智能辅导系统中的检索实践研究

Jeffrey Matayoshi, Hasan Uzun, Eric Cosyn
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引用次数: 5

摘要

检索练习(也称为测试效果或测试增强学习)是一种经过充分研究和建立的提高知识保留的技术。许多先前的工作已经证实了在实验室实验中涉及单词或事实记忆的检索练习的好处。在本研究中,我们在这些工作的基础上,分析了智能辅导系统中的检索实践。利用由近400万名学习数学和化学的学生的行为组成的大型数据集,我们研究了在ALEKS自适应学习和评估系统中检索实践的可能好处。我们比较了两种不同类型的检索实践——一种涉及学习材料的评估,另一种涉及建立在学习材料基础上的密切相关内容的学习——利用可用数据的规模来控制几个混杂变量。最后,我们看看系统中检索练习的时间以及它对遗忘的可能影响。结果表明,延迟检索练习与更好的记忆有关,而且,虽然对学习的材料进行评估是有益的,但学习密切相关的内容与更高的记忆率有关。
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Studying Retrieval Practice in an Intelligent Tutoring System
Retrieval practice (also known as testing effect or test-enhanced learning) is a well-studied and established technique for improving the retention of knowledge. Many previous works have confirmed the benefits of retrieval practice in laboratory experiments involving the memorization of words or facts. In this study, we build on these works and analyze retrieval practice in an intelligent tutoring system. Using a large data set composed of the actions of almost 4 million students studying math and chemistry, we look at the possible benefits of retrieval practice in the ALEKS adaptive learning and assessment system. We compare two different types of retrieval practice---one involving the assessment of learned material, and another involving the learning of closely related content that builds on the learned material---leveraging the scale of the available data to control for several confounding variables. Finally, we look at the timing of retrieval practice within the system and the possible effect it has on forgetting. The results indicate that a delay in retrieval practice is associated with better retention and that, while being assessed on learned material is beneficial, the learning of closely related content is associated with an even higher rate of retention.
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Trust, Sustainability and [email protected] L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022 L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior
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