An official website of the United States government
This study investigates how faculty, student, and course features are linked to student outcomes in Learning Assistant (LA) supported courses. Over 4,500 students and 17 instructors from 13 LA Alliance member institutions participated in the study. Each participating student completed an online concept inventory at the start (pre) and end (post) of their term. The physics concept inventories included Force and Motion Concept Evaluation (FMCE) and the Brief Electricity and Magnetism Assessment (BEMA). Concepts inventories from the fields of biology and chemistry were also included. Our analyses utilize hierarchical linear models that nest student level data (e.g. pre/post scores and gender) within course level data (e.g. discipline and course enrollment) to build models that examine student outcomes across institutions and disciplines. We report findings on the connections between students' outcomes and their gender, race, and time spent working with LAs as well as instructors' experiences with LAs.
more »
« less
The Impact of Learning Assistants on Inequities in Physics Student Outcomes
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.
more »
« less
- Award ID(s):
- 1525338
- PAR ID:
- 10099988
- Date Published:
- Journal Name:
- Proc. 2016 Physics Education Research Conference
- Page Range / eLocation ID:
- 360 to 363
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
The Learning Assistant (LA) model supports instructors in implementing research-based teaching practices in their own courses. In the LA model undergraduate students are hired to help facilitate research-based collaborative-learning activities. Using the Learning About STEM Student Out- comes (LASSO) database, we examined student learning from 112 first-semester physics courses that used either lecture-based instruction, collaborative instruction without LAs, or LA supported instruction. We measured student learning using 5959 students’ responses on the Force and Motion Conceptual Evaluation (FMCE) or Force Concept Inventory (FCI). Results from Hierarchical Linear Models (HLM) indicated that LA supported courses had higher posttest scores than collaborative courses without LAs and that LA supported courses that used LAs in laboratory and recitation had higher posttest scores than those that used LAs in lecture.more » « less
-
This paper presents the design and analysis of a pilot problem set deployed to engineering students to assess their retention of physics knowledge at the start of a statics course. The problem set was developed using the NSF-IUSE (grant #2315492) Learning Map project (LMap) and piloted in the spring and fall of 2024. The LMap process is rooted in the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model [1] and Backward Design [2,3], extending these principles to course sequences to align learning outcomes, assessments, and instructional practices. The primary motivation for this problem set (Statics Knowledge Inventory, SKI) was to evaluate students' understanding and retention of physics concepts at the beginning of a statics course. The SKI includes a combination of multiple-choice questions (MCQ) and procedural problems, filling a gap in widely-used concept inventories for physics and statics, such as the Force Concept Inventory (FCI) and Statics Concept Inventory (SCI), which evaluate learning gains within a course, rather than knowledge retention across courses. Using the LMap analysis and instructor consultations, we identified overlapping concepts and topics between Physics and Statics courses, referred to here as “interdependent learning outcomes” (ILOs). The problem set includes 15 questions—eight MCQs and seven procedural problems. Unlike most concept inventories, procedural problems were added to provide insight into students’ problem-solving approach and conceptual understanding. These problems require students to perform calculations, demonstrate their work, and assess their conceptual understanding of key topics, and allow the instructors to assess essential prerequisite skills like drawing free-body diagrams (FBDs), computing forces and moments, and performing basic vector calculation and unit conversions. Problems were selected and adapted from physics and statics textbooks, supplemented by instructor-designed questions to ensure full coverage of the ILOs. We used the revised 2D Bloom’s Taxonomy [4] and a 3D representation of it [5] to classify each problem within a 6x4 matrix (six cognitive processes x four knowledge dimensions). This classification provided instructors and students with a clear understanding of the cognitive level required for each problem. Additionally, we measured students’ perceived confidence and difficulty in each problem using two questions on a 3-point Likert scale. The first iteration of the problem set was administered to 19 students in the spring 2024 statics course. After analyzing their performance, we identified areas for improvement and revised the problem set, removing repetitive MCQs and restructuring the procedural problems into scaffolded, multi-part questions with associated rubrics for evaluation. The revised version, consisting of five MCQs and six procedural problems, was deployed to 136 students in the fall 2024 statics course. A randomly selected subset of student answers from the second iteration was graded and analyzed to compare with the first. This analysis will inform future efforts to evaluate knowledge retention and transfer in key skills across sequential courses. In collaboration with research teams developing concept inventories for mechanics courses, we aim to integrate these procedural problems into future inventories.more » « less
-
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.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1119/perc.2016.pr.085
