support HSIs with varying structures and diverse student populations, including newly designated HSIs, to engage in organizational change efforts to support equitable learning outcomes for all its students.

NSF HSI program seeks to improve efforts aimed at enhancing the preparation, participation, and contributions of groups that have been historically excluded and/or underserved in the STEM enterprise. As such, proposers are encouraged to use an intersectional lens[ 2 ] perspective in designing proposals across all tracks in the HSI program. Intersectionality is an approach that considers the interconnectedness of overlapping social identities and can help shape a project's design and conceptualization of inclusivity to better serve students at HSIs. An intersectional approach to institutional transformation in a student-centered learning environment could significantly support the ability to leverage the full spectrum of diverse talent that society has to offer which helps to increase the diversity of undergraduate STEM degrees awarded and STEM professionals across the nation.

The Equitable Transformation in STEM Education (ETSE) solicitation accepts proposals in four tracks. Additional opportunities for planning and conference proposals are also discussed below.

• Departmental/Division Transformation Track (DDTT): This new track focuses on strengthening STEM education through the transformation of academic departments or divisions which are in turn shaped by the personnel, leadership, practices, and disciplinary culture of these distinct and often interconnected units. These projects should provide opportunities for departments and divisions to scrutinize their policies and practices, invest in current and future leaders, and challenge narrow or exclusionary disciplinary norms that can sustainably drive positive student outcomes.
• Institutional Transformation Track (ITT) : The Institutional Transformation track should articulate a vision for unifying academic equity research, practice, and policy to strengthen an institutional understanding of student learning outcomes from the context of the diverse community it serves. These projects seek to 1) support the planning and implementation of institutional research infrastructure efforts which results in institution-wide efforts toward broadening participation in STEM and 2) engage students in STEM undergraduate best practices and effectively guide students toward careers in STEM and/or graduate programs.
• Emerging Faculty Research Track (EFRT ): The EFRT track is a new track that invites proposals from individual investigators at 2- and 4-year PUIs, including community colleges, to engage in STEM research, including undergraduate STEM education or STEM broadening participation research. The specific objectives of EFRT projects should (1) enhance faculty opportunities at PUIs and two-year colleges to conduct STEM Research, STEM education research and/or Broadening Participation research and (2) improve understanding of factors that advance positive student learning outcomes and effective STEM broadening participation efforts.
• HSI Program Resource Hubs (HSI Hubs) : Hub projects should be designed to promote research and support collaboration within the HSI community, including prospective PIs, to build capacity at HSIs. The HSI-Hubs will support initiatives and activities that address any area(s) of need in the HSI community, identified by the proposer and community, and supported by evidence. These should be designed to effectively serve the HSI STEM communities and increase the participation of the full spectrum of diverse talent to include historically underrepresented individuals/communities in STEM.

The ETSE Competition also accepts planning proposals for Departmental/Division Transformation and Institutional Transformation tracks. Please review PAPPG guidelines on how to submit a planning proposal.

II. Program Description

The HSI program is guided by student-centered frameworks that build an intentional and supportive environment for students and reinforce cultural and mindset shifts that support the success of all students at HSIs. Proposals should discuss project designs that are based on data-informed decision-making processes to operationalize an institution’s student-centered approach.

This competition is designed to leverage existing institutional strengths for advancing efforts toward student-centered environments[ 3 ]. Proposals to ETSE should impact the STEM learning landscape, result in equitable undergraduate STEM degree attainment for all students, and position students for successful transition and retention into the STEM workforce or graduate education.

Competition Tracks This competition accepts proposals for four project tracks. Additional opportunities for planning and conference proposals are discussed later in the document.

Departmental/Division Transformation Track (DDTT) . The Departmental/Division Transformation Track is new to the HSI program and focuses on supporting transformation through building STEM research capacity and infrastructure at the departmental, divisional- or college level. It is intended to provide opportunities for an end-to-end self-study of a discipline(s)’s culture, students’ experiences, and more granular academic outcomes. Proposals should prioritize “building people capacity” as a foundational element for institutional transformation and consider the collective needs of all stakeholders.

These projects should: (1) strengthen academic capacities, including investing in STEM leaders at the college, departmental, or division level; (2) develop and enhance sociocultural academic support to broaden participation in STEM education; (3) support the design and implementation of an organizational self-assessment to collect and analyze data to identify STEM inequities in a specific discipline or connected disciplines in a department, unit or college; and (4) develop a project design that takes into consideration a student-centered framework, such as “Servingness,[ 4 ]” “Intersectionality” or “Growth-Mindset[ 5 ]” to promote a learning environment that intentionally positions the student at the center of the academic experience to ensure that all students have meaningful opportunities to realize their fullest potential and as a result, strengthens the ability of academic programs to attract, retain, and graduate students in the STEM disciplines of focus.

The specific objectives of DDTT projects must: (1) increase student engagement in evidence-based practices that result in positive STEM student learning outcomes; and (2) develop and engage all members of the focal academic department or division, as well as administrators, staff, and both full-time and part-time faculty as appropriate.

The unit head, chair, or equivalent must be a PI or co-PI for the duration of the project, and the role of this individual should be central to the proposed project and clearly described in the project narrative. Proposals are also encouraged to devote funds towards a project coordinator who can help support data collection and analysis, organize project activities, and attend to the multifaceted requirements for STEM transformation.

An emphasis of this track is to also enable institutions with limited or no research capacity, including PUIs, two-year institutions, including community colleges, to expand and build STEM capacity. Proposals from PUIs and community colleges are encouraged to propose meaningful partnerships with external organizations to grow programs in workforce development, research and development (R&D), and/or the translation of research to practice in emerging technology fields.

Institutions whose goal is to advance from one research classification to another (e.g., achieving R2 Carnegie classification ) are also encouraged to submit to this track.

The project description for successful proposals to the DDTT are strongly encouraged to:

• Establish a direct connection to the long-term strategic plan of the host department(s).
• Discuss the adaptation/replication of known evidence-based strategies and/or design and implementation of new strategies that will impact the STEM discipline(s) that are the focus of the project. The approaches taken to improve undergraduate STEM education should clearly align with the data narrative and baseline data should inform the development of clear goals highlighting how the proposed change effort might close equity gaps or otherwise measurably improve student engagement, experiences, and outcomes.
• Include formal and informal leadership development activities for individuals across the unit (e.g., faculty, unit chairs or heads, staff members, leads for multi-section courses). Projects are encouraged to consider how the unit will identify and prepare future leaders and how equitable, engaged mentoring, advising, and other practices can serve as loci of leadership development and drivers of positive change.
• Discuss a plan for the assessment of division/department-level and institutional factors, including how the institutional designation as an HSI intertwines with its climate, culture, practices, and outcomes. Proposals are encouraged to leverage qualitative and quantitative data streams spanning all stakeholders as institutional research data alone cannot fully capture the breadth of the students’ lived experiences within an institution.

