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.

Table of Contents:
Executive Summary 9 Acknowledgements 15 Foreword 16 Introduction to Learning Analytics and Educational Technology Tools in Higher Education 18 Introduction 20 Purpose of the Monograph 22 Current Trends in Higher Education 23 Status of Learning Analytics Research in Higher Education 29 Framework for Examining Learning Analytics in Higher Education 32 Organizational Theory 33 Technology Alignment and Adoption 34 Faculty and Advisor Beliefs and Behaviors 34 Student Use and Action 35 Ethics and Privacy 36 Outline of the Monograph 37 How Organizational Context and Capacity and Technological Alignment Affect Learning Analytics Adoption 38 Introduction 39 An Organizational Model for Individual Decision Making 42 Individual Factors 43 Institutional Levels 44 Institutional Levers 45 Organizational Context 46 Organizational Change 47 Institutional Logics 48 Organizational Readiness and Capacity 50 Technology Adoption and Alignment 52 Technology Adoption Models 52 Traditional Adoption Models 52 Education-Focused Adoption Models 53 Technology Alignment 55 Conclusion and Future Work 57 Faculty, Advisor, and Student Decision Making Related to Use of Learning Analytics Data and Tools 58 Introduction 60 Faculty and Advisor Decision Making 61 Professional Identity 62 Professional Beliefs 63 Professional Behaviors 64 Impact of Identity, Beliefs, and Behavior and Future Work 66 Student Decision Making 67 Learning Analytics Dashboards 67 Impact of Learning Analytics Dashboards on Student Actions 69 Sensemaking and Trust 71 Conclusion and Future Work 73 Ethical and Privacy Concepts and Considerations 74 Introduction 76 Ethics and Privacy: Definitions, Conceptions, and Influences 78 Evolving Definitions and Concepts 79 Ethics and Privacy Within the Higher Education Context 82 Institutional, Individual, and Data Considerations 83 Institutional Contexts 83 Individual Contexts 85 Consent and Agency 86 Trust and Bias 87 Data Considerations 88 Algorithmic Bias 88 Transparency and Trust 89 Security, Access, and Ownership 90 Laws, Policies, and Codes of Practice 91 Laws and Regulations 91 Policies and Recommendations 94 Challenges in Practice 95 Emerging Codes of Practice 96 Conclusion and Future Work 98 Recommendations for Moving Forward: Considerations of Organizational Complexity, Data Fidelity, and Future Research 100 Learning Analytics in Higher Education: Model Considerations and Recommendations 101 Organizational Logic, Leadership, and Value 101 Faculty and Advisor Input, Trust, and Engagement 104 College Student Interpretation of and Context for Data 107 Ethics and Privacy: Transparency and Ownership 110 Data Concerns and Recommendations 113 Data Access, Provenance, and Fidelity 113 Use-Case/Scenario-Based Design of Systems 114 Work Practice Integration of Systems 114 Personalized Information to Stakeholders 115 Use-Inspired Research in Pasteur’s Quadrant: Integrated Education, Research, and Advising 115 Privacy, Accountability, Transparency, Security, and Trust 116 Suggestions for Future Research 116 Quasiexperimental Design of Intervention Impacts 117 Modeling Student Engagement 117 Modeling and Visualizing Student Learning Preferences and Prior Learning Outcomes 118 Developing Ethical Codes of Practice and Use 119 Conclusion 121 Resources 122 References 125 Name Index 137 Subject Index 143 About the Authors 147