Using online cognitive tasks to predict mathematics low school achievement

Authors:
Caroline Di Bernardi Luft;July Silveira Gomes;Daniel Priori;Emilio Takase
Affiliations:
Psychology Post-Graduation Programme, Federal University of Santa Catarina - UFSC, Florianopolis, Brazil;Psychology Post-Graduation Programme, Federal University of Santa Catarina - UFSC, Florianopolis, Brazil;Cinema, Federal University of Santa Catarina - UFSC, Florianopolis, Brazil;Psychology Post-Graduation Programme, Federal University of Santa Catarina - UFSC, Florianopolis, Brazil
Venue:
Computers & Education
Year:
2013

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Abstract

This study aimed to analyze the validity of an online cognitive screening battery to predict mathematic school achievement using artificial neural networks (ANNs). The tasks were designed to measure; selective attention, visuo-spatial working memory, mental rotation, and arithmetic ability in an online, game-like format. In the first study, we investigated the cognitive performance of students with low and typical achievement in mathematics and language. In the second study, we developed an ANN to classify mathematics school achievement. Finally, we tested the adequacy of this network to classify an unknown sample to the ANN. Most of the performance differences in the battery were related to mathematics achievement. The ANN was able to predict mathematics achievement with acceptable accuracy and presented equivalent results in a simulation involving a different sample. We suggest that this assessment model combining ANNs and online cognitive tasks may be a valuable tool to research low school achievement in school settings.