Understanding primary students’ self-regulated vocabulary learning behaviours on a mobile app via learning analytics and their associated outcomes: A case study

In this study, we investigated primary school students’ self-regulated vocabulary learning (SRVL) behaviours on a mobile app using learning analytics (LA) and their associated English vocabulary learning outcomes. Participants were 44 students in Grade 4 from one class in a primary school in Mainland China. Data collection included log data on the mobile app and pre-, mid- and post- vocabulary tests. Data analysis included LA using agglomerative hierarchical clustering and process-mining techniques to understand primary students’ SRVL behaviours in a mobile learning environment and quantitative data analysis to examine the association between students’ SRVL behaviours and their English vocabulary learning outcomes. The results show that three groups of students’ SRVL behaviours were identified using clustering. In addition, the similarities and differences of the characteristics of students’ SRVL behaviours among the identified three clusters were explored. Finally, the association between the identified three clusters and English vocabulary learning outcomes was discussed. The findings provide researchers, language teachers and learners with theoretical and practice insights into the characteristic of the dynamic SRVL behavioural learning patterns in a mobile learning environment and shed light on future research in making personalised recommendations to learners with different SRVL characteristics. Copyright © 2022 Beijing Normal University.