More Like this

1. Exploring the Role of Mentorship in Enhancing Engineering Students’ Innovation Self-Efficacy.

   Bolhari, Azadeh; Bielefeldt, Angela (July 2023, 2023 ASEE Annual Conference & Exposition, Baltimore, Maryland.)

   This paper explores a learning environment that may foster innovation in the engineering curriculum. In this study, the innovation self-efficacy of undergraduate environmental engineering students is explored in a target course before and after a curricular intervention which has been shown to have the potential to enhance innovation self-efficacy. A design mentor and an education mentor outside of the course supported the students through their engineering design process. During the start and end of this curricular intervention, a survey consisting of the Very Brief Innovation Self-Efficacy scale (ISE.5), the Innovation Interests scale (INI), and the Career Goals: Innovative Work scale (CGIW) was administered to measure students’ shift in: 1) Innovation Self-Efficacy, 2) Innovation Interests, and 3) Innovative Work. Formal feedback from the mentors was utilized in interpreting the survey outcomes. Results generated from this survey show a modest increase in innovation self-efficacy. Nevertheless, less impact was found compared to the previous year when innovation attitudes were weaker in the pre-survey. 

   more » « less
2. Work in Progress: Exploring Innovation Self-Efficacy in Neurodiverse Engineering Students.

   Bolhari, Azadeh; Bielefeldt, Angela (July 2023, 2023 ASEE Annual Conference & Exposition, Baltimore, Maryland.)

   It is critical to incorporate inclusive practices in the engineering curriculum which prepares neurodiverse students to achieve their full potential in the workforce. This work-in-progress paper seeks to capitalize on the unique strengths of marginalized neurodiverse engineering students. In this study, the innovation self-efficacy of engineering students who self-identify as neurodiverse is explored before and after a curricular intervention, which has been shown to have the potential to enhance innovation self-efficacy, in an environmental engineering target course. A previously validated Likert-type survey was used, which included the Very Brief Innovation Self-Efficacy scale, the Innovation Interests scale, and the Career Goals: Innovative Work scale. Among the 47 responses on the pre-survey, 13% of the students self-identified as neurodiverse and an additional 19% indicated that they were maybe neurodiverse. This included a much higher percentage of female than male students in the course (23% vs. 5% neurodiverse). There were no significant differences in the pre-survey or post-survey in the innovation self- efficacy and innovation interest among students who self-identified as neurodiverse, maybe neurodiverse, and not neurodiverse. Career goals based on the innovative work scale differed in the pre-survey among the three groups, being lowest among students who self-identified as maybe neurodiverse; there were no differences among the groups in the post-survey. It appeared that there were gains in the innovation self-efficacy between the pre and post-survey among the students who self-identified as neurodiverse and maybe neurodiverse but these differences were not statistically significant. A limitation of the study was the lack of ability to pair the data for individual students and a low number of neurodiverse students in the dataset. This preliminary work calls attention to the need to consider neurodiverse students in our instructional practices. In the future, we hope the research will expand our understanding of a neurodiverse-friendly curricular design in preparation for engineering students with autism spectrum disorder and other types of neurodiversity for the workforce, as well as assisting engineering educators in the adoption of practices that have the tendency to enhance innovation self-efficacy in neurodiverse students. 

   more » « less
3. Using Learning Maps and Bloom’s Taxonomy to Develop a New Instrument to Assess Knowledge Transfer from Physics to Statics Courses.

   https://doi.org/10.18260/1-2--57307  

   Wijesinghe, Priyantha; Seneviratne, Varuni; Medsker, Larry; Giles, Courtney; Ottati, Marlee (June 2025, ASEE Conferences)

   This paper presents the design and analysis of a pilot problem set deployed to engineering students to assess their retention of physics knowledge at the start of a statics course. The problem set was developed using the NSF-IUSE (grant #2315492) Learning Map project (LMap) and piloted in the spring and fall of 2024. The LMap process is rooted in the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model [1] and Backward Design [2,3], extending these principles to course sequences to align learning outcomes, assessments, and instructional practices. The primary motivation for this problem set (Statics Knowledge Inventory, SKI) was to evaluate students' understanding and retention of physics concepts at the beginning of a statics course. The SKI includes a combination of multiple-choice questions (MCQ) and procedural problems, filling a gap in widely-used concept inventories for physics and statics, such as the Force Concept Inventory (FCI) and Statics Concept Inventory (SCI), which evaluate learning gains within a course, rather than knowledge retention across courses. Using the LMap analysis and instructor consultations, we identified overlapping concepts and topics between Physics and Statics courses, referred to here as “interdependent learning outcomes” (ILOs). The problem set includes 15 questions—eight MCQs and seven procedural problems. Unlike most concept inventories, procedural problems were added to provide insight into students’ problem-solving approach and conceptual understanding. These problems require students to perform calculations, demonstrate their work, and assess their conceptual understanding of key topics, and allow the instructors to assess essential prerequisite skills like drawing free-body diagrams (FBDs), computing forces and moments, and performing basic vector calculation and unit conversions. Problems were selected and adapted from physics and statics textbooks, supplemented by instructor-designed questions to ensure full coverage of the ILOs. We used the revised 2D Bloom’s Taxonomy [4] and a 3D representation of it [5] to classify each problem within a 6x4 matrix (six cognitive processes x four knowledge dimensions). This classification provided instructors and students with a clear understanding of the cognitive level required for each problem. Additionally, we measured students’ perceived confidence and difficulty in each problem using two questions on a 3-point Likert scale. The first iteration of the problem set was administered to 19 students in the spring 2024 statics course. After analyzing their performance, we identified areas for improvement and revised the problem set, removing repetitive MCQs and restructuring the procedural problems into scaffolded, multi-part questions with associated rubrics for evaluation. The revised version, consisting of five MCQs and six procedural problems, was deployed to 136 students in the fall 2024 statics course. A randomly selected subset of student answers from the second iteration was graded and analyzed to compare with the first. This analysis will inform future efforts to evaluate knowledge retention and transfer in key skills across sequential courses. In collaboration with research teams developing concept inventories for mechanics courses, we aim to integrate these procedural problems into future inventories. 

