Framework of quality assurance of TEL integration into an educational organization
Bibliographic record
Abstract
This research paper addresses the issues of integration of technology enhanced learning (TEL) into an educational organization. Good practice experience cannot be directly transferred to new organisations due to different contextual conditions. The TEL integration depends significantly upon a very rapid development of services and information communication technologies (ICT). Some organizations have managed to go step by step with the developments and have become leaders in TEL provision, however others, though having successful examples, have not succeeded in reaching the service level they want. While many positive examples exist in research literature, it is rare that institutions have complete strategies or solutions for integrating TEL that meet their specific pre-conditions and satisfy quality assurance parameters at the same time. The research reported here aims at the development of a theoretical framework for quality assurance of TEL integration into educational organizations. During the research, the development of the TEL concept has been discussed, success indicators for TEL integration in an educational organization have been described, the quality parameters of TEL integration into an educational organization have been identified and the model for TEL integration into an organization has been developed.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".