Educational Data Mining Summer School

26-30 May 2014, 8:30 to 4:30
Room CTC 112

Hosted by:

The Ateneo Laboratory for the Learning Sciences
Department of Information Systems and Computer Science
Ateneo de Manila University, Loyola Heights, Quezon City

In cooperation with

The Educational Data Mining Laboratory
Teachers College, Columbia University
New York, New York

Resource Speakers:

Jaclyn Ocumpaugh, Ph.D.
Teachers College, Columbia University

Ma. Mercedes T. Rodrigo, Ph.D.
Ateneo de Manila University

The Ateneo Laboratory for the Learning Sciences (ALLS) is a research office that lies at the intersection of computer science and education. The Educational Data Mining Laboratory of Teachers College, Columbia University is headed by Dr. Ryan Baker, President of the International Educational Data Mining Society and Associate Editor of the Journal of Educational Data Mining. Both laboratories make use of statistical and data mining techniques to investigate behavioral and affective phenomena that mediate learning. These include in depth examinations of learning outcomes, prediction of STEM career choice, student help-seeking, carelessness, and conscientiousness as well as patterns of student confusion, frustration, and boredom.

The goal of the ALLS Summer School is to assist participants explore the quantitative analysis of student interactions with computer-based learning environments to derive insights about how students learn best. The Summer School will begin with a poster presentation in which participants will discuss their current work. The Summer School continues with an intensive five-day workshop that will familiarize participants with the data collection and analysis techniques that ALLS uses to conduct its research. Ample time will be provided for hands-on work. Each participant will be expected to produce a data analysis. The workshop ends with poster presentation in which resource people will critique student work and recommend ways in which the work may be extended.

The Summer School is open to current or prospective graduate students who wish to learn more about the learning sciences and its associated methods. The tentative schedule of activities is as follows:

26 May 2014
Introduction of participants
Poster presentations
Introduction to the learning sciences
Video annotation
Field observation methods

27 May 2014
Low-fidelity text replays annotation
Reliability checks
Sample interaction logs
Exploratory analysis

28 May 2014
Educational data mining

29 May 2014
Educational data mining

30 May 2014
Poster preparation
Poster presentations

To apply, prospective participants must email a 2-3 PDF document (12-point Times New Roman Font, 1 inch margins on all sides, double spaced) with the following information to Ma. Mercedes T. Rodrigo ( by 31 March 2014:

Full name
Email address
Highest degree earned or ongoing studies
Describe background in statistics and data mining. What statistics or data mining packages have you used? What techniques are already familiar to you?
If you are not currently enrolled in a graduate degree program, are you planning to enroll in graduate school? What is your time line? What specialization do you intend to pursue?
Why are you interested in attending the ALLS Summer School? How is it relevant to your current or future work? How do you see yourself applying what you learned? (The response to this question must be 500 to 600 words long.)

Successful applicants will be allowed to attend the Summer School free of charge. They will, however, be responsible for their own transportation, meals, and lodging. Current graduate students also have the option of officially registering for this course and taking it for credit as CS 214 User Modeling and User Profiling for Adaptive Systems.

Slots are limited, so please apply soon!

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