Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25435
Title: Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project
Authors: GELAN, Anouk 
Fastré, Greet
VERJANS, Martine 
MARTIN, Niels 
JANSSENSWILLEN, Gert 
CREEMERS, Mathijs 
LIEBEN, Jonas 
DEPAIRE, Benoit 
Thomas, Michael
Issue Date: 2018
Source: Computer Assisted Language Learning, 31 (3), p. 294-319
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.
Notes: Gelan, A (reprint author), Hasselt Univ, Ctr Appl Linguist, Hasselt, Belgium, anouk.gelan@uhasselt.be
Keywords: blended learning; flipped learning; learning analytics; learning dashboards; learning patterns; selfregulated learning; tracking data
Document URI: http://hdl.handle.net/1942/25435
ISSN: 0958-8221
e-ISSN: 1744-3210
DOI: 10.1080/09588221.2017.1418382
ISI #: 000429596700007
Rights: © 2018 Informa UK Limited, trading as Taylor & Francis Group
Category: A1
Type: Journal Contribution
Validations: ecoom 2019
Appears in Collections:Research publications

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