From Knowledge Management System to E-Learning Tool
Bibliographic record
Abstract
In this chapter, we try to bridge the gap between e-learning, knowledge management (KM), and the Semantic Web (SW) by identifying the principle properties and techniques that characterize each domain. We first note that although there is a major difference in the knowledge nature of each domain, however, there is a knowledge evolution and an interrelation throughout the three domains. Consequently, we should research methods of combining the strong techniques applied within each of them in order to satisfy the need of a particular work. In this perspective, we examine the similarities and differences, from a theoretical point of view, between the knowledge management systems (KMS) and the intelligent tutoring systems (ITS). We specifically focus on the knowledge transfer techniques in both systems such as the knowledge analysis needed to determine the knowledge content for both cases, the pedagogical planning for ITS, and the teaching model for KMS. Later, we examine the common task of ontology construction in the KM and SW domains and our recommendations. Next, we tackle the experimental issues by presenting our dynamic knowledge network system (DKNS), a general purpose KMS tool that is also used as self-learning software in several projects. This system is an appropriate tool for teaching procedural knowledge. Its functionality and simple implementation make it a user-friendly tool for both the lesson designer and the learner. We shall discuss and illustrate the didactic approach of DKNS in e-learning. Our goal is to teach laboratory users how to use the available equipment and software to create new-media artwork. Finally, we highlight some emerging trends in the three above-mentioned domains.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".