A Visual Approach towards Knowledge Engineering and Understanding How Students Learn in Complex Environments
Bibliographic record
Abstract
Exploratory learning environments, such as virtual labs, support divergent learning pathways. However, due to their complexity, building computational models of learning is challenging as it is difficult to identify features that (i) are informative with respect to common learning strategies, (ii) abstract similar actions beyond surface differences, and (iii) differentiate groups of learners. In this paper, we present a visualization tool that addresses these challenges by facilitating a novel analytic approach to aid in the knowledge engineering process, focusing on five main capabilities: data-driven hypotheses raising, visualizing behavior over time, easily grouping related actions, contrasting learners' behaviors on these actions, and comparing the behaviors of groups of learners. We apply this analytic approach to better understand how students work with a popular interactive physics virtual lab. By splitting learners by learning gains, we found that productive learners performed more active testing and adapted more quickly to the task at hand by focusing on more relevant testing instruments. Implications for online virtual labs and a broader class of complex learning environments are discussed throughout.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".