Machine learning methods outperformed logistic regression in classification accuracy across reviewed studies¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2019 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2022: tool and model accuracy changes quickly

Subclaims¶

q2 i? Across studies comparing algorithms, machine learning methods outperformed conventional logistic regression in classification accuracy. → Zawacki-Richter 2019

Evidence¶

Zawacki-Richter 2019¶

Zawacki-Richter, O., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-019-0171-0

q2 · i? · review · r2

The review synthesized accuracy figures from studies (Table 7) comparing ANN, RF/DT, SVM, NB, and J48 against logistic regression for admission, dropout, engagement, and performance prediction tasks.

"Table 7 shows that machine learning methods outperformed logistic regression in all studies in terms of their classification accuracy in percent."

Discussion¶

Related Claims¶

• Random forest algorithms yielded the best classification performance in K–8 MMLA studies comparing multiple machine learning models — a narrower finding that bears on this claim
• CatBoost achieved the best classification performance among tested algorithms for predicting course completion risk (F-measure .77, accuracy 78%, AUC .87) — a narrower finding that bears on this claim
• Decision trees achieve competitive accuracy with black-box models (LR, RF, SVM), which performed slightly better but lack interpretability — reports the opposite
• Reviewed studies reported good algorithm accuracy and efficiency for AI in STEM, with performance varying by algorithm and granularity — a broader claim this one bears on
• A random forest classified flagged versus non-flagged word problems with the best accuracy of five tested models (AUC = 0.75) — a narrower finding that bears on this claim
• Baseline LR and XGBoost models achieve comparable predictive performance on OULAD, with XGBoost slightly higher AUC-ROC — reports the opposite
• The DMO-GRU model achieves the best classification results among the compared models, with 91.2% accuracy, 90.8% F1-score, 90.1% precision and 92.5% recall — a narrower finding that bears on this claim
• Logistic regression performs poorly (mean accuracy 0.4243) and SVM moderately (0.7374) on physiological exam-outcome prediction — reports the opposite
• A stacking ensemble of LG, SVM, NB, KNN and XGB achieved the best fine-tuning F1-score (0.88), outperforming individual models such as SVM (0.86) — related
• Standard ML models predicting student pass/fail from VLE data exhibit biased true-positive rates across demographic subgroups — related
• A calibrated logistic-regression model predicts next-assessment pass/fail strongly on OULAD at both decision horizons — reports the opposite
• In static concurrent prediction, TabPFN outperformed Random Forest, XGBoost and Linear Regression across the four emotional and proficiency targets — related
• Boosting models (XGBoost) achieved the highest predictive accuracy among the compared ML models, while logistic regression showed the lowest — a narrower finding that bears on this claim