The inclusion of student voice and feedback is critical to DDTT, and proposals must include mechanisms to aggregate and analyze existing student feedback and collect quantitative and qualitative student data throughout the life of the proposed project. Proposals are encouraged to include student members as part of the project leadership team or advisory boards to serve as liaisons with their peers and ensure that their viewpoints are clear and understood. Student leaders should be appropriately compensated for their time and effort.

Institutional Transformation Track (ITT). Proposals to the Institutional Transformation track should articulate a vision for unifying academic equity research, practice, and policy to strengthen an institutional understanding of student learning outcomes from the context of the diverse community it serves. All institution types are encouraged to apply, especially PUIs (including community colleges). Proposals are encouraged to consider moving efforts from enrollment-driven strategies to student-centered principles. These projects seek to support the planning and implementation of institutional research infrastructure efforts which results in institutional-wide efforts toward broadening participation in STEM while engaging students in STEM undergraduate best practices to effectively guide students toward careers in STEM and/or graduate programs.

While ITT proposals do not need to carry out the proposed activities in all STEM disciplines at the institution, a substantial subset of those disciplines should be integrated into the transformation effort across the proposed project period. This should go substantively beyond an effort to transform undergraduate STEM education within a single department, division, school, or college. Furthermore, the sustainability plan presented should clearly discuss how the institution will implement successful practices into departments and disciplines that are not fully engaged in the proposed work during the project period.

ITT proposals should incorporate a theory of change that informs the overall project design and should further be grounded in STEM education research and broadening participation research to enhance student outcomes in STEM. The project design should lead to institutional infrastructure and policy changes to support long-term institutional changes that encourage and support faculty to implement evidence-based practices that enhance student outcomes in STEM.

ITT projects may include a plan to conduct research that advances understanding of institutional culture and identity on students' learning outcomes in undergraduate STEM education. Such research should result in a strategic understanding of the complex characteristics of students at HSIs and how multi-faceted strategies work synchronously to advance equity in STEM education. This may be achieved through posing one or more research questions that will be answered through the course of the study or through evaluation of project activities, impacts, or outcomes. Projects should include a well-designed plan to gather data and should specify methods of analysis that will be employed to address questions posed and mechanisms to evaluate the success of the project. Projects should also specify strategies for generating and using formative and summative assessments of project processes, outputs, and/or outcomes. Proposals that include a research plan must include a plan that discusses dissemination and must also discuss how the research will generate knowledge to make an impact on how HSIs can transform STEM education.

Project Descriptions for successful proposals to the Institutional Transformation Track (ITT) are strongly encouraged to:

• Discuss the proposal’s alignment with the institutional strategic plan to improve the enrollment, retention, and graduation of STEM associates and baccalaureate degrees.
• Discuss how the proposed ITT project will leverage and/or complement existing programs and initiatives to help the institution move towards a more student-centered undergraduate STEM ecosystem.
• Articulate the creation of institution-wide strategies to transform their policies or practices to foster inclusive STEM learning environments that promote equitable student learning and engagement in all STEM disciplines at the proposing HSI.
• Comprise a multidisciplinary team with the expertise and experience needed to implement the proposed project. The PI team may have members from other institutions or non-profit organizations to augment the team's expertise, which should be explained in the project description and management plan. (For more information on the project management plan see required components for all proposals in the Proposal Preparation section of the Competition.) The project team should include an upper-level administrator with institution-wide responsibility and authority over STEM education at the institution (i.e. Provost, VP of Academic Affairs or equivalent).
• Provide evidence of institutional commitment to the proposed work as part of the proposal.

Proposers should be aware that ITT projects will be formally reviewed via a formal Reverse Site Visit prior to the conclusion of the project's third year. If necessary, this may be followed by a formal site visit. Continued funding of ITT project will be contingent on the results of the reverse site visit and/or site visit review.

Common Expectations for proposals to DDTT and ITT Tracks

The sections must be included in the project description:

• An institutional data narrative to determine baselines, set goals, and evaluate impact. The institutional data should contextualize the institution's need and ability to provide undergraduate STEM education promoting a more competitive, diverse, and capable STEM workforce. The institutional data narrative should serve as a foundation for the adoption of an intersectional lens to the project design.
• A discussion on how the project design applies an intersectional lens that supports the context from which the institution proposes to address academic equity gaps in STEM. Intersectional perspectives are important for identifying academic equity challenges and solutions for underrepresented populations in STEM. Intersectional perspectives are also important for identifying factors that need attention to effectively support those populations whose social identities, in addition to gender, race, and ethnicity, such as age, disability; economic status (e.g., Pell recipients), and first-generation status impact the learning environment. As a result, an intersectional lens provides an opportunity to intentionally engage in strategies that leverage the full spectrum of diverse talent.
• A discussion on a theory of change[ 6 ] that supports the project in taking actionable steps to transform policies, practices, relationships, approaches, and/or mindsets, to make the STEM environment more inclusive, advance equity, and broadening participation in STEM at HSIs.
• A project evaluation plan that is based on S.M.A.R.T. (Specific, Measurable, Attainable, Realistic, and Time-bound) goals . Evaluation plans should include a logic model as a supplementary document. In addition to quantitative and/or qualitative methodologies, it is encouraged that evaluation plans include measures of success for non-academic outcomes (i.e. STEM identity, academic self-concept, graduate aspirations). Project evaluator(s) should be independent to the project and named in the Project Description section of the proposal. Proposals should: (1) describe the expertise of the evaluator(s); (2) explain how that expertise relates to the goals and objectives of the proposal; and (3) specify how the PI will report and use the results of the project's external review process and incorporate the recommendations to improve the project. The biosketch(es) of the external evaluator(s) should be uploaded as a supplementary document.
• Describe a dissemination plan that includes activities beyond conferences and journal articles to reach interested disciplinary communities, leaders, and scholars. While conferences and journals may be an integral part of a dissemination plan, PIs should consider how they will assemble and reach an audience that could benefit from their project’s findings.
• Describe a sustainability plan that demonstrates how activities and practices are being integrated into an institution’s overall undergraduate STEM culture and are transforming how institutions support and develop activities for enrollment in, and completion of a STEM degree. Proposals should include preliminary sustainability plans for the continuation of a project’s goals and efforts to achieve desired outcomes beyond the funding period.
• If hiring undergraduate students, a student mentoring plan is required. This solicitation specific required plan is separate and distinct from the mentoring plan required by the PAPPG of proposals that request funding to support postdoctoral scholars or graduate students and should be included as a supplementary document.
• Include a discussion that outlines a strategy for leveraging previous awards from the HSI program from NSF 22-611 , NSF 22-545 , or NSF 20-599 , if applicable.

