An Experimental Method for the Active Learning of Greedy Algorithms
ACM Transactions on Computing Education (TOCE)
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
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This paper has two contributions. Firstly, we present the results of using observations as a complementary method to evaluate an active approach to learning greedy algorithms based on the interactive assistant GreedEx. Two kinds of observations were conducted: questions to the teacher, and students' activity. Observations were useful in two ways: they reinforced findings of two evaluation methods ?usability questionnaires and analysis of students' reports?, and they allowed identifying new, interesting data to further enhance our approach. A second contribution is the methodological lessons learnt about how to use observations as a research method. In particular, we found human observation useful for some purposes but not with respect to software usage.