Jigsaw learning versus traditional lectures: Impact on student grades and learning experience
Bibliographic record
Abstract
Despite significant research supporting active learning, many professors continue to use traditional lectures as their primary teaching method, particularly in introductory level courses. This article explores whether jigsaw cooperative learning had a positive impact on student grades and enhanced their learning experience, as compared to the traditional lecture method. The question was answered by collecting data from an insurance and risk management introductory course in the business school. To answer the question on learning experience, students completed a validated survey on each pedagogy, consisting of 15 statements that they rated on a Likert scale of 1 to 5, strongly disagreeing or agreeing with the statements. The course content was taught using lectures for four learning modules and the jigsaw learning method for four learning modules. After each module, a quiz was written by each student, and these grades were compared to establish the impact of each teaching method on student grades. Data was analyzed using descriptive statistics and two-way ANOVA testing to determine significant differences. Data was collected from two student groups. One group was a traditional university group of diverse students and the other group consisted of international students from India. I compared the results of the two student groups to identify any differences. This research adds to the studies on active learning in insurance education, specifically jigsaw cooperative learning. It also contributes to literature on effective teaching strategies for international student groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".