Caroline Budwell, Ph.D. profile photo

Caroline Budwell, Ph.D.

Associate Professor


Dr. Caroline Budwell is a computer scientist focused on learning and engagement strategies.

Areas of Expertise

Software EngineeringComputer Science EducationLearning and Engagement StrategiesProblem Based LearningCollaborative Learning


Nova Southeastern University

Ph. D., Computer Science


Virginia Commonwealth University

M. S., Computer Science


University of Virginia

M. A., Teaching


University of Virginia

B. A., Mathematics


Media Appearances

Amazon Deal Puts Pressure On State To Boost Tech Faculty, Student Investments

WCVE  online


Duke: We have one position open, but there were two people that we really loved. It was a tough decision and our department chair went to the Dean and said, you know, we have these two people and we really wish we could have them both. And we get the second position approved, and that's why we have Dr. Budwell. Professor Caroline Budwell says it wasn’t an easy decision to leave her private-sector job as a business analyst. Budwell: I left a very lucrative job in the industry, but it was more for the flexibility and the challenge of teaching.

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Selected Articles

The SLAI Methodology: An Aspect-Oriented Requirement Identification Process | IEEE 2008 International Conference on Computer Science and Software Engineering


Caroline Budwell, Frank Mitropoulos

Aspect-oriented software development (AOSD) has great potential in reducing software complexity. Aspects have been defined in the implementation phase of software development, but lack clear understanding in the early phases of software development. Without this early focus on aspects, the benefits of aspect-oriented programming are lost. This paper proposes a definition of what an aspect is in the requirements phase of software development that focuses on both functional and non-functional requirements. In addition, this paper presents a methodology, the SLAI (Structured Lexicon for Aspectual Identification) Methodology, for the systematic identification of aspects at this stage. This methodology examines all the vocabulary used to define the requirements of the system to ensure that all terms are reused as much as possible, eliminating similar terms for the same concepts. The SLAI was used in a case study where requirements were systematically analyzed and aspects were identified from both functional and non-functional requirements.

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Ability Grouping on Lower Level Math Students’ Self-Concept on Achievement | ERIC (Educational Resources Information Center)


Caroline Collins

The differences in effects of heterogeneous and homogeneous regrouping for math on academic ability and self-concept in math were investigated. Five sixth-grade students from both grouping placements were interviewed to determine their self-concept of their math abilities. All students labeled as average or below from both placements were given an assessment of basic grade-level math skills. The classes containing these students were observed, and each placement had the same teacher providing all math instruction. The results indicated that the homogeneous students liked their math classes better and were more likely to compare themselves above their classmates in ability than the heterogeneous students. However, there were almost no differences between overall self-concept in math between the placements, and the scores on the assessment substantially favored the heterogeneous placement. It was concluded that heterogeneous regrouping in math did not have any substantial negative consequences. Nine appendixes provide: (1) interview format and questions; (2) interview transcripts; (3) the assessment measure; (4) categorization of interviewed students; (5) students' perceptions of their math ability; (6) math class placement levels; (7) overall test results; (8) test scores eliminating fraction questions; and (9) percentage of differences between the two groupings.

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