A Cognitive-Motivational Model of Tasks, Emotions, and Achievement

• In Event: Putting Tasks Under the Microscope: Task Features Triggering Emotions and Modulating Their Effects on Learning

Abstract

Objectives

Emotions are critically important in understanding students’ learning and cognitive performance. However, research on the performance effects of emotions is in a fragmented state. With aims to integrate theoretical perspectives, we present an expanded version of the cognitive-motivational model of emotion effects that is part of Pekrun’s (2006, 2018) control-value theory. We also report empirical evidence on implications of the model for emotions and scientific reasoning in mathematics.

Theoretical Framework

We argue that emotions can influence various cognitive, motivational, and regulatory mechanisms that mediate learning and performance. Most important are effects on (a) working memory and executive processes (attentional focus, switching, distractibility); (b) long-term memory encoding and retrieval (activation, inhibition, retrieval-induced forgetting and facilitation); (c) accommodative and assimilative modes of changing cognitive schemas; (d) intrinsic and extrinsic forms of motivation; and (e) metacognition and regulatory strategies. Effects of emotions are predicted to depend on the interplay of these mechanisms, with task demands influencing their interactions. We outline implications for two core components of scientific reasoning, two tasks, the generation and evaluation of scientific ideas.

Methods

Development of the model is based on a meta-review of existing reviews and meta-analyses on emotions, learning, and performance. Implications for mathematical reasoning were tested in an experimental study (N=243 university students in mathematics, 103 females) using autobiographical recall and music induction to generate a positive (enjoyment), negative (anxiety), or neutral emotional state. Dependent variables were measures of performance on authentic mathematical tasks, namely, generating and evaluating ideas for geometrical proofs.

Results

There were significant differences between the emotion conditions in indicators for generative reasoning, including fluency, originality, and flexibility. Planned pairwise comparisons showed that scores on all three indicators were significantly higher for the enjoyment condition than for the anxiety and neutral conditions. There were also significant differences in evaluative reasoning. Scores for the identification of correct ideas as correct, and of incorrect ideas as incorrect, were higher for the anxiety condition than for the other two conditions.

Conclusions

Supporting our hypotheses, the findings show that it would be misleading to equate positive emotions with positive effects on cognitive performance, and negative emotions with negative effects. Rather, the effects depend on the interplay of type of emotion and type of task. Meta-analyses suggest that students’ positive emotions show positive overall relations, and negative emotions negative relations with achievement (e.g., Barroso et al., 2021; Camacho-Morles et al., 2021). However, these overall relations may mask differential effects under different task conditions. Our model and findings suggest that is important to attend to the cognitive and motivational power of both positive and negative emotions.

Scientific Significance

Although there is rich evidence on single mechanisms of emotions and their overall relations with academic achievement, theory and evidence on the interplay of different mechanisms and types of academic tasks are largely lacking. Our work contributes to a better understanding of the impact of emotions on students’ learning, thus helping to elucidate the full range of competencies required to thrive in the 21st century.

Authors

• Reinhard Pekrun, University of Essex
  Presenting Author

• Sandra Schubert, University of Munich
  Non-Presenting Author