Exploring Adaptive Social Comparison for Online Practice
Bibliographic record
Abstract
Students experience motivational issues during online learning which has led to explorations of how to better support their self-regulated learning. One way to support students uses social reference frames or social comparison in student-facing learning analytics dashboards (LADs) and open learner models (OLMs). Usually, the social reference frame communicates class averages. Despite the positive effects of class-average-based social comparison on students’ activity levels and learning behaviors, comparison to class average can be misleading for some students and offer an irrelevant reference frame, motivating only low or high performers. Such conflicting findings highlight a need for an investigation of social reference frames that are not based on the “average” student. We extend the research on social comparison in education by conducting two complementary classroom studies. The first explores the effects of different fixed social reference frames in a non-mandatory practice system, while the second introduces an adaptive social reference frame that dynamically selects the peers who serve as a comparison group when students are engaged in online programming practice. We reported our analyses from both studies and shared students’ subjective evaluations of the system and its adaptive comparison functionality.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".