Cohesion network analysis: Predicting course grades and generating sociograms for a romanian moodle course

Maria Dorinela Dascalu, Mihai Dascalu, Stefan Ruseti, Mihai Carabas, Stefan Trausan-Matu, Danielle S. McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Online collaborative learning environments open new research opportunities, for example, the analysis of learning outcomes, the identification of learning patterns, the prediction of students’ behaviors, and the modeling and visualization of social relations and trends among students. Moodle is an online educational platform which supports both students and teachers, and can be effectively employed to encourage collaborative learning. Moodle is often used to make inquiries on student homework, exams, to request clarifications, and to make announcements. Our goal is to predict student success based on Cohesion Network Analysis (CNA) and to identify interaction patterns between students (n = 71 who had a sufficient level of participation on the forum) and 4 tutors together with 19 teaching assistants in a Romanian Moodle course. CNA visualizations consider a hierarchical clustering that classifies members into central, active, and peripheral groups. Weekly snapshots are generated to better understand students’ evolution throughout the course, while correlating their activities with specific course events (e.g., homework deadlines, tests, holidays, exam, etc.). Several regression models were trained based on the generated CNA indices and the best model achieves a mean average error below.5 points when predicting partial course grades, prior to the final exam, on a 6-point scale.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 16th International Conference, ITS 2020, Proceedings
EditorsVivekanandan Kumar, Christos Troussas
PublisherSpringer
Pages174-183
Number of pages10
ISBN (Print)9783030496623
DOIs
StatePublished - 2020
Event16th International Conference on Intelligent Tutoring Systems, ITS 2020 - Athens, Greece
Duration: Jun 8 2020Jun 12 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12149 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Intelligent Tutoring Systems, ITS 2020
CountryGreece
CityAthens
Period6/8/206/12/20

Keywords

  • Cohesion Network Analysis
  • Interaction patterns
  • Moodle
  • Sociograms
  • Student success

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Dascalu, M. D., Dascalu, M., Ruseti, S., Carabas, M., Trausan-Matu, S., & McNamara, D. S. (2020). Cohesion network analysis: Predicting course grades and generating sociograms for a romanian moodle course. In V. Kumar, & C. Troussas (Eds.), Intelligent Tutoring Systems - 16th International Conference, ITS 2020, Proceedings (pp. 174-183). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12149 LNCS). Springer. https://doi.org/10.1007/978-3-030-49663-0_21