Drivers of Competitive Behavior in Persuasive Technology in Education
Bibliographic record
Abstract
Competition has been identified as an intrinsic motivation that could lead to successful outcomes in education. However, in Persuasive Technology in Education (PTE) research, there are limited studies showing its possible predictors. To advance research in this area, we conducted an empirical study among university students (N = 243) to uncover how extrinsic factors and social influence, which are external to learners, influence students' susceptibility to Competition. Specifically, we investigated how Social Learning, Social Comparison, and Reward, which are widely applied in persuasive technologies (PTs), influence Competition. Our results show that Social Comparison and Reward have significant influence on Competition, with Social Comparison (β = 0.52, p < 0.001) having a stronger influence than Reward (β = 0.29, p < 0.001). However, Social Learning (β = -0.07, p = n.s) has no significant effect on Competition. Our model accounts for about 41% of the variance of Competition. Moreover, our multigroup analysis reveals that there are no significant differences between males and females, indicating that our findings generalize across gender. These findings suggest that Social Comparison, Reward, and Competition are compatible strategies, which can be implemented together in a persuasive system for education. We discuss the implications of our findings
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".