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E-raamat: Data Mining and Learning Analytics: Applications in Educational Research

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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 minings 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 EDMs emerging role in helping to advance educational researchfrom 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.
Notes On Contributors xi
Introduction: Education At Computational Crossroads xxiii
Samira El Atia
Donald Ipperciel
Osmar R. Zalane
Part I At The Intersection Of Two Fields: EDM 1(78)
Chapter 1 Educational Process Mining: A Tutorial And Case Study Using Moodle Data Sets
3(26)
Cristobal Romero
Rebeca Cerezo
Alejandro Bogarin
Miguel Sanchez-Santillin
1.1 Background
5(2)
1.2 Data Description and Preparation
7(9)
1.2.1 Preprocessing Log Data
7(4)
1.2.2 Clustering Approach for Grouping Log Data
11(5)
1.3 Working with ProM
16(10)
1.3.1 Discovered Models
19(4)
1.3.2 Analysis of the Models' Performance
23(3)
1.4 Conclusion
26(1)
Acknowledgments
27(1)
References
27(2)
Chapter 2 On Big Data And Text Mining In The Humanities
29(12)
Geoffrey Rockwell
Bettina Berendt
2.1 Busa and the Digital Text
30(2)
2.2 Thesaurus Linguae Graecae and the Ibycus Computer as Infrastructure
32(3)
2.2.1 Complete Data Sets
33(2)
2.3 Cooking with Statistics
35(2)
2.4 Conclusions
37(1)
References
38(3)
Chapter 3 Finding Predictors In Higher Education
41(14)
David Eubanks
William Evers Jr
Nancy Smith
3.1 Contrasting Traditional and Computational Methods
42(3)
3.2 Predictors and Data Exploration
45(5)
3.3 Data Mining Application: An Example
50(2)
3.4 Conclusions
52(1)
References
53(2)
Chapter 4 Educational Data Mining: A MOOC Experience
55(12)
Ryan S. Baker
Yuan Wang
Luc Paquette
Vincent Aleven
Octav Popescu
Jonathan Sewall
Carolyn Rose
Gaurav Singh Tomar
Oliver Ferschke
Jing Zhang
Michael J. Cennamo
Stephanie Ogden
Therese Condit
Jose Diaz
Scott Crossley
Danielle S. McNamara
Denise K. Comer
Collin F. Lynch
Rebecca Brown
Tiffany Barnes
Yoav Bergner
4.1 Big Data in Education: The Course
55(2)
4.1.1 Iteration 1: Coursera
55(1)
4.1.2 Iteration 2: edX
56(1)
4.2 Cognitive Tutor Authoring Tools
57(1)
4.3 Bazaar
58(1)
4.4 Walkthrough
58(7)
4.4.1 Course Content
58(3)
4.4.2 Research on BDEMOOC
61(4)
4.5 Conclusion
65(1)
Acknowledgments
65(1)
References
65(2)
Chapter 5 Data Mining And Action Research
67(12)
Ellina Chernobilsky
Edith Ries
Joanne Jasmine
5.1 Process
69(2)
5.2 Design Methodology
71(1)
5.3 Analysis and Interpretation of Data
72(3)
5.3.1 Quantitative Data Analysis and Interpretation
73(1)
5.3.2 Qualitative Data Analysis and Interpretation
74(1)
5.4 Challenges
75(1)
5.5 Ethics
76(1)
5.6 Role of Administration in the Data Collection Process
76(1)
5.7 Conclusion
77(1)
References
77(2)
Part II Pedagogical Applications Of EDM 79(94)
Chapter 6 Design Of An Adaptive Learning System And Educational Data Mining
81(18)
Zhiyong Liu
Nick Cercone
6.1 Dimensionalities of the User Model in ALS
83(2)
6.2 Collecting Data for ALS
85(1)
6.3 Data Mining in ALS
86(4)
6.3.1 Data Mining for User Modeling
87(1)
6.3.2 Data Mining for Knowledge Discovery
88(2)
6.4 ALS Model and Function Analyzing
90(4)
6.4.1 Introduction of Module Functions
90(3)
6.4.2 Analyzing the Workflow
93(1)
6.5 Future Works
94(1)
6.6 Conclusions
94(1)
Acknowledgment
95(1)
References
95(4)
Chapter 7 The "Geometry" Of Naive Bayes: Teaching Probabilities By "Drawing" Them
99(22)
