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1. Investigating the impact of peer supplemental instruction on underprepared and historically underserved students in introductory STEM courses

   https://doi.org/10.1186/s40594-022-00372-w  

   Anfuso, Chantelle ; Awong-Taylor, Judy ; Curry Savage, Jamye ; Johnson, Cynthia ; Leader, Tirza ; Pinzon, Katherine ; Shepler, Benjamin ; Achat-Mendes, Cindy ( September 2022 , International Journal of STEM Education)

   Supplemental instruction (SI) is a well-established mode of direct academic support, used in a wide variety of courses. Some reports have indicated that SI and similar peer-led academic support models particularly benefit students identifying with historically underserved racial/ethnic groups in STEM. However, these studies have not explicitly examined the role of prior academic experiences, an important consideration in college success. We report on the impact of a modified SI model, Peer Supplemental Instruction (PSI), on student success in introductory STEM courses at a diverse access institution. This study focuses on PSI’s impact on the academic performance of students identifying with historically underserved racial/ethnic groups, while also considering the effects of prior academic experiences.

   Data were aggregated for nine courses over five semesters to produce a robust data set (n = 1789). PSI attendees were representative of the overall student population in terms of previous academic experiences/performance (as determined by high school GPA) and self-identified racial/ethnic demographics. Frequent PSI attendance was correlated with a significant increase in AB rates (average increase of 29.0 percentage points) and reduction in DFW rates (average decrease of 26.1 percentage points) when comparing students who attended 10 + vs. 1–2 PSI sessions. Overall, students identifying as Black/African American received the largest benefit from PSI. These students experienced a significant increase in their final course GPA when attending as few as 3–5 PSI sessions, and exhibited the largest increase in AB rates (from 28.7 to 60.5%) and decrease in DFW rates (from 47.1 to 14.8%) when comparing students who attended 10 + vs. 1–2 sessions. However, students with similar HS GPAs experienced similar benefits from PSI, regardless of self-identified race/ethnicity.

   The data presented here suggest that PSI particularly benefitted underprepared students in their introductory STEM courses. Since students identifying with historically underserved racial/ethnic groups have traditionally had inequitable K–12 educational experiences, they enter college less prepared on average, and thus particularly benefit from PSI. PSI, in conjunction with additional strategies, may be a useful tool to help rectify the results of systemic educational inequities for students identifying with historically underserved racial/ethnic groups.

    

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2. STEM courses are harder: evaluating inter-course grading disparities with a calibrated GPA model

   https://doi.org/10.1186/s40594-022-00343-1  

   Tomkin, Jonathan H. ; West, Matthew ( March 2022 , International Journal of STEM Education)

   Grades in college and university STEM courses are an important determinant of student persistence in STEM fields. Recent studies have used the grade offset/grade penalty method to explore why students have lower grades in STEM courses than their GPAs would predict. The results of these studies are in doubt; however, as they use GPA as a reliable measure of academic performance, which is a disputed assumption. Using a predictive model of student performance, it is possible to produce a more accurate measure of academic performance than the observed GPA and discover if STEM courses are graded more stringently, and under which circumstances.

   A weighted logistic model of GPA better predicts academic performance than the observed GPA. Using this calibrated GPA it is found that the grade offset method predicts that STEM courses, departments, and programs grade significantly more stringently than non-STEM courses. The average grade difference between STEM and non-STEM course grades and GPAs is around four tenths of a grade point. An exception is general education courses offered by STEM departments, which are graded with the same leniency as non-STEM courses. Grade offset calculations that use the observed GPA systematically underestimate the negative offset in STEM grading relative to calculations that use the calibrated GPA. The calibrated GPA is much more highly correlated with standardized tests such as the ACT (r = 0.49) than the observed GPA is (r = 0.25).

   Observed GPA is a systematically biased measure of academic performance, and should not be used as a basis for determining the presence of grading inequity. Logistic models of GPA provide a more reliable measure of academic performance. When comparing otherwise academically similar students, we find that STEM students have substantially lower grades and GPAs, and that this is the consequence of harder (more stringent) grading in STEM courses.

    

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3. Factors Influencing Course Withdrawal in Fundamental Engineering Courses in a Research 1 University

   Woods, J.C. ; Chowdhury, T.M. ; Murzi, H. ; Soledad, M ; Knight, D.B. ; Grohs, J.R. ; Case, S.W. ; Smith, N. ( June 2019 , ASEE Annual Conference proceedings)

