Learning Analytics

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    Intelligent Tutoring Systems: Why Teachers Abandoned a Technology Aimed at Automating Teaching Processes
    ( 2021-01-05) Utterberg Modén, Marie ; Tallvid, Martin ; Lundin, Johan ; Lindström, Berner
    Expectations that technology will improve and streamline education are high. However, technology often introduces new problems. This study aims to explore the challenges mathematics teachers encounter when they implement a digital mathematics textbook with an integrated intelligent tutoring system. A formative intervention was conducted in a two-year project with 16 secondary school teachers. The method was based on activity theory and required the teachers to collaborate with researchers in analyzing their work activity when the new teaching tool was introduced. In this paper, we show that an intelligent tutoring system created systemic contradictions for the teachers. Those contradictions involved predictability, division of labor, individual versus collective learning, accountability, and expectations versus experience. The teachers all tried to resolve the contradictions, but eventually felt compelled to abandon the intelligent tutoring system. The findings contribute to a better understanding of teachers’ responses to a technology aimed at automating teaching processes.
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    Do They Even Care? Measuring Instructor Value of Student Privacy in the Context of Learning Analytics
    ( 2021-01-05) Jones, Kyle ; Vanscoy, Amy ; Bright, Kawanna ; Harding, Alison
    Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether or not faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. The findings detail faculty perspectives of their own privacy, students’ privacy, and the high degree to which they value both. Data indicate that faculty believe that privacy is important to intellectual behaviors and learning. This work reports initial findings of a multi-phase, grant-funded research project that will further uncover instructor views of learning analytics and its student privacy issues.
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    An analysis of learners’ intentions toward virtual reality online learning systems: a case study in Taiwan
    ( 2021-01-05) Wang, Yi-Ting ; Lin, Kuan-Yu ; Huang, Travis
    The virtual reality technology, VR, features feeling of blending in, interactivity and imagination. It not only improves on the limitations of conventional learning methods but also enhances the communication of learning contents and messages via interactivity and imitation. Over the past decade, the incorporation of information technology in education has been an important research topic. Following the computer-aided teaching, interactive online learning, distance teaching and mobile learning of the earlier days, the VR technology is now regarded as a new wave of trend facilitating the incorporation of application technology into education. To explore the factors that affect users’ use of virtual reality online learning systems (VROLS), this study based on the theory of cost-benefit perspective and incorporating the perceived value and the flow theories proposes an integrated research model elaborating on why people opt for VROLS services. A total of 296 valid samples were collected from online questionnaires and the data were analyzed using the structural equation modeling (SEM) approach. The findings suggest that both perceived value and flow experience are important roles in users’ use of VROLS services, in which benefits (spatial presence, enjoyment, service compatibility) and costs (complexity and visual fatigue) are the critical factors affecting users’ perceived value and flow experience. The implications of these findings are further discussed.
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    A Framework for Informal Learning Analytics - Evidence from the Literacy Domain
    ( 2021-01-05) Rizk, Aya ; Rodriguez, Adrian
    Multidisciplinary approaches to learning analytics (LA) have the potential to provide important insights into student learning beyond interactions within learning management systems (LMS). In this paper we demonstrate the benefits of such an approach by proposing a framework that adds the contextual elements of task design, tools and technologies and datasets to established LA processes. Our framework was developed as a design science research (DSR) artifact, working with teachers of English at two Swedish secondary schools. The results highlight the importance of valid task design for generating relevant, useful insights and provide a basis for simplifying and automating in-situ LA that can be used by teachers in their everyday work. The study also provided important insights for the field of online research and comprehension (ORC) both in relation to methodology and how students engage with a task that requires locating and synthesizing information on the open Internet in a second language.
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    Introduction to the Minitrack on Learning Analytics
    ( 2021-01-05) Deeva, Galina ; Willermark, Sara ; Islind, Anna Sigridur ; Oskarsdottir, Maria