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Supporting self-efficacy beliefs and interest as educational inputs and outcomes: Framing AI and Human partnered task experiences
Learning and Individual Differences ( IF 3.8 ) Pub Date : 2020-05-18 , DOI: 10.1016/j.lindif.2020.101850
Luke K. Fryer , Andrew Thompson , Kaori Nakao , Mark Howarth , Andrew Gallacher

Interest and self-efficacy are crucial to academic success. This study addresses two gaps in our understanding of their development and support during university courses: how prior self-efficacy and interest plays a role in, and how different classroom activities build toward the development of students' future interest and self-efficacy. In this study the interplay between ability-beliefs (self-efficacy/self-concept) and interest at three levels of specificity (Domain, Course and Task) were tested across a Japanese university language course (n = 128). Within this test, students' interest in two language practice tasks (i.e., Human and then Chatbot partners) were assessed and compared. Prior interest was a robust predictor of all future task/course interest. Only Human-Human task interest directly predicted future course self-efficacy, but was mediated by course interest for future domain interest. For future interest, Human practice partners are superior to AIs. Supporting prior domain and later course interest should be a focus for university educators.



中文翻译:

支持自我效能感信念和兴趣作为教育投入和成果:构筑AI和人类合作的任务经验

兴趣和自我效能对于学术成功至关重要。这项研究解决了我们在大学课程中对他们的发展和支持的理解上的两个空白:以前的自我效能感和兴趣如何发挥作用,以及不同的课堂活动如何促进学生未来的兴趣和自我效能感的发展。在这项研究中,能力信念(自我效能/自我概念)与兴趣在三个特定级别(域,课程和任务)之间的相互作用在日本大学的日语课程中进行了测试(n  =  128 。在该测试中,评估并比较了学生对两种语言练习任务的兴趣(即,人类,然后是Chatbot合作伙伴)。先前的兴趣是所有未来任务/课程兴趣的可靠预测指标。只有人与人之间的任务兴趣直接预测了未来课程的自我效能,但受到课程兴趣对未来领域兴趣的调节。为了将来的利益,人类实践合作伙伴要优于AI。支持以前的领域和以后的课程兴趣应该是大学教育者的重点。

更新日期:2020-05-18
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