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Auflistung nach Autor:in "Eiband, Malin"

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  • 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.
  • Dissertation
    Supporting users in understanding intelligent everyday systems
    (2019) Eiband, Malin
    Intelligent systems have permeated many areas of daily life like communication, search, decision-making, and navigation, and thus present an important meeting point of people and artificial intelligence in practice. These intelligent everyday systems are in focus of this thesis. Intelligent everyday systems exhibit the characteristics of so-called complex systems as defined in cognitive science: They serve ill-defined user goals, change dynamically over time, and comprise a large number of interrelated variables whose dependencies are not transparent to users. Due to this complexity, intelligent everyday systems can violate established usability guidelines of user interface design like transparency, controllability and easy error correction. This may introduce uncertainty to interaction that users have to overcome in order to reach a goal. I introduce a perspective from cognitive science, where users do so through knowledge. The work presented in this thesis aims at assisting users in gaining this knowledge, or supporting users in understanding intelligent everyday systems, for example, through explanation, control, correction or feedback. To this end, the work included in this thesis makes three main contributions: First, I present a method for eliciting user need for support and informing adequate solutions through practical user problems with intelligent everyday systems in daily interaction. In a first phase, the presented method uses passive data collection to extract user problems with intelligent everyday systems through a combination of automated and manual analyses. In the second phase, these problems are then enriched and validated through active data collection to derive solutions for support. In addition, I report on the application of this method to uncover user problems with four popular commercial intelligent everyday systems (Facebook, Netflix, Google Maps and Google Assistant). Second, I introduce a conceptual framework for categorising and differentiating prevailing notionsin the field of how users should be supported in understanding intelligent systems related to what users seek to know, how they acquire knowledge, and what kind of knowledge they acquire. The presented framework can be used to make these notions explicit and thus introduces an overarching structure that abstracts from the field’s fractured terminological landscape. It aims at helping other researchers become aware of existing approaches and locate and reflect on their own work. Third, I present a number of case studies and arguments as an exploration of how users can be supported in the face of real-world challenges and trade-offs. My research reflects two possible perspectives to approach this question, a normative and a pragmatic one. As part of a critical reflection on the normative perspective, the work shows that explanations without information can similarly foster user trust in a system compared to real explanations, and discusses how user support can be exploited to deceive users. From the pragmatic perspective emerges a stage-based participatory design process that incorporates different stakeholder needs and a study assessing how support can be interwoven with users’ primary tasks. In summary, this thesis adopts a perspective on interaction with intelligent everyday systems, where understanding is a fundamental process towards reaching a user-set goal. On this basis, I introduce a research agenda for future work that incorporates the presented contributions and also includes challenges beyond the scope of this work, such as considering user empowerment. I hope that this agenda, along with the presented method, framework and design exploration, will help future work to shape interaction with intelligent everyday systems in a way that allows people to use them better, and to better ends and outcomes.
  • Konferenzbeitrag
    Understanding Algorithms through Exploration: Supporting Knowledge Acquisition in Primary Tasks
    (Mensch und Computer 2019 - Tagungsband, 2019) Eiband, Malin; Anlauff, Charlotte; Ordenewitz, Tim; Zürn, Martin; Hussmann, Heinrich
    We investigate exploration as an alternative to explanation to improve user understanding of algorithms and algorithmic decision-making. Drawing on complex problem-solving as defined in cognitive science, we conducted a think-aloud study in the lab (N=10) as well as an MTurk online study (N=123) using a flight booking scenario to see if and how exploration supports \textit{knowledge acquisition} in two different tasks. One group was told to focus on booking the cheapest flight (knowledge acquisition as a secondary task), the other on understanding the system logic (knowledge acquisition as a primary task). Our results indicate that exploration, even as a secondary task, may contribute to knowledge about the underlying algorithm. However, our study also suggests that the overall knowledge acquired through exploration is limited in the sense that it gives people an idea of how a system works, rather than teaching them concrete rules they can recall. Overall, we conclude that exploration presents a design opportunity to interweave knowledge acquisition with users' primary task, and may thus contribute to (but not substitute) existing design solutions for supporting users in understanding algorithmic decision-making.
  • 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.