LPR: an adaptive learning path recommendation system using ACO and meaningful learning theory
Bibliographic record
Abstract
In recent years, the educational community has been interested in enabled learning systems. That is, having a personalized learning system that can adapt itself while providing learning support to different learners to overcome the weakness of ‘one size fits all’ approaches in technology-enabled learning systems. In this thesis, we address one known problem in adaptive learning systems called curriculum sequencing. We design and implement a learning path recommendation (LPR) system that selects an appropriate learning path for learners based on their characteristics and needs. There are two components to the LPR system: searching for the learning paths and clustering the learners into groups based on their prior knowledge. Using bioinspired ant colony optimization (ACO) algorithm and meaningful learning theory of Ausubel, the ACO path finder component searches for a suitable learning path for the learner. This component incorporates continuous learner’s improvement in the process of a learning path selection. The LPR system, uses the pre-assessment/familiar degree calculator to gauge learner’s prior knowledge and produces a learner’s familiar degree of concepts. The clustering component uses Fuzzy C-Mean (FCM) algorithm. The LPR system can recommend more than one learning path to learners located on the cluster boundaries. We implement an interface to provide the recommendation to the learner. We evaluate the effectiveness of the LPR system by designing and developing a database course and ask actual learners to complete the course. The results of our experiment show that the group that used the LPR system have higher performance and knowledge improvement in the course than the control group. The performance and knowledge improvement differences between the two groups are statistically significant. Based on the statistical tests, the LPR system has a moderate to large impact on the learners’ performance and knowledge improvement. Although the course completion time for the LPR group was slightly less than the control group, no statistically significant difference is found between the time completion of both groups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".