Design and Implementation of an Adaptive Tutoring System for Enhanced E-Learning
Bibliographic record
Abstract
The increasing offer of new information and communication technologies has changed the educational field, e-learning emerged as an important complement to traditional face-to-face education and often a good alternative in many contexts. This shift has been emphasized by global challenges such as the COVID-19 pandemic, which highlighted the importance of remote learning platforms and their effectiveness in such situations. However, many challenges such as the costs and the need for personalized and interactive learning environments remain an obstacle. To address these issues, adaptive e-learning systems and Intelligent Tutoring Systems (ITS) are increasingly being developed and given support by education communities and governments. These systems aim to adapt content to the learner’s cognitive abilities and individual learning styles, for better understanding and retention. This paper explores the design and development of an adaptive ITS, which integrates Artificial Intelligence and data analytics to provide better learning experience. This paper puts the light on the role of adaptive hypermedia in educational interactions, analyzing its key features and how they can be leveraged to enhance learning outcomes. By incorporating learning success metrics, our study provides a comprehensive perspective on the potential of ITS to revolutionize adaptive and personalized e-learning systems, driving significant improvements in both learner engagement and achievement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".