Automatic Learning Path Creation Using OER: A Systematic Literature Mapping
Bibliographic record
Abstract
Learning paths are curated sequences of resources organized in a way that a learner has all the prerequisite knowledge needed to achieve their learning goals. In this article, we systematically map the techniques and algorithms that are needed to create such learning paths automatically. We focus on open educational resources (OER), though a similar approach can be used with other types of learning objects. Our method of mapping goes through three passes of selected literature. First, we selected all articles mentioning OER and machine learning from IEEE, SCOPUS, and ACM. This resulted in a set of 347 papers after removing duplicates. Of these, 13 were selected as relating to learning paths and their references and citations were identified and organized into eight categories identified in this article (metadata, linked data, recommendation systems, concept maps, knowledge graphs, classification, and learning paths). After identifying these topics, a manual review was conducted resulting in the final set of 112 papers. This article combines the found categories into three steps for learning path creation, which are then discussed in detail. These steps are as follows: 1) concept extraction; 2) relationship mapping; and 3) path creation. Current research relates primarily to enhancing concept extraction and relationship mapping. We identify directions for potential future research that focus on automatically augmenting previously created learning paths in accordance with the changing needs of learners.
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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 | 0.040 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.106 | 0.042 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".