Visualizations and Dashboards for Learning Analytics

ISBN-13: 9783030812249 / Angielski / Miękka / 2022 / 611 str.

Muhittin Sahin; Dirk Ifenthaler

Opis

This edited volume fills the gaps in existing literature on visualization and dashboard design for learning analytics. To do so, it presents critical tips to stakeholders and acts as guide to efficient implementation. The book covers the following topics: visualization and dashboard design for learning analytics, visualization and dashboard preferences of stakeholders, learners’ patterns on the dashboard, usability of visualization techniques and the dashboard, dashboard and intervention design, learning and instructional design for learning analytics, privacy and security issues about the dashboard, and future directions of visualization and dashboard design.This book will be of interest to researchers with interest in learning analytics and data analytics, teachers and students in higher education institutions and instructional designers, as it includes contributions from a wide variety of educational and psychological researchers, engineers, instructional designers, learning scientists, and computer scientists interested in learning analytics.

Spis treści

Part I: Theoretical and Technological Perspectives Linking Visualization and Dashboard Design.- Chapter 1: Visualizations And Dashboards For Learning Analytics: A Systematic Literature Review.- Chapter 2: The Current Landscape Of Research And Practice On Visualizations And Dashboards For Learning Analytics.- Chapter 3: Designing Theory-Driven Analytics-Enhanced Self-Regulated Learning Applications.- Chapter 4: Data Visualizations To Foster Self-Regulated Learning With Intelligent Programming Tutors.- Chapter 5: Effectiveness Of Dashboard And Intervention.- Chapter 6: What do MOOC Dashboards Present To Learners?.- Chapter 7: Powerful Student-Facing Dashboard Design Through Effective Feedback, Visualization, And Gamification.- Chapter 8: Visualizing Your Visualizations: The Role Of Metavisualization In Learning Analytics.- Part II: Practices and Evidence from the Learner’s Perspective.- Chapter 9: User-Centered Design For A Student-Facing Dashboard Grounded In Learning Theory.- Chapter 10: Learning Analytics For Students.- Chapter 11:Students’ Emotional Reactions To Social Comparison Via A Learner Dashboard.- Chapter 12: Navigational Behavior Patterns Of Learners On Dashboards Based On Assessment Analytics.- Chapter 13: Development And Evaluation Of A Student-Facing Gamified Learning Analytics Dashboard.- Chapter 14: Evaluating LA Dashboard In Secondary School And Higher Education: Fostering Goal Setting And Students’ Self-Regulation.- Chapter 15: “We Know What You Were Doing”.- Part III:Practices and Evidence from the Educator’s Perspective.- Chapter 16: Teachers’ Perspectives On The Promises, Needs And Challenges Of Learning Analytics Dashboards: Insights From Institutions Offering Blended And Distance Learning.- Chapter 17: Learning Analytics Dashboard Use In Online Courses: Why And How Instructors Interpret Discussion Data.- Chapter 18: Expanding Teacher Assessment Literacy With The Use Of Data Visualizations In Game-Based Assessment.- Part IV:Systems Design for Learning Analytics Applications.- Chapter 19: Visualization Of Learning For Students: A Dashboard For Study Progress.- Chapter 20: Visualization of Student-Item Interaction Matrix.- Chapter 21: Discovering Generative Uncertainty In Learning Analytics Dashboards.- Chapter 22: Designing And Developing A Learning Analytics Dashboard To Support Self-Regulated Learning.- Chapter 23: User-Centred Guidelines For The Design Of Curriculum Analytics Dashboards.- Chapter 24: Learning Analytics Dashboards In Educational Games.- Part V:Future Directions of Visualization and Dashboard.- Chapter 25: Maximizing Student Achievement Through The Collection And Visualization Of Assessment Data.- Chapter 26: Linking Assessment Results And Feedback Representations In E-Assessment: Evidence Centered Assessment Analytics Process Model.- Chapter 27: Visualization And Dashboards: Challenges And Future Directions.

O autorze

Muhittin Sahin is post-doc researcher at University of Mannheim, Germany, and academic staff

in the Department of Computer Education and Instructional Technology at Ege University, Turkey. His research interests deal with educational technology, learning analytics, educational data mining, multi-criteria decision making, data analysis, and e-assessment.

Dirk Ifenthaler is Professor and Chair of Learning, Design and Technology at University of Mannheim,

Germany, and UNESCO Deputy Chair of Data Science in Higher Education Learning and Teaching at Curtin University, Australia. Dirk’s research focuses on the intersection of cognitive psychology, educational technology, data analytics, and organisational learning.