Evaluating the Effectiveness of Problem Based Learning As a Method of Engaging Year One Law Students.
Bibliographic record
Abstract
The focus of this paper is a critical assessment of the effectiveness of a problem-based learning exercise introduced to increase student engagement within a year one, core law degree module at Northumbria University. Problem-based learning as a teaching method was developed by medical schools in the US and Canada in the 1960s and 1970s and has steadily grown in popularity, particularly within law schools. Presenting students with a reality-based problem, and requiring students to resolve that problem in teams, should mean that students find an increased motivation to learn. This paper explores the rationale behind the use of problem-based learning as a means of engaging students and will outline how the project was designed and implemented in the curriculum. Although both students and lecturers verbally reported an improvement in confidence and an increase in engagement, formal student perceptions were obtained through the use of an evaluation questionnaire. The paper details a full breakdown of the survey, which covers not only issues relating to student engagement but also student perceptions of group work and skills acquired.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".