Learning Analytics in Higher Education ASHE

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.

Volume Editors: Jaime Lester is an associate professor in the Higher Education Program at George Mason University. Carrie Klein is a PhD research and teaching assistant in the Higher Education Program at George Mason University. Huzefa Rangwala is an associate professor in the Department of Computer Science at George Mason University. Aditya Johri is an associate professor in the Information Sciences and Technology Department at George Mason University. Series Editors: Kelly Ward and Lisa E. Wolf-Wendel

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

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Learning Analytics in Higher Education ASHE Higher Education Report

Summary

Author Biography

Table of Contents

Supplemental Materials