Impact of Time Granularity on Histories Binary Correlation Analysis

Paolo Mengoni*, Alfredo Milani, Yuanxi Li

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Activities taken by students within a Virtual Learning Environment (VLE) can be represented by using binary student histories. Virtual Learning Environments allow educators to track most of the students’ individual activities that can be used to elicit the students social communities. In this work, we analyse the impact of granularity in the social community elicitation. Granularity can be seen as the resolution of the student history vectors where each time slot is directly dependent from this value. Indeed, the higher is the resolution of the students histories the more precise is the representation of their actions within the VLE. When comparing the histories using various similarity measures to elicit the students’ groups, we find the optimal granularity and demonstrate that there is a resolution limit where the similarity measures will not help to distinguish the social communities.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2019
Subtitle of host publication19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part II
EditorsSanjay Misra, Osvaldo Gervasi, Beniamino Murgante, Elena Stankova, Vladimir Korkhov, Carmelo Torre, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan, Eufemia Tarantino
PublisherSpringer, Cham.
Pages323-335
Number of pages13
ISBN (Electronic)9783030242961
ISBN (Print)9783030242954
DOIs
Publication statusPublished - 2019
Event19th International Conference on Computational Science and Its Applications, ICCSA 2019 - Saint Petersburg, Russian Federation
Duration: 1 Jul 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11620 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Computational Science and Its Applications, ICCSA 2019
Country/TerritoryRussian Federation
CitySaint Petersburg
Period1/07/194/07/19

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Cluster analysis
  • Community elicitation
  • Data analysis
  • Learning analytics

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