Learner Perspectives of Online Problem-Based Learning and Applications from Cognitive Load Theory
Bibliographic record
Abstract
Problem-based learning (PBL) courses have historically been situated in physical classrooms involving in-person interactions. As online learning is embraced in higher education, programs that use PBL can integrate online platforms to support curriculum delivery and facilitate student engagement. This report describes student perspectives of the online PBL experience, interpreted through the lens of Cognitive Load Theory. Fifty-two undergraduate health professional students participated in this descriptive survey. The responses revealed that, overall, learners perceived the platform to be suitable for conducting online PBL, that distractions in the online environment were no greater than those experienced in physical classroom, and that online PBL was as effective as in-class PBL for learning. In online PBL, the technological capabilities and limitations of the online platform were identified by students as the key sources of hindrances and facilitators to learning. Suggestions for implementing online PBL using instructional principles from Cognitive Load Theory and multimedia learning are offered.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".