Giorgio Maria Di Nunzio
7.1 Introduction
99(3)
7.1.1 Main Contribution
100(1)
7.1.2 Related Works
101(1)
7.2 The Geometry of NB Classification
102(3)
7.2.1 Mathematical Notation
102(1)
7.2.2 Bayesian Decision Theory
103(2)
7.3 Two-Dimensional Probabilities
105(6)
7.3.1 Working with Likelihoods and Priors Only
107(1)
7.3.2 De-normalizing Probabilities
108(1)
7.3.3 NB Approach
109(1)
7.3.4 Bernoulli Naive Bayes
110(1)
7.4 A New Decision Line: Far from the Origin
111(3)
7.4.1 De-normalization Makes (Some) Problems Linearly Separable
112(2)
7.5 Likelihood Spaces, When Logarithms make a Difference (or a SUM)
114(4)
7.5.1 De-normalization Makes (Some) Problems Linearly Separable
115(1)
7.5.2 A New Decision in Likelihood Spaces
116(1)
7.5.3 A Real Case Scenario: Text Categorization
117(1)
7.6 Final Remarks
118(1)
References
119(2)
Chapter 8 Examining The Learning Networks Of A MOOC
121(18)
Meaghan Brugha
Jean-Paul Restoule
8.1 Review of Literature
122(2)
8.2 Course Context
124(1)
8.3 Results and Discussion
125(8)
8.4 Recommendations for Future Research
133(1)
8.5 Conclusions
134(1)
References
135(4)
Chapter 9 Exploring The Usefulness Of Adaptive Elearning Laboratory Environments In Teaching Medical Science
139(18)
Thuan Thai
Patsie Polly
9.1 Introduction
139(2)
9.2 Software for Learning and Teaching
141(11)
9.2.1 Reflective Practice: ePortfolio
141(2)
9.2.2 Online Quizzes
143(1)
9.2.3 Online Practical Lessons
144(1)
9.2.4 Virtual Laboratories
145(2)
9.2.5 The Gene Suite
147(5)
9.3 Potential Limitations
152(1)
9.4 Conclusion
153(1)
Acknowledgments
153(1)
References
154(3)
Chapter 10 Investigating Co-Occurrence Patterns Of Learners' Grammatical Errors Across Proficiency Levels And Essay Topics Based On Association Analysis
157(16)
Yutaka Ishii
10.1 Introduction
157(2)
10.1.1 The Relationship between Data Mining and Educational Research
157(1)
10.1.2 English Writing Instruction in the Japanese Context
158(1)
10.2 Literature Review
159(1)
10.3 Method
160(2)
10.3.1 Konan-JIEM Learner Corpus
160(2)
10.3.2 Association Analysis
162(1)
10.4 Experiment 1
162(1)
10.5 Experiment 2
163(1)
10.6 Discussion and Conclusion
164(1)
Appendix A: Example of Learner's Essay (University Life)
164(1)
Appendix B: Support Values of all Topics
165(3)
Appendix C: Support Values of Advanced, Intermediate, and Beginner Levels of Learners
168(1)
References
169(4)
Part III EDM And Educational Research 173(104)
Chapter 11 Mining Learning Sequences In Moocs: Does Course Design Constrain Students' Behaviors Or Do Students Shape Their Own Learning?
175(32)
Lorenzo Vigentini
Simon McIntyre
Negin Mirriahi
Dennis Alonzo
11.1 Introduction
175(3)
11.1.1 Perceptions and Challenges of MOOC Design
176(1)
11.1.2 What Do We Know About Participants' Navigation: Choice and Control
177(1)
11.2 Data Mining in MOOCs: Related Work
178(2)
11.2.1 Setting the Hypotheses
179(1)
11.3 The Design and Intent of the LTTO MOOC
180(4)
11.3.1 Course Grading and Certification
183(1)
11.3.2 Delivering the Course
183(1)
11.3.3 Operationalize Engagement, Personal Success, and Course Success in LTTO
184(1)
11.4 Data Analysis
184(7)
11.4.1 Approaches to Process the Data Sources
185(1)
11.4.2 LTTO in Numbers
186(1)
11.4.3 Characterizing Patterns of Completion and Achievement
186(3)
11.4.4 Redefining Participation and Engagement
189(2)
11.5 Mining Behaviors and Intents
191(7)
11.5.1 Participants' Intent and Behaviors: A Classification Model
191(3)
11.5.2 Natural Clustering Based on Behaviors
194(4)
