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University of Wisconsin-Milwaukee

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Representative Papers

Search or Chat? Comparing How We Learn About Debated Topics

Aug 14, 2026

This study investigates the differential effects of conversational and search-based tools on informal learning regarding controversial topics. Through a crowdsourced comparative experiment, we analyzed how user characteristics and interaction patterns influence learning outcomes and critical reflection. Results indicate no significant differences between the two tools in terms of learning gains or critical reflection. Instead, individual traits such as attitude strength and intellectual humility were found to be stronger determinants of immediate learning efficacy than the specific information retrieval modality employed. These findings suggest that in the context of controversial subject matter, individual cognitive attributes exert a more pivotal influence than technological form factors. Consequently, this work offers novel insights into learning mechanisms within human-computer interaction by highlighting the primacy of learner dispositions over tool design in shaping educational outcomes for contentious issues.

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Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification

Jun 21, 2026

This study addresses the lack of systematic comparison among convolutional neural networks (CNNs), vision Transformers (ViTs), and hybrid architectures for colorectal histopathological image classification. Leveraging the Kather dataset, the authors evaluate twelve prominent models—including ResNet34, ConvNeXt-Tiny, ViT-B/16, and EVA-02—under a unified pipeline of ImageNet pretraining, transfer learning, and fine-tuning. For the first time, the three major deep learning paradigms are comprehensively benchmarked under standardized conditions, with performance analyzed through accuracy, interpretability, and per-class metrics to identify strengths and challenging categories. Results show that all models achieve high accuracy ranging from 93.2% to 97.1%, with the hybrid model EVA-02 attaining the best performance at 97.1%. Vision Transformers generally outperform CNNs, while modern CNNs offer a favorable trade-off between accuracy and computational complexity.

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Latest Papers

Search or Chat? Comparing How We Learn About Debated Topics

Aug 14, 2026

This study investigates the differential effects of conversational and search-based tools on informal learning regarding controversial topics. Through a crowdsourced comparative experiment, we analyzed how user characteristics and interaction patterns influence learning outcomes and critical reflection. Results indicate no significant differences between the two tools in terms of learning gains or critical reflection. Instead, individual traits such as attitude strength and intellectual humility were found to be stronger determinants of immediate learning efficacy than the specific information retrieval modality employed. These findings suggest that in the context of controversial subject matter, individual cognitive attributes exert a more pivotal influence than technological form factors. Consequently, this work offers novel insights into learning mechanisms within human-computer interaction by highlighting the primacy of learner dispositions over tool design in shaping educational outcomes for contentious issues.

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Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification

Jun 21, 2026

This study addresses the lack of systematic comparison among convolutional neural networks (CNNs), vision Transformers (ViTs), and hybrid architectures for colorectal histopathological image classification. Leveraging the Kather dataset, the authors evaluate twelve prominent models—including ResNet34, ConvNeXt-Tiny, ViT-B/16, and EVA-02—under a unified pipeline of ImageNet pretraining, transfer learning, and fine-tuning. For the first time, the three major deep learning paradigms are comprehensively benchmarked under standardized conditions, with performance analyzed through accuracy, interpretability, and per-class metrics to identify strengths and challenging categories. Results show that all models achieve high accuracy ranging from 93.2% to 97.1%, with the hybrid model EVA-02 attaining the best performance at 97.1%. Vision Transformers generally outperform CNNs, while modern CNNs offer a favorable trade-off between accuracy and computational complexity.

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