Predictive analytics in education- enhancing student achievement through machine learning
Bibliographic record
Abstract
This study investigates the application of predictive analytics and machine learning models to enhance student achievement in educational settings. The experiment involved a dataset of 24,005 student records collected from institutional academic records at Wollo University and the Kombolcha Institute of Technology, spanning the years 2017–2022. The data were systematically gathered from student demographic information, academic performance metrics, and contextual features such as studied credits, entrance results, and number of previous attempts. Unlike prior works, this study proposes a novel hybrid architecture that combines Convolutional Neural Networks (CNNs) and Random Forests with XGBoost as a meta-learner, achieving superior accuracy (88 %) compared to individual models such as Random Forest (85 %). Accuracy and other performance metrics (precision, recall, F1-score, and AUC-ROC) were calculated using a hold-out validation approach, with 80 % of the data used for training and 20 % for testing. This architecture effectively captures complex feature interactions and provides actionable insights for educators. Additionally, key predictive factors such as studied credits, entrance results, and regional differences were identified, offering a comprehensive understanding of student performance. The study addresses gaps in feature diversity and demonstrates the applicability of hybrid models in educational settings, paving the way for targeted interventions and improved resource allocation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".