About

Sara Hooshangi is a Collegiate Professor and the Director of the Master of Engineering in Computer Science program at Virginia Tech. She is located at the Institute for Advanced Computing in Alexandria, Virginia. Her research interests include computer science education. She holds a Ph.D. in electrical engineering from Princeton University and a B.S. in electrical engineering from McGill University. Her professional contact information includes email shoosh@vt.edu and multiple office locations, including the Blacksburg campus and the Virginia Tech Research Center in Arlington, Virginia.

Research topics

• Computer Science
• Artificial Intelligence
• Multimedia
• World Wide Web
• Psychology
• Internet privacy
• Quantum mechanics
• Demographic economics
• Engineering
• Astrophysics
• Mathematics education
• Physics
• Theoretical physics
• Statistics
• Statistical physics
• Operations management
• Data science

Selected publications

• Evaluating Assessment Practices in Team-Based Computing Capstone Projects

  Falmouth University Research Repository (FURR) (Falmouth University) · 2026-01-03

  article1st authorCorresponding

  Team-based capstone projects are vital in preparing computer science students for real-world work by developing teamwork, communication, and industry-relevant technical skills. Their assessment, however, is challenging, requiring alignment between academic criteria and external stakeholder expectations, fair evaluation of individual contributions, recognition of diverse skills, and clarity on external partners’ involvement in the evaluation process. The high stakes of these projects further demand transparent and equitable assessment methods that are perceived as fair by all involved. Our working group (WG) addresses the challenges of capstone project assessment by examining the perspectives of instructors, students, and external stakeholders to support fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we used a mixed-methods approach combining online surveys (quantitative) and in-depth interviews (qualitative) with instructors, students, and external stakeholders. In total, we collected 66 survey responses and conducted 30 interviews across multiple countries and institutions, capturing a diverse range of global perspectives on capstone course assessments. Insights from instructors and students revealed several commonalities, for example, in the types of assessed components and the challenges of identifying and addressing non-contributing group members. Our findings also revealed clear variation between instructor and student perspectives on how contributions are measured and weighted. Instructors were reluctant to rely heavily on peer or self-evaluation due to concerns about reliability, preferring scaffolded assessments and early-warning systems to gather contribution data and moderate team dynamics. They viewed contribution-based grading as positive but resource-intensive. Students, in contrast, emphasized the need for more transparency, formative feedback, and accurate recognition of individual contributions. They also expressed concerns about the lack of recognition for hidden labor (e.g., project management, team coordination), assessor inconsistency, and a reluctance to critique peers. Instructors treated peer input as supplementary evidence, whereas students perceived it as high-stakes and socially risky. Stakeholder involvement in assessment was generally limited to providing formative feedback and participating in final showcase events. We also identified generative AI as a rapidly evolving challenge, with both students and instructors seeking guidance on acceptable use and exploring opportunities to automate aspects of assessment. Our results offer actionable evidence-based guidance for designing transparent and equitable assessment practices in team-based computing capstones.

  Publisher

• Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study

  2025-01-22 · 8 citations

  articleOpen access1st authorCorresponding

  Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional development. The majority of the instructors reported positive experiences working with external partners and did not report any issues with Non-Disclosure Agreements (NDAs) or disputes about Intellectual Property (IP). In most institutions, students own the IP of their work, and clients understand that. We use the global perspective that this work has given us to provide guidelines for institutions to better support capstone instructors.

  PublisherDOI

• Transfer Students in Computer Science: Examining Barriers, Success Metrics, and Research Gaps

  2025-06-13 · 1 citations

  articleOpen accessSenior author

  Transfer students play an important role in enhancing diversity within computer science programs. As first-generation college students, minorities, rural residents, and individuals from low-income backgrounds, transfer students represent a demographic critical to fostering innovation and inclusion in the field. However, while significant research has been conducted on transfer students in STEM disciplines, studies specifically addressing their experiences in computer science remain limited. This paper presents a preliminary systematic review of the literature to explore the challenges, successes, and gaps in the support of transfer students in computer science.

