CALL FOR CHAPTERS
for an edited reference book entitled "Data Mining and Learning Analytics in Educational Research" To be published by Wiley and Blackwell

In a 2010 article published in The Chronicles of Higher Education, Marc Parry maintains that academia is “at a computational crossroads” when it comes to big data and analytics in education. With this in mind, this edited volume will bring together interdisciplinary articles in which advances in data mining and education are showcased. It will address issues of data mining research and its use, implementation, challenges, benefits, and consequences in education and educational research with the aim of presenting a comprehensive picture and in-depth analyses of the current situation from the fields of Education and Computing Sciences.

For over a decade, data mining has solidly established itself as a research tool within institutions of higher education. Defined as the “analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owners (Han and Kamber, 2006)”, data mining is a multidisciplinary field that integrates methods at the intersection of artificial intelligence, machine learning, natural language processing, statistics and database systems. Data mining techniques are used to analyze large scale data and discover meaningful patterns such as natural grouping of data records (cluster analysis), unusual records (anomaly and outlier detection) and dependencies (association rule mining). It has made major advances in medical, biomedical, engineering and business fields. Educational Data Mining (EDM) emerged in the last few years from computer sciences as a field in its own right with the goal of integrating data mining techniques to advance teaching, learning and research in higher education. It has matured enough to have its own international conference, its 7th annual gathering being held in London in the summer of 2014
(http://www.educationaldatamining.org/EDM2014/).

Yet, most of the advances in EDM are mostly led by and confined to computing sciences. Educators from “education fields,” unfortunately, play a minor role in EDM. Consequently, advances in EDM often lack the grounding in pedagogical and educational research; which thus far, have played a peripheral role in this strongly emerging field that could greatly benefit and shape education and educational research.

We intend this proposed book to be a foundation for the intersection between DM, LA, EDM and education from a social sciences perspective. The chapters in this book will collectively address the impacts of DM on education from various perspectives: insights, challenges, issues,expectations, and practical implementation of DM within educational mandates. It will be a common interdisciplinary platform for both scientists at the cutting edge of EDM
and educators seeking to use and integrate DM and LA in addressing educational issues, improving education and advancing educational research.

Being at the crossroads of two intertwined disciplines, this book will offer practitioners, educators, computer scientists, graduate students, university, school and board administrators, teachers and students a reference in both fields. It will showcase the work of scholars who work with a deep understanding and respect for both the social sciences and computer sciences.

Table of contents:

The book will be divided into three major parts:

Part 1: At the Intersection of two fields: EDM
A collection of chapters presenting a general overview and definition of DM, LA, data
warehousing, and the data collection models in the context of educational research. Chapters will
have a technical aspects and algorithm development in DM. Throughout this first part, the reader
will not only have a better understanding of DM, DW and LA and how they operate, but also of
the type of data and the organization of data needed for carrying EDM studies within an
education context at both macro (e.g. programs, large scale studies) and micro (e.g. classroom,
learner-centered) levels. We welcome studies that shed light on what is possible to collect in the
context of education and why it is useful and meaningful to collect, as well as those that present
details on how to collect data and how to approach educational contexts and research.

Part 2: Pedagogical Applications of EDM
Chapters will showcase both the applications and challenges to using DM and LA in pedagogical
settings. They will highlight effective classroom practices where EDM can advance learning and
teaching. In order to ensure that we have a broad representation of various educational setting,
we will not limit the submission to the education field per se, but we will seek studies that
sought to apply EDM in teaching of Business, Medicine and Sciences (outside of CS, such as the
teaching of mathematics or physics).Some of the studies may focus on social networking in classroom setting, students’ interactions, feedback, responses analyses. etc. Assessment is another component where EDM has proven
effective and we will seek submissions that target assessment from a diagnostic and formative
perspective within the classroom from elementary to post-secondary practices in education.
Some chapters could also address pitfalls in EDM, so that the book showcases not only what can
be done, but also what should be avoided and what does not work and is not possible to do in
educational contexts. For example, DM and LA may have a great applicability in business but, if
applied to pedagogy, would yield counterproductive results.

Part 3: EDM and Educational Research
Chapters will exclusively focus on EDM in educational research. An important aspect and use of
EDM is the potential role it could play in providing new research tools and new perspectives for
advancing educational research. EDM is an innovative research method that has the potential to
revolutionize research in education: instead of following pre-determined research questions and
predefined variables, EDM could be used as a means to look at data holistically, longitudinally,
and transversally and to ‘let’ data reveal more than if we restricted it to specific variables within
a time constraint. Submissions that studied this aspect for research in education and on large
scale educational data sets such as the MET data (Measures of Effective Teaching data from the
University of Michigan) are welcome.

Chapters’ topics
The book will cover various topics that may include, but are not limited to, the following areas:
1. Educational data mining
2. Learning analytics
3. The technical requirements for DM in education
4. Technical challenges to using and implementing DM in education
5. Educational assessment and DM
6. Pedagogical models using DM
7. The relative merits of DM humanities and social sciences
8. Educational research and DM
9. Data mining and policy/legislation in education
10. Implementations in the classroom
11. Adaptive learning environment
12. Broader socio-cultural and political implications for data mining in Education
13. Social networking and education
14. Identifying at-risk students
15. Action research in education using DM & LA
16. Teacher and peer conferencing and interactions
17. Diagnostics and formative assessment for learning
18. Application of DM & LA in other education setting: Medicine, Sciences etc.

For proposal submission
Please include the following:
- A page with title, author’s name, affiliation and contact information
- A one page proposal (250-300 words)
- A short bio of 50 words
Please send proposals to:
Dr. Samira ElAtia: selatia@ualberta.ca
Dr. Osmar Zaiane: zaiane@ualberta.ca
Dr. Donald Ipperciel: di@ualberta.ca
Inquiries may be sent to the same email addresses.

Timetables

Stage 1 – Submission of Abstracts Deadline
Proposal –one page October 10, 2014
Invitations to submit a manuscript- November10, 2014

Stage 2 – Submission of Manuscripts
Submission of manuscripts April 30, 2015
Results of peer review July 30 2015
Submission of revised manuscripts October 30, 2015

Tentative date of Publication December, 2015