Technology in problem-based learning: helpful or hindrance?
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between student motivation and technology in the implementation of problem-based learning (PBL) in a technologically enhanced active learning classroom (ALC). Design/methodology/approach PBL was implemented in an undergraduate course in human osteology (n=49) at a large Canadian University. Numerous activities using the ALC technology were conducted to engage students in self-directed active learning. Students wrote critical self-reflections at the beginning of the course and with each PBL report. They completed a survey at the end of the course using a Likert scale that included written comments on their motivation toward different uses of technology. Findings Students generally had high motivation toward PBL at the end of the course. Their evaluation of the technology to support PBL was dependent on the activity. Students (88 percent) appreciated the use of an overhead camera to visualize anatomical elements, and short problem-solving exercises using the whiteboard but they negatively evaluated the real-time projection of PBL sessions through a discussion board (52 percent). Almost half of the class (43 percent) felt that technology was a hindrance to their learning process in PBL. Originality/value This study demonstrates the complex relationship between student motivation toward active learning, the learning environment, and technology. Instructors and students influence the learning environment through their conceptions of effective teaching. According to this framework, technology should be implemented not only according to the teaching method, but consider teaching conceptions and the learning environment.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".