Research
Research in the Design, Technology, and Data Science (DTDS) area focuses on how design, data, and technology shape teaching and learning across classrooms and communities. Faculty and students collaborate on projects that develop innovative digital tools, explore how data can guide decision-making, and apply design thinking to create meaningful learning experiences. This work highlights both practical applications and new possibilities for the future of education.
Programs
The DTDS area of study offers undergraduate courses and graduate programs.
Undergraduate Courses
Graduate
Our graduate programs include:
- A Certificate in Educational Data Science which offers foundational knowledge and applied experience for working with data in education.
- A Master of Science (M. S.) in Instructional Technology which is designed to prepare educators and professionals to create technology-rich learning environments.
- A Doctor of Philosophy (Ph. D.) in Learning, Design, and Technology which provides advanced study and research opportunities for those seeking to shape the future of technology and learning.
More on Certificate in Educational Data Science
Design, Technology, and Data Science is about imagining what’s possible for learning—bringing creativity, innovation, and evidence together to design experiences that make a lasting impact in classrooms and communities.
Design, Technology, and Data Science Faculty
Sharing Recent Work From Faculty and Students
The North American Chapter of the International Group for the Psychology of Education
Boles, K. L. (2024). Spatial equity considerations for calculus and statistics as secondary mathematics endpoints. In K. W. Kosko, J. Caniglia, S. A. Courtney, M. Zolfaghari, & G. A. Morris. (Eds). Envisioning the Future of Mathematics Education in Uncertain Times: Proceedings of the 46th Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (PME-NA) 2024 (pp. 211-216). Cleveland, OH. Kent State University. https://doi.org/10.51272/pmena.46.2024
Computer Science Education
Moudgalya, S. K., Lachney, M., Yadav, A., & Allen Kuyenga, M. (2024). What does the phrase “diverse students” mean? An exploration of CS teachers’ ideas of race, culture, and community in their classrooms. Computer Science Education, 1-29.
International Journal of Engineering Education
Carter,B., Reeves, S.M., Caro, V., Garbocci- Schmittlen, J., Millunchick, J. M. (2025). Material Science Education Research for Effective Teaching: Systemic Review. International Journal of Engineering Education, 41(5),1-12.
Educational Technology Research and Development
Romero-Hall, E., Allen, E., Duan, Y. & Morris, A. (2025). A Decade of HyFlex Learning: A Systematic Review in Undergraduate Education. Educational Technology Research and Development.https://doi.org/10.1007/s11423-025-10544-4
Harvard Data Science Review
Rosenberg, J., & Jones, R. S. (2024). Data science learning in grades K–12: Synthesizing research across divides. Harvard Data Science Review, 6(3), 1-30. https://doi.org/10.1162/99608f92.b1233596
International Journal of Technology in Higher Education
Sung, H. & Nathan, M. J. (2025). Unraveling temporally entangled multimodal interactions: investigating verbal and nonverbal contributions to collaborative construction of embodied math knowledge. International Journal of Educational Technology in Higher Education, 22(8). https://doi.org/10.1186/s41239-025-00504-6
Journal of Research on Technology in Education
Song, Y., Kim, J., Xing, W., Liu, Z., Li, C., Oh, H. (2025). Elementary school students’ and teachers’ perceptions towards creative mathematical writing with generative AI. Journal of Research on Technology in Education. DOI: https://doi.org/10.1080/15391523.2025.2455057
Teachers College Record
Rosenberg, J. M., Romero-Hall, E., Veletsianos, G., & Allen, E. (in press). The accessibility of published educational research. Teachers College Record.https://osf.io/mvh87_v1/