Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project

Abstract

Learning analytics (LA) has emerged as a field that offers promising new ways to support failing or weaker students, prevent drop-out and aid retention. However, other research suggests that large datasets of learner activity can be used to understand online learning behaviour and improve pedagogy. While the use of LA in language learning has received little attention to date, available research suggests that understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways. This paper first discusses previous research in the field of language learning and teaching based on learner tracking and the specific affordances of LA for CALL, as well as its inherent limitations and challenges. The second part of the paper analyses data arising from the European Commission (EC) funded VITAL project that adopted a bottom-up pedagogical approach to LA and implemented learner activity tracking in different blended or distance learning settings.Referring to data arising from 285 undergraduate students on a Business French course at Hasselt University which used a flipped classroom design, statistical and process-mining techniques were applied to map and visualise actual uses of online learning resources over the course of one semester.Results suggested that most students planned their self-study sessions in accordance with the flipped classroom design, both in terms of their timing of online activity and selection of contents. Other metrics measuring active online engagement -a crucial component of

Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy

“…While the framework proposed is promising for a holistic approach to student engagement in a broader context of schooling, the OSE model as proposed by Dixson (2015) aligns quite well with the conceptual arguments of Kahu (2013) in the context of students' engagement in online courses. Gelan et al (2018) measure online engagement by the number of times students log in the VLE to follow a learning session. They also found that students who tend to show higher regularity level in their online interaction and by attending more learning session were successful, compared to non-successful students.…”

Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy

“…Dixson (2015) reported a signi cant correlation between application learning behaviours and OSE scale and a non-signi cant association between observation learning behaviours and OSE. Gelan et al (2018) measure online engagement by the number of times students log in the VLE to follow a learning session. They also found that students who tend to show higher regularity level in their online interaction and by attending more learning session were successful, compared to non-successful students.…”

Use of learning analytics in the flipped classroom: a systematic review

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