In this article, we offer and test a nonsurvey-based method to characterize learner emotions. Our method, instead of using surveys, uses logs of learner behaviors in learning management systems (LMS) to reason about the emotional state of the e-learner. We use the control value theory (CVT) as the theoretical base of measuring emotions. Using this theory, learner emotions are directly tied to their achievements. We develop two fuzzy inference systems, one to measure academic self-efficacy (ASE), that we call ASEMEL, and another to measure task value, TAVAMEL. These two factors, according to the CVT, can identify the prospective outcome emotions in a learning environment. We conducted our experiment in an LMS with a sample of 30 students and validated the performance of our nonsurvey-based systems by comparing the results with the measures of an equivalent survey-based method. Finally, by linking our ASEMEL and TAVAMEL results, our system anticipated “hopelessness,” “anticipated relief” and “no emotion” with 97% accuracy, “hope/anxiety” with 77% accuracy, and “anticipatory joy” with 87% accuracy compared with the self-reports of the students.

中文翻译：

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使用行为线索识别电子学习者情绪的自动方法


在本文中，我们提供并测试了一种基于非调查的方法来表征学习者的情绪。我们的方法不使用调查，而是使用学习管理系统（LMS）中的学习者行为日志来推断电子学习者的情绪状态。我们使用控制值理论（CVT）作为测量情绪的理论基础。根据这一理论，学习者的情绪与他们的成就直接相关。我们开发了两种模糊推理系统，一种用于衡量学术自我效能感（ASE），我们称之为 ASEMEL，另一种用于衡量任务价值，即 TAVAMEL。根据 CVT，这两个因素可以识别学习环境中的预期结果情绪。我们在 LMS 中以 30 名学生为样本进行了实验，并通过将结果与基于调查的等效方法的测量结果进行比较来验证我们的非调查系统的性能。最后，通过关联我们的 ASEMEL 和 TAVAMEL 结果，我们的系统预测“绝望”、“预期宽慰”和“无情绪”的准确度为 97%，“希望/焦虑”的准确度为 77%，“预期喜悦”的准确度为 87%与学生的自我报告进行比较。