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Auflistung nach Schlagwort "Mobile Language Learning"

1 - 3 von 3
  • Konferenzbeitrag
    Auto-Generating Multimedia Language Learning Material for Children with Off-the-Shelf AI
    (Mensch und Computer 2022 - Tagungsband, 2022) Draxler, Fiona; Haller, Laura; Schmidt, Albrecht; Chuang, Lewis L.
    The unique affordances of mobile devices enable the design of novel language learning experiences with auto-generated learning materials. Thus, they can support independent learning without increasing the burden on teachers. In this paper, we investigate the potential and the design requirements of such learning experiences for children. We implement a novel mobile app that auto-generates context-based multimedia material for learning English. It automatically labels photos children take with the app and uses them as a trigger for generating content using machine translation, image retrieval, and text-to-speech. An exploratory study with 25 children showed that children were ready to engage to an equal extent with this app and a non-personal version using random instead of personal photos. Overall, the children appreciated the independence gained compared to learning at school but missed the teachers’ support. From a technological perspective, we found that auto-generation works in many cases. However, handling erroneous input, such as blurry images and spelling mistakes, is crucial for children as a target group. We conclude with design recommendations for future projects, including scaffolds for the photo-taking process and information redundancy for identifying inaccurate auto-generation results.
  • Konferenzbeitrag
    Comparing Concepts for Embedding Second-Language Vocabulary Acquisition into Everyday Smartphone Interactions
    (Mensch und Computer 2021 - Tagungsband, 2021) Schneegass, Christina; Sigethy, Sophia; Eiband, Malin; Buschek, Daniel
    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.
  • Zeitschriftenartikel
    UnlockLearning – Investigating the Integration of Vocabulary Learning Tasks into the Smartphone Authentication Process
    (i-com: Vol. 21, No. 1, 2022) Schneegass, Christina; Sigethy, Sophia; Mitrevska, Teodora; Eiband, Malin; Buschek, Daniel
    Frequent repetition of vocabulary is essential for effective language learning. To increase exposure to learning content, this work explores the integration of vocabulary tasks into the smartphone authentication process. We present the design and initial user experience evaluation of twelve prototypes, which explored three learning tasks and four common authentication types. In a three-week within-subject field study, we compared the most promising concept as mobile language learning (MLL) applications to two baselines: 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.