Learning to program and learning to think: what's the connection?
Communications of the ACM
Programming pedagogy—a psychological overview
ACM SIGCSE Bulletin
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Working group reports from ITiCSE on Innovation and technology in computer science education
Mining student CVS repositories for performance indicators
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
A Motivation Guided Holistic Rehabilitation of the First Programming Course
ACM Transactions on Computing Education (TOCE)
Toward the application of argumentation to interactive learning systems
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
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
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In this paper we present a concept for three-phase measuring method, which can be used to obtain data on student learning. The focus of this method lies on the technical aspects of learning programming, answering questions like which programming constructs students applied and how large portion of the students understood the concepts of programming language. The model is based on three consecutive measurements, which are used to observe the student errors, applied programming structures and an application of a Bayesian learning model to determine the programming knowledge. So far the model has produced results which confirm prior knowledge on student learning, indicating that the concept is feasible for further development. Despite of the early development phase of the method, it offers a straightforward way for teacher to assess the course contents and student performance.