Closing the Loop in Feedback Driven Learning Environments Using Trust Decision Making and Utility Theory
Bibliographic record
Abstract
Contemporary learning systems are an integration of learning resources with human interactions. To close the loop in feedback driven learning environments, the utility of learning objectives needs to be measured. To this end, a comprehensive trust evaluation model for decision making is required to utilize feedback ratings along with other key parameters such as previous course result percentage, active participation and reputation of learners. This paper proposes a novel utility theory based trust evaluation model, wherein the utility of a learning objective is computed in terms of trust applicable to big data-sets. The utility is computed by allowing users to weigh the course related attributes according to their preferences. The utility value facilitates learners to select trustworthy learning objectives and enables instructors to improve different aspects of learning objectives. In addition, a satisfaction index is proposed for the assessment of the usefulness of the computed utility value. The performance of the model is evaluated on a big data-set, which is collected from learners enrolled in different courses of a postgraduate degree program for the purposes of decision making. The results indicate that the proposed unique intelligent model is effective for dynamic and user-specified trust evaluations of learning objectives for the purposes of decision making.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".