   Engineering students develop competencies in fundamental engineering courses (FECs) that are critical for success later in advanced courses and engineering practice. Literature on the student learning experience, however, associate these courses with challenging educational environments (e.g., large class sizes) and low student success rates. Challenging educational environments are particularly prevalent in large, research-intensive institutions. To address concerns associated with FECs, it is important to understand prevailing educational environments in these courses and identify critical points where improvement and change is needed. The Academic Plan Model provides a systematic way to critically examine the factors that shape the educational environment. It includes paths for evaluation and adjustment, allowing educational environments to continuously improve. The Model may be applied to various levels in an institution (e.g., course, program, college), implying that a student’s entire undergraduate learning experience is the result of several enacted academic plans that are interacting with each other. Thus, understanding context-specific factors in a specific educational environment will yield valuable information affecting the undergraduate experience, including concerns related to attrition and persistence. In order to better understand why students are not succeeding in large foundational engineering courses, we developed a form to collect data on why students withdraw from certain courses. The form was included as a requirement during the withdrawal process. In this paper, we analyzed course withdrawal data from several academic departments in charge of teaching large foundational engineering courses, and institutional transcript data for the Spring 2018 semester. The withdrawal dataset includes the final grades that students expected to receive in the course and the factors that influenced their decision to withdraw. Institutional transcript data includes demographic information (e.g., gender, major), admissions data (e.g., SAT scores, high school GPA), and institutional academic information (e.g., course grades, cumulative GPA). Results provide a better understanding of the main reasons students decide to withdraw from a course, including having unsatisfactory grades, not understanding the professor, and being overwhelmed with work. We also analyzed locus of control for the responses, finding that the majority of students withdrawing courses consider that the problem is outside of their control and comes from an external source. We provide analysis by different departments and different specific courses. Implications for administrators, practitioners, and researchers are provided. 

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4. For physics majors, gender differences in introductory physics do not inform future physics performance

   https://doi.org/10.1088/1361-6404/ab9f1d  

   Whitcomb, Kyle M. ; Singh, Chandralekha ( October 2020 , European Journal of Physics)

   Analysis of institutional data for physics majors showing predictive relationships between required mathematics and physics courses in various years is important for contemplating how the courses build on each other and whether there is need to make changes to the curriculum for the majors to strengthen these relationships. We used 15 years of institutional data at a US-based large research university to investigate how introductory physics and mathematics courses predict male and female physics majors’ performance on required advanced physics and mathematics courses. We used structure equation modeling (SEM) to investigate these predictive relationships and find that among introductory and advanced physics and mathematics courses, there are gender differences in performance in favor of male students only in the introductory physics courses after controlling for high school GPA. We found that a measurement invariance fully holds in a multi-group SEM by gender, so it was possible to carry out analysis with gender mediated by introductory physics and high school GPA. Moreover, we find that these introductory physics courses that have gender differences do not predict performance in advanced physics courses. In other words, students could be using invalid data about their introductory physics performance to make their decision about whether physics is the right field for them to pursue, and those invalid data in introductory physics favor male students. Also, introductory mathematics courses predict performance in advanced mathematics courses which in turn predict performance in advanced physics courses. Furthermore, apart from the introductory physics courses that do not predict performance in future physics courses, there is a strong predictive relationship between the sophomore, junior and senior level physics courses.

    

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5. GPA Patterns of Black Mechanical Engineering Students

   Manning, Jessica ; Mobley, Catherine ; Orr, Marisa ; Brawner, Catherine ; Brent, Rebecca ; Tidwell, Michael L. ( August 2022 , Proceedings of the 2022 Annual Conference of the American Society for Engineering Education. https://peer.asee.org/41089)

   In recent years, research has associated grade point average (GPA) with a variety of student outcomes during their undergraduate careers. The studies link higher GPAs to students being more likely to graduate in their major, while lower GPAs have been linked to students switching majors or leaving the institution. Further research, which focuses on how Black female and male students remain successful in different engineering degrees, is necessary to identify the underlying elements contributing to their entrance into and exit from engineering disciplines. This quantitative examination of trends among the GPAs of Black women and men is part of a larger NSF-funded mixed-methods study that includes in-depth student interviews of Black students who persisted in and switched from ME. In this quantitative paper, we examine the GPA patterns of Black students in Mechanical Engineering (ME). Students who have ever enrolled in ME have four potential, mutually exclusive, outcomes: 1) they can persist for 12 semesters without graduating; 2) they can graduate in ME within 12 semesters; 3) they can switch to another major; or 4) they can leave school. In this research, we identify the most common GPA patterns associated with graduated ME students. We hypothesize a relationship between distinct GPA patterns and whether a student persists in ME, graduates in ME, switches away from ME, or leaves the institution altogether. This quantitative investigation uses the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) to collect the cumulative GPA of ME students at each term. We use a functional cluster analysis approach to group similar patterns. First, a function is fit to each student record. Then a cluster analysis is conducted on the function parameters to identify natural groupings in the data. Once students are grouped according to their GPA profile, we examine the other characteristics and outcomes of the group. We present a visual quantitative analysis of the patterns in the GPAs of Black women and men who enroll in ME. Clustering analysis suggests that first-time-in-college (FTIC) Black female students in ME who graduated have a higher proportion of students in the higher GPA clusters than the proportion of FTIC Black male students who graduated in ME. A higher proportion of the male student population is clustered in the lower GPA cluster groups as compared to women in the lower GPA cluster groups. A higher proportion of students who graduated are in the higher GPA clusters than the proportion of graduated students in the lower GPA clusters. 

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