Novice mistakes: are the folk wisdoms correct?
Communications of the ACM
Analyzing the high frequency bugs in novice programs
Papers presented at the first workshop on empirical studies of programmers on Empirical studies of programmers
An analysis of patterns of debugging among novice computer science students
ITiCSE '05 Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Mining student CVS repositories for performance indicators
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Methods and tools for exploring novice compilation behaviour
Proceedings of the second international workshop on Computing education research
ClockIt: collecting quantitative data on how beginning software developers really work
Proceedings of the 13th annual conference on Innovation and technology in computer science education
Retina: helping students and instructors based on observed programming activities
Proceedings of the 40th ACM technical symposium on Computer science education
Another look at the behaviors of novice programmers
Proceedings of the 40th ACM technical symposium on Computer science education
Affective and behavioral predictors of novice programmer achievement
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
Comparing effective and ineffective behaviors of student programmers
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Flexible, reusable tools for studying novice programmers
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Coarse-grained detection of student frustration in an introductory programming course
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Online identification of learner problem solving strategies using pattern recognition methods
Proceedings of the fifteenth annual conference on Innovation and technology in computer science education
Predicting at-risk novice Java programmers through the analysis of online protocols
Proceedings of the seventh international workshop on Computing education research
Modeling how students learn to program
Proceedings of the 43rd ACM technical symposium on Computer Science Education
Web-scale data gathering with BlueJ
Proceedings of the ninth annual international conference on International computing education research
A fast measure for identifying at-risk students in computer science
Proceedings of the ninth annual international conference on International computing education research
Towards improving programming habits to create better computer science course outcomes
Proceedings of the 18th ACM conference on Innovation and technology in computer science education
SnapViz: visualizing programming assignment snapshots
Proceedings of the 18th ACM conference on Innovation and technology in computer science education
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In this paper, we report on the analysis of a novel type of automatically recorded detailed programming session data collected on a university-level web programming course. We present a method and an implementation of collecting rich data on how students learning to program edit and execute code and explore its use in examining learners' behavior. The data collection instrument is an in-browser Python programming environment that integrates an editor, an execution environment, and an interactive Python console and is used to deliver programming assignments with automatic feedback. Most importantly, the environment records learners' interaction within it. We have implemented tools for viewing these traces and demonstrate their potential in learning about the programming processes of learners and of benefiting computing education research and the teaching of programming.