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

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.
Original languageEnglish
Title of host publicationArtificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016
EditorsChristo Dichev, Gennady Agre
PublisherSpringer
Pages109-118
ISBN (Electronic)978-3-319-44748-3
ISBN (Print)978-3-319-44747-6
DOIs
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 Sep 201610 Sep 2016
https://link.springer.com/book/10.1007/978-3-319-44748-3
https://www.springer.com/la/book/9783319447476

Publication series

Name Lecture Notes in Computer Science
PublisherSpringer
Volume9883
NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume9883

Conference

ConferenceInternational Conference on Artificial Intelligence: Methodology, Systems, and Applications
Abbreviated titleAIMSA 2016
CountryBulgaria
CityVarna
Period7/09/1610/09/16
Internet address

Fingerprint

Clustering algorithms
Feedback
Monitoring
Experiments

Keywords

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

Cite this

Mihăescu, M. C., Tănasie, A. V., & Dascalu, M. (2016). Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics. In C. Dichev, & G. Agre (Eds.), Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016 (pp. 109-118). ( Lecture Notes in Computer Science; Vol. 9883), (Lecture Notes in Artificial Intelligence; Vol. 9883). Springer. https://doi.org/10.1007/978-3-319-44748-3_11
Mihăescu, Marian Cristian ; Tănasie, Alexandru Virgil ; Dascalu, Mihai. / Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics. Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. editor / Christo Dichev ; Gennady Agre. Springer, 2016. pp. 109-118 ( Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence).
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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.",
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Mihăescu, MC, Tănasie, AV & Dascalu, M 2016, Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics. in C Dichev & G Agre (eds), Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science, vol. 9883, Lecture Notes in Artificial Intelligence, vol. 9883, Springer, pp. 109-118, International Conference on Artificial Intelligence: Methodology, Systems, and Applications, Varna, Bulgaria, 7/09/16. https://doi.org/10.1007/978-3-319-44748-3_11

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics. / Mihăescu, Marian Cristian; Tănasie, Alexandru Virgil ; Dascalu, Mihai.

Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. ed. / Christo Dichev; Gennady Agre. Springer, 2016. p. 109-118 ( Lecture Notes in Computer Science; Vol. 9883), (Lecture Notes in Artificial Intelligence; Vol. 9883).

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

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Mihăescu MC, Tănasie AV, Dascalu M. Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics. In Dichev C, Agre G, editors, Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Springer. 2016. p. 109-118. ( Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-319-44748-3_11