The Benefits of Participating in a Learning Assistant Program on the Metacognitive Awareness and Motivation of Learning Assistants
Bibliographic record
Abstract
Learning assistant (LA) programs train undergraduate students to foster peer discussion and facilitate active-learning activities in undergraduate science, technology, engineering, and mathematics (STEM) classes. Students who take courses that are supported by LAs demonstrate better conceptual understanding, lower failure rates, and higher satisfaction with the course. There is less work, however, on the impact that participating in LA programs has on the LAs themselves. The current study implements a pretest-posttest design to assess changes in LAs' metacognition and motivation to succeed in STEM across their first and second quarters as an LA. Our findings suggest that participating in this program may help LAs become more reflective learners, as was demonstrated by an increase in their scores on the Metacognitive Awareness Inventory (MAI) after the first quarter. LAs also showed increases on the Intrinsic Motivation and Self-Efficacy subscales of the Science Motivation Questionnaire. Students who participated in the program for an additional quarter continued to show increases in their MAI scores and maintained the gains that were observed in their motivation. Taken together, this work suggests that, in addition to benefiting the learner, LA programs may have positive impacts on the LAs themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".