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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Related Experiment Video

Updated: Jul 11, 2025

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
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An empirical study on learners' learning emotion and learning effect in offline learning environment.

Xiangwei Mou1,2, Yu Xin1, Yongfu Song2

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Facial emotion recognition in offline learning environments reveals significant correlations between specific emotions like joy and anxiety, and student learning effects. This study models these crucial relationships for educational evaluation.

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Area of Science:

  • Educational Psychology
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Facial emotion recognition is increasingly used in educational evaluation.
  • Existing research primarily focuses on virtual learning environments.
  • Sparse research exists on facial emotion's impact in offline learning settings.

Purpose of the Study:

  • To investigate the relationship between learner facial emotions and learning effects in an offline educational context.
  • To address the research gap concerning non-cognitive factors in traditional learning environments.
  • To develop a predictive model for learning outcomes based on observed emotions.

Main Methods:

  • An emotion observation experiment was conducted in an offline learning environment.
  • Data collected from 127 college students included facial emotion types and learning effects.
  • Statistical analysis explored correlations and built an explanatory model.

Main Results:

  • Eight distinct learner emotions were identified, including joy, relaxation, surprise, meekness, contempt, disgust, sadness, and anxiety.
  • Significant correlations were found between specific emotions (joy, relaxation, surprise, meekness, contempt, anxiety) and learning effects.
  • An explanatory model linking learner emotion to learning effect in offline settings was successfully constructed.

Conclusions:

  • Learner facial emotions are demonstrably linked to learning outcomes in offline educational settings.
  • The developed model provides insights into how emotions influence academic performance.
  • This research highlights the importance of considering affective states for effective educational evaluation.