Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
In support of student pair-programming
Proceedings of the thirty-second SIGCSE technical symposium on Computer Science Education
Houston, we have a problem: there's a leak in the CS1 affective oxygen tank
Proceedings of the 35th SIGCSE technical symposium on Computer science education
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Alternative pacing in an introductory java sequence
CITC5 '04 Proceedings of the 5th conference on Information technology education
Intention-based scoring: an approach to measuring success at solving the composition problem
Proceedings of the 36th SIGCSE technical symposium on Computer science education
Programming: factors that influence success
Proceedings of the 36th SIGCSE technical symposium on Computer science education
What can computer science learn from a fine arts approach to teaching?
Proceedings of the 36th SIGCSE technical symposium on Computer science education
Increasing student retention in computer science through research programs for undergraduates
ACM SIGGRAPH 2006 Educators program
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Automatic prediction of frustration
International Journal of Human-Computer Studies
Game2Learn: building CS1 learning games for retention
Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Proceedings of the 39th SIGCSE technical symposium on Computer science education
Using group-based projects to improve retention of students in computer science major
Journal of Computing Sciences in Colleges
Proceedings of the 14th European conference on Cognitive ergonomics: invent! explore!
Affect and Usage Choices in Simulation Problem-Solving Environments
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Experiencing programming assignments in CS1: the emotional toll
Proceedings of the Sixth international workshop on Computing education research
CS majors' self-efficacy perceptions in CS1: results in light of social cognitive theory
Proceedings of the seventh international workshop on Computing education research
Predicting at-risk novice Java programmers through the analysis of online protocols
Proceedings of the seventh international workshop on Computing education research
Automatic identification of affective states using student log data in ITS
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A Motivation Guided Holistic Rehabilitation of the First Programming Course
ACM Transactions on Computing Education (TOCE)
Recording and analyzing in-browser programming sessions
Proceedings of the 13th Koli Calling International Conference on Computing Education Research
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We attempt to automatically detect student frustration, at a coarse-grained level, using measures distilled from student behavior within a learning environment for introductory programming. We find that each student's average level of frustration across five lab exercises can be detected based on the number of pairs of consecutive compilations with the same edit location, the number of pairs of consecutive compilations with the same error, the average time between compilations and the total number of errors. Attempts to detect frustration at a finer grain-size, identifying individual students' fluctuations in frustration between labs, were less successful. These results indicate that it is possible to detect frustration at a coarse-grained level, solely from coarse-grained data about students' behavior within a learning environment.