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Publications

Incorporating Teacher Effect When Modeling Student Engagement in Smart STEM classrooms: A Cluster Analysis

Student engagement during learning serves as a critical predictor of academic success and plays a pivotal role in nurturing interest and readiness for future careers.

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Healthcare Data Science, Artificial Intelligence, and Machine Learning: Exploring Context-Based Learning for High School Students

This paper reports perspectives of high school students and their teachers on how context-based learning can support students with varied educational and vocational aspirations to engage with and learn about emerging fields.

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Increasing Data Literacy in High School Students Through Data Science and Healthcare: Part 1 of 2 Leading to AI Instruction

High school science and biomedical pathway teachers need effective strategies to build student data literacy and prepare them to conduct experiments.

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Individual Showcase: What do high school students experience and learn during a two-day datathon?

What do high school students learn from a two-day datathon during which they tackle data to visualize the impact of biased data on healthcare decisions? How do they interact with their team of high school students, data scientists, clinicians, and teachers?

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Exploring Teachers’ Perspectives on Enacting Context-based Learning of Artificial Intelligence (AI) and Data Science to Support Students’ Engagement and Learning

This paper presents an empirical study of high school teachers’ perspectives on context-based learning about Artificial Intelligence (AI) and data science. Four teachers were interviewed after they had enacted a curriculum contextualized in healthcare.

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Development of a machine-learning-driven digital teaching assistant that utilises student engagement data to improve access to and success in K-12 STEM education

Student engagement is a key predictor of academic achievement and is closely linked to career awareness, interest, and preparedness. Measuring student engagement during STEM learning is challenging for teachers, given the dynamic and ever-changing nature of these learning environments.

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This material is based upon work supported by the National Science Foundation under Grant Nos. DRL-1312022, 1614697 and 1949200. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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