2023 ASEE Annual Conference & Exposition

Background: The growing demand for computing skills in all science and engineering-related fields begs the question of how college graduates in science and engineering can be best equipped with computational thinking and computer programming skills. Therefore, computational practices need to be integrated into the science and engineering curricula sooner and more often.

Purpose: This study investigated undergraduate students pursuing biomedical and agricultural engineering majors and the changes in their self-beliefs about programming for approaching engineering problems. Specifically, we wanted to understand if the students' self-beliefs changed as a result of implementing three two-week-long computational assignments throughout the semester facilitated through computational pedagogy. The computational pedagogy was embedded within computational notebooks and was grounded in evidence-based practices aligned with cognitive apprenticeship methods.

Methods: The study was conducted in a second-year thermodynamics course offered at a large Mid-Western University. The objective of the course was to understand and exploit basic principles of thermodynamics as they apply to biological systems and biological processes and model these processes using computer code. Pre and post-data were collected using a survey instrument at the beginning and the end of the course. The survey instrument captured students' perceptions toward five aspects related to their experience with programming, i.e., programming self-efficacy, programming self-concept, programming interest, programming anxiety, programming aptitude mindset, and motivation to programming. The survey had a 5-point Likert scale ranging from strongly disagree (1 point) to strongly agree (5 points). The population consisted of 100 students completing both surveys, which was considered for the final analysis.

Results: Based on the constructs used to capture students' programming experience, i.e., self-efficacy, self-concept, interest, anxiety, aptitude mindset, and motivation, results indicate an average positive increase in only programming self-efficacy. The rest of the constructs maintained a neutral or undecided position.

Implications: The study indicates that undergraduate engineering students reported a poor to neutral experience during programming in the computational modeling course, specifically for programming self-concept, interest, anxiety, aptitude mindset and motivation. These findings can be potentially useful for understanding engineering dropout rates and implementing course interventions to improve engineering students experience.