Abstract
Learning style identification is important for improving the learning and teaching experience in the massive open online courses (MOOCs). To identify learning styles automatically, a very large quantity of labeled data is necessary. However, labeling data manually is tedious and impractical. A known solution to this problem is to cluster MOOCs learning data and label them with the general characteristics of the cluster to which they belong. In this paper, we propose two distance measures suitable for forming canopies in MOOCs, and incorporate the canopy approach into the K-means clustering algorithm. This improves the stability of the clustering results and the quality of the data labeling. Experimental results with four popular classifiers show that the proposed method can improve both the overall identification of learning styles and the identification of each individual learning style.
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Acknowledgments
We thank Dr. Brahim Hmedna for providing the source code of the K-LS method and dataset for experimentation. This work was partially supported by the National Natural Science Foundation of China (61977001) and the Great Wall Scholar Program (CIT&TCD20190305).
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Song, W., Wang, Z. (2022). Improved Clustering Strategies for Learning Style Identification in Massive Open Online Courses. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1744. Springer, Singapore. https://doi.org/10.1007/978-981-19-9297-1_18
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DOI: https://doi.org/10.1007/978-981-19-9297-1_18
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