Data Mining and Learning Analytics: Applications in Educational Research

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning

This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields.

•  Includes case studies where data mining techniques have been effectively applied to advance teaching and learning
• Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students
• Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students
• Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics

Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Table of Contents

Part I At The Intersection Of Two Fields: Edm
Chapter 1 Educational Process Mining: A Tutorial And Case Study Using Moodle Data Sets
Chapter 2 On Big Data And Text Mining In The Humanities
Chapter 3 Finding Predictors In Higher Education
Chapter 4 Educational Data Mining: A Mooc Experience
Chapter 5 Data Mining And Action Research

Part II Pedagogical Applications Of Edm
Chapter 6 Design Of An Adaptive Learning System And Educational Data Mining
Chapter 7 The “Geometry” Of Naïve Bayes: Teaching Probabilities By “Drawing” Them
Chapter 8 Examining The Learning Networks Of A Mooc
Chapter 9 Exploring The Usefulness Of Adaptive Elearning Laboratory Environments In Teaching Medical Science
Chapter 10 Investigating Co-Occurrence Patterns Of Learners’ Grammatical Errors Across Proficiency Levels And Essay Topics Based On Association Analysis

Part III Edm And Educational Research
Chapter 11 Mining Learning Sequences In Moocs: Does Course Design Constrain Students’ Behaviors Or Do Students Shape Their Own Learning?
Chapter 12 Understanding Communication Patterns In Moocs: Combining Data Mining And Qualitative Methods
Chapter 13 An Example Of Data Mining: Exploring The Relationship Between Applicant Attributes And Academic Measures Of Success In A Pharmacy Program
Chapter 14 A New Way Of Seeing: Using A Data Mining Approach To Understand Children’S Views Of Diversity And “Difference” In Picture Books
Chapter 15 Data Mining With Natural Language Processing And Corpus Linguistics: Unlocking Access To School Children’S Language In Diverse Contexts To Improve Instructional And Assessment Practices