How Do Learners Regulate their Emotions? Amber Chauncey Strain1, Sidney D’Mello2, and Melissa Gross1 1
Institute for Intelligent Systems, University of Memphis 365 Innovation Drive, Memphis, TN, 38152
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University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN, 46556
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Abstract. In an online survey, one hundred and thirteen college students were asked to describe the emotion regulation strategies they frequently use during learning. We found that learners tend to report using certain strategies more frequently than others, and that generally the strategies that are used most often are considered by leaners to be the most effective. We discuss the implications of these findings for the development of intelligent tutoring systems that train and scaffold effective strategies to help learners regulate their emotions.
Keywords: emotion regulation, intelligent tutoring systems
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Introduction
There is a complex interplay between emotion and cognition during learning and problem solving [1]. Researchers are now testing emotion regulation (ER) strategies to help learners regulate their emotions so they might pursue more positive trajectories of thought and feeling. The present study analysed the types of reappraisal strategies that are commonly used during learning with an eye for implementing a subset of these strategies in next generations ITSs.
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Method and Results
One hundred and thirteen (N=113) participants from a large public U.S. university were recruited for this experiment. The key online material for this study was an open-ended ER strategy questionnaire. This questionnaire was a six-item measure that provided definitions and examples of emotion regulation strategies that are commonly used in the literature (situation selection/modification, attentional deployment, cognitive change, suppression) [2]. After the description of each strategy was presented, participants were asked to describe a time they used that particular strategy during learning. In particular, participants were prompted to describe the specific way in which they used the strategy, and whether they thought that strategy was effective.
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We used a subset of participants’ responses on the open-ended emotion regulation questionnaire to develop a coding scheme to identify the types of reappraisal strategies learners use. The strategies we identified were: quiet-seeking/stimulation seeking (seeking out a quiet/stimulating place to study), self-reward (providing oneself with rewards for accomplishing goals), prioritizing (selecting the order in which to accomplish tasks in a way that will minimize negative emotions), taking a break (disengaging from the learning task and engaging in a non-academic task), strategy use (engaging in a learning strategy that might help minimize negative emotions), positive/negative rumination (choosing to attend to positive/negative feelings), self-talk (giving oneself a sense of reassurance by talking through the emotion), value focus (thinking about the personal value of the task), role play (imagining or acting out a particular role other than the role of a student or learner), and making a game (making a game of the learning task so that it has elements of fun or competition). After the coding scheme was developed, two trained coders independently coded each response for the type of reappraisal strategy used, and obtained an inter-rater agreement of 97%. Results indicated that quiet seeking was the most frequently used ER strategy, along with taking a break, positive and negative rumination, and making a game. Interestingly, we also found that with the exception of negative rumination, learners reported that each of the most frequently used ER strategies were also the most effective, indicating that learners are perhaps metacognitively aware of which strategies are the most beneficial and tend to engage more frequently in those strategies.
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Discussion
While more research in this area is certainly needed, our study serves as an initial point towards gaining knowledge about the types of reappraisal strategies that are used in real learning contexts. The next step is to implement a subset of these strategies in ITSs and other advanced learning technologies.
Acknowledgments This research was supported by the NSF (ITR 0325428, HCC 0834847, DRL 1108845). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.
References 1.
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Linninbrink, L. A. (2007). The role of affect in student learning: A multi-dimensional approach to considering the interaction of affect, motivation, and engagement. In P. A. Schutz, & R. Pekrun, Emotion in Education (pp. 13–36). Amsterdam. Gross, J. (2008). Emotion regulation. In M. Lewis, J. Haviland-Jones & L. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 497-512). New York, NY: Guilford.
