Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

Jonna Malmberg, Oliver Fincham, Héctor J. Pijeira-Díaz, Sanna Järvelä, Dragan Gašević

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in the context of collaborative learning. The participants (N = 12, age 16–17 years, three females and nine males) in the study were high school students enrolled in an advanced physics course. The results show that during collaborative learning, the students engaged in monitoring in each self-regulated learning phase such as task understanding, planning and goal setting, task enactment, adaptation and reflection. The results of the HMM indicated that the learners' physiological reactivity was low when monitoring occurred. The associations between the states based on the HMM provide insights not only into how learners engage in metacognitive monitoring but also about their level of physiological reactivity in each state. In conclusion, exploring aspects of metacognitive monitoring in collaborative learning can be done with the help of physiological reactions.

Original languageEnglish
Pages (from-to)861-874
Number of pages14
JournalJournal of Computer Assisted Learning
Volume37
Issue number3
DOIs
Publication statusPublished - Jun 2021

Keywords

  • collaborative learning
  • hidden Markov models
  • metacognitive monitoring
  • physiological reactivity

Cite this