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Dergiler / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2026 / Volume: 15 Issue: Special Issue Special Issue

Human-Centered AI for Discovering Student Engagement Profiles on Large-Scale Educational Assessments

Hongwen Guo(ETS)0000-0002-1751-0918
Matthew Johnson(ETS Research Institute)0000-0003-3157-4165
Luis Saldivia(ETS)0009-0007-3482-7654
Michelle Worthington(ETS)0009-0006-0480-3769
Kadriye Ercikan(ETS)0000-0001-8056-9165
Sayfa
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Yayın Tarihi
2024-12-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These engagement profiles are expected to generate rich and meaningful feedback for educators and stakeholders.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Large-scale assessments play a key role in education: they provide insights for educators and stakeholders about what students know and are able to do, which can inform educational policies and interventions.
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Temel Bulgular & Veri:Besides overall performance scores and subscores, educators need to know how and why students performed at certain proficiency levels to improve learning.
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Sonuç & Katkı:These engagement profiles are expected to generate rich and meaningful feedback for educators and stakeholders.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Multi-source data · machine learning · human-in-the-loop

Abstract

Large-scale assessments play a key role in education: they provide insights for educators and stakeholders about what students know and are able to do, which can inform educational policies and interventions. Besides overall performance scores and subscores, educators need to know how and why students performed at certain proficiency levels to improve learning. Process/log data contain nuanced information about how students engaged with and acted on tasks in an assessment, which hold promise of contextualizing a performance score. However, one isolated action event observed in process data may be open to multiple interpretations. To address this challenge, in the current study, we propose to integrate sequential process data with response data to create engagement profiles to better reflect students' test-taking processes. Most importantly, we propose to use AI algorithms to assist and amplify human expertise in the creation of students’ engagement profiles, so that the information extraction from the multi-source (performance and process) data can be scaled up to enhance the value of large-scale assessments in teaching and learning. We leveraged various machine learning techniques and developed a general framework of the human-centered AI approach to help human experts efficiently and effectively make sense of the multi-source data. Using a mathematics item block from the National Assessment of Educational Progress (NAEP) for illustrations, data from over 14,000 students resulted in ten preliminary profiles, more than half of which were associated with low performing students. These engagement profiles are expected to generate rich and meaningful feedback for educators and stakeholders.

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Bu Makaleye Atıf Yap

Guo, H., Johnson, M., Saldivia, L., Worthington, M., Ercikan, K. (2024). Human-Centered AI for Discovering Student Engagement Profiles on Large-Scale Educational Assessments. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Volume: 15 Issue: Special Issue Special Issue. https://doi.org/10.21031/epod.1532846

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