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Diagnosing learners’ problem-solving strategies using learning environments with algorithmic problems in secondary education
Kiesmüller U. ACM Transactions on Computing Education (TOCE)9 (3):1-26,2009.Type:Article
Date Reviewed: Apr 23 2010

“One of the goals of science education is to develop the learners’ ability to acquire knowledge in specific subjects and to improve their problem-solving skills” [1]. This paper actively participates in science education, covering problems related to learners’ ability to acquire problem-solving skills and techniques that are of crucial importance for today’s knowledge workers.

“Problem solving requires overcoming all the impediments in reaching a goal. Many researchers indicate that the use of problem-solving instructional models and techniques to teach science influences the problem-solving skill of students” [1]. Kiesmüller starts with the hypothesis that learners need help from teachers--and more than just technical help. Since learners have their own solving strategies, Kiesmüller proposes finding a learning environment that automatically identifies problem-solving strategies. This idea is further developed for an adaptive learning environment, by recording the learner-system interactions during the problem-solving process. Since the process of designing a learning environment requires diagnostic modules that identify problem-solving strategies and create individualized system messages, Kiesmüller uses the Kara environment tool for further analysis.

Kara is one of several useful software tools for teaching programming via a language. It is particularly attractive for introductory courses. Kiesmüller uses Kara to analyze elementary problem-solving strategies. The central goal of the research methodology is to overcome the challenge of solving algorithmic problems that are often difficult for programming novices. Kiesmüller assumes that, in order to improve a learning environment, the learners’ various strategies must be identified and categorized automatically. Then, he associates his research findings with the problem-solving process, analyzing learners’ problem-solving strategies. Kiesmüller exposes the correlations between the task’s difficulty and the problem-solving strategy. His research is based on the average time spent solving the problem, the average number of errors made by the learner, and the quality of the learner’s solution. He concludes that we should have different tools for obtaining information about a learner’s problem-solving strategies, since pure diagnostic software is not enough--there needs to be additional observation by tools, as well as human observation. Thus, the author uses Kara in a secondary school environment, expanding the tool to the students’ algorithmic problems.

Kiesmüller excellently presents his research findings. The paper should be of great value to secondary school educators who teach problem-solving strategies and techniques that prepare students for computer programming. It is also a valuable contribution to the literature on teaching problem solving.

Reviewer:  F. J. Ruzic Review #: CR137929 (1009-0956)
1) Bilgin, I.; Karakirik, E. A computer based problem solving environment in chemistry. The Turkish Online Journal of Educational Technology (TOJET) 4, 3(2005), 7–11.
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