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arXiv:2505.02853v2cs.CY 등록 2025-05-02 업데이트 2026-11-11

A Computational Model of Teacher-Student Co-Adaptation

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.

arXiv 원문 상세

arXiv:2410.21024v2astro-ph.EP 등록 2024-10-28 업데이트 2026-10-30

Breccia and basalt classification of thin sections of Apollo rocks with deep learning

Human exploration of the moon is expected to resume in the next decade, following the last such activities in the Apollo programme time. One of the major objectives of returning to the Moon is to continue retrieving geological samples, with a focus on collecting high-quality specimens to maximize scientific return. Tools that assist astronauts in making informed decisions about sample collection activities can maximize the scientific value of future lunar missions. A lunar rock classifier is a tool that can potentially provide the necessary information for astronauts to analyze lunar rock samples, allowing them to augment in-situ value identification of samples. Towards demonstrating the value of such a tool, in this paper, we introduce a framework for classifying rock types in thin sections of lunar rocks. We leverage the vast collection of petrographic thin-section images from the Apollo missions, captured under plane-polarized light (PPL), cross-polarised light (XPL), and reflected light at varying magnifications. Advanced machine learning methods, including contrastive learning, are applied to analyze these images and extract meaningful features. The contrastive learning approach fine-tunes a pre-trained Inception-Resnet-v2 network with the SimCLR loss function. The fine-tuned Inception-Resnet-v2 network can then extract essential features effectively from the thin-section images of Apollo rocks. A simple binary classifier is trained using transfer learning from the fine-tuned Inception-ResNet-v2 to 98.44\% ($\pm$1.47) accuracy in separating breccias from basalts.

arXiv 원문 상세