Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project. Issue 3 (4th March 2018)

• Record Type: Journal Article
• Authors: Gelan, Anouk; Fastré, Greet; Verjans, Martine; Martin, Niels; Janssenswillen, Gert; Creemers, Mathijs; Lieben, Jonas; Depaire, Benoît; Thomas, Michael
• Is Part Of: Computer assisted language learning. Volume 31:Issue 3(2018)
• Page Start: 294
• Page End: 319
• Publication Date: 2018-03-04
• Subjects: Blended learning -- flipped learning -- learning analytics -- learning dashboards -- learning patterns -- self-regulated learning -- tracking data; Language and languages -- Computer-assisted instruction -- Periodicals; 418.00285
• DOI: 10.1080/09588221.2017.1418382
• Languages: English
• ISSNs: 0958-8221

Abstract

ABSTRACT: Learning analytics (LA) has emerged as a field that offers promising new ways to 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 LA 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 CALL research based on learner tracking and 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 VITAL project that implemented LA in different blended or distance learning settings. Statistical and process-mining techniques were applied to data from 285 undergraduate students on a Business French course. Results suggested that most students planned their self-study sessions in accordance with the flipped classroom design. Other metrics measuring active online engagement indicated significant differences between successful and non-successful students' learner patterns. The research implied that valuable insights can be acquired through LA and the use of visualisation and process-mining tools.