Kula, F.
f.kula@utwente.nl
University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, The Netherlands
Abstract
This study aims to address the challenges faced by engineering students in understanding and applying statistical inference, particularly in relation to the conceptual difficulties that arise when trying to integrate statistical reasoning into real-world applications. Despite the critical role of statistical inference in data-driven decision-making within engineering, students often struggle to apply theoretical concepts to practical problems. Teaching methods typically and to some extend unavoidably prioritize procedural knowledge over conceptual understanding. To respond to these challenges, this study explores the adoption of the construction direction model—a pedagogical approach designed to promote active learning, foster deeper conceptual engagement, and improve the application of statistical reasoning (Kula & Koçer, 2020).
The construction direction model involves students in the active creation of statistical concepts, such as the sample mean, by using their own datasets. This hands-on approach encourages participation, enabling students to build a stronger understanding of statistical inference through the integration of theoretical knowledge with practical experience. By emphasizing collaborative learning and real-world applications, the model aims to enhance engagement and comprehension, making statistical inference more meaningful and accessible to students.
Existing literature supports the need for teaching strategies that activate students‘ learning through the use of technology to address the conceptual barriers they face in learning statistical inference (e.g. Tintle et al. 2015). Research highlights the importance of bridging procedural knowledge with conceptual understanding through active learning and experiential approaches (Van DijkeDroogers et al, 2022).
The construction direction model has been implemented with second-year engineering students as part of a specially designed lesson plan. During this implementation, a teacher was present for observation, and students worked with their own data to engage with statistical concepts. Students‘ responses were gathered via an interactive collaboration platform. In the analysis phase, interviews with students will be conducted, and the findings will be presented in the full paper submission. Data collection includes students‘ responses to interactive questions, classroom observations, and interviews. Preliminary results, while not yet fully analyzed, suggest that students demonstrate a better understanding of statistical inference and exhibit increased engagement during the teaching sessions.
Keywords
Statistical inference, Engineering education, Conceptual understanding, Construction direction model.
References
Kula, F. and Koçer, R.G. (2020) Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework. Teaching Mathematics and its Applications: An International Journal of the IMA, Vol. 39 (No. 4):248–265. https://doi.org/10.1093/teamat/hrz014.
Tintle, N., Chance, B., Cobb, G., Roy, S., Swanson, T. and VanderStoep, J. (2015) Combating Anti-Statistical Thinking Using Simulation-Based Methods Throughout the Undergraduate Curriculum. The American Statistician, Vol. 69 (No. 4):362–370. https://doi.org/10.1080/00031305.2015.1081619
Van Dijke-Droogers, M., Drijvers, P. and Bakker, A. (2022) Introducing Statistical Inference: Design of a Theoretically and Empirically Based Learning Trajectory. International Journal of Science and Mathematics Education, Vol. 20:1743–1766. https://doi.org/10.1007/s10763-021-10208-8