Instruction-Embedded Assessment for Reading Ability in Adaptive Mathematics Software
Instruction-Embedded Assessment for Reading Ability in Adaptive Mathematics Software
复制标题
自适应数学软件中的指令嵌入式阅读能力评估
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Steven Ritter
中科院分区：
文献类型：
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作者：
H. Almoubayyed;Stephen E. Fancsali;Steven Ritter
适应性教育软件可能会更好地支持更广泛和更多样化的学习者，考虑更全面的意见（或模型），这些学习者。例如，最近的研究提出了对“非数学”因素（如阅读理解）进行推断，而学生则使用数学自适应软件来更好地支持和适应学习者。我们建立在这一建议的方法，更全面的学习建模提供了一个实证基础，使学生的阅读能力的推理，从他们的表现在自适应软件的数学活动。我们提出了一种方法来预测中学生的阅读能力，使用他们的表现在卡内基学习的MATHia，一个广泛使用的智能数学辅导系统的活动。我们专注于如何表现在早期，介绍性活动作为一个特别强大的地方，考虑嵌入式评估非数学因素，如阅读理解，以指导适应的基础上，如阅读能力的因素。最后，我们讨论的机会，以扩大这项工作的重点是特定的知识组成部分或跟踪MATHIA的技能，可以提供重要的“杠杆”驾驶适应的基础上，学生的阅读能力，而他们学习和实践数学。
Adaptive educational software is likely to better support broader and more diverse sets of learners by considering more comprehensive views (or models) of such learners. For example, recent work proposed making inferences about “non-math” factors like reading comprehension while students used adaptive software for mathematics to better support and adapt to learners. We build on this proposed approach to more comprehensive learning modeling by providing an empirical basis for making inferences about students’ reading ability from their performance on activities in adaptive software for mathematics. We lay out an approach to predicting middle school students’ reading ability using their performance on activities within Carnegie Learning’s MATHia, a widely used intelligent tutoring system for mathematics. We focus on how performance in an early, introductory activity as an especially powerful place to consider instruction-embedded assessment of non-math factors like reading comprehension to guide adaptation based on factors like reading ability. We close by discussing opportunities to extend this work by focusing on particular knowledge components or skills tracked by MATHia that may provide important “levers” for driving adaptation based on students’ reading ability while they learn and practice mathematics.