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Updated: Jul 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predicting LD on the basis of motivation, metacognition, and psychopathology: an ROC analysis
Georgios D Sideridis1, Paul L Morgan, George Botsas
1Department of Psychology, University of Crete, Rethimnon. sideridis@psy.soc.uoc.gr
Abstract:
We examined how strongly motivation, metacognition, and psychopathology acted as predictors of learning disabilities (LD). The results from five studies suggested that level of motivation (as shown through self-efficacy, motivational force, task avoidance, goal commitment, or self-concept) was highly accurate in classifying students with or at risk for LD. Metacognition and psychopathology were also strong predictors. Classification accuracy using receiver operating characteristic (ROC) curves ranged between 77% and 96%. These rates were much higher than the chance-level (i.e., 50%-55%) rates sometimes yielded by cognitive indices. Linear discriminant function (LDF) analysis substantiated classification accuracy. These results suggest that motivation, metacognition, and psychopathology are strong predictors of LD. Understanding the influence of these characteristics may help researchers and practitioners more accurately screen and treat students with LD.
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