Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity
Bibliographic record
Abstract
Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a student model that can both evaluate learning as well inform relevant feedback. Building such a model for interactive simulations is especially challenging because the exploratory nature of the interaction makes it hard to know a priori which behaviors are conducive to learning. To address this problem, in this paper we leverage the student modeling framework proposed in (Kardan and Conati, 2011 ) to specifically address the challenge of modeling students in interactive simulations. The framework has already been successfully applied to build a student model and to give adaptive interventions for an interactive simulation for constraint satisfaction. We seek to investigate the generality of the framework by building student models for a more complex simulation on electric circuits called Circuit Construction Kit (CCK). We evaluate alternative representations of logged interaction data with CCK, capturing different amounts of granularity and feature engineering. We then apply the student modeling framework proposed in (Kardan and Conati, 2011 ) to group students based on their interaction behaviors, map these behaviors into learning outcomes and leverage the resulting clusters to classify new learners. Data collected from 100 college students working with the CCK simulation indicates that the proposed framework is able to successfully classify students in groups of high and low learners and identify patterns of productive behaviors that are common across representations that can inform real-time feedback. In addition to presenting these results, we discuss trade-offs between levels of granularity and feature engineering in the tested interaction representations in terms of their ability to evaluate learning, classify students, and inform feedback.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".