Innovative Learning Analytics for Evaluating Instruction: A Big Data Roadmap to Effective Online Learning

Description

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. Cover Half Title Title Page Copyright Page Table of Contents Foreword Preface Chapter Summaries List of Tables, Figures, and Maps 1 Learning Journeys in Education Metaphor of a Journey: The Oregon Trail The State-Trait Approach to Measurement: Quantitative Methods Individual Episodic Stories: Qualitative Methods Qualitative Temporal Mapping that is Quantifiable and Generalizable: A Third Alternative for Educational Research Methods The Larger Problem in Educational Research References 2 Overview of the Big Study A Tale of Two Learning Journeys Learning Journey #1: Sam’s Case Learning Journey #2: Melinda’s Case Design and Structure of IPTAT Using First Principles of Instruction Problem-Centered Principle Activation Principle Demonstration Principle Application Principle Integration Principle Results from 936,417 Learning Journeys through IPTAT in the Big Study in 2019 and 2020 Activation Results Demonstration Results Application Principle Integration Principle Overall Unique Pageviews What Does All This Mean? References 3 The Indiana University Plagiarism Tutorials and Tests Introduction Major Redesign of IPTAT in 2015 Authentic Problems Principle Activation Principle Demonstration Principle Application Principle Integration Principle Certification Tests Summary of Changes to the Newly Designed IPTAT in 2015 What’s the Same? Usage of IPTAT from 2019 and 2020 Minimalists Traditionalists Dabblers Registrants User-Developer Feedback Loop What About COVID? References 4 More Details of the Big Study Discovery of Google Analytics for Doing APT Important Concepts for Doing Analysis of Patterns in Time Two Fundamental Ways of Temporal Segmenting: Prediction and Retrodiction Temporal Segmenting by Quarters One at a Time Retrodictive APT Queries Endpoint Condition Defined Users Who Passed Users Who Have Not Passed Apply the Endpoint Condition to the GA Audience Reporting Tool (UA) Using GA to Find Matches of Pageviews within Segments Wash, Rinse, and Repeat, then Combine Some Key Issues We Identified and Resolved to do APT of IPTAT Data Streams Created by GA Can You Do APT with GA4? Who Are the Registered IPTAT Users in 2019 and 2020 (from Our MySQL Database at IU)? Summary References 5 Analysis of Patterns in Time as a Research Methodology Introduction APT of Direct Instruction and Academic Learning Time: Joint Event Occurrences Linear Models Approach APT Approach APT of Teacher-Student Interaction in Class: Frequency of Sequential Events APT of Asynchronous Online Discussion: Sequential Patterns of Comments APT Outside of Education Moneyball Google Analytics References 6 Using Analysis of Patterns in Time for Formative Evaluation of a Learning Design Introduction Simulation Fidelity Evaluation of Fidelity Using APT for Model Verification The Diffusion Simulation Game Applying the APT Procedure to the DSG Data Analysis and Results Conclusion References 7 Analysis of Patterns in Time with Teaching and Learning Quality Surveys APT of Course Evaluations The Goal: Creating a Table from a Spreadsheet Formation of TALQ Scales Transferring MOO-TALQ Survey Responses to a Spreadsheet Creating Spreadsheet Formulas for Each TALQ Scale Creating Further Derived Scores for Scale Agreement (Yes or No) Creating a Table for the Combinations of Categories Summary References 8 Analysis of Patterns in Time as an Alternative to Traditional Approaches Making Inductive Inferences with APT Big Data in Education Approaches to Big Data Methods Used to Analyze Big Data Learning Analytics and Instructional Design The Value of Theory to Guide Educational Research Extending APT References Epilogue Abbreviations and Symbols Index