Emerging Faculty Research Track (EFRT). The EFRT track is a new track that invites proposals from individual investigators at two-year institutions, including community colleges and primarily undergraduate institutions (PUIs) to engage in STEM research, including undergraduate STEM education Research or STEM broadening participation research. Proposals from individuals looking to develop a new scholarly program or have an established record of scholarship in these areas are equally welcome.

Awards through this track are intended to strengthen the community of teacher-scholars at these institutions, allow investigators to strengthen existing scholarly endeavors or explore new opportunities, have a positive impact on faculty and student development, and/or develop inclusive environments in STEM.

EFRT projects are expected to increase research activity at primarily undergraduate institutions, including community colleges. As result, EFRT projects should increase knowledge about effective STEM education practices on engaged student learning and broadening participation at HSIs. The specific objectives of proposed EFRT projects should (1) improve understanding of what leads to positive student learning outcomes and effective broadening participation efforts and (2) strengthen the community of undergraduate STEM education or broadening participation researchers at PUIs and two-year colleges.

Proposals to EFRT will support single-investigators' research in all disciplines supported by NSF. These include: (1) theoretical or applied STEM research that is inter-, multi-, or trans-disciplinary, (2) discipline-based STEM education research, and/or (3) STEM broadening participation research. Regardless of focus, research should support the overarching goals of the HSI program which seeks to improve and enhance undergraduate STEM education, including undergraduate student research experiences. Proposals should discuss alignment with the long-term plans of the investigator’s department, division, school/college, or institution. This includes the institutional mission and plans for expanding institutional research capacity and increasing the production of STEM baccalaureate degrees.

Engaging undergraduate researchers in authentic research experiences is an established high-impact practice. Proposals that include opportunities for undergraduates in any NSF-supported discipline to engage in STEM research, including the core education or broadening participation research are encouraged. Proposals which include the support the success of students who have historically not engaged in STEM undergraduate research activities and are impacted by academic inequities are strongly encouraged. Projects that involve undergraduates should include a specific discussion of students’ roles, duties, and training. Proposals should also address the PI’s readiness to engage in supporting undergraduate research and mentoring students of diverse backgrounds. Please note that a student mentoring plan should also be submitted as a supplementary document for any project that involves undergraduates involved in roles other than as study participants.

Interdisciplinary research projects and projects focused on training students in emerging technologies or areas of national interest (i.e. artificial intelligence, environmental change, quantum information systems, advanced manufacturing, etc.), as outlined in the NSF Strategic Plan 2022-2026 , are strongly encouraged.

The Project Description for each EFRT proposal must contain the following elements:

• An overview of the PIs overall research, education, and professional goals.
• Background and justification for proposed research, supported by the relevant literature, along with appropriate research questions and hypotheses. Theoretical or conceptual frameworks should be incorporated as appropriate for the specific nature of the proposed study.
• Information on how the proposed research will contribute to the literature on how to effectively impact broadening participation in STEM and/or advance STEM education research at HSIs.
• A discussion of data streams, sampling methods, and methodologies to be employed. Proposals that include the development or adaptation of surveys, rubrics, or other data collection tools should also include a clear plan for validating those items. Please note that the EFRT track does not favor any particular approach, method, or type of data, but rather asks proposers to carefully consider which approaches would be best suited to address the issues and research questions presented.
• A detailed timeline that clearly presents data collection points and timeframes for analysis, and other key research activities.
• A plan for how the progress of the project will be assessed. Proposals from investigators new to STEM education or broadening participation research are encouraged to include an advisory board or formal, experienced mentor to guide their scholarly journey throughout the project.
• A plan for dissemination of project outcomes.
• A letter of commitment from the PI's Department Chair or Dean stating that the PI will have institutional support in terms of allowance to utilize project funds for release time, travel for research purposes, or access to existing research facilities, as appropriate. This should be included as a supplementary document.

Budget: Funds requested for EFRT proposals are intended to support investigators’ specific needs and may include, but are not limited to the following: faculty release time; technical support for research; faculty and student professional development; travel to conferences; acquisition or upgrading of research equipment; development of special topics or seminar courses; and collaborative research efforts including travel to collaborating institutions or travel for collaborators to visit the PI at their home institution. The budget may include support for student trainees or post-doctoral fellows. EFRT proposals can be used to support sabbatical activities, including providing salary supplements in cases where the proposing institution does not provide full salary support.

HSI Program Resource Hubs (HSI-Hubs) . Through the ETSE competition the HSI program will continue to support the HSI Hubs, as part of the HSI-Net infrastructure. HSI-Hubs will provide support for specific areas of need and of importance to the HSI community and will serve the HSI community at large, and its stakeholders, including current and potential HSI awardees. The Hub proposal may focus on one or several critical aspects of HSIs such as institutional transformation, capacity building for specific institution types or specific disciplines, and research on broadening participation that may effectively impact STEM degree production.

Possible topics may include institutional transformation, capacity building at HSIs, STEM leadership development of all faculty to include scholars from historically underrepresented groups, research and dissemination, intersectionality and partnerships, effective frameworks designed for HSI; or any other area critical to the HSI community that supports the goals and strategies of the HSI program. This listing of possible thematic areas is not meant to be exclusive. Rather, NSF expects prospective PIs to define the need, cite evidence establishing the needs at HSIs, and offer a clear recommended plan with activities and measurable objectives and solutions. PIs are encouraged to put forward critical areas and ideas that are important to the HSI community and its unique and diverse ecosystem. All HSI- Hubs must propose and budget for activities related to the hub's critical areas.

