Innovative Learning Analytics for Evaluating Instruction

Innovative Learning Analytics for Evaluating Instruction covers the application of a forward-thinking research methodology that uses big data to evaluate the effectiveness of online instruction. Analysis of Patterns in Time (APT) is a practical analytic approach that finds meaningful patterns in massive data sets, capturing temporal maps of students' learning journeys by combining qualitative and quantitative methods. Offering conceptual and research overviews, design principles, historical examples, and more, this book demonstrates how APT can yield strong, easily generalizable empirical evidence through big data; help students succeed in their learning journeys; and document the extraordinary effectiveness of First Principles of Instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods.

Theodore W. Frick is Professor Emeritus in the Department of Instructional Systems Technology in the School of Education at Indiana University Bloomington, USA. Rodney D. Myers is Instructional Consultant in the School of Education at Indiana University Bloomington, USA. Cesur Dagli is Research Analyst in the Office of Analytics & Institutional Effectiveness at Virginia Polytechnic Institute and State University, USA. Andrew F. Barrett is Co-founder of ScaleLearning, Inc. and leads the Learning Technology team at Shopify, Inc., Canada

1. Learning Journeys in Education 2. Overview of the Big Study 3. The Indiana University Plagiarism Tutorials and Tests 4. More Details of the Big Study 5. Analysis of Patterns in Time as a Research Methodology 6. Using Analysis of Patterns in Time for Formative Evaluation of a Learning Design 7. Analysis of Patterns in Time with Teaching and Learning Quality Surveys 8. Analysis of Patterns in Time as an Alternative to Traditional Approaches