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1. The Impact of Learning Assistants on Inequities in Physics Student Outcomes

   https://doi.org/10.1119/perc.2016.pr.085  

   Van Dusen, Ben ; White, Jada-Simone S. ; Roualdes, Edward A. ( December 2016 , Proc. 2016 Physics Education Research Conference)

   This study investigates how Learning Assistants (LAs) and related course features are associated with inequities in student learning in introductory university physics courses. 2,868 physics students’ paired pre- and post-test scores on concept inventories from 67 classes in 16 LA Alliance member institutions are examined in this investigation. The concept inventories included the Force Concept Inventory, Force and Motion Conceptual Evaluation, and the Conceptual Survey of Electricity and Magnetism. Our analyses include a multiple linear regression model that examines the impact of student (e.g. gender and race) and course level variables (e.g. presence of LAs and Concept Inventory used) on student learning outcomes (Cohen’s d effect size) across classroom contexts. The presence of LAs was found to either remove or invert the traditional learning gaps between students from dominant and non-dominant populations. Significant differences in student performance were also found across the concept inventories. 

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2. Systemic inequities in introductory physics courses: the impacts of learning assistants

   https://doi.org/10.1119/perc.2017.pr.095  

   Van Dusen, Ben ; Nissen, Jayson M. ( January 2018 , Proc. 2017 Physics Education Research Conference)

   Creating equitable performance outcomes among students is a focus of many instructors and researchers. One focus of this effort is examining disparities in physics student performance across genders, which is a well-established problem. Another less common focus is disparities across racial and ethnic groups, which may have received less attention due to low representation rates making it difficult to identify gaps in their performance. In this investigation we examined associations between Learning Assistant (LA) supported courses and improved equity in student performance. We built Hierarchical Linear Models of student performance to investigate how performance differed by gender and by race/ethnicity and how LAs may have moderated those differences. Data for the analysis came from pre-post concept inventories in introductory mechanics courses collected through the Learning About STEM Student Outcomes (LASSO) platform. Our models show that gaps in performance across genders and races/ethnicities were similar in size and increased from pre to post instruction. LA-support is meaningfully and reliably associated with improvement in overall student performance but not with shifts in within-course performance gaps. 

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3. The impacts of learning assistants on student learning of physics

   https://doi.org/10.1119/perc.2016.pr.091  

   White, Jada-Simone S. ; Van Dusen, Ben ; Roualdes, Edward A. ( December 2016 , Proc. 2016 Physics Education Research Conference)

   This study investigated whether and how Learning Assistant (LA) support is linked to student outcomes in Physics courses nationwide. Paired student concept inventory scores were collected over three semesters from 3,753 students, representing 69 courses, and 40 instructors, from 17 LA Alliance member institutions. Each participating student completed an online concept inventory at the beginning (pre) and end (post) of each term. The physics concept inventories tested included the Force Concept Inventory (FCI), Conceptual Survey of Electricity and Magnetism (CSEM), Force and Motion Concept Evaluation (FMCE) and the Brief Electricity and Magnetism Assessment (BEMA). Across instruments, Cohen’s d effect sizes were 1.4 times higher, on average, for courses supported by LAs compared to courses without LA support. Preliminary findings indicate that physics students' outcomes may be most effective when LA support is utilized in laboratory settings (1.9 times higher than no LA support) in comparison to lecture (1.4 times higher), recitations (1.5 times higher), or unknown uses (1.3 times higher). Additional research will inform LA-implementation best practices across disciplines. 

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4. Longitudinal Associations between Learning Assistants and Instructor Effectiveness

   https://doi.org/10.1119/perc.2017.pr.015  

   Caravez, Daniel ; De La Torre, Angelica ; Nissen, Jayson M. ; Van Dusen, Ben ( January 2018 , Proc. 2017 Physics Education Research Conference)

   A central goal of the Learning Assistant (LA) model is to improve students’ learning of science through the transformation of instructor practices. There is minimal existing research on the impact of college physics instructor experiences on their effectiveness. To investigate the association between college introductory physics instructors’ experiences with and without LAs and student learning, we drew on data from the Learning About STEM Student Outcomes (LASSO) database. The LASSO database provided us with student-level data (concept inventory scores and demographic data) for 4,365 students and course-level data (instructor experience and course features) for the students’ 93 mechanics courses. We performed Hierarchical Multiple Imputation to impute missing data and Hierarchical Linear Modeling to nest students within courses when modeling the associations be- tween instructor experience and student learning. Our models predict that instructors’ effectiveness decreases as they gain experience teaching without LAs. However, LA supported environments appear to remediate this decline in effectiveness as instructor effectiveness is maintained while they gain experience teaching with LAs. 

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5. Computational Thinking Growth During a First-Year Engineering Course

   https://doi.org/10.1109/FIE44824.2020.9274250  

   Mendoza Diaz, Noemi V. ; Meier, Russ ; Trytten, Deborah A. ; Yoon Yoon, So ( October 2020 , 2020 IEEE Frontiers in Education Conference (FIE))

   null (Ed.)

   This full research-track paper demonstrates growth in computational thinking in a cohort of engineering students completing their first course in engineering at a large Southwestern university in the United States. Computational thinking has been acknowledged as a key aspect of engineering education and an intrinsic part of multiple ABET outcomes. However, computing is an area where some students have more privileges (e.g. access and exposure to meaningful use of computers) than others. Integrating computing into engineering, especially early in the curriculum, may exacerbate existing experiential disadvantages students from excluded social identities experience. Most introductory engineering programs have a component of programming and/or computational thinking. A comprehensive literature review showed that no existing computational thinking framework fully met the needs of students and professors in engineering and computer science. As a result, this team created the Engineering Computational Thinking Diagnostic (ECTD). This diagnostic was assessed and improved during the 2019-2020 academic year. Data was collected from a cohort in a first-year engineering course that included topics in mathematics, engineering problem solving, and computation. Pre- and post-test data analysis with 62 participants documents statistically significant student growth in computational thinking in this course. Significant differences were not found by gender or a limited racially-based analysis. This diagnostic is of interest and relevance to all institutions providing engineering and computing programs. The short-term impact of this research includes an innovative approach to gauge student abilities in computational thinking early in a course in order to add appropriate intervention activities into lesson plans. The long-term impact is the creation of a measurement of student learning of computational thinking in engineering for courses and programs that wish to develop this important skill in their students. 

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