Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact
Personalizing Algebra to Students’ Individual Interests in an Intelligent Tutoring System: Moderators of Impact
复制标题
在智能辅导系统中根据学生的个人兴趣个性化代数：影响的调节因素
DOI：
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发表时间：
2018
影响因子：
4.9
通讯作者：
Matthew L. Bernacki
中科院分区：
文献类型：
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作者：
Candace A. Walkington;Matthew L. Bernacki
学生在日常生活中体验数学，因为他们在体育或电子游戏等领域追求个人兴趣。本研究探讨了如何利用代数智能辅导系统（ITS）来联系学生的个人兴趣来实现个性化学习。我们研究了这样一种观点，即个性化的影响可能会受到学生对校外兴趣的定量参与程度的调节。我们还研究了旨在利用学生对个人兴趣的深层次（即实际定量经验）或表层（即问题主题的表面变化）知识的数学问题是否会产生不同的影响。结果表明，将数学教学与学生的校外兴趣联系起来有利于 ITS 中的学习，并减少对系统的欺骗。然而，只有当学生对校外兴趣的定量参与程度与个性化问题的编写深度相匹配时，好处才可能实现。当问题涉及其兴趣领域的表面方面时，对其兴趣的定量参与最少的学生可能会受益最多。当个人兴趣深深融入数学问题的定量结构时，那些对自己的兴趣进行大量定量参与的学生可能会受益最多。我们还发现，更深层次的个性化问题可能会激发所有学生的积极情感状态并避免消极情感状态。研究结果表明，深度是个性化学习的一个关键特征，对理论和人工智能教学设计具有影响。
Students experience mathematics in their day-to-day lives as they pursue their individual interests in areas like sports or video games. The present study explores how connecting to students’ individual interests can be used to personalize learning using an Intelligent Tutoring System (ITS) for algebra. We examine the idea that the effects of personalization may be moderated by students’ depth of quantitative engagement with their out-of-school interests. We also examine whether math problems designed to draw upon students’ knowledge of their individual interests at a deep level (i.e., actual quantitative experiences) or surface level (i.e., superficial changes to problem topic) have differential effects. Results suggest that connecting math instruction to students’ out-of-school interests can be beneficial for learning in an ITS and reduces gaming the system. However, benefits may only be realized when students’ degree of quantitative engagement with their out-of-school interests matches the depth at which the personalized problems are written. Students whose quantitative engagement with their interests is minimal may benefit most when problems draw upon superficial aspects of their interest areas. Students who report significant quantitative engagement with their interests may benefit most when individual interests are deeply incorporated into the quantitative structure of math problems. We also find that problems with deeper personalization may spur positive affective states and ward off negative ones for all students. Findings suggest depth is a critical feature of personalized learning with implications for theory and AI instructional design.
DOI：
10.5860/choice.41-1317
发表时间：
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期刊：
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影响因子：
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作者：
通讯作者：
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影响因子：
4
作者：
Fredricks, JA;Eccles, JS
通讯作者：
Eccles, JS