Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment.
Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment.
Jaime Lester is Associate Professor of Higher Education at George Mason University, USA. Carrie Klein is a PhD Candidate and Research and Teaching Assistant in the Higher Education Program at George Mason University, USA. Aditya Johri is Associate Professor of Information Sciences and Technology at George Mason University, USA. Huzefa Rangwala is Associate Professor of Computer Science at George Mason. University, USA.
Inhaltsangabe
Contents List of Tables List of Figures Preface Acknowledgments Chapter 1: Absorptive capacity and routines: Understanding barriers to learning analytics adoption in higher education Aditya Johri Chapter 2. Analytics in the field: Why locally grown continuous improvement systems are essential for effective data driven decision-making Matthew T. Hora Chapter 3: Big data, small data, and data shepherds Jennifer DeBoer and Lori Breslow Chapter 4: Evaluating scholarly teaching: A model and call for an evidence-based approach Daniel L. Reinholz, Joel C. Corbo, Daniel J. Bernstein, and Noah D. Finkelstein Chapter 5: Discipline-focused learning analytics approaches with users instead of for usersDavid B. Knight, Cory Brozina, Timothy J. Kinoshita, Brian J. Novoselich, Glenda D. Young, and Jacob R. Grohs Chapter 6: Student consent in learning analytics: The devil in the details? Paul Prinsloo and Sharon Slade Chapter 7: Using learning analytics to improve student learning outcomes assessment in higher education: Potential, constraint, & possibility Carrie Klein, and Richard M. Hess Chapter 8: Data, data everywhere: Implications and considerations Matthew D. Pistilli Contributor Bios
Contents List of Tables List of Figures Preface Acknowledgments Chapter 1: Absorptive capacity and routines: Understanding barriers to learning analytics adoption in higher education Aditya Johri Chapter 2. Analytics in the field: Why locally grown continuous improvement systems are essential for effective data driven decision-making Matthew T. Hora Chapter 3: Big data, small data, and data shepherds Jennifer DeBoer and Lori Breslow Chapter 4: Evaluating scholarly teaching: A model and call for an evidence-based approach Daniel L. Reinholz, Joel C. Corbo, Daniel J. Bernstein, and Noah D. Finkelstein Chapter 5: Discipline-focused learning analytics approaches with users instead of for usersDavid B. Knight, Cory Brozina, Timothy J. Kinoshita, Brian J. Novoselich, Glenda D. Young, and Jacob R. Grohs Chapter 6: Student consent in learning analytics: The devil in the details? Paul Prinsloo and Sharon Slade Chapter 7: Using learning analytics to improve student learning outcomes assessment in higher education: Potential, constraint, & possibility Carrie Klein, and Richard M. Hess Chapter 8: Data, data everywhere: Implications and considerations Matthew D. Pistilli Contributor Bios
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