Supporting second language learners’ development of affective self-regulated learning skills through the use and design of mobile technology

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Abstract

Self-regulated learning (SRL) is associated with students’ academic performance, but it is difficult for learners. It can be learnt and taught. This study investigates how we can support second language learners in fostering affective learning skills needed for the successful acquisition of the targeted language. In the research literature, the affective domain has received little attention compared to other aspects of SRL. In this design-oriented case study, a prototype of a cultural-appropriate tool (the AffecTive LeArning Srl (ATLAS) mobile app) aiming to support students with affective learning in their SRL process, was designed and evaluated with 13 Japanese language students through semi-structured interviews and usability testing. The app’s design has been informed by Zimmerman’s SRL model, and its affective learning part - by the psychoevolutionary theory of emotions. Design Science Research was adopted as the overall methodology. The findings show that the majority of the respondents exhibited positive attitudes towards the use of the affective learning scenarios embedded in the ATLAS app to enhance their SRL. Especially, the perceived usefulness of the ATLAS’ affective learning part was seen to increase their awareness of their individual SRL process, as well as their engagement in- and motivation for self-regulated language learning across learning settings. In regard to the SRL phases (i.e., plan, monitor and reflect), the results indicate that the students would prefer to perform affective learning activities during each SRL phase. From a practical perspective, this study presents a culturally adapted SRL tool and provides relevant design guidelines.

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APA

Viberg, O., Mavroudi, A., & Ma, Y. (2020). Supporting second language learners’ development of affective self-regulated learning skills through the use and design of mobile technology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12315 LNCS, pp. 173–186). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57717-9_13

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