Students' self‐report and observed learning orientations in blended university course design: How are they related to each other and to academic performance?

• Published in: Journal of computer assisted learning Vol. 36; no. 6; pp. 969 - 980
• Main Authors: Han, Feifei, Pardo, Abelardo, Ellis, Robert A.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.12.2020; Wiley; Wiley Subscription Services, Inc
• Subjects: Academic Achievement; Blended Learning; blended university course design; Clustering; Cognitive Style; College Students; Colleges & universities; Contingency tables; Course Content; Curriculum Design; Distance learning; Educational Environment; Electronic Learning; Instructional Improvement; Learning Analytics; learning orientations; Markov chains; observed data; Orientation; Self report; self‐report data; student approaches to learning; Students; Teaching Methods; Undergraduate Students
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.12453

This study examines the extent to which the learning orientations identified by student self‐reports and the observation of their online learning events were related to each other and to their academic performance. The participants were 322 first‐year engineering undergraduates, who were enrolled in a blended course. Using students' self‐report on a questionnaire about their approaches to learning and perceptions of the blended learning environment, ‘understanding’ and ‘reproducing’ learning orientations were identified. Using observations of student activity online, a Hidden Markov Model (HMM) and agglomerative sequence clustering detected four qualitatively different patterns of online learning orientations. Cross‐tabulations showed significant and logical associations amongst the learning orientations derived by the self‐report and observational methods. Significant differences were also consistently found in the students' academic performance across the mid‐term and final assessments based on their learning orientations detected by both self‐report and observational methods, results which have important implications for learning research. Lay Description What is currently known about the subject matter Learning analytics research uses observational data to investigate learning. Students Approaches to Learning research uses self‐report data to examine learning experiences. Little research combines observational and self‐report data to investigate variations in learning outcomes. What this paper adds This study uses both observational and self‐report data to investigate learning orientations. The learning orientations predicted mid‐ and final‐ assessment marks. Significant associations were found between learning orientations identified by observational and self‐report data. Implications for practitioners Combining data sources enables teachers to better understand students' learning orientations. Interventions can be designed based on an understanding of different learning orientations to improve learning. The approach used in this study is transferrable to other similar research.