11.5.3 Stated Intents and Behaviors: Are They Related?
198(1)
11.6 Closing the Loop: Informing Pedagogy and Course Enhancement
198(3)
11.6.1 Conclusions, Lessons Learnt, and Future Directions
200(1)
References
201(6)
Chapter 12 Understanding Communication Patterns In MOOCs: Combining Data Mining And Qualitative Methods
207(16)
Rebecca Eynon
Isis Hjorth
Taha Yasseri
Nabeel Gillani
12.1 Introduction
207(2)
12.2 Methodological Approaches to Understanding Communication Patterns in MOOCs
209(1)
12.3 Description
210(3)
12.3.1 Structural Connections
211(2)
12.4 Examining Dialogue
213(1)
12.5 Interpretative Models
214(1)
12.6 Understanding Experience
215(1)
12.7 Experimentation
216(1)
12.8 Future Research
217(1)
References
218(5)
Chapter 13 An Example Of Data Mining: Exploring The Relationship Between Applicant Attributes And Academic Measures Of Success In A Pharmacy Program
223(14)
Dion Brocks
Ken Cor
13.1 Introduction
223(2)
13.2 Methods
225(3)
13.3 Results
228(2)
13.4 Discussion
230(4)
13.4.1 Prerequisite Predictors
230(2)
13.4.2 Demographic Predictors
232(2)
13.5 Conclusion
234(1)
Appendix A
234(2)
References
236(1)
Chapter 14 A New Way Of Seeing: Using A Data Mining Approach To Understand Children's Views Of Diversity And "Difference" In Picture Books
237(18)
Robin A. Moeller
Hsin-liang Chen
14.1 Introduction
237(1)
14.2 Study 1: Using Data Mining to Better Understand Perceptions of Race
238(10)
14.2.1 Background
238(1)
14.2.2 Research Questions
239(1)
14.2.3 Methods
240(1)
14.2.4 Findings
240(8)
14.2.5 Discussion
248(1)
14.3 Study 2: Translating Data Mining Results to Picture Book Concepts of "Difference"
248(4)
14.3.1 Background
248(1)
14.3.2 Research Questions
249(1)
14.3.3 Methodology
250(1)
14.3.4 Findings
250(2)
14.3.5 Discussion and Implications
252(1)
14.4 Conclusions
252(1)
References
252(3)
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
255(22)
Alison L. Bailey
Anne Blackstock-Bernstein
Eve Ryan
Despina Pitsoulakis
15.1 Introduction
255(1)
15.2 Identifying the Problem
256(5)
15.3 Use of Corpora and Technology in Language Instruction and Assessment
261(5)
15.3.1 Language Corpora in ESL and EFL Teaching and Learning
261(1)
15.3.2 Previous Extensions of Corpus Linguistics to School-Age Language
262(1)
15.3.3 Corpus Linguistics in Language Assessment
263(1)
15.3.4 Big Data Purposes, Techniques, and Technology
264(2)
15.4 Creating a School-Age Learner Corpus and Digital Data Analytics System
266(3)
15.4.1 Language Measures Included in DRGON
267(1)
15.4.2 The DLLP as a Promising Practice
268(1)
15.5 Next Steps, "Modest Data," and Closing Remarks
269(2)
Acknowledgments
271(1)
Apendix A: Examples of Oral and Written Explanation Elicitation Prompts
272(1)
References
272(5)
Index 277
Samira ElAtia is Associate Professor of Education at The University of Alberta, Canada. She has published numerous articles and book chapters on topics relating to the use of technology to support pedagogical research and education in higher education. Her current research focuses on using e-learning environment and big data for fair and valid longitudinal assessment of, and for, learning within higher education.

Donald Ipperciel is Principal and Professor at Glendon College, York University, Toronto, Canada and was the Canadian Research Chair in Political Philosophy and Canadian Studies between 2002 and 2012. He has authored several books and has contributed chapters and articles in more than 60 publications.  Ipperciel has dedicated many years of research on the questions of e-learning and using technology in education. He is co-editor of the Canadian Journal of Learning and Technology since 2010.

Osmar R. Zaiane is Professor of Computing Science at the University of Alberta, Canada and Scientific Director of the Alberta Innovates Centre of Machine Learning. A renowned researcher and computer scientist, Dr. Zaiane is former Secretary Treasurer of the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining. He obtained the IEEE ICDM Outstanding Service Aware in 2009 as well as the ACM SIGKDD Service Award the following year.
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