A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments
Bibliographic record
Abstract
Despite a growing development of virtual laboratories which use the advantages of multimedia and the Internet for distance education, learning by means of such tutorial tools would be more effective if they were specifically tailored to each student needs. The virtual teaching process would be well adapted if an artificial tutor (integrated into the lab) could identify the correct acquired knowledge. The training approach could be more personalised if the tutor is also able to recognise the erroneous learner’s knowledge and to suggest a suitable sequence of pedagogical activities to improve significantly the level of the student. This chapter proposes a knowledge representation model which judiciously serves the remediation process to students’ errors during learning activities via a virtual laboratory. The chapter also presents a domain knowledge generator authoring tool which attempts to offer a user-friendly environment that allows modelling graphically any subject-matter domain knowledge according to the proposed knowledge representation and remediation approach. The model is inspired by artificial intelligence research on the computational representation of the knowledge and by cognitive psychology theories that provide a fine description of the human memory subsystems and offer a refined modelling of the human learning processes. Experimental results, obtained thanks to practical tests, show that the knowledge representation and remediation model facilitates the planning of a tailored sequence of feedbacks that considerably help the learner.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".