Novice mistakes: are the folk wisdoms correct?
Communications of the ACM
AAA and CS 1: the applied apprenticeship approach to CS 1
SIGCSE '95 Proceedings of the twenty-sixth SIGCSE technical symposium on Computer science education
Programming pedagogy—a psychological overview
ACM SIGCSE Bulletin
Design guidelines for the lab component of objects-first CS1
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
An approach to teaching Java using computers
ACM SIGCSE Bulletin
Enhancing apprentice-based learning of Java
Proceedings of the 35th SIGCSE technical symposium on Computer science education
Helping novice programming students succeed
Journal of Computing Sciences in Colleges
Instructional design of a programming course: a learning theoretic approach
Proceedings of the third international workshop on Computing education research
A survey of literature on the teaching of introductory programming
Working group reports on ITiCSE on Innovation and technology in computer science education
Clean Code: A Handbook of Agile Software Craftsmanship
Clean Code: A Handbook of Agile Software Craftsmanship
Extreme apprenticeship method in teaching programming for beginners
Proceedings of the 42nd ACM technical symposium on Computer science education
Three years of design-based research to reform a software engineering curriculum
Proceedings of the 13th annual conference on Information technology education
Using POGIL to help students learn to program
ACM Transactions on Computing Education (TOCE) - Special Issue on Alternatives to Lecture in the Computer Science Classroom
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Programming is a craft that can be efficiently learned from people who already master it. Our previous work introduced a teaching method we call Extreme Apprenticeship (XA), an extension to the cognitive apprenticeship model. XA is based on a set of values that emphasize doing and best programming practices, together with continuous feedback between the master and the apprentice. Most importantly, XA is individual instruction that can be applied even in large courses. Our initial experiments (n = 67 and 44) resulted in a significant increase in student achievement level compared to previous courses. In this paper, we reinforce the validity of XA by larger samples (n = 192 and 147) and a different lecturer. The results were similarly successful and show that the application of XA can easily suffer if the core values are not fully adhered to.