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Student Online Activity in Blended Learning: A Learning Analytics Perspective of Professional Teacher Education Studies in Finland

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  • Arto O. Salonen
  • Annukka Tapani
  • Sami Suhonen

Abstract

Distance learning is rapidly gaining ground globally. In this case study, we focused on professional (vocational) teacher education (PTE) student online activity in a blended learning context. We applied learning analytics (LA) to identify students’ ( n  = 19) online study patterns. Our key interest was in determining when and what kinds of online activity and behavior PTE students engage in during their studies. We applied quantitative content analysis to analyze the students’ behavior. Moodle’s event log enabled us to identify active hours and days, variation in use of learning materials, the impact of interventions, and stumbling blocks to student learning in the study unit. Based on our data, educator availability is an essential factor for good student engagement in digital learning environments. Interaction forums are important for PTE students effective learning. Monday and Tuesday afternoons are the most effective times for educators to be available for PTE students. There is a clear need for contact learning in professional teacher education, even when operating in digital learning environments. It plays an essential role in keeping students’ activity alive. It could be beneficial to plan a post-process for students who do not graduate as planned, including regular group meetings for supporting studies, receiving guidance, and meeting peers. PTE students’ behavior in a distance learning environment in the context of blended learning follows Zipf’s law, which models the occurrence of distinct objects in particular sorts of collections.

Suggested Citation

  • Arto O. Salonen & Annukka Tapani & Sami Suhonen, 2021. "Student Online Activity in Blended Learning: A Learning Analytics Perspective of Professional Teacher Education Studies in Finland," SAGE Open, , vol. 11(4), pages 21582440211, October.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:4:p:21582440211056612
    DOI: 10.1177/21582440211056612
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    References listed on IDEAS

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    1. Diego Buenaño-Fernández & David Gil & Sergio Luján-Mora, 2019. "Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    2. Arto O. Salonen & Carina Savander-Ranne, 2015. "Teachers’ Shared Expertise at a Multidisciplinary University of Applied Sciences," SAGE Open, , vol. 5(3), pages 21582440155, July.
    3. Wim Ectors & Bruno Kochan & Davy Janssens & Tom Bellemans & Geert Wets, 2019. "Exploratory analysis of Zipf’s universal power law in activity schedules," Transportation, Springer, vol. 46(5), pages 1689-1712, October.
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    Cited by:

    1. Inusah Salifu & Flora Chirani & Solomon Kofi Amoah & Ebenezer Darkwah Odame, 2023. "Training Teachers by the Distance Mode: Implications for Quality Teacher Performance in Pre-Tertiary Schools," SAGE Open, , vol. 13(4), pages 21582440231, December.

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