Evaluating a Prototype of a Recommender‐Driven Online Learning System
Bibliographic record
Abstract
ABSTRACT Recommender‐driven online learning systems (ROLS) are at the forefront of new computer‐based learning. They incorporate machine learning to allow learning‐by‐doing, generating personalized recommendations in the process. This article describes the evaluations of a new type of online learning systems, ROLS. This evaluation was carried out in three phases using a design science research approach. In Phase I, building the ROLS prototype validated the conceptual framework used. In Phase II, building an instantiation of ROLS to teach Structured Query Language (SQL), SQL‐with‐Ease, validated the ROLS prototype. In Phase III, a laboratory experiment evaluated learning outcomes from using SQL‐with‐Ease compared with two other traditional forms of learning. A set of qualitative interviews carried out with learners soon after using the system confirmed that the system was effective. They indicated that more work on fine‐tuning recommendations generated by the system could further improve learner satisfaction. The key implication for practitioners is that ROLS have the potential to improve learning outcomes significantly. Implications for researchers are that evaluations of ROLS, which include formative and summative evaluations, are essential to improve their performance and that developing innovative approaches to evaluation can advance these learning technologies.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".