The project description must:

• Provide a description for the development and implementation of a resource hub that would provide services, resources, and/or knowledge generation pertaining to specific areas of need in the HSI community.
• Articulate a discussion on how the hub identifies, develops, and promotes promising innovative research or initiatives and successful practices and frameworks that generate valuable new knowledge and systemic change for STEM education at HSIs.
• Develop a plan to provide intellectual infrastructure for collaborations with the potential to expand the knowledge base about HSIs.
• Discuss mechanisms for the dissemination of successful practices at HSIs, the context in which they work, and research results.
• Include a discussion to ensure that the HSI-Hub's activities are inclusive of the broad collection of institutions within the HSI typology (which includes 2-year colleges, rural colleges, PUIs, comprehensive public institutions, universities in Puerto Rico, private institutions, and research-intensive universities).
• Include a timeline of when activities would occur and who is responsible for key activities.
• Describe a sustainability plan that demonstrates the continuation of the Hub’s goals and efforts to achieve desired outcomes beyond the funding period.
• Include a project evaluation plan that is based on S.M.A.R.T. (Specific, Measurable, Attainable, Realistic, and Time-bound) goals. Evaluation plans should include a logic model as a supplementary document. In addition to quantitative and/or qualitative methodologies, it is encouraged that evaluation plans include measures of success for non-academic outcomes (i.e. STEM identity, academic self-concept, graduate aspirations.

It should also include a strategy to adapt successful existing frameworks for effectively diversifying the STEM enterprise and for student success at HSIs.

Proposers should be aware that Hub projects may be formally reviewed by NSF via a Site Visit or Reverse Site Visit during their second year to determine whether satisfactory progress has been made. Continued funding contingent on the result of the second-year review.

Additional Opportunities

Planning Proposals. The ETSE competition welcomes planning proposals for DDTT and ITT to develop, organize, and/or strengthen key data, human, and educational resources. Proposers should refer to Chapter II.F.1 of the NSF PAPPG for specific budget and proposal preparation guidelines relating to planning proposals and should note the target dates provided for this mechanism. As detailed in the PAPPG, PIs must contact a program director on the ETSE Competition to discuss their proposal idea and determine if a planning grant is appropriate. Furthermore, written permission to submit a planning proposal must be obtained from an HSI program director and uploaded at the time of submission.

Planning proposals can focus on the development of a future submission to the DDTT or ITT tracks. Examples of planning proposals include, but are not limited to the following:

• Identifying or developing models, frameworks, research or evaluation designs central to the development of a strong future submission to the DDTT or ITT Tracks.
• Developing or revising strategic plans for STEM education that leverage student-centered frameworks and practices.
• Strengthening collaborations among faculty, administration, and staff in STEM departments, divisions, schools, colleges.
• Strengthening collaborations among institutions of higher education, including two-year colleges and rural institutions.
• Establishing partnerships with industry and/or community organizations.
• Piloting systems and approaches to collect, organize, and analyze student data.

Workshops and Conferences. Proposals for workshops and conferences addressing topics that contribute to the goals of the HSI Program may be submitted at any time following consultation with an HSI Program Officer. Proposals for conferences addressing critical challenges in undergraduate STEM education and broadening STEM participation at HSIs may be submitted at any time following consultation with an HSI program officer.

III. Award Information

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

IV. Eligibility Information

Proposals may only be submitted by the following: With the exception of conference proposals, proposals may only be submitted by the following: To be eligible for funding an institution must meet the following criteria: Be an accredited institution of higher education. Offer Undergraduate STEM educational programs that result in certificates or degrees. Satisfy the definition of an HSI as specified in section 502 of the Higher Education Act of 1965 (20 U.S.C. 1101a) and meet the eligibility of an HSI by the U.S. Department of Education definition. Documentation (eligibility letter) from the Department of Education confirming HSI designation must be submitted as a supplemental document. Additional requirements to be eligible for funding in the Emerging Faculty Research Track (EFRT), the institution must meet the four criteria listed above at the time of submission and: Be an eligible Primarily Undergraduate Institution (PUI)[ 1 ] . Eligible PUIs are accredited colleges and universities (including two-year community colleges) that award Associate's degrees, Bachelor's degrees, and/or Master's degrees in NSF-supported fields, but have awarded 20 or fewer Ph.D./D.Sc.. degrees in all NSF-supported fields during the combined previous two academic years.

Additional Eligibility Info:

With the exception of conference proposals, proposals may only be submitted by the following: To be eligible for funding an institution must meet the following criteria: Be an accredited institution of higher education. Offer Undergraduate STEM educational programs that result in certificates or degrees. Satisfy the definition of an HSI as specified in section 502 of the Higher Education Act of 1965 (20 U.S.C. 1101a) and meet the eligibility of an HSI by the U.S. Department of Education definition. Documentation (eligibility letter) from the Department of Education confirming HSI designation must be submitted as a supplemental document. Additional requirements to be eligible for funding in the Emerging Faculty Research Track (EFRT), the institution must meet the four criteria listed above at the time of submission and: Be an eligible Primarily Undergraduate Institution (PUI)[ 1 ] . Eligible PUIs are accredited colleges and universities (including two-year community colleges) that award Associate's degrees, Bachelor's degrees, and/or Master's degrees in NSF-supported fields, but have awarded 20 or fewer Ph.D./D.Sc.. degrees in all NSF-supported fields during the combined previous two academic years.

V. Proposal Preparation And Submission Instructions

Full Proposal Preparation Instructions : Proposers may opt to submit proposals in response to this Program Solicitation via Research.gov or Grants.gov.

• Full Proposals submitted via Research.gov: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] . The Prepare New Proposal setup will prompt you for the program solicitation number.
• Full proposals submitted via Grants.gov: Proposals submitted in response to this program solicitation via Grants.gov should be prepared and submitted in accordance with the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov . The complete text of the NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: ( https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ). To obtain copies of the Application Guide and Application Forms Package, click on the Apply tab on the Grants.gov site, then click on the Apply Step 1: Download a Grant Application Package and Application Instructions link and enter the funding opportunity number, (the program solicitation number without the NSF prefix) and press the Download Package button. Paper copies of the Grants.gov Application Guide also may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

In determining which method to utilize in the electronic preparation and submission of the proposal, please note the following:

Collaborative Proposals. All collaborative proposals submitted as separate submissions from multiple organizations must be submitted via Research.gov. PAPPG Chapter II.E.3 provides additional information on collaborative proposals.

See PAPPG Chapter II.D.2 for guidance on the required sections of a full research proposal submitted to NSF. Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions.

