How teachers’ self-regulation, emotions, perceptions, and experiences predict their capacities for learning analytics dashboard: A Bayesian approach

Yiming LIU, Lingyun HUANG, Tenzin DOLECK

Research output: Contribution to journalArticlespeer-review

Abstract

Learning analytics dashboards (LADs) are emerging tools that convert abstract, complex information with visualizations to facilitate teachers’ data-driven pedagogical decision-making. While many LADs have been designed, teachers’ capacities for using such LADs are not well articulated in the literature. To fill the gap, this study provided a conceptual definition highlighting data visualization literacy and integrating abilities as two critical components in LAD capacities. Moreover, this study assessed teachers’ LAD capacities through a knowledge test and examined the combined effect of teachers’ self-regulation, emotions, perceptions of LAD usefulness and ease of use, and online teaching experience on teachers’ achievements of the LAD capacity knowledge test. The results of a Bayesian path analysis based on the sample of 150 teachers show that (1) teachers’ self-regulation and perceived LAD usefulness were the two main factors that made significant impacts on their LAD capacities, (2) the factors of negative emotions and perceived ease of use had effects on teachers’ LAD capacities, but such effects were mediated by self-regulation and perceived usefulness, and (3) online teaching experience had little effect on LAD capacities. This is the first study that conceptually researches teachers’ capacities for LAD uses. The findings offer novel perspectives into the complexity of LAD using process and demonstrate the importance of teachers’ self-regulation, emotions, and perceptions of usefulness in enhancing teachers’ abilities to use LADs for pedagogical decisions and actions. Copyright © 2023 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Original languageEnglish
JournalEducation and Information Technologies
Early online dateOct 2023
DOIs
Publication statusE-pub ahead of print - Oct 2023

Citation

Liu, Y., Huang, L., & Delock, T. (2023). How teachers’ self-regulation, emotions, perceptions, and experiences predict their capacities for learning analytics dashboard: A Bayesian approach. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-023-12163-z

Keywords

  • Learning analytics dashboard capacities
  • Emotions
  • Perceived usefulness
  • Perceived ease of use
  • Self-regulation
  • Bayes path analysis

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