Wednesday, April 13
10:45 AM-11:15 AM
PDT
Tidepool 4

Emotional behavior in quantitative research methods course for preservice teachers. Learning analytics approach.

Full Paper (F2F) ID: 59538
  1. aaa
    Erkko Sointu
    University of Eastern Finland
  2. Mohammed Saqr
    University of Eastern Finland
  3. aaa
    Teemu Valtonen
    University of Eastern Finland
  4. Susanne Hallberg
    University of Eastern Finland
  5. Sanna Väisänen
    University of Eastern Finland
  6. aaa
    Jenni Kankaanpää
    University of Eastern Finland
  7. Ville Tuominen
    Valamis
  8. aaa
    Laura Hirsto
    University of Eastern Finland

Abstract: Preservice teacher training is research intensive in Finland. Additionally, teaching as profession is highly valued among young people. However, quantitative methods courses are challenging for teacher students from many reasons. Particularly, this is due to previous negative experiences and emotions (among other things). Thus, new approaches for teaching quantitative methods are warranted. In this research we used Flipped Learning, online teaching and learning analytics to support the content learning. The aim of this research was to investigate teacher students’ (N = 40) emotional profiles (i.e., cluster) based on their emotional level (anxiety, boredom and enjoyment) towards quantitative research methods studies and online behavior. For creating profiles, we used questionnaire data. These profiles were then further analyzed with learning analytics data, more precisely, time-ordered data of teacher students’ interactions (i.e., frequencies). Based on the results, three distinct profiles were found: “medium”, “pro quantitative”, and “scared” teacher students towards quantitative research methods. Further investigation revealed that scared students demonstrated statistically significant transitions in majority of the learning management systems compared to other profiles. Interestingly, pro quantitative had the lowest and medium teacher students had no difference in these results. The results are discussed further in the conclusions.

Presider: Fawzi Benmessaoud, IU Luddy School of Informatics, Computing & Engineering

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