A Hierarchical Bayesian Model of Adaptive Teaching

IF 2.3 2区 心理学 Q2 PSYCHOLOGY, EXPERIMENTAL
Alicia M. Chen, Andrew Palacci, Natalia Vélez, Robert D. Hawkins, Samuel J. Gershman
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引用次数: 0

Abstract

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.

Abstract Image

自适应教学的层次贝叶斯模型
教师如何了解学习者已经掌握的知识?学习者如何通过向教师提供他们的背景知识信息和他们感到困惑的知识来帮助教师?我们使用分层贝叶斯教学法模型将这一合作推理过程正规化。然后,我们在两个在线行为实验(N = 312 名成人)中对这一模型进行了评估。在实验 1 中,我们发现教师在选择例子时会考虑到学习者的背景知识,并根据学习者的反馈调整他们的例子。在实验 2 中,我们发现当教师的示例偏离学习者的背景知识时,学习者会有策略地提供更多反馈。这些发现为将计算教学法扩展到更丰富的互动环境奠定了基础。
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来源期刊
Cognitive Science
Cognitive Science PSYCHOLOGY, EXPERIMENTAL-
CiteScore
4.10
自引率
8.00%
发文量
139
期刊介绍: Cognitive Science publishes articles in all areas of cognitive science, covering such topics as knowledge representation, inference, memory processes, learning, problem solving, planning, perception, natural language understanding, connectionism, brain theory, motor control, intentional systems, and other areas of interdisciplinary concern. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers in cognitive science and its associated fields, including anthropologists, education researchers, psychologists, philosophers, linguists, computer scientists, neuroscientists, and roboticists.
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