Peer Assessment for Action Learning of Data Structures and Algorithms

Machanick, P.

    This paper describes an experience with use of peer assessment in tutorials as a tool to promote deep learning from early stages of a course on Data Structures and Algorithms. The goal was to improve the utility of tutorials in encouraging more efficient learning habits. Since assessment forms a key part of the actual curriculum, tutorial exercises were for credit, but the emphasis was on formative assessment. The novelty in this approach is that peer assessment has not been extensively studied in Computer Science Education for content of the kind covered in this course. Evaluation is limited by the fact that other details of the course were changed. Two surveys were conducted, one soon after the first assignment, the other soon after the second assignment. Of various aspects of the course surveyed, the tutorial quizzes were the least popular, but improved in popularity between the two surveys. The overall effect based on general observation of the class appeared to be positive. Results were closer to a normal distribution than for the previous 2 years. Performance in the first assignment, which required understanding of how the theory is applied in a practical situation, suggested that deep learning had taken place.
Cite as: Machanick, P. (2005). Peer Assessment for Action Learning of Data Structures and Algorithms. In Proc. Seventh Australasian Computing Education Conference (ACE2005), Newcastle, Australia. CRPIT, 42. Young, A. and Tolhurst, D., Eds. ACS. 73-82.
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