  PublisherDOI

• Confidence, Interest, and Gender Perception in non-Computer Science Majors: an Instrument Re-validation Study

  2025-06-13

  articleOpen access1st authorCorresponding

  To broaden participation in the computer science (CS) field and its workforce, it is important to consider how students from non-CS majors enter the field at various points along the educational pipeline. Gaining insight into these students' attitudes and interests toward CS requires a validated, reliable instrument that can capture the factors influencing their perceptions. While several tools have been developed to measure motivation, attitudes, knowledge, and self-efficacy in CS, few are specifically designed to focus on non-CS majors who may hold peripheral or emerging interests in the discipline. In this study, exploratory factor analysis was used to re-validate the Engineering Students' Attitudes towards CS survey initially created by Hoegh and Moskal using a population of non-CS majors. Results indicated that a 1-factor solution best fits the data for the Interest, Confidence, and Gender Equality Perceptions (GEP) constructs. Unique to this study, is support for a shortened 5-item GEP subscale. Results showed that the 5-item GEP performed as well as (and at times better than) the 10-item GEP. Based on these results, we recommend researchers wishing to examine Gender Equality Perceptions use a shortened version of the subscale utilizing only the 5 positively worded items. As a secondary interest of the work, results indicated women were nearly a full standard deviation higher on GEP subscales (Cohen's d = .961 and .837). This is considered a large effect size in social science research and indicates women had higher ratings of gender equality in CS than men did.

  PublisherDOI

• Gender Diversity in Computer Science Through a Global Lens

  2025-11-02

  articleSenior author

  This research full paper presents a global exploration of diversity in Computer Science (CS) with a focus on gender parity. Through a systematic and methodologically rigorous analysis of 260 research papers from various countries, the study identifies context-specific strategies, challenges, and effective solutions. The comprehensive analysis provides insights for academia, industry, and policymakers, emphasizing the critical link between gender diversity and innovation in CS. The findings highlight global trends, regional variations, research gaps, and culturally tailored solutions that can enhance equity in CS education and professional environments.

  PublisherDOI

• Evaluating Assessment Practices in Team-Based Computing Capstone Projects

  2025-06-27

  articleOpen access1st authorCorresponding

  Team-based capstone projects are vital in preparing computer science students for real-world work by developing teamwork, communication, and industry-relevant technical skills. Their assessment, however, is challenging, requiring alignment between academic criteria and external stakeholder expectations, fair evaluation of individual contributions, recognition of diverse skills, and clarity on external partners' involvement in the evaluation process. The high stakes of these projects further demand transparent and equitable assessment methods that are perceived as fair by all involved. Our working group (WG) addresses the challenges of capstone project assessment by examining the perspectives of instructors, students, and external stakeholders to support fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we used a mixed-methods approach combining online surveys (quantitative) and in-depth interviews (qualitative) with instructors, students, and external stakeholders. In total, we collected 66 survey responses and conducted 30 interviews across multiple countries and institutions, capturing a diverse range of global perspectives on capstone course assessments. Insights from instructors and students revealed several commonalities, for example, in the types of assessed components and the challenges of identifying and addressing non-contributing group members. Our findings also revealed clear variation between instructor and student perspectives on how contributions are measured and weighted. Instructors were reluctant to rely heavily on peer or self-evaluation due to concerns about reliability, preferring scaffolded assessments and early-warning systems to gather contribution data and moderate team dynamics. They viewed contribution-based grading as positive but resource-intensive. Students, in contrast, emphasized the need for more transparency, formative feedback, and accurate recognition of individual contributions. They also expressed concerns about the lack of recognition for hidden labor (e.g., project management, team coordination), assessor inconsistency, and a reluctance to critique peers. Instructors treated peer input as supplementary evidence, whereas students perceived it as high-stakes and socially risky. Stakeholder involvement in assessment was generally limited to providing formative feedback and participating in final showcase events. We also identified generative AI as a rapidly evolving challenge, with both students and instructors seeking guidance on acceptable use and exploring opportunities to automate aspects of assessment. Our results offer actionable evidence-based guidance for designing transparent and equitable assessment practices in team-based computing capstones.

  PublisherDOI

• Unpacking Intersectional Race-Gender Gaps in Computing Courses Through Institutional Data

  2025-11-02

  articleSenior author

  This full research paper presents race-gender intersectional enrollment rates, course passing rates by intersectional group, and group-specific retention rates across core computer science courses, using a multi-year institutional dataset of 8,868 students who enrolled in at least one of CS1, CS2, or CS3 at a large research-focused U.S. institution. Furthermore, this work explores the various pathways students use by analyzing how frequently each race-gender group utilizes AP CSA or transfer credits to bypass CS1. The goal of this study is to identify the most prominent race-gender intersectional disparity gaps in enrollment, performance, and retention of core computer science courses. In CS1, only 62.7% of Black female students pass the course with a grade sufficient to progress to CS2, compared to the overall passing average of 86.9% for the course. In terms of retention, 83.0% of students eligible to take CS2 continue from CS1, though this rate varies considerably across intersectional groups. For instance, White female and Hispanic female students have retention rates of 76.4% and 78.0%, respectively. The findings underscore the importance of examining intersectional identities and highlight the value of analyzing retention, enrollment, and course performance data to identify disparity gaps. This study emphasizes the need for institutions to evaluate their student populations through an intersectional lens in order to design targeted interventions that promote enrollment, academic success, and retention for all student populations.

