Article: Evaluation of teaching effectiveness in data analysis courses using a behavioural big data model Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.10 pp.22 - 41 Abstract: This study proposes a behavioural big data model for evaluating teaching effectiveness in data analysis courses across primary, secondary, and higher education levels. The framework integrates learning management system data, classroom engagement indicators, and student interaction behaviours to provide a comprehensive understanding of how teaching strategies influence learning outcomes. At the primary level, the model captures early learning patterns such as task attention, problem-solving attempts, and basic data reasoning through gamified digital activities. For secondary students, behavioural indicators - including learning persistence, collaboration, and response accuracy - are used to assess the development of analytical thinking and computational skills. Machine learning and statistical techniques, such as clustering, regression, and correlation analysis, identify patterns linking teaching approaches with student performance and motivation. With a predictive accuracy of 89%, the model demonstrates strong adaptability across age groups. Findings show that interactive, feedback-rich, and project-based learning environments significantly enhance students' comprehension and retention of data analysis concepts. Inderscience Publishers - linking academia, business and industry through research

Title: Evaluation of teaching effectiveness in data analysis courses using a behavioural big data model

Authors: Yixu Wang

Addresses: Teacher Digital Literacy Enhancement Center, Jilin Provincial Institute of Education, Changchun, Jilin, 130000, China

Abstract: This study proposes a behavioural big data model for evaluating teaching effectiveness in data analysis courses across primary, secondary, and higher education levels. The framework integrates learning management system data, classroom engagement indicators, and student interaction behaviours to provide a comprehensive understanding of how teaching strategies influence learning outcomes. At the primary level, the model captures early learning patterns such as task attention, problem-solving attempts, and basic data reasoning through gamified digital activities. For secondary students, behavioural indicators - including learning persistence, collaboration, and response accuracy - are used to assess the development of analytical thinking and computational skills. Machine learning and statistical techniques, such as clustering, regression, and correlation analysis, identify patterns linking teaching approaches with student performance and motivation. With a predictive accuracy of 89%, the model demonstrates strong adaptability across age groups. Findings show that interactive, feedback-rich, and project-based learning environments significantly enhance students' comprehension and retention of data analysis concepts.

Keywords: data analysis education; behavioural big data; learning analytics; teaching effectiveness; student performance evaluation; big data in education; data-driven pedagogy.

DOI: 10.1504/IJICT.2026.151688

International Journal of Information and Communication Technology, 2026 Vol.27 No.10, pp.22 - 41

Received: 27 Sep 2025
Accepted: 22 Oct 2025
Published online: 13 Feb 2026 *

Title: Evaluation of teaching effectiveness in data analysis courses using a behavioural big data model

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