Project Data Form : A Project Data Form must be submitted as part of all proposals. The information on this form is used to direct proposals to appropriate reviewers and to determine the characteristics of projects supported by the NSF Division of Undergraduate Education (DUE). In Research.gov, this form will appear as a required section of the proposal only after the ETSE solicitation number has been selected in Step 1 of the Proposal Creation Wizard. Grants.gov users should refer to Section VI.5.2. of the NSF Grants.gov Application Guide for specific instructions on how to submit the DUE Project Data Form.

Project Description: The project description should follow the requirements outlined in the NSF PAPPG and this solicitation. The narrative is limited to 15 single-spaced pages except for Planning proposals, which should adhere to the page limitation presented in the PAPPG. The Project Description must explain the project's motivating rationale, goals, objectives, deliverables, and describe how they address the goals of the HSI program. In addition to the required sections, all proposals to ETSE must include the track specific requirements noted in Section II and below. The following sections must be included in the 15-page project description with a bold heading.

Results from Prior NSF Support : If applicable the Project Description must include a section on results from prior NSF support. This must include support for projects pertaining to the proposed project that the PI or any of the co-PIs have been involved with (including sub-awards from NSF supported projects). This section should be aligned with the requirements given in the NSF PAPPG and contain specific outcomes and results to demonstrate the impact of the project. If the project team has had no prior support pertaining to the new proposal, this should be stated in the proposal. It is not required to have prior support to be successful in the HSI program.

Project Rationale, Significance and Objectives: The proposal should contain specific objectives that address the goals of the HSI program. The project rationale should build a compelling case for the proposed work, its approach, and how the work will advance knowledge regarding STEM education at HSIs. Proposals are expected to build on prior fundamental and/or applied research in STEM education or provide theoretical and empirical justification for the proposed project as needed. Justification may be accomplished through a combination of relevant literature, institutional data, and summaries of results from prior work.

Broader Impacts: Please note that per guidance in Chapter II of the NSF PAPPG, the Project Description must contain a separate section within the narrative labeled "Broader Impacts." This section should provide a discussion of the Broader Impacts of the proposed activities. Proposers may decide where to include this section within the Project Description.

Institutional Data Narrative: All DDTT and ITT proposals must include an Institutional Data Narrative to demonstrate the need for and potential benefits of the project. Proposers are encouraged to make appropriate use of disaggregated data in order to examine the intersectional identities of their students. These data may use any metrics that are appropriate for the project and may be tabular, graphical, or narrative in nature.

Commitment and Sustainability: All proposals must document an institutional commitment to faithfully carry out the project. This may include a discussion of how the institution will allocate existing and new resources to benefit the project. All proposals must demonstrate an institutional commitment to build upon or sustain any successful results of the project beyond the funding period.

Research Plan: All ETSE proposals must clearly describe efforts to generate knowledge through assessment, research, and/or evaluation. Projects must be situated in the existing practice, literature, and theory in the context of STEM education at an HSI and address questions of significance to those who work in and support HSIs. Assessing the impact of efforts as part of knowledge generation may be carried out by the PI and co-PIs or in partnership with an education researcher, evaluator, institutional research offices or other colleagues with measurement expertise.

Project Evaluation: All ETSE proposals must include a section that will describe how the project will assess progress, document outcomes, and evaluate success in achieving the project’s goals.

Guidelines for ETSE Proposals: All ETSE proposals must include a detailed evaluation plan, executed by an experienced and independent evaluator, that will provide both formative and summative feedback on the project’s progress towards its stated goals. Evaluation plans for IEP proposals should: (1) describe the aspects of the proposed project to be evaluated, (2) demonstrate the alignment between project activities and evaluation efforts, and (3) provide the design of the evaluation plan, including mechanisms for formative evaluation. Furthermore, evaluation plans for IEP proposals should include clear evaluation questions, quantitative and/or qualitative data streams beyond baseline institutional research data, specified methods for data analysis, and a mechanism for providing a written evaluation report to the project team at least annually.

The selected project evaluator should be independent from the project team but may be an individual from the same institution who has expertise in evaluation and assessment. Evaluators are expected to adhere to the American Evaluation Association's Guiding Principles for Evaluators ( https://www.eval.org/About/Guiding-Principles ), and project evaluations are expected to be consistent with standards established by the Joint Committee on Standards for Educational Evaluation ( http://www.jcsee.org/program-evaluation-standards-statements ).

If the submitting organization requires external evaluation consultants to be selected through a competitive bid process after an award is made, the proposer should mention the policy and describe the plans to select and collaborate with the evaluator once an award is made. Proposals without a named evaluator due to such a restriction should still include an evaluation plan reflecting the guidance provided above.

Project Management Plan : All proposals should include a project management plan indicating the roles and responsibilities of anyone serving as PI, co-PI, or senior personnel on the proposed project. Multi-institutional proposals including subawards should describe how project management responsibilities will be distributed across institutions as appropriate. The description provided should enable reviewers to assess the alignment of the team's experience and professional capabilities that are relevant to the proposed project. The project management plan may additionally describe other contributors as appropriate for the project, including STEM professionals, collaborators, researchers, advisory board members, evaluators, consultants, and contractors.

Dissemination Plan: All ETSE projects must include a plan to disseminate project outcomes to interested stakeholders and members of the HSI community. Innovative approaches that will strategically engage specific or broad audiences are encouraged.

Facilities, Equipment & Other Resources: See PAPPG Chapter II.D.2.g

Senior Personnel Documents: See PAPPG Chapter II.D.2.h.

Data Management and Sharing Plan: Proposers should provide a detailed data management and sharing plan. Transparency requires that the Federal agencies share how they are maximizing outcomes of Federal STEM investments and activities and ensuring broad benefit to the public. Proposers are highly encouraged to review Directorate-specific data management plan guidance, which can be accessed at https://www.nsf.gov/bfa/dias/policy/dmpdocs/ehr.pdf .

Mentoring Plan (if applicable): Required when funding is requested to support postdoctoral scholars or graduate students. See PAPPG Chapter II for instructions for the preparation of this item.

Special Information and Supplementary Documents : Please refer to the PAPPG Chapter II for additional guidance on Supplementary Documents. There is a distinction between supplementary documents and an appendix. Documents outside of what is described below may be interpreted as an appendix and can result in the proposal being returned without review.

