Can Competition Though Leaderboards Lead to Better Engagement and Learning of Data Science Concepts? An Experimental Study
Bibliographic record
Abstract
This paper examines the effect of gamification in engaging students to learn introductory concepts of data science, in particular, we study competitiveness through leaderboards. A between-subject experiment was conducted with 37 students and included two conditions: 1) playing against fictious opponents with a competitive leaderboard and, 2) playing alone with a leaderboard ranking only the subject's score. Our results show no effect of the competitive nature of leaderboards on learning and engagement. However, we found that highly efficacious participants with prior predictive modelling knowledge demonstrated higher levels of emotional arousal despite having a lower probability of increasing their knowledge on the subject matter. This suggests that individual differences such as self-efficacy and prior knowledge need to be accounted for when developing data science training that is augmented with competition through leaderboards as these factors may impact the learner’s ability to engage with the content.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| 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".