Abstract

The capstone project is a bridge from the university to industry. During the project, students not only learn the process of integrating previously learned engineering concepts into an actual product but also practice essential professional skills such as communication, teamwork, and project delivery. How can universities ensure effective capstone student learning and good outcomes? To answer this question, the capstone project is studied as a system comprising of products, people, and processes. People refer to the students and instructors involved. Students develop products by following development processes. Instructors guide students using instruction processes. In educational context there limited control over the students enrolling for the course, instructors available to guide the students, and products that client-sponsors want students to develop. Consequently, development and instructional processes emerge as the key levers through which the capstone system can be improved. Currently, at RIT like many other schools, a single standard development process is used to develop all types of products and a single instructional process is used to teach all students. Early experiments provided evidence of correlations between certain student characteristics and project outcomes, disparities in student experiences, and the potential effectiveness of process interventions in improving outcomes. The quality of outcomes improves when a capstone system can anticipate and address challenges. These findings motivated the central research question: Can poor student learning outcomes be predicted based on student and product characteristics? If so, what specific factors are associated with increased risk? Using machine learning, this research successfully predicts poor student learning outcomes in a capstone course based on product and student characteristics. This study adopts a supervised learning approach using Decision Trees and Random Forest to predict student learning outcomes, which are measured using the standardized rubrics. Input features include product and student characteristics available at the start of each project. Product characteristics include factors such as the type of product being developed and the type of client organization. People characteristics encompass data sets like students’ academic performance, demographic information, personality types, and instructor background. Out of eleven student learning outcomes, six were predicted with a recall of at least 0.75 and an F1-score of at least 0.60. Successfully predicted outcomes were applying engineering design to solve problems, experimentation, written communication, oral communication, teamwork, and independent learning. Risk factors systematically leading to bad outcomes include type of product, type of client organization, student personality, and instructor characteristics. These findings confirm that a single standard capstone process does not work equally well for everyone. In light of these findings, attention turned to identifying process customizations that could prevent poor outcomes. Using qualitative methods, the experiences of expert project guides were elicited. The findings revealed that the standard capstone process is already being implicitly customized. Additionally, certain products possess characteristics that demand greater design effort to resolve ambiguity and uncertainty. During the interviews, project guides emphasized the importance of explicitly confronting problems. Each guide helped student teams overcome emergent technical and non-technical contextual challenges in a distinctive way. This doctoral research advances knowledge at the intersection of integrated product development, capstone education, and applied machine learning. It provides new insights into how process customization can serve as a mechanism to improve student learning outcomes, and introduces methodological innovations that leverage existing student datasets to develop an early warning system for identifying poor outcomes. Through a mixed-method investigation, the study explores how customizing processes can help improve capstone project outcomes. This work lays a strong foundation for future studies and offers potential applications in both research and pedagogy. Ultimately, this work emphasizes the importance of evidence-based approaches in improving systems that involve both people and products, and underscores the importance of interdisciplinary perspectives in addressing the challenges of tomorrow.

Library of Congress Subject Headings

Engineering--Research; Engineering--Study and teaching (Higher); Education, Higher--Evaluation; Education, Higher--Aims and objectives; Personality assessment

Publication Date

8-2025

Document Type

Dissertation

Student Type

Graduate

Degree Name

Mechanical and Industrial Engineering (Ph.D)

Department, Program, or Center

Mechanical Engineering

College

Kate Gleason College of Engineering

Advisor

Elizabeth A. DeBartolo

Advisor/Committee Member

Katie McConky

Advisor/Committee Member

Kathleen Lamkin-Kennard

Campus

RIT – Main Campus

Plan Codes

MIE-PHD

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