A debate on teaching computing science
Communications of the ACM
Introduction to algorithms
The case for case studies of programming problems
Communications of the ACM
Studying the Novice Programmer
Studying the Novice Programmer
Elaborating heuristic reasoning and rigor with mathematical games
ACM SIGCSE Bulletin
Learning from wrong and creative algorithm design
Proceedings of the 39th SIGCSE technical symposium on Computer science education
Design Disciplines and Non-specific Transfer
ISSEP '08 Proceedings of the 3rd international conference on Informatics in Secondary Schools - Evolution and Perspectives: Informatics Education - Supporting Computational Thinking
Active learning of greedy algorithms by means of interactive experimentation
ITiCSE '09 Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education
A method to construct counterexamples for greedy algorithms
Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education
ACM Inroads
Constructive use of errors in teaching CS1
Proceeding of the 44th ACM technical symposium on Computer science education
Novice difficulties with interleaved pattern composition
ISSEP'13 Proceedings of the 6th international conference on Informatics in Schools: Situation, Evolution, and Perspectives
An Experimental Method for the Active Learning of Greedy Algorithms
ACM Transactions on Computing Education (TOCE)
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Educators' approach towards their students' mistakes can have significant impact on the students. This paper presents a rather less considered approach of teaching by capitalizing on mistakes. In the course of teaching our students algorithm design, we noticed the phenomenon of students' "over-reliance" on intuition rather than rigor. In particular, we noticed a repeated erroneous trend of turning to intuitive, but inadequate greedy algorithmic solutions. We capitalized on the student errors for influencing their attitude and beliefs regarding intuition and rigor. The paper displays the student errors and our capitalization-on-errors approach, with colorful and novel algorithmic tasks.