SMFSOP: A semantic-based modelling framework for student outcome prediction
Bibliographic record
Abstract
Over the past two decades, studying the various factors affecting student performance became essential. Knowing these factors assist in enhancing student’s performance, teaching practices and policy decisions. This research proposes a framework named ”Semantic-based Modeling Framework for Student Outcome Prediction”(SMFSOP), to automatically map students’ activities within their learning environment to a standardized behavioral model (Community of Inquiry model (CoI)). The generated student representation is utilized to cluster students and predict an outcome based on their cluster. The framework is divided into three phases: Data gathering and pre-processing, automated mapping, clustering and prediction. The automatic mapping uses semantic similarity between student attribute names/descriptions, and CoI model indicators. Path and BERT similarities were identified as the best performers compared to human annotators. K-means, DBSCAN, and Kernel K-means are used for the clustering step, followed by LassoCV for regression-based prediction, & K-nearest neighbors for classification-based prediction. In order to prove that the proposed framework is generally applicable, three real life datasets were used as a case study. Best-performing trials enhanced outcome prediction as follows: In StudentLife Dataset, Adjusted R2 is enhanced by 3% (95% to 98%), and MSE decreased by 2.375 % (0.126 to 0.031). In social network dataset, Adjusted R2 was enhanced by 17% (65% to 82%). The MSE decreased by 4.4% (0.164 to 0.12). For the ”Open university learning Analytics dataset” (OULAD), accuracy is improved by 1.56%, F1-score enhanced by 0.014. Precision is enhanced by 3.1%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".