Neural network models predicted students' writing performance groups with 94–100% accuracy from self-regulation characteristics¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2017 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2020: tool and model accuracy changes quickly

Subclaims¶

q2 i? Neural network methodology predicted poor, mid, or high writing performance groups with accuracy ranging 94–100% based on 56 predictors of the self-regulation system. → Panadero E 2017

Evidence¶

Panadero E 2017¶

Panadero E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Front. Psychol. 8:422. https://doi.org/10.3389/fpsyg.2017.00422

q2 · i? · review · r2

Statistical learning analysis reviewed by the article: neural networks predicted writing performance group membership from 56 OMQ-based predictors "with high accuracy (ranging 94 and 100%)." Accuracy is not a standardized effect size, so impact is null.

"It was found that neural networks could predict with high accuracy (ranging 94 and 100%) which students would be in the poor, mid, or high performance groups, based on 56 predictors."

Discussion¶

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