Hidden Markov models for speech recognition
Technometrics
Constructivism in computer science education
SIGCSE '98 Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education
C5 '04 Proceedings of the Second International Conference on Creating, Connecting and Collaborating through Computing
Human Problem Solving
Creativity support tools: accelerating discovery and innovation
Communications of the ACM
ACM Transactions on Computing Education (TOCE)
How do students solve parsons programming problems?: an analysis of interaction traces
Proceedings of the ninth annual international conference on International computing education research
Toward facilitating assistance to students attempting engineering design problems
Proceedings of the ninth annual international ACM conference on International computing education research
Recording and analyzing in-browser programming sessions
Proceedings of the 13th Koli Calling International Conference on Computing Education Research
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Learning and programming environments used in computer science education give feedback to the users by system messages. These are triggered by programming errors and give only "technical" hints without regard to the learners' problem solving process. To adapt the messages not only to the factual but also to the procedural knowledge of the learners, their problem solving strategies have to be identified automatically and in process. This article describes a way to achieve this with the help of pattern recognition methods. Using data from a study with 65 learners aged 12 to 13 using a learning environment for programming, a classification system based on hidden Markov models is trained and integrated in the very same environment. We discuss findings in that data and the performance of the automatic online identification, and present first results using the developed software in class.