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In Event: The Power and Possibilities of Physiological Data to Explore Students' and Teachers' Experiences
According to the Job Demands-Resources-Model, every occupation has its own specific demands and resources, which influence the stress level of employees (Demerouti & Bakker, 2011). Concerning teachers, most knowledge about demands is based on questionnaires and interviews. Previous results range from contextual factors like class size (e.g. Schutz & Zembylas, 2009), other interactional factors like disruptive and unmotivated student behaviors (e.g. Zhang, 2018), and personal factors like low self-efficacy beliefs (e.g. Robertson, 2013). However, survey and interview data could be biased by acquiescence (Watson, 1992), hindsight (Blank, Musch & Pohl, 2007), and social desirability (Nederhof, 1985).
Therefore, the aim of this study was to expand our knowledge about the real-time correlation between teacher-student interactions and teacher stress, measured by teachers’ physiological arousal.
To do so, we filmed lessons of 40 Dutch secondary teachers during one lesson each. The heart rate of the teachers was measured with the VU-AMS. We controlled for the influence of physical activity in line with Myrtek’s (2004) Additional Heart Rate approach. On average, we identified six interactions per teacher, in which the heart rate was higher than 2 standard deviations for at least 5 seconds. We identified and examined the same number of random interactions to have a control measurement for usual lesson interactions. Overall, 480 events, consisting of the stressful interactions as well as what happened 5 seconds before and after, were coded using the Flanders Interaction Analysis Categories (Flanders, 1970). Categories like “praises or encourages” (indirect influence of the teacher), “gives directions” (direct Influence of the teacher), or “student talk-responses” (student talk) were used to describe how teacher-student interactions sequentially evolved. An interrater-reliability of Krippendorff's α = .78 was reached based on the categorization of three trained raters. For correlational analysis, multilevel modeling was used, with events as level 1 and persons as level 2.
Preliminary results reveal that repeated teacher instructions or explanations that follow an action of a student are much more likely to correlate with stress than typical Initiate-Response-Evaluate (IRE) patterns (Mehan, 1985; Turner et al., 2002), which were found to be the main interactional type for random events. These findings suggest that teachers’ activity caused by inexplicit instructions or inattentive pupils may cause situational stress.
Further research could analyze the exact characteristics of the distinctly stressful teacher-student interactions and search for influencing variables. Such an approach would significantly advance our knowledge about stress emergence in the teaching profession. It could also optimize teacher training in terms of self-efficacy beliefs and burnout prevention.