Predictive Analysis of Computer Science Student Performance: An ACM2013 Knowledge Area Approach
Bibliographic record
Abstract
In the realm of Educational Data Mining (EDM), the predictive analysis of student performance has emerged as a pivotal area of interest, particularly within computer science education.This investigation employs machine learning and data mining techniques to project academic outcomes of computer science undergraduates, anchoring its framework on the Association for Computing Machinery's (ACM) 2013 Body of Knowledge (Bok), as delineated in the curriculum guidelines for undergraduate computing programs.Encompassing 18 Knowledge Areas (KAs), each with multiple Knowledge Units (KUs), the ACM2013 guideline serves as a comprehensive scaffold for curriculum development, ensuring an inclusive coverage of essential skills and subjects.Through an analysis of data from 2,756 students across nine years at Qassim University's College of Computer Science, this study aims to pinpoint performance levels across various KAs and semesters.Linear regression models were constructed to predict student performance, with their accuracy evaluated through Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE).The predictive accuracy varied across courses, with "Systems Programming" and "Graduation_1" demonstrating high alignment with actual scores, while courses like "Artificial Intelligence" and "Compiler Design" revealed significant discrepancies.A correlation analysis between predicted and actual scores further assessed the models' precision.Findings underscore the utility of EDM in academic settings, especially for tailoring predictive models that enhance student performance prediction in computer science.The identification of KAs with high predictive accuracy corroborates the curriculum's alignment with student achievements, whereas lower accuracy areas highlight potential gaps in curriculum or pedagogy, offering vital insights for educators and curriculum designers to refine educational strategies and resources for improved student outcomes.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 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".