Full Description
Learning analytics (or educational big data) tools are increasingly being deployed on campuses to improve student performance, retention and completion, especially when those metrics are tied to funding. Providing personalized, real-time, actionable feedback through mining and analysis of large data sets, learning analytics can illuminate trends and predict future outcomes. While promising, there is limited and mixed empirical evidence related to its efficacy to improve student retention and completion. Further, learning analytics tools are used by a variety of people on campus, and as such, its use in practice may not align with institutional intent. This monograph delves into the research, literature, and issues associated with learning analytics implementation, adoption, and use by individuals within higher education institutions. With it, readers will gain a greater understanding of the potential and challenges related to implementing, adopting, and integrating these systems on their campuses and within their classrooms and advising sessions. This is the fifth issue of the 43rd volume of the Jossey-Bass series ASHE Higher Education Report. Each monograph is the definitive analysis of a tough higher education issue, based on thorough research of pertinent literature and institutional experiences. Topics are identified by a national survey. Noted practitioners and scholars are then commissioned to write the reports, with experts providing critical reviews of each manuscript before publication.
Contents
Executive Summary 9Acknowledgements 15Foreword 16Introduction to Learning Analytics and Educational Technology Tools in Higher Education 18Introduction 20Purpose of the Monograph 22Current Trends in Higher Education 23Status of Learning Analytics Research in Higher Education 29Framework for Examining Learning Analytics in Higher Education 32Organizational Theory 33Technology Alignment and Adoption 34Faculty and Advisor Beliefs and Behaviors 34Student Use and Action 35Ethics and Privacy 36Outline of the Monograph 37How Organizational Context and Capacity and Technological Alignment Affect Learning Analytics Adoption 38Introduction 39An Organizational Model for Individual Decision Making 42Individual Factors 43Institutional Levels 44Institutional Levers 45Organizational Context 46Organizational Change 47Institutional Logics 48Organizational Readiness and Capacity 50Technology Adoption and Alignment 52Technology Adoption Models 52Traditional Adoption Models 52Education-Focused Adoption Models 53Technology Alignment 55Conclusion and Future Work 57Faculty, Advisor, and Student Decision Making Related to Use of Learning Analytics Data and Tools 58Introduction 60Faculty and Advisor Decision Making 61Professional Identity 62Professional Beliefs 63Professional Behaviors 64Impact of Identity, Beliefs, and Behavior and Future Work 66Student Decision Making 67Learning Analytics Dashboards 67Impact of Learning Analytics Dashboards on Student Actions 69Sensemaking and Trust 71Conclusion and Future Work 73Ethical and Privacy Concepts and Considerations 74Introduction 76Ethics and Privacy: Definitions, Conceptions, and Influences 78Evolving Definitions and Concepts 79Ethics and Privacy Within the Higher Education Context 82Institutional, Individual, and Data Considerations 83Institutional Contexts 83Individual Contexts 85Consent and Agency 86Trust and Bias 87Data Considerations 88Algorithmic Bias 88Transparency and Trust 89Security, Access, and Ownership 90Laws, Policies, and Codes of Practice 91Laws and Regulations 91Policies and Recommendations 94Challenges in Practice 95Emerging Codes of Practice 96Conclusion and Future Work 98Recommendations for Moving Forward: Considerations of Organizational Complexity, Data Fidelity, and Future Research 100Learning Analytics in Higher Education: Model Considerations and Recommendations 101Organizational Logic, Leadership, and Value 101Faculty and Advisor Input, Trust, and Engagement 104College Student Interpretation of and Context for Data 107Ethics and Privacy: Transparency and Ownership 110Data Concerns and Recommendations 113Data Access, Provenance, and Fidelity 113Use-Case/Scenario-Based Design of Systems 114Work Practice Integration of Systems 114Personalized Information to Stakeholders 115Use-Inspired Research in Pasteur's Quadrant: Integrated Education, Research, and Advising 115Privacy, Accountability, Transparency, Security, and Trust 116Suggestions for Future Research 116Quasiexperimental Design of Intervention Impacts 117Modeling Student Engagement 117Modeling and Visualizing Student Learning Preferences and Prior Learning Outcomes 118Developing Ethical Codes of Practice and Use 119Conclusion 121Resources 122References 125Name Index 137Subject Index 143About the Authors 147