• Letters of Collaboration : Proposals are encouraged to include letters of collaboration from internal and external partners and project contributors outside of the project PIs and co-PIs. The format of these letters should closely align with the suggested language provided in the PAPPG.
• Letters of Support from Key Administrators : All DDTT, ITT and Hub proposals must include letters of support from upper-level institutional administrators, at the level of Dean or higher, with responsibility for academic affairs and/or undergraduate STEM education in the proposal’s focal unit(s). These letters should outline concrete mechanisms for institutionalization and sustainability of the project activities and should be uploaded as supplemental documents. EFRT proposals should Include a letter of commitment from the PI's Department Chair or Dean stating that the PI will have institutional support in terms of allowance to utilize project-funded for release time, travel for research purposes, and access to existing research facilities. This should be included as a supplementary document.
• Biographical Sketch of the External Evaluator: If an evaluator is named in the proposal, then a biographical sketch can be included as a supplementary document. This must follow the NSF format for biosketches and must not be a resume, CV, or quote for services.
• Letter of Eligibility : The institution submitting a proposal to the ETSE program for tracks: DTT, ITT and EFRT must be a Hispanic-serving institution as defined by law in Section 502 of the Higher Education Act of 1965 (20 U.S.C. 1101a). A copy of the most recent Letter of Eligibility from the Department of Education must be included as a supplementary document. For collaborative proposals from multiple institutions, each submitting institution must be a Hispanic-serving institution and submit an Eligibility Letter. For collaborative proposals from a single institution, an Eligibility Letter is required only from the lead institution.
• Undergraduate Student Mentoring Plan : All ETSE proposals that plan to financially support undergraduate students, for instance as tutors, peer mentors, research assistants, or other trainees must include a student mentoring plan of a maximum of 1 page as a supplementary document. This document should discuss specific strategies that will be utilized to provide academic, professional, and other valuable types of mentoring to these students. A student mentoring plan is not required if a project solely intends to provide incentives to students serving as research subjects without additional training requirements or duties. This solicitation specific required plan is separate and distinct from the mentoring plan required by the PAPPG for proposals that request funding to support postdoctoral scholars or graduate students.

Information regarding the preparation of a Conference Proposal can be found in Section II of this solicitation and in PAPPG Chapter II.F.9.

Information regarding the preparation of a Planning Proposal can be found in Section II of this solicitation and in PAPPG Chapter II.F.1

Cost Sharing:

Other Budgetary Limitations

Funds requested for EFRT proposals are intended to support investigators’ specific needs and may include, but are not limited to the following: faculty release time; technical support for research; faculty and student professional development; travel to conferences; acquisition or upgrading of research equipment; development of special topics or seminar courses; and collaborative research efforts including travel to collaborating institutions or travel for collaborators to visit the PI at their home institution. The budget may include support for student trainees or post-doctoral fellows.

EFRT proposals can be used to support sabbatical activities, including providing salary supplements in cases where the proposing institution does not provide full salary support.

Collaborative Funding for non-HSIs:

Except for the ITT, the ETSE solicitation welcomes collaborative proposals. Collaborative Proposals from Multiple Institutions (PAPPG Chapter II.E.3.b) are encouraged as long as each lead and non-lead Institution is an HSI. If the collaboration involves institution(s) that are not HSIs, these institution(s) must be included as a non-lead subaward (PAPPG Chapter II.E.3.a) from the lead HSI. Collaborative proposals involving non-HSIs may not be submitted as Collaborative Proposals from Multiple Institutions (PAPPG Chapter II.E.3.b)

ETSE project funds may not be used for:

• Student scholarships (please see the S-STEM, SFS, or Robert Noyce Teacher Scholarship programs for scholarships for students).
• Equipment or instrumentation that does not significantly improve instructional capability, please see the Educational Instrumentation Track in the ELSPE solicitation.
• Teaching aids (e.g., films, slides, projectors, "drill and practice" software).
• Vehicles, trailers, laboratory furnishings, or general utility items such as office equipment, benches, tables, desks, chairs, storage cases, and routine supplies.
• Maintenance equipment and maintenance or service contracts.
• Modification, construction, or furnishing of laboratories or other buildings.
• Installation of equipment or instrumentation (as distinct from the on-site assembly of multi-component instruments--which is an allowable charge).

In accordance with 2 CFR § 200.413, the salaries of administrative and clerical staff should normally be treated as indirect costs (F&A). Direct charging of these costs may be appropriate only if all the conditions specified in 2 CFR § 200.413 are met.

Budget Preparation Instructions:

In FY 2024, the HSI program expects to fund new awards totaling $20,000,000, subject to the availability of funds.

Budgets and budget justifications submitted to this solicitation should reflect an equitable distribution of funds based on the proposed scope of the project. All budget requests must be consistent with the proposed scope and duration of the project in its track and cannot exceed the maximum permitted in its track. Proposers to the ETSE solicitation should provide a budget for each year of support requested.

D. Research.gov/Grants.gov Requirements

For Proposals Submitted Via Research.gov:

To prepare and submit a proposal via Research.gov, see detailed technical instructions available at: https://www.research.gov/research-portal/appmanager/base/desktop?_nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission.html . For Research.gov user support, call the Research.gov Help Desk at 1-800-381-1532 or e-mail [email protected] . The Research.gov Help Desk answers general technical questions related to the use of the Research.gov system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

For Proposals Submitted Via Grants.gov:

Before using Grants.gov for the first time, each organization must register to create an institutional profile. Once registered, the applicant's organization can then apply for any federal grant on the Grants.gov website. Comprehensive information about using Grants.gov is available on the Grants.gov Applicant Resources webpage: https://www.grants.gov/applicants . In addition, the NSF Grants.gov Application Guide (see link in Section V.A) provides instructions regarding the technical preparation of proposals via Grants.gov. For Grants.gov user support, contact the Grants.gov Contact Center at 1-800-518-4726 or by email: [email protected] . The Grants.gov Contact Center answers general technical questions related to the use of Grants.gov. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation.

Submitting the Proposal: Once all documents have been completed, the Authorized Organizational Representative (AOR) must submit the application to Grants.gov and verify the desired funding opportunity and agency to which the application is submitted. The AOR must then sign and submit the application to Grants.gov. The completed application will be transferred to Research.gov for further processing.

The NSF Grants.gov Proposal Processing in Research.gov informational page provides submission guidance to applicants and links to helpful resources including the NSF Grants.gov Application Guide , Grants.gov Proposal Processing in Research.gov how-to guide , and Grants.gov Submitted Proposals Frequently Asked Questions . Grants.gov proposals must pass all NSF pre-check and post-check validations in order to be accepted by Research.gov at NSF.

