Data Mining and Learning Analytics

INTRODUCTION: EDUCATION AT COMPUTATIONAL CROSSROADS

Samira ElAtia¹ Donald Ipperciel², and Osmar R. Zaïane³

¹ Campus Saint‐Jean, University of Alberta, Edmonton, Alberta, Canada

² Glendon College, York University, Toronto, Ontario, Canada

³ Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada

For almost two decades, data mining (DM) has solidly grounded its place as a research tool within institutions of higher education. Defined as the “analysis of observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owners (Han, Kamber, and Pei, 2006),” DM is a multidisciplinary field that integrates methods at the intersection of artificial intelligence (AI), machine learning, natural language processing (NLP), statistics, and database systems. DM 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 biomedical, medical, engineering, and business fields. Educational data mining (EDM) emerged in the last few years from computer sciences as a field in its own right that uses DM techniques to advance teaching, learning, and research in higher education. It has matured enough to have its own international conference (http://www.educationaldatamining.org). In 2010, ...