Abstract

Emotional skills have been proposed as distal determinants of achievement emotions. However, the specific role they play in such emotions and their effects on learning has not been defined. The purpose of this comparative study is to provide an ex post facto assessment of emotional intelligence (EI) as a predictor of the enjoyment and anger expressed while studying and as a mediator and/or moderator of its effects on the use of deep learning strategies. A sample comprising 603 university students (85.7% women; Medad=25.16, SDedad=8.42) completed the Trait Meta-Mood State-24 to assess perceived EI, the Achievement Emotions Questionnaire-AEQ for enjoyment and anger, and the Deep Learning Strategies Questionnaire. The stepwise regression, multiple mediation, and moderation analyses conducted showed that EI explains enjoyment and anger, although its different skills predict them differently. Enjoyment increases the use of deep learning strategies, an effect that is partially mediated by emotional repair. In anger there is less use of these strategies, and this effect is partially mediated by high emotional attention with little emotional clarity and repair. Similarly, a medium and high level of EI moderates the negative effect of anger on the use of deep learning strategies. These results show the benefits of being emotionally intelligent since it mitigates the negative consequences of negative emotions, stressing the need to teach emotional skills or promote EI in educational and cognitive contexts.

Full Text
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