Bridging the Gap Between Domain Models and Computational Models: A Case Study of COVID-19

Zhang, Yepeng; Wang, Jianxu; Li, Guanding; Zhang, Xuyun; Ye, Qing; Yu, Huixue; Xue, Xiao

doi:10.1007/978-981-19-6901-0_67

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, knowledge of a variety of different cognitive mechanisms involved in learning may aid teachers to better help and adapt to students (Koedinger, Corbett, & Perfetti, 2012). Future work building tools to scale up adaptive reasoning processes (e.g., by incorporating natural language and more complex teacher–learner interactions) should incorporate a diverse range of data sources, classroom interventions, and collaborations with teachers and education researchers to enhance our understanding of these processes in real‐world settings (e.g., Demszky, Wang, Geraghty, & Yu, 2024; Wang, Wirawarn, Goodman, & Demszky, 2023; Wang, Zhang, Robinson, Loeb, & Demszky, 2024).…”

Section: Discussionmentioning

confidence: 99%

A Hierarchical Bayesian Model of Adaptive Teaching

Chen,

Palacci,

Vélez

et al. 2024

Cognitive Science

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%