Claims

Decision tree classifiers trained on slide interaction features predict end-of-module outcomes with high accuracy across four K-12 programming courses

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? DTs with and without feature selection achieved 85.2-90.7% accuracy, 0.88-0.92 precision, 0.78-0.98 recall and 0.83-0.92 F1 across all four courses, considerably surpassing the majority-class baseline. → Vincent Zhang 2025

Evidence

Vincent Zhang 2025

Vincent Zhang, Bryn Jeffries, Irena Koprinska. (2025). A Machine Learning Approach for Predicting Student Progress in Online Programming Education. Int J Artif Intell Educ. https://doi.org/10.1007/s40593-025-00510-9

q2 · i? · associational · r2

Evaluation on log data from four large-scale online programming courses (approximately 35,000 enrolled students), with a separate DT model per module and 10-fold stratified cross-validation. The article reports "85.2-90.7% accuracy" for DTs with and without feature selection, all surpassing the majority-class baseline.

"The DTs with and without feature selection show consistently high predictive performance on all courses - 85.2-90.7% accuracy, precision of 0.88-0.92 precision, 0.78-0.98 recall and 0.83-0.92 F1 score. All DTs considerably surpass the majority class baseline."

Discussion

Page metadata
Field Value
type claim
id dt-slide-interaction-features-predict-module-outcomes
title Decision tree classifiers trained on slide interaction features predict end-of-module outcomes with high accuracy across four K-12 programming courses
description Decision tree classifiers trained on slide interaction features predict end-of-module outcomes with high accuracy across four K-12 programming courses
status review
evidence_strength moderate
newest_evidence 2025
recheck_by 2028
generated by process:wiki-ingest on 2026-10-12
trust unverified — no one has confirmed this page's content
sources 1 — vincent-zhang-2025

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