Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Carrie Grace Aponte, Kansas State University; Safia Malallah, Kansas State University; Lior Shamir, Kansas State University

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

work and do notreflect the views of the NSF.References [1] World Economic Forum, “How to address disinformation,” October 2022. Accessed: 2025-01-13. [2] C. Engledowl and T. Weiland, “Data (Mis)representation and COVID-19: Leveraging Misleading Data Visualizations For Developing Statistical Literacy Across Grades 6–16,” Journal of Statistics and Data Science Education, vol. 29, pp. 160–164, Aug. 2021. Publisher: Taylor & Francis eprint: https://doi.org/10.1080/26939169.2021.1915215. [3] S. Yeom, “Teaching and assessing data literacy for adolescent learners,” in Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies, pp. 93–123, IGI Global, 2021. [4] K. Janacsek, J. Fiser, and D. Nemeth

Conference Session

DSAI Technical Session 1: K–12 and Early Exposure to Data Science and AI

Collection

2025 ASEE Annual Conference & Exposition

Authors

Faiza Zafar, Rice University; Carolyn Nichol, Rice University; Matthew Cushing, Rice University

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

-González, M., & Robles, G. (2020b). LearningML: A Tool to Foster Computational Thinking Skills through Practical Artificial Intelligence Projects; Revista de Educación a Distancia; 20(63).[5] Rodríguez-García, J. D., Moreno-León, J., Román-González, M., & Robles, G. (2021). Evaluation of an online intervention to teach artificial intelligence with learningML to 10-16-year-old students. In Proceedings of the 52nd ACM technical symposium on computer science education (pp. 177–183).[6] Sakulkueakulsuk, B. S.;Witoon, P. Ngarmkajornwiwat, P. Pataranutaporn, W. Surareungchai, P. Pataranutaporn, P. Subsoontorn (2018). “Kids making AI: Integrating Machine Learning, Gamification, and Social Context in STEM

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Declan Kirk Bracken, University of Toronto; Sinisa Colic Ph.D., University of Toronto

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

, noisy text, and reorganize large sequences of strings into a columnar struc-ture. These results suggest that with more data and continuous improvement, these systems couldbe implemented to greatly support the admissions process in the future.References [1] R. Avyodri, S. Lukas, and H. Tjahyadi, “Optical character recognition (ocr) for text recogni- tion and its post-processing method: A literature review,” in 2022 1st International Confer- ence on Technology Innovation and Its Applications (ICTIIA), 2022, pp. 1–6. [2] S. Paliwal, V. D, R. Rahul, M. Sharma, and L. Vig, “Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images,” CoRR, vol. abs/2001.01469, 2020. [Online

Conference Session

DSAI Technical Session 6: Academic Success, Performance & Complexity

Collection

2025 ASEE Annual Conference & Exposition

Authors

Cristian Saavedra-Acuna, Universidad Andres Bello, Concepcion, Chile; Monica Quezada-Espinoza, Universidad Andres Bello, Santiago, Chile; Danilo Alberto Gomez, Universidad Andres Bello, Concepcion, Chile

Tagged Topics

Diversity

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

-Aguilar, P. R. Álvarez-Pérez, and P. A. Toledo- Delgado, "Dropping out of higher education: Analysis of variables that characterise students who interrupt their studies," Acta Psychologica, vol. 252, p. 104669, Feb. 2025, doi: 10.1016/j.actpsy.2024.104669.[2] Cruz L., Li T., Ciner L., Douglas K., Greg C., (2022) Predicting learning outcome in a first-year engineering course: a human-centered learning analytics approach. Recuperado de: https://peer.asee.org/predicting-learning-outcome-in-a-first-year- engineering-course-a-human-centered-learning-analytics-approach.pdf[3] G. Bilquise, S. Abdallah, and T. Kobbaey, "Predicting Student Retention Among a Homogeneous Population Using Data Mining," in Proceedings

Conference Session

DSAI Technical Session 5: Educational Technology and Innovative Tools

Collection

2025 ASEE Annual Conference & Exposition

Authors

Handan Liu, Northeastern University

Tagged Divisions

Data Science and Artificial Intelligence (DSAI) Constituent Committee

one to provide aprogressive learning experience.Part 1: High-Performance Computing (HPC) Architectures and Parallel ProgrammingModelsHigh-performance computing (HPC) architectures are designed to solve complex computationalproblems by leveraging the parallel processing capabilities of multiple computing resources.These architectures typically consist of clusters of interconnected nodes, each with its ownprocessor(s), memory, and storage, working collaboratively to execute tasks simultaneously.HPC systems rely on efficient interconnect networks for high-speed communication betweennodes and often utilize specialized hardware, such as GPUs or accelerators, to enhanceperformance for specific workloads. Software frameworks like MPI (Message