Students’ pre-tutorial learning behaviors and their relationship with tutorial engagement and learning outcomes

Education and Information Technologies, vol. 31, pp. 5989–6015

Abstract

Abstract In blended learning, pre-tutorial learning lays the foundation for in-tutorial learning but also presents challenges due to its emphasis on self-regulated learning. To understand the relationship between pre-tutorial and in-tutorial learning, this study examined variations in pre-tutorial behaviors using both online and offline learning data, identified students’ pre-class learning patterns, and investigated how these patterns relate to both tutorial engagement and learning outcomes. Our quantitative data were collected from 51 first-year bachelor students. Our qualitative data were collected from a subset of 20 students through interviews. The findings show an overall declining participation in pre-tutorial activities. Instructional aspects, such as pedagogy and course format, were reported in interviews as primary reasons for not participating in pre-tutorial learning activities. Three learning behavior profiles have been identified through Latent profile analysis, but neither tutorial engagement nor learning outcomes differed significantly across these profiles according to ANCOVA. However, self-regulation was found to be a predictor of tutorial engagement. This study extends previous blended learning analytics research, which has primarily focused on online learning, by integrating both online and offline pre‑tutorial data and examining their relationship to tutorial engagement and outcomes. Future research should focus on refining methods for identifying learning profiles that comprehensively represent students’ engagement and learning outcomes, for example, by including students’ self-regulation skills in the profile analysis. In addition, future research should investigate the pedagogical design of blended learning to more effectively support students’ learning, using tools such as AI-driven dashboards and learning analytics.

Authors 4

1. L Wang corresponding Aachen

   Leiden University · University of Hong Kong

   Affiliation as printed

   Faculty of Education, Hong Kong University, Hong Kong, China

   ICLON Graduate School of Teaching, Leiden University, P.O. Box 905, Leiden, 2300 AX, the Netherlands

2. Arjen de Vetten Aachen

   Leiden University

   Affiliation as printed

   ICLON Graduate School of Teaching, Leiden University, P.O. Box 905, Leiden, 2300 AX, the Netherlands

3. Roeland Matthijs van der Rijst Aachen

   Leiden University

   Affiliation as printed

   ICLON Graduate School of Teaching, Leiden University, P.O. Box 905, Leiden, 2300 AX, the Netherlands

4. Wilfried ADMIRAAL

   OsloMet – Oslo Metropolitan University

   Affiliation as printed

   Center for the Study of Professions, Oslo Metropolitan University, PO Box 4 , St. Olavs Plass, Oslo, N-0130, Norway