When submitting via Grants.gov, NSF strongly recommends applicants initiate proposal submission at least five business days in advance of a deadline to allow adequate time to address NSF compliance errors and resubmissions by 5:00 p.m. submitting organization's local time on the deadline. Please note that some errors cannot be corrected in Grants.gov. Once a proposal passes pre-checks but fails any post-check, an applicant can only correct and submit the in-progress proposal in Research.gov.

Proposers that submitted via Research.gov may use Research.gov to verify the status of their submission to NSF. For proposers that submitted via Grants.gov, until an application has been received and validated by NSF, the Authorized Organizational Representative may check the status of an application on Grants.gov. After proposers have received an e-mail notification from NSF, Research.gov should be used to check the status of an application.

VI. NSF Proposal Processing And Review Procedures

Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review. All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process. Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional. Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards. A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1.

A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www.nsf.gov/bfa/dias/policy/merit_review/ .

Proposers should also be aware of core strategies that are essential to the fulfillment of NSF's mission, as articulated in Leading the World in Discovery and Innovation, STEM Talent Development and the Delivery of Benefits from Research - NSF Strategic Plan for Fiscal Years (FY) 2022 - 2026 . These strategies are integrated in the program planning and implementation process, of which proposal review is one part. NSF's mission is particularly well-implemented through the integration of research and education and broadening participation in NSF programs, projects, and activities.

One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy. NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports.

A. Merit Review Principles and Criteria

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education. To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes." NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects.

1. Merit Review Principles

These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards. Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply:

• All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge.
• NSF projects, in the aggregate, should contribute more broadly to achieving societal goals. These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project activities may be based on previously established and/or innovative methods and approaches, but in either case must be well justified.
• Meaningful assessment and evaluation of NSF funded projects should be based on appropriate metrics, keeping in mind the likely correlation between the effect of broader impacts and the resources provided to implement projects. If the size of the activity is limited, evaluation of that activity in isolation is not likely to be meaningful. Thus, assessing the effectiveness of these activities may best be done at a higher, more aggregated, level than the individual project.

With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project. Thus, individual projects should include clearly stated goals, specific descriptions of the activities that the PI intends to do, and a plan in place to document the outputs of those activities.

These three merit review principles provide the basis for the merit review criteria, as well as a context within which the users of the criteria can better understand their intent.

2. Merit Review Criteria

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.D.2.d(i). contains additional information for use by proposers in development of the Project Description section of the proposal). Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.D.2.d(i), prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit: The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts: The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

• Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
• Benefit society or advance desired societal outcomes (Broader Impacts)?
• To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
• Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
• How well qualified is the individual, team, or organization to conduct the proposed activities?
• Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and other underrepresented groups in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the United States; and enhanced infrastructure for research and education.

Proposers are reminded that reviewers will also be asked to review the Data Management and Sharing Plan and the Mentoring Plan, as appropriate.

Additional Solicitation Specific Review Criteria

In addition to the two NSF criteria for Intellectual Merit and Broader Impacts, the additional HSI proposal review criteria for DDTT, ITT and Hub proposals are as follows:

• How effectively does the design of project activities (e.g., student supports, evaluation, research, etc.) take into account students’ membership in populations described by demographic characteristics or lived experiences (e.g., low-income, commuter, parenting, first-generation, or veteran status) to reflect the HSI context and the community it serves?

The following criterion is also in effect for ITT and DDTT proposals.

• How effectively do the proposed goals, objectives, and activities demonstrate potential to drive institutional or departmental transformation that will result in a more student-centered STEM environment and increase the likelihood of success for students across a diversity of populations?

B. Review and Selection Process

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review.

Reviewers will be asked to evaluate proposals using two National Science Board approved merit review criteria and, if applicable, additional program specific criteria. A summary rating and accompanying narrative will generally be completed and submitted by each reviewer and/or panel. The Program Officer assigned to manage the proposal's review will consider the advice of reviewers and will formulate a recommendation.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF strives to be able to tell proposers whether their proposals have been declined or recommended for funding within six months. Large or particularly complex proposals or proposals from new recipients may require additional review and processing time. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director acts upon the Program Officer's recommendation.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements or the Division of Acquisition and Cooperative Support for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

VII. Award Administration Information

A. notification of the award.

Notification of the award is made to the submitting organization by an NSF Grants and Agreements Officer. Organizations whose proposals are declined will be advised as promptly as possible by the cognizant NSF Program administering the program. Verbatim copies of reviews, not including the identity of the reviewer, will be provided automatically to the Principal Investigator. (See Section VI.B. for additional information on the review process.)

B. Award Conditions

An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto; (2) the budget, which indicates the amounts, by categories of expense, on which NSF has based its support (or otherwise communicates any specific approvals or disapprovals of proposed expenditures); (3) the proposal referenced in the award notice; (4) the applicable award conditions, such as Grant General Conditions (GC-1)*; or Research Terms and Conditions* and (5) any announcement or other NSF issuance that may be incorporated by reference in the award notice. Cooperative agreements also are administered in accordance with NSF Cooperative Agreement Financial and Administrative Terms and Conditions (CA-FATC) and the applicable Programmatic Terms and Conditions. NSF awards are electronically signed by an NSF Grants and Agreements Officer and transmitted electronically to the organization via e-mail.

*These documents may be accessed electronically on NSF's Website at https://www.nsf.gov/awards/managing/award_conditions.jsp?org=NSF . Paper copies may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

More comprehensive information on NSF Award Conditions and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Administrative and National Policy Requirements

Build America, Buy America

As expressed in Executive Order 14005, Ensuring the Future is Made in All of America by All of America’s Workers (86 FR 7475), it is the policy of the executive branch to use terms and conditions of Federal financial assistance awards to maximize, consistent with law, the use of goods, products, and materials produced in, and services offered in, the United States.

Consistent with the requirements of the Build America, Buy America Act (Pub. L. 117-58, Division G, Title IX, Subtitle A, November 15, 2021), no funding made available through this funding opportunity may be obligated for an award unless all iron, steel, manufactured products, and construction materials used in the project are produced in the United States. For additional information, visit NSF’s Build America, Buy America webpage.

Special Award Conditions:

HSI Program Evaluation: Projects are required to cooperate and participate in additional program efforts to gather data and information to support HSI program monitoring and evaluation. Projects are furthermore required to participate, if asked, in any efforts to synthesize and disseminate program outcomes via current or future HSI-Net Centers.

