Towards Classroom Affective Analytics. Validating an Affective State Self-reporting tool for the medical classroom

  • Antoniou P
  • Spachos D
  • Kartsidis P
  • et al.
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Abstract

This article was migrated. The article was not marked as recommended. Background: Emotion and cognition are closely interconnected. Specifically in learning engagement and motivation are crucial pillars of effectively absorbing the educational material and being able to utilize it. For this reason, one of the key challenges in the classroom, especially in pre-clinical medical education, is the assessment of the audience's mood in order to apply effective engagement strategies in it. This work presents the first efficacy and robustness results of a subjective classroom affective state measurement instrument. Methods: Students recorded affective states using a range of positive to negative smileys in lightweight web based and mobile applications. A total of 225 pre lecture, 220 post lecture and 20 mid lecture recordings were taken throughout 5 lectures. In one of these (lecture 2), as an intervention, the mood of the students was positively altered using an impressive demonstration. Results: Kolmogorov Smirnov tests revealed samples not following normal distributions. Independent samples Mann-Wittney U Tests presented statistically significant variation between pre and post recordings only for lecture 2 at significance p<0.01 (U=1416, p=0.006). Kruskal-Walis one-way ANOVA for post lecture data revealed statistically significant difference lecture 2 at p<0.01 (p =0.000) with no variations presented in other post and all pre-lecture recordings, further establishing the robustness of the tool. Post-hoc pairwise test of post lecture tests revealed that the detected difference in lecture 2 was strongly positive (z between 4.495 and 2.039 between lecture 2 and all others). Conclusions: Demonstrated efficacy of such a simple and readily available tool for affective measurements opens new classroom strategies through easily accessible web based and mobile technologies. Self- reported affective classroom analytics can readily be aggregated from such instruments and used for even real time improvement of audience engagement.

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APA

Antoniou, P. E., Spachos, D., Kartsidis, P., Konstantinidis, E. I., & Bamidis, P. D. (2017). Towards Classroom Affective Analytics. Validating an Affective State Self-reporting tool for the medical classroom. MedEdPublish, 6, 134. https://doi.org/10.15694/mep.2017.000134

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