Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Marian Cristian Mihăescu, Alexandru Virgil Tănasie, Mihai Dascalu

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

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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.
Original languageEnglish
Title of host publicationArtificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016
EditorsChristo Dichev, Gennady Agre
ISBN (Electronic)978-3-319-44748-3
ISBN (Print)978-3-319-44747-6
Publication statusPublished - 18 Aug 2016
Externally publishedYes
EventInternational Conference on Artificial Intelligence: Methodology, Systems, and Applications: Artificial Intelligence: Methodology, Systems, and Applications - Varna, Bulgaria
Duration: 7 Sept 201610 Sept 2016

Publication series

SeriesLecture Notes in Computer Science
SeriesLecture Notes in Artificial Intelligence (subseries)


ConferenceInternational Conference on Artificial Intelligence: Methodology, Systems, and Applications
Abbreviated titleAIMSA 2016
Internet address


  • clustering quality metrics
  • pattern extraction
  • k-means clustering
  • learner performance


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