arXiv cs.AI· Francesco Balzan, Pedro P. Santos, Maurizio Gabbrielli, Mahault Albarracin, Manuel Lopes·· 3 小时前AI 评分27
师生协同适应的计算模型：一个贝叶斯框架
A Computational Model of Teacher-Student Co-Adaptation
AI 导读
一篇 arXiv 论文提出贝叶斯计算框架，研究部分可观测概念学习任务中的师生协同适应：学生按可观测特征分成异质小组，教师从有限反馈推断其约束。论文在 100 次独立匹配仿真中比较主动教学、主动学习、轮替交互、自适应教学与完全协同适应五种交互模式和三种教师主导对照；经验水平 40 时，完全协同适应平均交互步数少于主动学习、轮替交互及全部教师主导对照。
正文 · 原文
Abstract:Effective teaching involves mutual adaptation: teachers adjust instruction based on student responses, while students actively seek information to resolve uncertainty. Yet many computational models of teaching treat instruction as a one-way process. This paper presents a Bayesian computational framework for studying teacher-student co-adaptation under partial observability in a stylized concept-learning task. Students are organized in heterogeneous groups that differ in which features of a target concept they can observe, and the teacher infers these constraints from limited feedback. We compare five interaction modes (active teaching, active learning, turn-based interaction, adaptive teaching and full co-adaptation) and three teacher-led controls across 100 independent matched simulation runs. Learner-initiated queries resolve information gaps left by the baseline teachers' stopping rules, while teacher-led continuation also achieves complete identification in the observed episodes. Bayesian belief tracking reduces the mean interaction steps required by full co-adaptation as teacher experience accumulates. At experience level 40, full co-adaptation requires fewer interaction steps on average than active learning, turn-based interaction and all three teacher-led controls, with complete observed coverage shared by these policies. Analyses by group and student type, together with sensitivity analyses, clarify the conditions and distributional limits of these results. The framework is interpretable and modular. Its findings provide a computational proof of concept for investigating interaction structure in heterogeneous groups, rather than direct evidence about classroom practice.
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2505.02853 [cs.CY] |
| (or arXiv:2505.02853v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2505.02853 arXiv-issued DOI via DataCite |
Submission history
From: Francesco Balzan Dr [view email]
[v1]
Fri, 2 May 2025 12:26:31 UTC (607 KB)
[v2]
Tue, 10 Nov 2026 08:45:20 UTC (829 KB)
来源：arXiv cs.AI · arxiv.org