Simulating a ZPD-based Knowledge Tracing Model for Enhanced Adaptive Practicing
Bibliographic record
Abstract
Self-paced online learning demands strong self-regulation, a skill many learners lack, causing high attrition rates in this educational model. Learners need a means to assess their dynamic knowledge states to clearly understand their ability throughout the course learning. While knowledge tracing function in adaptive learning systems (ALS) helps, traditional models usually overlook the side information of metacognition and the consideration of learner control. To address these issues, our study introduces the Zone of Proximal Development-based Knowledge Tracing (ZPD-KT) model, a novel adaptive practicing framework for online STEM education. ZPD-KT integrates confidence-based assessment, dynamic ZPD, and traditional knowledge tracing principles to enable an AI-learner shared control approach. Through a large-scale simulation involving 8,000 synthetic learners, the ZPD-KT model was benchmarked against traditional Bayesian Knowledge Tracing (BKT). Initial results show a significant increase in the effectiveness of adaptive practice when traditional KT algorithms are augmented with ZPD. Furthermore, the study highlights the critical role of metacognitive signals (e.g., self-reported confidence) in refining KT beyond binary correctness metrics. For smart education ecosystems, ZPD-KT offers a scalable, ethically grounded solution that balances AI-driven personalization with learner autonomy, advocating for systems that prioritize pedagogical equity and adaptability in self-paced online learning environments.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".