An Intelligent Pedagogical Agent for In-The-Wild Interaction in an Open-Ended Learning Environment for Computational Thinking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adaptive support can help learners in Open-Ended Learning Environments (OELEs), where the free-form nature of the interaction can be confusing to students. In this paper, we design and evaluate an Intelligent Pedagogical Agent (IPA) for an OELE designed to foster Computational Thinking (CT). Specifically, we design help interventions for an in-the-wild scenario where students interact with the OELE in an unmonitored, self-directed manner. We build a student model by extracting meaningful student behaviors on real-world interaction data obtained during interaction in online classrooms and including expert insights. We show that these student models perform better than a baseline and have the potential for adaptive support in self-directed interaction with the OELE. We design an IPA with the help of teachers, leveraging the student behaviors extracted from data. Lastly, we get insights into the value of these help interventions by empirically evaluating the IPA in a formal user study.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it