Tackling the anxiety of learning statistics: The mystery of the red envelope
Bibliographic record
Abstract
Abstract Most graduate programs in management require students to carry out a substantive research project. However, few management students have a comfortable command of the statistical techniques needed to realize such quantitative projects. This can lead to student anxiety and stress, which challenges instructors to devise ways to build students’ self‐efficacy with statistical analysis. Drawing on game‐based learning principles, we developed an exercise to help students in a graduate‐level research methods course practice these statistical techniques. Designed around a series of four gamified challenges, students perform basic statistical analyses (correlations, t ‐tests, and simple linear regression) to solve puzzles and unlock a reward hidden in a mysterious red envelope. We used the exercise on seven occasions (five times in the methods course and twice in a graduate program preparatory course). After launching it in fall 2021, we observed that students were engaged and enthusiastic about the exercise. To ascertain its effectiveness more systematically, we collected data in five subsequent sections using a pretest/posttest design ( N = 84) which showed that perceptions of statistics self‐efficacy increased following the exercise. We conclude by suggesting that our exercise can be tailored to other learning contexts such as management and statistics‐centered courses.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".