Analyzing the consistency in within-activity learning patterns in blended learning

Varshita Sher, Marek Hatala, Dragan Gaševic

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

2 Citations (Scopus)


Performance and consistency play a large role in learning. This study analyzes the relation between consistency in students' online work habits and academic performance in a blended course. We utilize the data from logs recorded by a learning management system (LMS) in two information technology courses. The two courses required the completion of monthly asynchronous online discussion tasks and weekly assignments, respectively. We measure consistency by using Data Time Warping (DTW) distance for two successive tasks (assignments or discussions), as an appropriate measure to assess similarity of time series, over 11-day timeline starting 10 days before and up to the submission deadline. We found meaningful clusters of students exhibiting similar behavior and we use these to identify three distinct consistency patterns: highly consistent, incrementally consistent, and inconsistent users. We also found evidence of significant associations between these patterns and learner's academic performance.

Original languageEnglish
Title of host publicationLAK 2020 Conference Proceedings
EditorsVitomir Kovanović, Maren Scheffel, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450377126
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th (Website) (Website)


ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
Internet address


  • Learner performance and Consistency
  • Regularity
  • Student Persistence
  • Time Management
  • Time-series Analysis
  • Work Habits

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