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  1. A comprehensive external evaluation of the First2 Network. 
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    Free, publicly-accessible full text available September 30, 2025
  2. Almerico, Gina ; Hall, Kelly (Ed.)
    For many years, studies have shown that undergraduate research experiences have numerous positive effects on students. This study seeks to determine if students get more benefit from having multiple undergraduate research experiences. Specifically, this study considered how students' self-efficacy and STEM belonging changed as a result of multiple experiences and how students' perceptions of the mentoring they receive related to the growth in their self­efficacy. In-depth interviews produced several case studies that outline specific benefits students gained from having multiple undergraduate research experiences. Faculty interviews enlightened the research related to the types of undergraduate research offered in various colleges and universities. Results indicate that students do benefit greaUy from having multiple undergraduate research experiences. Self-efficacy and STEM belonging are significantly higher for students who have had multiple experiences versus those students who only had one experience. There is also a correlation between the instrumental mentoring students receive and the development of their self-efficacy. Students shared the excellent benefits they gained from having multiple undergraduate research experiences and the life-altering consequences of instrumental mentoring. 
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    Free, publicly-accessible full text available August 6, 2025
  3. Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on variables available in the first few weeks of the class which could potentially allow for early interventions to help at-risk students. Multiple types of variables were used in the model: in-class variables (average homework and clicker quiz scores), institutional variables [college grade point average (GPA)], and noncognitive variables (self-efficacy). The substantial imbalance between the pass and fail rates of the course, with only about 10% of students failing, required modification to the machine learning algorithms. Decision threshold tuning and upsampling were successful in improving performance for at-risk students. Logistic regression combined with a decision threshold tuned to maximize balanced accuracy yielded the strongest classifier, with a DF accuracy of 83% and an ABC accuracy of 81%. Measures of variable importance involving changes in balanced accuracy identified homework grades, clicker grades, college GPA, and the fraction of college classes successfully completed as the most important variables in predicting success in introductory physics. Noncognitive variables added little predictive power to the models. Classification models with performance near the best-performing models using the full set of variables could be constructed with very few variables (homework average, clicker scores, and college GPA) using straightforward to implement algorithms, suggesting the application of these technologies may be fairly easy to include in many physics classes.

    Published by the American Physical Society2024 
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    Free, publicly-accessible full text available May 1, 2025
  4. Horzempa, Joseph (Ed.)

    Mathematics serves as the cornerstone of every STEM major, and proficiency in this subject is paramount for achieving academic success, societal integration, and meaningful participation in the workplace. However, numerous students encounter challenges in mastering mathematical concepts. In West Virginia, students face an additional hurdle, poverty, with households in the state ranking among the lowest earners in the nation. This can have significant implications for the funding and resources available to the West Virginia School System. Recognizing the interplay between socioeconomic factors and academic performance is crucial for devising targeted interventions and providing necessary support. In pursuit of this understanding, during the 2023-2024 school year, we administered a survey in 50 STEM courses at Fairmont State University (n = 447) and collected data from the LEAD Center on tutoring. Preliminary results show that math made up 50% and chemistry 37% of requested tutoring subjects in STEM and 69.7% receiving tutoring were in their first two years. Survey demographics are the following: gender identity: 212 male, 212 female, and 4 non-binary; economic status: 82% were middle class, 13% working class, and 4% on assistance; 29.5% are first generation; 62% are within their first two years of study. The most common math completed in high school was algebra (24.8%), trigonometry (34%), and calculus (24.4%). There was a strong correlation between major and enjoying math (0.99) and having a good math teacher in high school (0.62). Through data analysis and working with students directly, our goal is to discover why students struggle with math and how to better prepare future generations.  

     
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    Free, publicly-accessible full text available April 18, 2025
  5. Prompting students to practice metacognition, and encouraging the growth of metacognitive strategies improves student success in chemistry coursework. In this study, students were encouraged to submit weekly metacognitive reports providing (i) their time devoted to the course, including hours devoted to out-of-class additional study, and (ii) detailed summaries of additional study activities. This study was administered to students in two different courses, introductory chemistry and general chemistry I. Introductory chemistry students submitted 1,513 metacognitive reports with self-reported overall means of 2.81 hour per week devoted to attending lecture, 4.75 hours per week engaged in additional study, and 7.56 hours per week devoted to the course overall. Weekly patterns indicate that students’ additional study was focused on days of the week that preceded formal assessments. Our expectation was that general chemistry I students would report more time devoted to additional study outside of class than introductory chemistry students because of the preparatory nature of the course. General chemistry I students submitted 3,551 weekly metacognitive reports with self-reported overall means of 2.83 hours per week devoted to attending lecture, 4.83 hours per week engaged in additional study, and 7.66 hours per week devoted to the course overall. We will discuss the seeming equivalence of time spent on out-of-class additional study for the two populations of students. In addition, we will present qualitative analysis of students’ out-of-class study strategies, including classification of study strategies as deep or surface-level. This work is partially supported by the NSF-funded First2 Network.

     
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    Free, publicly-accessible full text available April 18, 2025
  6. It is well known that high impact practices such as student learning communities and peer-mentoring, early engagement in research and faculty-student relationship building can have a positive influence on undergraduate student engagement and retention in their chosen field of study. The First2 Network is a Networked Improvement Community comprised of students, faculty, staff and administrators from eight higher education institutions in West Virginia and one in Kentucky, that has, over the past six years, engaged in creating interventions to implement and test these high impact practices with the aim of doubling the graduation rate of first-generation and other underrepresented STEM students. Relying on data collected, mostly from WVU, we see very promising outcomes on the persistence of STEM majors. These results will be shared. However, our data story is incomplete as there is no mechanism to sort data on first-generation status across all universities and colleges in the state. The value of this information and its inclusion in a future state funding formula will be explored.

     
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    Free, publicly-accessible full text available April 18, 2025
  7. Free, publicly-accessible full text available April 1, 2025
  8. The First2 Network is an alliance of higher education institutions across the State of West Virginia striving to improve science, technology, engineering or math (STEM) education by supporting rural, first-generation, and underrepresented college students pursuing STEM majors.  Over the summers of 2019 and 2020, the First2 Network delivered two-week summer research immersion experiences at various institutions throughout West Virginia. The 2019 program was delivered on-campus at four universities while the 2020 program was delivered virtually, due to the COVID-19 pandemic, across nine sites. Before and after the immersion experience, students who participated in the program completed a variety of survey questionnaires for the assessment of their interests, expectations, identity and belonging in STEM. We found that the in-person experience in 2019 had better outcomes compared to the virtual experience, suggesting students conducting research directly under their faculty supervisors in-person and on-site will have a more positive impact on their STEM education and career. However, participation in the virtual format still resulted in an improvement in belonging and STEM identity, indicating that connecting with students remotely is still worthwhile when it is the most viable option. The student population in Appalachia faces a number of academic barriers, so there is much to gain by finding new ways to reach as many students as possible with early career development programs. 
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    Free, publicly-accessible full text available March 1, 2025
  9. Lamberg, T ; Moss, Diana (Ed.)