Student Motivation in Response to Problem-based Learning
Bibliographic record
Abstract
Problem-based learning (PBL) is a self-directed learning strategy where students work collaboratively in small groups to investigate open-ended relatable case scenarios. Students develop transferable skills that can be applied across disciplines, such as collaboration, problem-solving and critical thinking. Despite the extensive research on problem-based learning, an examination of variables that affect student engagement through the implementation of PBL is lacking (Savin-Baden, 2014; 2016). Our research question examined student motivation during problem-based learning implementation in an undergraduate anthropology course (N = 49) with students with diverse subject matter experience and no previous exposure to active learning. Student motivation was examined through surveys, peer-evaluations, and self-reflection exercises. The results showed that student motivation was higher in students with more subject matter experience at the beginning of the course. During the course, motivation decreased in relation to subject matter experience, but by the end of the course the majority of students (76.7%) increased their motivation toward problem-based learning. Based on their subject matter experience, we were surprised that a particular subset of students had low motivation at the end of the course (78%). We discuss some challenges of implementing problem-based learning in a traditional curriculum, and provide suggestions to successfully implement PBL for diverse student populations.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".