2024 ASEE Annual Conference & Exposition

Though recognition of the importance of diversity and inclusion in engineering education has grown in recent years [1], little is known about the best practices for supporting neurodiverse students [2-3]. It has been suggested that neurodiverse students benefit from course assessments that allow for a more flexible mode of expressing knowledge [3]. However, evidence for improved learning outcomes on different types of course assessments is largely anecdotal. Characteristics associated with different forms of neurodiversity, such as attention deficit hyperactivity disorder (ADHD), autism spectrum, depression, and anxiety, are suggested to be normally distributed in the population [2]. Indeed, research suggests that these conditions are best conceptualized as dimensional [4-6] and that varying levels of these characteristics are associated with similar functional outcomes [7-8]. Thus, assessing how variation in neurodiverse characteristics of all students predicts performance on different types of engineering course assessments should help to shed light on how engineering faculty can support students who learn and think in different ways. To this end, undergraduate engineering students (N = 50) in a Soil Mechanics course participated in a study to determine if neurodiverse characteristics differentially predict performance on different types of course assessments. At the beginning of the Fall 2023 semester, students completed self-report assessments of neurodiverse characteristics (ADHD, autism spectrum, depression, and anxiety) and personal resources (self-efficacy, engagement, and motivation) using an online survey. Students also provided permission to record their grades on course assignments for analysis. Following the end of the semester, participating students’ scores were recorded for the following: (1) Average of scores for homework assignments; (2) Average of scores on quizzes; (3) Average of scores for each of three phases of the term project; (4) Average of scores for three mid-terms; (5) Score for class participation. Data will be analyzed using multiple regression models. The proposed paper will describe the course structure and design of the course assignments, which differ in their level of flexibility, as well as the results and conclusions of the analyses.