   more » « less
4. The Wooden Bike Frame Challenge: Learning Statics Through Hands-On Design

   https://doi.org/10.18260/1-2--48150  

   Buckley, Jenni; Trauth, Amy; De_Rosa, Alexander; Doty, Heather (June 2024, ASEE Conferences)

   Statics is a core course taken by undergraduate mechanical engineers in their freshmen or sophomore years. The course involves characterizing structures that remain still (static) under load. Statics concepts traditionally build in complexity from isolated particles, then to rigid bodies, and finally to structures formed by multiple rigid bodies. Structural analysis, otherwise known as “frames and machines,” is thus one of the more complex topics covered in Statics because it integrates prior knowledge of particle and rigid body equilibrium with new concepts like two-force members and internal loads. Traditionally, students become proficient in structural analysis by solving textbook problems where implicitly or explicitly, these problems classify the structure as either a “frame” or a “machine.” This classification in problem wording hints at the solution method and typically requires students to calculate the loads at a particular connector or cross section at risk of failure, thus reducing opportunities for structural analysis before computation. In actual practice, structural analysis is less straightforward; engineers must thoughtfully examine the structure to determine the best method of analysis and likely failure location(s). Prior studies have introduced project-based learning (PBL) experiences for Statics courses that involve more realistic open-ended design, analysis, and validation. However, the prototyping component of these studies often falls short of actual practice by limiting students to scale model designs in craft grade materials, e.g., table-top sized bridges constructed from balsa wood. While economical and logistically simplistic, scale model designs do not reinforce industry-relevant design and fabrication skills, e.g., CAD/CAM and shop skills. Furthermore, scale models cannot be subjected to realistic loading conditions, which disconnects the analysis and validation portions of the project from actual engineering practice. In this study, we introduce a novel PBL exercise – the Wooden Bike Frame Challenge – for Statics courses that focuses on structural analysis and involves fabrication of a full-scale wooden bike frame using CAD/CAM techniques. The complete set of instructional materials, including problem statements, assignments, and rubrics, are included in this study for open-source use by other engineering educators. We evaluated the efficacy of this exercise in reinforcing students’ knowledge of statics concepts and previously acquired prototyping skills using a mixed-methods approach. Study subjects were sophomore year mechanical engineering students who were teamed (n=158 students in 37 teams). The effect of the PBL exercise on content knowledge was determined by comparing pre- and post-PBL solutions to structural analysis textbook problems, as well as the more open-ended structural analysis of the bike frame designs. Post-PBL, students individually completed a survey assessing their level of engagement with the analytical and design aspects of the PBL exercise and perceived value of the project. The Wooden Bike Frame Challenge demonstrates the value of embedding full-scale design experiences into core courses like Statics, not only for strengthening newly acquired knowledge like structural analysis, but also for reinforcing industry-standard design and fabrication skills from prior coursework. 

   more » « less
5. Self-Efficacy Versus Gender: Project-Based Active Learning Techniques in Biomedical Engineering Introductory Computer Programming Courses

   https://doi.org/10.1115/1.4047924  

   Cyrus Rezvanifar, S.; Amini, Rouzbeh (November 2020, Journal of Biomechanical Engineering)

   null (Ed.)

   Abstract Engineering education has increasingly embraced active learning techniques within a variety of curricula. In particular, project-based active learning techniques have a significant potential to enhance students' learning experience. In this study, we implemented project-based techniques in biomedical engineering (BME) classes, and we investigated the effects of active learning on students' self-efficacy as an effective predictor of students' academic persistence and their career decision-making. Differences in self-efficacy were compared across genders. A high level of internal consistency was observed for both academic and career-oriented scales, as determined by Cronbach's alpha values of 0.908 and 0.862, respectively. While average scores of all survey questions indicated improvement in students' academic and career-oriented self-efficacy measures, significant improvements were observed in “clearer vision of programming application in engineering” and “BME careers,” as well as in “expectation of success in a future BME career that involves developing medical devices” after the completion of the project-based activity (p = 0.002, 0.023, and 0.034, respectively). For two of the survey questions, female students reflected a significantly lower “self-confidence about understanding the most complex course material” as well as a significantly lower “willingness to have a future career in BME that involves intensive computer programing” as compared to male students (p = 0.035 and 0.024, respectively). We have further discussed possible explanations for the observed differences and multiple potential ways to enhance gender equality in STEM fields from a self-efficacy standpoint. 

   more » « less