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Title: Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics
Authors: Mihaescu, Marian
Tanasie, Alexandru
Dascalu, Mihai
Trausan-Matu, Stefan
Keywords: clustering quality metrics
pattern extraction
k-means clustering
learner performance
Issue Date: 2016
Publisher: Springer
Citation: Mihaescu, M. C., Tanasie, A. V., Dascalu, M., & Trausan-Matu, S. (2016). Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics. In 15th Int. Conf. on Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016) (pp. 109–118). Varna, Bulgaria: Springer
Abstract: Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.
Appears in Collections:1. RAGE Publications

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