Pre-test mathematical performance and self-efficacy are the most important predictors of post-test math performance in a random forest model¶
Claim · All claims
Evidence · 1 study · causalr2·q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person
Design limits · Hou 2024: no comparison group (read from the study by two models, 2026-10-26)
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? A random forest regression model predicting post-test math performance achieved test MSE 0.671 and R = 0.187, with pre-test performance showing the highest feature importance. → Hou 2024
Evidence¶
Hou 2024¶
Hou, Z., Li, X., Yan, J., & Xu, S. Y. (2024). Enhancing mathematical learning outcomes through a low-cost single-channel BCI system. npj Science of Learning, 9, 65. https://doi.org/10.1038/s41539-024-00277-z
q2 · i? · causal · r2
Secondary exploratory analysis: the random forest model was trained on 51 samples, validated on 13, and tested on 16, implemented in scikit-learn. The article reports the test metrics including "a correlation coefficient ( R) of 0.187", indicating weak predictive accuracy.
"The model achieved an MSE of 0. 671, a root mean squared error (RMSE) of 0.819, a mean absolute error (MAE) of 0.654, a mean absolute percentage error (MAPE) of 118.25%, and a correlation coefficient ( R) of 0.187."
Discussion¶
Learner Variables¶
- Algebraic Thinking — predictor: learners who differ on it differ in outcomes
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Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
pretest-performance-selfefficacy-predict-posttest-math |
title |
Pre-test mathematical performance and self-efficacy are the most important predictors of post-test math performance in a random forest model |
description |
Pre-test mathematical performance and self-efficacy are the most important predictors of post-test math performance in a random forest model |
status |
review |
evidence_strength |
weak |
newest_evidence |
2024 |
generated |
by process:wiki-ingest on 2026-10-26 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — hou-2024 |
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