User behavior pattern detection in unstructured processes – a learning management system case study

David Codish, Eyal Rabin, Gilad Ravid

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Process mining methodologies are designed to uncover underlying business processes, deviations from them, and in general, usage patterns. One of the key limitations of these methodologies is that they struggle in cases in which there is no structured process, or when a process can be performed in many ways. Learning Management Systems are a classic case of unstructured processes since each learner follows a different learning process. In this paper, we address this limitation by proposing and validating the user behavior pattern detection (UBPD) methodology which is based on detecting very short user activities and clustering them based on shared variance to construct a more meaningful behavior. We develop and validate this methodology by using two datasets of unstructured processes from different implementations of a learning management system. The first dataset uses a gamified course where users have the freedom to choose how to use the system, and the second dataset uses data from a massive online open course, where again, system usage is based on personal learning preferences. The key contribution of the methodology is its ability to discover user-specific usage patterns and cluster users based on them, even in noisy systems with no clear process. It provides great value to course designers and teachers trying to understand how learner interact with their system and sets the foundation for additional research in this class of systems.

Original languageEnglish
Pages (from-to)699-725
Number of pages27
JournalInteractive Learning Environments
Volume27
Issue number5-6
DOIs
StatePublished - 18 Aug 2019

Keywords

  • Learning analytics
  • gamification
  • learning management systems
  • pattern detection
  • process mining
  • spaghetti processes

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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