Classroom behavior mining in adolescents: a cognitive and data-driven approach using BEHAVE_Apriori

Abstract

Adolescence is a critical developmental stage that leads to essential changes in social, emotional, and cognitive domains that affect conduct in the classroom. Students' perceptions, processing, and reactions to their learning environment are better-understood thanks to cognitive psychology. However, contemporary data mining techniques frequently ignore the environmental, emotional, and cognitive elements influencing teenage behavior in learning environments. This research presents a comprehensive approach to analyzing teenage college students' classroom behavior by integrating cognitive psychology with data-driven methods to identify key behavioral traits shaped by both external and internal factors. A brand-new algorithm called the behavioral evaluation via hybrid attributes and valuable extraction using the Apriori (BEHAVE_Apriori) approach is presented. Also, a variety of feature selection (FS) strategies, including information gain (IG), chi-squared (CS), and tree-based approaches, are used for FS. Then, using the Apriori algorithm, association rules are found that relate behavior patterns to elements like family history, academic involvement, and peer influence. The IG-based FS combined with the Apriori algorithm delivered the best performance, generating 95 rules in 0.0241 seconds, outperforming CS (154 rules, 0.0629s) and tree-based FS (251 rules, 0.1394s), while the unfiltered dataset produced 514 rules in 0.2853 seconds.