Multi-agent approach towards intelligent e-learning system
Bibliographic record
Abstract
Web technology is becoming more universal in the world; accordingly, the use of the Web to provide learners and helpers (tutors or others) with a real-time learning environment at a distance is also continuing to grow rapidly. Furthermore, the Web as a distance learning environment has wonderful potential to replace traditional means of teaching distance learners. However, the ability to offer collaboration along with adaptation within the same system is still a challenging and important problem. To address this issue, we developed a new approach called dominant meaning. This research has applied a multi-agent technique based on the dominant meaning approach to design a multi-user e-learning system to provide learners with a Collaboration Adaptive Distance Learning Environment (CADLE). The proposed system is intended to create more collaboration between online learners and an individualized approach to adapt course presentation without the need for additional input from a user. This dissertation consists of three areas of research regarding the implications of CADLE. The first area establishes flexible domain knowledge and a dynamic user profile in order to establish a cohesive collaboration and provide individual adaptation. This is achieved by a combination of the dominant meaning approach and machine learning algorithms. This combination is a novel approach in the field of machine learning. Second, based on the dominant meaning approach, this research has investigated the technical feasibility and also the utility of applying a multi-agent technique to perform CADLE. An e-learning system called Confidence Intelligent Tutoring System (CITS) has been implemented to determine the viability of this approach. The CITS has been implemented, tested, and evaluated. The prototype of CITS is based on six types of agents: user interface agent, cognitive agent, behavior agent, guide agent, context-based information agent, and confidence agent. Third, results from an experiment conducted on each agent are presented separately. Moreover, the results of the evaluation of the whole system show that the CITS is a functional and robust system. In the same sense, the utility of using dominant meaning approach along with machine learning algorithms to improve Web search results and to analyze users' browsing activity in CITS was proven, and the technical feasibility has been established.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".