Swarm Intelligence in Educational Data Mining
نویسندگان
چکیده
This paper explores the potential intersection of two fascinating and increasingly burgeoning fields: Swarm Intelligence and Educational Data Mining. A thorough review of the existing works in both fields has revealed a lack of Swarm Intelligence applications in Educational Data Mining, despite its successful applications in other data mining domain. Consequently, this paper launches the intersection of the two emerging fields by applying Swarm Intelligence techniques to an Educational Data Mining classification problem. More specifically, it proposes the use of Particle Swarm Classification to classify teachers' classroom questions into the cognitive levels identified in Bloom's taxonomy. To do so, a dataset of questions has been collected and classified manually into Bloom's cognitive levels. Preprocessing steps have been applied to convert questions into a suitable representation. Using the dataset, the performance of Particle Swarm Classification is evaluated against several traditional machine learning approaches. The initial results provide evidences on the superiority of Particle Swarm Classification technique over the best traditional machine learning approaches.
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