Evaluating Model for E-learning Modules According to Selected Criteria: An Object Oriented Approach
Bibliographic record
Abstract
This paper aims at developing an applied system to evaluate the e-Learning Modules (eLMs) depending on selected related criteria. Those criteria concern with many fields as well as many factors like, learning theories, computer science, instructional computer, software engineering, educational sciences, language criteria, economical factors, etc. Therefore the authors expressed the term of selected criteria to reflect the meaning of integrated factors of the environment deals with the developed eLM. However eLM for any subject requires for a systematic steps of integrated work depending on the model which is considered by the developer. Mostly the output of a stage represents the input of the next step. Our model will cover all steps in details. Thus such model could be considered not only to evaluate the developed eLMs but it could be used during process of developing because many items of criteria are designed so as to be a guide for developer during development of eLM. Developer should consider eLMs while he/she develops an eLM. Finally the authors presented selected eLM to apply the evaluating model, outcomes of the evaluations process lead to the needful conclusion.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".