  PublisherDOI

• Assessing Team-Based Capstone Projects: Challenges and Recommendations

  2025-06-13

  article1st authorCorresponding

  eam-based capstone projects are vital in preparing computer science students for real-world challenges by fostering teamwork, communication, and industry-relevant technical skills. However, their assessment presents challenges, such as aligning academic criteria with other stakeholders' expectations, evaluating individual contributions within teams, fairly addressing the diverse skills required, and determining the appropriate level of external partners' involvement in the evaluation process. Moreover, the high stakes of these projects necessitate transparent and equitable assessment methods that all stakeholders perceive as fair. Our working group (WG) aims to address the challenges of assessing capstone projects by examining the perspectives of instructors, students, and other stakeholders to ensure fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we will employ a mixed-methods approach to explore the issues faced by various stakeholders in assessing capstone projects and to capture both common challenges experienced (quantitative), and delve into nuanced individual experiences (qualitative). By conducting this research in a multi-national, multi-institutional context, we aim to capture a diverse range of global perspectives while accounting for the variation in capstone courses. Our goal is to provide actionable recommendations that enhance assessment practices, improve learning outcomes, and foster effective team collaboration in team-based capstone courses, ultimately preparing students for real-world challenges.

  PublisherDOI

• “I Always Feel Dumb in Those Classes”: A Narrative Analysis of Women’s Computing Confidence

  2024-08-03 · 1 citations

  articleOpen accessSenior author

  The lack of women in computer science is a decades old problem.Numerous studies have looked at contributing factors that lead to this problem, one of which is lack of self confidence in female students.Having less confidence than their male peers lead women to feel uncomfortable asking questions and speaking out in class, feel isolated in the field, and ultimately steer them away from computer science.The purpose of this study is to understand how women's computing confidence is shaped by their experiences in introductory computer science courses and to understand how their experiences lead to negative attitudes towards computer science.To answer these questions, this study uses a narrative analysis approach.Four female, non-computer science students at a large public university were interviewed, using a semi-structured protocol.Interviews were then qualitatively coded using thematic analysis, and analyzed using the theoretical frameworks of self-efficacy and self-concept.Results show that while participants were highly successful in their course (reporting a high mark in the class) and had relatively high self-efficacy when discussing specific programming problems, they lacked computing self-concept in whether or not they were good at programming in general.Some participants directly said they were not good at coding, while others noted that they knew they could be successful but then used unconfident language such as stating they often asked 'stupid questions' or believed they were only successful due to the help of instructors and TAs.Results also show a common theme in which most participants believed that if they had to work hard in the course, then they were not good at computer science.Understanding how women grapple with self-confidence even while being highly successful in computing courses is needed to better understand how to create environments that are welcoming and inclusive of women.While self-efficacy can be built through mastery experiences, this study suggests that mastery experiences are not enough to build general computing self-concept.Since a lack of computing confidence in women can cause negative attitudes towards the field of computer science, future work should focus on ways in which this confidence can be increased so as to try and minimize the number of women avoiding or leaving the field of computer science.

  PublisherOA PDFDOI

• Algot: A Visual, Hands-On Approach to Introductory Computer Science

  2024-03-14

  articleSenior author

  Algot is a newly developed visual programming language that seeks to bridge the syntax-semantics gap in programming via a novel implementation of programming by demonstration. Preliminary research, which will be presented separately at SIGCSE this year, suggests that Algot may be useful for teaching foundational computer science concepts at both secondary and tertiary levels. In this proposed SIGCSE demo session, attendees will have a chance to interact with Algot and learn about its potential benefits in their own classrooms.

  PublisherDOI

Recent grants

• Pathway for Adult-learners, Community college and non-Traditional Students (PACTS)

  NSF · $611k · 2014–2020

Frequent coauthors

• Ron Weiss

  Massachusetts Institute of Technology

  9 shared

• William E. Bentley

  University of Maryland, College Park

  8 shared

• Jelle van Assema

  University of Amsterdam

  5 shared

• Margaret Ellis

  Virginia Tech

  5 shared

• Ruth Lennon

  Letterkenny Institute of Technology

  4 shared

• Rukiye Altın

  Kiel University

  3 shared

• Jürgen Börstler

  3 shared

• Harald Störrle

  3 shared