Année : 2019
Lieu de publication de l'article :
Résumé de l'article
With increasing abundance and ubiquity of mobile phones, desk-top PCs, and tablets in the last decade, we are seeing studentsintermixing these modalities to learn and regulate their learning.However, the role of these modalities in educational settings is stilllargely under-researched. Similarly, little attention has been paidto the research on the extension of learning analytics to analyzethe learning processes of students adopting various modalities dur-ing a learning activity. Traditionally, research on how modalitiesaffect the way in which activities are completed has mainly reliedupon self-reported data or mere counts of access from each modal-ity. We explore the use of technological modalities in regulatinglearning via learning management systems (LMS) in the contextof blended courses. We used data mining techniques to analyzepatterns in sequences of actions performed by learners (n = 120)across different modalities in order to identify technological modal-ity profiles of sequences. These profiles were used to detect thetechnological modality strategies adopted by students. We found amoderate effect size (ϵ2 = 0.12) of students’ adopted strategies onthe final course grade. Furthermore, when looking specifically atonline discussion engagement and performance, students’ adoptedtechnological modality strategies explained a large amount of vari-ance (η2 = 0.68) in their engagement and quality of contributions.The result implications and further research are discussed.CCS CONCEPTS• Information systems → Data mining; • Human-centeredcomputing → Mobile devices; • Applied computing → Edu-cation;KEYWORDSMobile Learning, Trace Analysis, Multi-device use, Blended learn-ing, Online discussions, Learning analyticsACM Reference Format:Varshita Sher, Marek Hatala, and Dragan Gašević. 2019. On multi-deviceuse: Using technological modality profiles to explain differences in students’learning. In The 9th International Learning Analytics Knowledge Conference(LAK19), March 4–8, 2019, Tempe, AZ, USA. ACM, New York, NY, USA,Article 4, 10 pages. https://doi.org/10.1145/3303772.3303790Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.LAK19, March 4–8, 2019, Tempe, AZ, USA© 2019 Association for Computing Machinery.ACM ISBN 978-1-4503-6256-6/19/03. . . $15.00https://doi.org/10.1145/3303772.33037901
Mots-clés
Knowledge acquisition,Web application,Pedagogical agent,Color gradient,Final Exam,LL parser,Library (computing),Data logger,Randomness,Dalton Pritchard,Causality,Gosper curve,Hubbard model,Morgan,
Français
English
