NSF Award
2407487
Computational tools have gain popularity across engineering curricula in the past decade. Computational notebooks, such as Jupyter, Google Colab, and Matlab Live, allow a dynamic interaction between instructors and students with immediate feedback on the concepts at hand. From programming to data visualization, computational notebooks engage students in complex engineering problems in a hands-on fashion. The design of such notebooks is still in its early stages in the context of undergraduate engineering courses. Most notebooks are adapted on a trial-and-error basis from instructor experience and student feedback. The engineering education community has been intentional in promoting the use of such notebooks in undergraduate classrooms with tutorials and templates for instructors that can be customized for the concepts of interest in their class. Nonetheless, there is very limited literature on evidence-based design and empirical evaluation of computational notebooks, and in the context of engineering curricula that objectively identify computational notebooks as better scaffolds for undergraduate learning. This project aims to provide research evidence to answer this question examining how students learn and as part of the mentoring plan for the lead researcher in formal engineering education research. The specific deliverables are in the context of undergraduate education in chemical engineering, yet the expected outcomes can be extended to other engineering disciplines. <br/><br/>This project will provide objective analysis of computational notebooks as tools to engage and facilitate learning of statistics for undergraduate students in engineering. Data collected during the project will serve to inform the design principles of computational notebooks and produce an evaluation rubric for the notebook design in terms of learning outcomes, student interaction and performance. As a direct result of the proposed research, there will be measurable and objective improvements to an undergraduate statistics course in chemical engineering. The cognitive apprenticeship model will serve to quantify the effectiveness of the computational notebooks considering student performance and their actual learning process through questionnaires, in class interactions and observations, an online discussion board, and class assignments and examinations. The same model will be employed by the lead mentors to evaluate and adapt the training of the lead researcher. The following research questions will guide experiment design, data collection tools, and analysis in this project: (1) Does systematic scaffolding of computational notebooks for threshold concepts in undergraduate statistics facilitate student learning? (2) What research-based elements constitute effective design of computational notebooks to reinforce student learning? Upon completion of this project, the research team will contribute to the engineering community systematic, evidence-based computational notebook design guidelines to facilitate student learning in engineering. The research outcomes align with the mission of the RIEF program and the project deliverables will benefit the communities of educators and researchers affiliated with the American Society for Engineering Education, the American Institute for Chemical Engineers Education Division, and Computer Aids for Chemical Engineering.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
