Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments
Proceedings of the 11th international conference on Intelligent user interfaces
Modeling and understanding students' off-task behavior in intelligent tutoring systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Motivating programming: using storytelling to make computer programming attractive to middle school girls
Flexible, reusable tools for studying novice programmers
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Exploring Wonderland: Java Programming Using Alice and Media Computation
Exploring Wonderland: Java Programming Using Alice and Media Computation
ACM Transactions on Computing Education (TOCE)
Alice, Greenfoot, and Scratch -- A Discussion
ACM Transactions on Computing Education (TOCE)
Modeling how students learn to program
Proceedings of the 43rd ACM technical symposium on Computer Science Education
The fairy performance assessment: measuring computational thinking in middle school
Proceedings of the 43rd ACM technical symposium on Computer Science Education
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There is growing interest in how we can use computer logging data to improve computational tools and pedagogies to engage children in complex thinking and self-expression, but our techniques lag far behind our theories. Only recently have learning scientists begun to measure, collect, analyze, and report how data informs the science of children's learning. In this paper, we describe our initial efforts towards developing tools to mine computer logging data for information on how to enhance learning opportunities. The data were collected as part of an NSF-funded project, and include logs from 320 middle school students using Alice to program computer games in semester-long courses. We describe some lessons learned and decisions made in the process of reconstructing high-level user actions in Alice from low-level Alice logs.