Cognitive assessment of motivation to perform classroom or online math tasks among engineering students

Abstract

This study explored the cognitive algebra mechanism underlying mathematical motivation in 672 engineering students. The experimental design included the combination of four factors (task modality versus task difficulty versus task structure versus task relevance) to compose 36 written experimental scenarios. Each one described a hypothetical situation about assigned activities in math class. The participant's task was to read each scenario and estimate how much motivation they would experience if performing the assigned math activity. The results indicated five cognitive motivational patterns among the participants. All the clusters considered the task's relevance as an essential factor in judging their mathematical motivation. Besides this, Clusters 1, 2, 3, and 5 considered the assigned task's difficulty and structure in judging their degree of motivation, but they evaluated the factors differently. The low math motivation cluster integrated the factors according to a summative cognitive rule. Clusters 2, 3, and 5 used a multiplicative rule to integrate the information, and Cluster 4 did not show an information integration systematic mechanism. These findings pointed to the diversity of motivational cognitive profiles among students. This type of cognitive characterization can help design programs that encourage students to learn and enjoy science subjects that will impact their professional development and daily life.