Assessing a novel problem‐based learning approach with game elements in a business analytics course
Bibliographic record
Abstract
Abstract Business education has traditionally relied on case‐based learning as its main form of active learning. However, this method is not always appropriate in introductory undergraduate business analytics courses, which require students to first master analytical techniques, best taught through examples of numerical problems. Building on established problem‐based learning (PBL) pedagogy, we propose a new approach in which students solve well‐structured problems in a gamified environment. The learner is challenged to solve a series of numerical problems at their own pace in a self‐directed manner. The series of problems are designed such that the student must find the correct solution to the first problem to unlock and progress to the next problem, and so on. To assess our method, both student outcomes and experience were evaluated in a controlled study that compared it to traditional lecturing. While student outcomes were similar, students perceived traditional lectures as more effective. Our results indicate that the game elements in our approach did not sufficiently increase student engagement to counteract negative student perceptions of PBL, which are well‐documented in the literature. We conclude with a discussion of advantages and drawbacks of this new approach, considerations for adapting it to virtual settings, and opportunities for expanding game elements to increase student engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".