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

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

Institutions

Publication Details

Journal
Education and Information Technologies
Published
2026-05-16
DOI
https://doi.org/10.1007/s10639-026-14027-8
Primary Topic
Innovative Teaching and Learning Methods
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

L Wang, Arjen de Vetten, Wilfried Admiraal, Roeland van der Rijst
Education and Information Technologies
Innovative Teaching and Learning Methods
article

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

L Wang, Arjen de Vetten, Wilfried Admiraal, Roeland van der Rijst
article en

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.

Education and Information Technologies
Leiden University (NL), Universidad Metropolitana (PR), University of Hong Kong (HK)
China Scholarship Council
Quality Education
Openalex Percentile: Top 2%
Innovative Teaching and Learning Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.