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    Using Analytics to Encourage Student Responsibility for Learning and Identify Course Designs That Help

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    Fritz_umbc_0434D_11433.pdf (2.807Mb)
    Permanent Link
    http://hdl.handle.net/11603/15659
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    • UMBC Theses and Dissertations
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    Author/Creator
    Unknown author
    Date
    2016-01-01
    Type of Work
    Text
    dissertation
    Department
    Language, Literacy & Culture
    Program
    Language Literacy and Culture
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
    Distribution Rights granted to UMBC by the author.
    Subjects
    Instructional Technology
    Learning Analytics
    Student Success
    Abstract
    The purpose of this study is to demonstrate how instructional technology impacts teaching and learning. Specifically, in this study I show how learning analytics could be implemented to encourage student responsibility for learning and identify effective faculty course designs that help. Typically, learning analytics focuses on data mining student use of an online learning management system (LMS), the most widely used instructional technology in higher education. However, key challenges include a relative lack of empirical studies, the field?s predisposition toward prediction vs. intervention, and a lack of understanding about the role of faculty LMS course design on student usage. Accordingly, I explore how system-generated feedback to students about their LMS use compared to peers can serve as a metacognitive "nudge" toward improved responsibility for learning and academic performance. I also explore how this approach might shine light on effective faculty LMS course designs. I show how analytics provides both a theoretical and methodological foundation for implementing interventions based on the learning sciences, including self-efficacy, self-regulated learning and instructional technology. Finally, my findings contribute to the dialogue about scalable institutional approaches to improving student retention, persistence and success. Learning analytics is made possible through the technology of data mining, but I believe it also serves as a mirror to reflect (if not assess) the impact of instructional technology on teaching and learning.


    Albin O. Kuhn Library & Gallery
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    www.umbc.edu/scholarworks

    Contact information:
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    Phone: 410-455-3544


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.