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Combined fine-grained video coding and process mining for sequential collaborative-learning analysis

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article introduces a methodological contribution that combines fine-grained video analysis with process mining to study sequential patterns linking challenges and collaborative regulatory activities. Coded event logs were analysed with the BupaR package in R, producing process models in which "boxes represent challenge and regulatory categories, with numbers indicating their frequency of occurrence" and arrows depict sequential transitions. Unlike coarse-grained or aggregated measures, this method captures micro-level events as they occur in real-time collaborative interaction, offering a structured framework for analysing sequential interactional patterns in large-scale educational interaction data.

Accounts

  • Combined fine-grained video coding and process mining method for analysing sequential SSRL patterns: The article introduces a methodological contribution that combines fine-grained video analysis with process mining to study sequential patterns linking challenges and collaborative regulatory activities. Coded event logs were analysed with the BupaR package in R, producing process models in which "boxes represent challenge and regulatory categories, with numbers indicating their frequency of occurrence" and arrows depict sequential transitions. Unlike coarse-grained or aggregated measures, this method captures micro-level events as they occur in real-time collaborative interaction, offering a structured framework for analysing sequential interactional patterns in large-scale educational interaction data. (Faisal Channa et al. (2026))

Claims

Key Sources

  • Faisal Channa, Piia Näykki, Päivi Häkkinen, Kristóf Fenyvesi, Takumi Yada. (2026). Sequential patterns of challenges and socially shared regulation of learning: a comparison of pre-service and in-service teachers’ collaborative learning. Instructional Science. https://doi.org/10.1007/s11251-026-09818-y
Page metadata
Field Value
type research-method
id combined-fine-grained-video-coding-and-process-mining-for-sequential-collaborative-learnin
title Combined fine-grained video coding and process mining for sequential collaborative-learning analysis
description The article introduces a methodological contribution that combines fine-grained video analysis with process mining to study sequential patterns linking challenges and collaborative regulatory activities.
status review
generated by process:settle-candidates on 2026-10-18
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