This page is archived content from LAK14

The goal of this tutorial is to share data mining tools and techniques used by computer scientists with educational social scientists. We broadly define educational social scientists as being made up of people with backgrounds in the learning sciences, cognitive psychology, and educational research. The learning analytics community is heavily populated with researchers of these backgrounds, and we believe those that find themselves at the intersection of research, theory, and practice (the theme of LAK14) have a particular interest in expanding their knowledge of data-driven tools and techniques.

By the end of this course we expect that attendees are able to: 
  1. Describe the differences between supervised and unsupervised classification 
  2. Understand how to choose a classification method for a particular research question 
  3. Frame different kinds of educational datasets in a way that is appropriate for data mining 
  4. Contextualize the results of a J48 decision tree to their research questions 
  5. Contextualize the results of k-means clustering to their research questions 
  6. Apply knowledge of the Weka toolkit to create decision trees or clusters of new educational datasets

While there are many education and learning conferences that include computer scientists and educational social scientists (e.g. EDM, AIED, ITS, LAK, ICLS, CSCL, etc.), to our knowledge there has been no tutorial focused explicitly on introductory methods of data mining. The closest relevant resource we have observed is the recently offered free course on Data Mining with Weka offered by the University of Waikato an excellent way to learn the Weka toolkit, it has not been largely advertised in the educational technology and learning sciences communities, and it does not focus on educational datasets or questions of learning outcomes. We view our proposed tutorial at LAK14 as one way of contextualizing the kind of instruction that is available in this complimentary online course. 

Dr. Christopher Brooks is a Research Fellow at the University of Michigan School of Information with an interest in learning analytics and quantifying the effects of educational technology on the teaching and learning process. He earned his PhD in Computer Science from the University of Saskatchewan in 2012. 

Dr. Zachary Pardos is an Assistant Professor at UC Berkeley in a joint position between the School of Information and Graduate School of Education. He earned his PhD in Computer Science at Worcester Polytechnic Institute in the Tutor Research Group in 2012.

Craig Thompson is a Ph.D. candidate at the University of Saskatchewan and is presently undertaking a research internship at the UofS University Learning Centre. His doctoral research interests include Machine Learning and Image Recognition applied to labelling images on the web. While on internship, Craig is pursuing a variety of projects involving Learning Analytics and Data Mining with institutional enrolment information, student survey responses, and LCMS data.