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Comparing Concepts for Embedding Second-Language Vocabulary Acquisition into Everyday Smartphone Interactions

Title data

Schneegass, Christina ; Sigethy, Sophia ; Eiband, Malin ; Buschek, Daniel:
Comparing Concepts for Embedding Second-Language Vocabulary Acquisition into Everyday Smartphone Interactions.
In: Mensch & Computer 2021 : Tagungsband. - New York : The Association for Computing Machinery , 2021 . - pp. 11-20
ISBN 978-1-4503-8645-6
DOI: https://doi.org/10.1145/3473856.3473863

Project information

Project title:
Project's official titleProject's id
AI Tools - Continuous Interaction with Computational Intelligence ToolsNo information

Abstract in another language

We present a three-week within-subject field study comparing three mobile language learning (MLL) applications with varying levels of integration into everyday smartphone interactions: We designed a novel (1) UnlockApp that presents a vocabulary task with each authentication event, nudging users towards short frequent learning session. We compare it with a (2) NotificationApp that displays vocabulary tasks in a push notification in the status bar, which is always visible but learning needs to be user-initiated, and a (3) StandardApp that requires users to start in-app learning actively. Our study is the first to directly compare these embedding concepts for MLL, showing that integrating vocabulary learning into everyday smartphone interactions via UnlockApp and NotificationApp increases the number of answers. However, users show individual subjective preferences. Based on our results, we discuss the trade-off between higher content exposure and disturbance, and the related challenges and opportunities of embedding learning seamlessly into everyday mobile interactions.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Mobile Language Learning; Embedded Interaction; Micro Interactions
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 20 Sep 2021 08:53
Last Modified: 20 Sep 2021 08:53
URI: https://eref.uni-bayreuth.de/id/eprint/67060