Cluster-Based Performance of Student Dropout Prediction as a Solution for Large Scale Models in a Moodle LMS
Bibliographic record
Abstract
Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS?” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".