Using Educational Data Mining to Study Problem Behaviors in Online Learning
|Saturday, November 9, 2013|
|4:00 PM–4:50 PM |
|Regency Ballroom A & B|
|Area: EDC; Domain: Conceptual/Theoretical|
|Instruction Level: Basic|
|CE Instructor: Ryan Baker, Ph.D.|
|Chair: Ronnie Detrich (The Wing Institute)|
|RYAN BAKER (Columbia University)|
|Dr. Ryan Shaun Joazeiro de Baker is an associate professor of learning analytics at Teachers College, Columbia University. He earned his Ph.D. in human-computer interaction from Carnegie Mellon University. Dr. Baker was previously an assistant professor of psychology and the learning sciences at Worcester Polytechnic Institute, and he served as the first technical director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding president of the International Educational Data Mining Society, and as associate editor of the Journal of Educational Data Mining. His research combines educational data mining and quantitative field observation methods to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, multi-user virtual environments, and educational games.|
Increasingly, students' educational experiences occur in the context of online learning environments, creating opportunities to study student behavior in a fashion that is both longitudinal and very fine-grained. In this talk, Dr. Baker will discuss the use of Educational Data Mining methods on this type of data to automatically infer student problem behaviors during online learning, and to make basic discoveries about the factors that lead students to engage in these behaviors. He will illustrate this process through discussing his research group's work to leverage a combination of field observation and data mining to develop automated detectors that infer when a student engages in a range of problem behaviors, including gaming the system, off-task behavior, and carelessness. Dr. Baker will then discuss his group's work studying the ways that these behaviors and emotions are influenced by student interaction with online learning environments, and how that work influences developing next-generation online learning environments that students are more likely to choose to use appropriately and effectively.
|Target Audience: |
Anyone who is interested in educational data mining and online learning.
|Learning Objectives: At the conclusion of the presentation, participants should be able to:
--Define educational data mining, and contrast it with learning analytics.
--Cite an example of how data mining can inform educators and instructional designers about student engagement and emotional responses to instruction.
--Cite an example of how student interaction with online learning environments influences the design of next-generation online learning.
|Keyword(s): Data mining, online learning|