ITEST Resources

As healthcare systems increasingly adopt artificial intelligence (AI), engineering students need greater awareness of how measurement bias can influence medical care, downstream AI models, and clinical decisions. This short paper describes a hands-on pulse oximetry experiment that is part of a semester-long, high school course on data science and AI in healthcare that was implemented across 10 high schools ( approximately 400 students) in one northeastern U.S. state. Pulse oximeters estimate blood oxygen saturation using red and infrared light and are widely used despite documented accuracy

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. As digital platforms become increasingly important to learning, it is essential that we understand how the interactions that students have with them reflects their engagement with learning. Previous research has often modeled engagement in a fully online context, where students pursue lessons independently and outside the influence of the classroom, paced and structured by digital systems. However, in STEM (Science, Technology

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. Specifically, through analyses of qualitative data from student and teacher interviews, this study explores how a healthcare context-based curriculum featuring in-class activities and an out-of-class datathon can introduce students to core concepts, practices, and the role of fields like data science, AI, and ML. Suggested citation: A.D. Bopardikar, M. Cassidy, A. Gardiner

High school science and biomedical pathway teachers need effective strategies to build student data literacy and prepare them to conduct experiments. This first article of two presents the data collection, visualization, and analysis lessons (Lessons 6–10) from the Data Science, AI and You (DSAIY) in Healthcare curriculum, co-developed and tested by teachers in diverse classroom settings using the Scoutlier online learning platform. The lessons guide students in collecting and visualizing data (Lessons 6–7) and in more advanced data interpretation, analysis, and application (Lessons 8–10)

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? What did we, the developers and leaders of the datathon, learn? How would we approach it differently next year? Our goal is to answer these questions plus share lessons learned. We will then divide the audience into teams to brainstorm ways to approach and solve some of the problems we experienced and hopefully recruit some audience members

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. The data were coded for teachers’ perspectives on what students learned; on the kinds of tasks that engaged students; and on the challenges and needs in teaching and learning about these fields. While context-based learning has the potential to promote students’ career awareness and appreciation of AI and data science, future research needs to

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. Even when engagement data can be collected, leveraging this information to refine and personalise instruction requires significant experience and time. To address this, we are developing Scoutlier EngagEd, a digital teaching assistant that embeds in existing Learning Management Systems (LMS) to automatically and

There is a growing need in the United States for a workforce trained in quantum information science and technology (QIST), a disciplinary topic that is rarely addressed in precollege science, mathematics, and computer science curricula. University quantum physics and physics education researchers designed and initiated a 4-week, 12-h QIST professional development workshop for 𝑁=5⁢1 preservice and in-service secondary school science, mathematics, and computer science educators. A STEM integration framework guided the workshop structure, which incorporated a situated cognition model for learning

In Massachusetts, the Latino population increased by 475 percent between 1980 and 2017, marking a dramatic growth. This diverse ethnic community of Puerto Ricans, Dominicans, Salvadoreans, Colombians, Brazilians, and more also contains a wide range of cultures, immigration and migration experiences, languages, and socio-political backgrounds. However, there are numerous commonalities involving education. Recent research at the Mauricio Gastón Institute for Latino Community Development and Policy reveals that Latino students in Massachusetts are more likely to attend public schools, in

Quickly disseminating an innovative, timely afterschool program raises challenges, from recruitment and professional development to assessment, program fidelity, and quality. In this paper, we describe our experience as project developers, trainers, and researchers working with an afterschool network, Imagine Science, to disseminate a middle school club program about epidemic diseases and data. What we learned from working with this network may be useful to others who have created an afterschool science, technology, engineering, and mathematics (STEM) program they hope to spread widely