Project-based learning in life sciences statistics courses: Dynamics, pitfalls, and educational gains

Authors

DOI:

https://doi.org/10.52041/iase25.105

Abstract

This study explores how project-based learning (PBL) enhances statistics education for students in the two departments—biotechnology and medical laboratory sciences. Semester-long practical projects enable students to simulate real-world data analysis and develop research skills by selecting topics, building databases, reviewing literature, applying various methods using analytical software, interpreting results, and drawing conclusions. MedLab students, who take the course early in their studies, express strong motivation to take initiative and work independently throughout the project. In contrast. Biotechnology students, who are at a more advanced stage in their academic studies, rate communication with peers and lecturer very high. The study draws on two surveys: a Likert-scale questionnaire completed by 64 students to assess PBL experiences, and an open-ended survey of 27 students analyzed using natural language processing to identify sentiment and themes. Findings reveal key differences and similarities, informing how PBL can be tailored to enhance learning in STEM education.

References

Chowdhary, K. R. (2020). Natural Language Processing. In K. R. Chowdhary (Ed.), Fundamentals of Artificial Intelligence (pp. 603–649). Springer. https://doi.org/10.1007/978-81-322-3972-7_19

Gibson, J. P., & Mourad, T. (2018). The growing importance of data literacy in life science education. American Journal of Botany, 105(12), 1953–1956. https://www.jstor.org/stable/26617166

Granado-Alcón, M. del C., Gómez-Baya, D., Herrera-Gutiérrez, E., Vélez-Toral, M., Alonso-Martín, P., & Martínez-Frutos, M. T. (2020). Project-Based Learning and the Acquisition of Competencies and Knowledge Transfer in Higher Education. Sustainability, 12(23). https://doi.org/10.3390/su122310062

Horton, N. J., & Hardin, J. S. (2015). Teaching the Next Generation of Statistics Students to “Think With Data”: Special Issue on Statistics and the Undergraduate Curriculum. The American Statistician, 69(4), 259–265. https://doi.org/10.1080/00031305.2015.1094283

Khalemsky, A., Gelbard, R., & Stukalin, Y. (2025). Constructing a Course on Classification Methods for Undergraduate Non-STEM Students: Striving to Reach Knowledge Discovery. Journal of Statistics and Data Science Education, 33(1), 68–76. https://doi.org/10.1080/26939169.2024.2320218

Khalemsky, A., & Stukalin, Y. (2024). Effect of students´ ethnic and cultural backgrounds on the success of practical projects within statistics and data science courses. Statistics Education Research Journal, 23(2), Article 2. https://doi.org/10.52041/serj.v23i2.721

Khalemsky, A., & Stukalin, Y. (2023). Teaching classification module in data science course for undergraduate non-STEM students using project based learning methodology. In E.M. Jones (Ed.), Fostering Learning of Statistics and Data Science. Proceedings of the Satellite conference of the International Association for Statistical Education (IASE). ISI/IASE. https://doi.org/10.52041/iase2023.104

Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project-based learning: A review of the literature. Improving Schools, 19(3), 267–277. https://doi.org/10.1177/1365480216659733

Kurczewska, A., Doryń, W., & Wawrzyniak, D. (2020). An Everlasting Battle between Theoretical Knowledge and Practical Skills? The Joint Impact of Education and Professional Experience on Entrepreneurial Success. Entrepreneurial Business and Economics Review, 8(2), 219–237. https://doi.org/10.15678/EBER.2020.080212

Li, B., Jia,X., Chi, Y., Liu,X, & Jia, B. (2020). Project-based learning in a collaborative group can enhance student skill and ability in the biochemical laboratory: A case study. Journal of Biological Education, 54(4), 404–418. https://doi.org/10.1080/00219266.2019.1600570

Montoyo, A., Martínez-Barco, P., & Balahur, A. (2012). Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments. Decision Support Systems, 53(4), 675–679. https://doi.org/10.1016/j.dss.2012.05.022

Movahedzadeh, F., Patwell, R., Rieker, J. E., & Gonzalez, T. (2012). Project-Based Learning to Promote Effective Learning in Biotechnology Courses. Education Research International, 2012, Article 536024. https://doi.org/10.1155/2012/536024

Pacella, D., Fabbricatore, R., D’Enza, A. I., Galluccio, C., & Palumbo, F. (2022). Teaching STEM Subjects in Non-STEM Degrees: An Adaptive Learning Model for Teaching Statistics. In F. Ouyand, P. Jiao, B. M. McLaren & A. H. Alavi (Eds.), Artificial Intelligence in STEM Education (chapter 5). CRC Press.

Parviz, M. (2024). AI in education: Comparative perspectives from STEM and Non-STEM instructors. Computers and Education Open, 6, Article 100190. https://doi.org/10.1016/j.caeo.2024.100190

Robeva, R. S., Jungck, J. R., & Gross, L. J. (2020). Changing the Nature of Quantitative Biology Education: Data Science as a Driver. Bulletin of Mathematical Biology, 82(10), Article 127. https://doi.org/10.1007/s11538-020-00785-0

Vaughn, P., & Turner, C. (2016). Decoding via Coding: Analyzing Qualitative Text Data Through Thematic Coding and Survey Methodologies. Journal of Library Administration, 56(1), 41–51. https://doi.org/10.1080/01930826.2015.1105035

Watson, J. M. (2017). Linking Science and Statistics: Curriculum Expectations in Three Countries. International Journal of Science and Mathematics Education, 15(6), 1057–1073. https://doi.org/10.1007/s10763-015-9673-y

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Published

2026-02-21

Conference Proceedings Volume

Section

Topic 3: Advancing Educational Practices to Enhance Understanding in Statistics and Data Science