Open Access to Project Products: Developers of new materials are required to license all work (except for computer software source code, discussed below) created with the support of the grant under either the 3.0 Unported or 3.0 United States version of the Creative Commons Attribution (CC BY), Attribution-ShareAlike (CC BY-SA), or Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. These licenses allow subsequent users to copy, distribute, transmit, and adapt the copyrighted work and requires such users to attribute the work in the manner specified by the grantee. Notice of the specific license used would be affixed to the work and displayed clearly when the work is made available online. For general information on these Creative Commons licenses, please visit http://creativecommons.org/licenses/ .

It is expected that computer software source code developed or created with NSF funds be released under an intellectual property license that allows others to use and build upon the work. The grantee may release all new source code developed or created with IUSE grant funds under an open license acceptable to the Free Software Foundation ( http://gnu.org/licenses/ ) and/or the Open-Source Initiative ( http://opensource.org/licenses/ ).

C. Reporting Requirements

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period. (Some programs or awards require submission of more frequent project reports). No later than 120 days following expiration of a grant, the PI also is required to submit a final annual project report, and a project outcomes report for the general public.

Failure to provide the required annual or final annual project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award. PIs should examine the formats of the required reports in advance to assure availability of required data.

PIs are required to use NSF's electronic project-reporting system, available through Research.gov, for preparation and submission of annual and final annual project reports. Such reports provide information on accomplishments, project participants (individual and organizational), publications, and other specific products and impacts of the project. Submission of the report via Research.gov constitutes certification by the PI that the contents of the report are accurate and complete. The project outcomes report also must be prepared and submitted using Research.gov. This report serves as a brief summary, prepared specifically for the public, of the nature and outcomes of the project. This report will be posted on the NSF website exactly as it is submitted by the PI.

More comprehensive information on NSF Reporting Requirements and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

VIII. Agency Contacts

Please note that the program contact information is current at the time of publishing. See program website for any updates to the points of contact.

General inquiries regarding this program should be made to:

For questions related to the use of NSF systems contact:

• NSF Help Desk: 1-800-381-1532
• Research.gov Help Desk e-mail: [email protected]

For questions relating to Grants.gov contact:

Grants.gov Contact Center: If the Authorized Organizational Representatives (AOR) has not received a confirmation message from Grants.gov within 48 hours of submission of application, please contact via telephone: 1-800-518-4726; e-mail: [email protected] .

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences . Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website .

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov .

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.F.7 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

The National Science Foundation promotes and advances scientific progress in the United States by competitively awarding grants and cooperative agreements for research and education in the sciences, mathematics, and engineering. To get the latest information about program deadlines, to download copies of NSF publications, and to access abstracts of awards, visit the NSF Website at 2415 Eisenhower Avenue, Alexandria, VA 22314 (NSF Information Center) (703) 292-5111 (703) 292-5090 Send an e-mail to: or telephone: (703) 292-8134 (703) 292-5111 Privacy Act And Public Burden Statements The information requested on proposal forms and project reports is solicited under the authority of the National Science Foundation Act of 1950, as amended. The information on proposal forms will be used in connection with the selection of qualified proposals; and project reports submitted by proposers will be used for program evaluation and reporting within the Executive Branch and to Congress. The information requested may be disclosed to qualified reviewers and staff assistants as part of the proposal review process; to proposer institutions/grantees to provide or obtain data regarding the proposal review process, award decisions, or the administration of awards; to government contractors, experts, volunteers and researchers and educators as necessary to complete assigned work; to other government agencies or other entities needing information regarding proposers or nominees as part of a joint application review process, or in order to coordinate programs or policy; and to another Federal agency, court, or party in a court or Federal administrative proceeding if the government is a party. Information about Principal Investigators may be added to the Reviewer file and used to select potential candidates to serve as peer reviewers or advisory committee members. See System of Record Notices , NSF-50 , "Principal Investigator/Proposal File and Associated Records," and NSF-51 , "Reviewer/Proposal File and Associated Records.” Submission of the information is voluntary. Failure to provide full and complete information, however, may reduce the possibility of receiving an award. An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0058. Public reporting burden for this collection of information is estimated to average 120 hours per response, including the time for reviewing instructions. Send comments regarding the burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to: Suzanne H. Plimpton Reports Clearance Officer Policy Office, Division of Institution and Award Support Office of Budget, Finance, and Award Management National Science Foundation Alexandria, VA 22314 X. Appendix References: 1 Definition of PUI: https://carnegieclassifications.acenet.edu/carnegie-classification/classification-methodology/basic-classification/ 2 Núñez, A.M., 2014. Advancing an intersectionality framework in higher education: Power and Latino postsecondary opportunity. In Higher education: Handbook of theory and research (pp. 33-92). Springer, Dordrecht. 3 McNair et al. (2022) Becoming a Student-Ready College: A New Culture of Leadership for Student Success. Hoboken, NJ: Josey-Bass. 4 Garcia, Gina A. “Defining “Servingness” at HSIs in Practice at Hispanic-Serving Institutions (HSIs).” Hispanic Serving Institutions (HSIs) in Practice. Charlotte: Information Age Publishing, 2020, xi-xxvi. 5 Dweck, C. S., & Yeager, D. S. (2019). Mindsets: A View From Two Eras. Perspectives on Psychological Science, 14(3), 481-496. https://doi.org/10.1177/1745691618804166 6 Developing a Theory of Change: Practical Theory if Change Guidance, Templates and Examples. https://www.aecf.org/resources/theory-of-change?gad_source=1&gclid=CjwKCAiAjrarBhAWEiwA2qWdCAc_GqSz611wuDm742yvbRLUAJoTe4BdLo4wHiOFAiBOyA8YKYHepxoCESMQAvD_BwE 425 IMAGES 130+ Correlational Research Topics: That You Need To Know Best 151+ Quantitative Research Topics for STEM Students 120+ Great Correlational Research Topics For Students [2024] 120+ Great Correlational Research Topics For Students [2024] 151+ Great Quantitative Research Topics For STEM Students 189+ Good Quantitative Research Topics For STEM Students VIDEO Unit 1: Correlational Research (AP Psychology) BIOLOGY 1st QUARTER TOPICS [Grade 12 STEM Subject] #shs #stem #generalbiology #stemeducation Experimental- Descriptive- Correlational research l Types of Research Lesson 4 : Correlational Research 1st Quarter Chemistry Topics || STEM Grade 11 Subject || #shs #stem Introduction to Psychology: Correlational Research Strategies COMMENTS 130+ Correlational Research Topics: That You Need To Know Correlation Topic Examples for STEM Students. 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