Publications

2026

1. Evaluating Assessment Practices in Team-Based Computing Capstone Projects

   Sara Hooshangi, Asma Shakil, Steve Riddle, Ilknur Aydin, Nayla Nasir, Tejasvi Parupudi, Attiqa Rehman, Michael James Scott, Jan Vahrenhold, Amali Weerasinghe, and Xi Wu

   In Proceedings of the 2025 Working Group Reports on Innovation and Technology in Computer Science Education, Netherlands, 2026

   Abs DOI

   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.

2025

1. Gender Diversity in Computer Science Through a Global Lens

   Nawar Wali and Sara Hooshangi

   In 2025 IEEE Frontiers in Education Conference (FIE), Nov 2025

   Abs DOI

   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.

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

   Jennifer Alexandra Thompson and Sara Hooshangi

   In 2025 IEEE Frontiers in Education Conference (FIE), Nov 2025

   DOI
3. Transfer Students in Computer Science: Examining Barriers, Success Metrics, and Research Gaps

   Nawar Wali and Sara Hooshangi

   In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1, Nijmegen, Netherlands, Jul 2025

   Abs DOI

   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.

4. Assessing Team-Based Capstone Projects: Challenges and Recommendations

   Sara Hooshangi, Asma Shakil, Steve Riddle, Ilknur Aydin, Nayla Nasir, Tejasvi Parupudi, Attiqa Rehman, Michael James Scott, Jan Vahrenhold, Amali Weerasinghe, and Xi Wu

   In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2, Nijmegen, Netherlands, Jul 2025

   Abs DOI

   Team-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.

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

   Sara Hooshangi, Khushi Parajuli, and Brandi A. Weiss

   In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1, Nijmegen, Netherlands, Jul 2025

   Abs DOI

   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.

6. Instructors’ Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study

   Sara Hooshangi, Asma Shakil, Subhasish Dasgupta, Karen C. C. Davis, Mohammed Farghally, KellyAnn Fitzpatrick, Mirela Gutica, Ryan Hardt, Steve Riddle, and Mohammed Seyam

   In 2024 Working Group Reports on Innovation and Technology in Computer Science Education, Milan, Italy, Jul 2025

   Abs DOI

   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.

7. Machine Learning Insights into Academic Success in CS3: The Role of Mathematics and CS Coursework

   Nawar Wali and Sara Hooshangi

   In 2025 ACM Capital Region Celebration of Women in Computing (CAPWIC), Washington, DC, Mar 2025

   Abs

   This research paper aims to understand how mathematics and computer science education influence the academic trajectory and performance of students in Computer Science degree programs, with a particular focus on CS3. By analyzing data from 3905 students over a decade at an R1 institution, this study examines course terms, grades, course completion, and program participation for all Computer Science and Math courses. Through extensive statistical and machine learning analysis on this comprehensive dataset, we investigate the correlation between students’ performance in mathematics and previous CS courses and their grades in the upper-level CS course, CS3. This study provides a nuanced understanding of the factors contributing to student success in advanced Computer Science coursework.

2024

1. AI in and for K-12 Informatics Education. Life after Generative AI.

   Erik Barendsen, Violetta Lonati, Keith Quille, Rukiye Altin, Monica Divitini, Sara Hooshangi, Oscar Karnalim, Natalie Kiesler, Madison Melton, Calkin Suero Montero, and Anna Morpurgo

   In Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 2, Virtual Event, NC, USA, Mar 2024

   Abs DOI

   The use and adoption of Generative AI (GenAI) has revolutionised various sectors, including computing education. However, this narrow focus comes at a cost to the wider AI in and for educational research. This working group aims to explore current trends and explore multiple sources of information to identify areas of AI research in K-12 informatics education that are being underserved but needed in the post-GenAI AI era. Our research focuses on three areas: curriculum, teacher-professional learning and policy. The denouement of this aims to identify trends and shortfalls for AI in and for K-12 informatics education. We will systematically review the current literature to identify themes and emerging trends in AI education at K-12. This will be done under two facets, curricula and teacher-professional learning. In addition, we will conduct interviews and surveys with educators and AI experts. Next, we will examine the current policy (such as the European AI Act, and European Commission guidelines on the use of AI and data in education and training as well as international counterparts). Policies are often developed by both educators and experts in the domain, thus providing a source of topics or areas that may be added to our findings. Finally, by synthesising insights from educators, AI experts, and policymakers, as well as the literature and policy, our working group seeks to highlight possible future trends and shortfalls.

2. Tracking Undergraduate Students’ Perception of Early Exposure to Practical Computing Skills Over Time

   Tyler Buxton, Margaret Ellis, and Sara Hooshangi

   In 2024 IEEE Frontiers in Education Conference (FIE), Oct 2024

   Abs DOI

   This innovative practice full paper describes the impact of early exposure to practical computing skills on students’ learning experiences. In a longitudinal study, we evaluated the effects of incorporating co-curricular activities and hands-on skills into the undergraduate Computer Science (CS) curriculum, while also tracking students’ sustained interest and utilization of these skills over time. Over a three-year period, more than 900 students majoring in CS were surveyed at the end of a required introductory course (post-course survey) that introduced various computing practical skills. These skills, encompassing topics such as version control, SQL, command line tools, and web development, were strategically integrated into the course to enhance students’ engagement and equip them for subsequent co-curricular computing endeavors. Students’ interests in these topics were measured at the completion of the course and again re-evaluated during their senior year three years later (senior-year survey). In the senior year, over 500 students responded to the survey. Similar questions related to interest, motivation, and the use of skills were asked in this follow-up survey. The results of the post-course and senior-year surveys were compared at the aggregated level. We were also able to map the responses of 68 students individually and compare their post-course and senior-year survey responses. The results of the individual students were consistent with the overall aggregated data between post-course and senior-year data. Our data analysis offers information on the timing and occasion of students applying practical skills, their sentiments regarding co-curricular activities, and the favorable influence of practical skills on the overall student experience. As students mature and accumulate experiences in their academic journey, their perceptions about these early exposures and practical skills also evolve. By the time they reach their senior year, basic practical skills do not appear as crucial, given their acquired proficiency. Nevertheless, this observation is significant since educators may not always be attuned to the challenges faced by novice students, emphasizing the importance of early exposure. Additionally, our longitudinal results affirms that both the utility and interest levels persist over the span of their undergraduate degree, reinforcing sustained motivation.

3. Experiences of Instructors Who Teach Capstone Courses in Computing Fields

   Sara Hooshangi, Asma Shakil, Subhasish Dasgupta, Karen C. Davis, Mohammed Farghally, KellyAnn Fitzpatrick, Mirela Gutica, Ryan Hardt, Ellie Lovellette, Steve Riddle, and Mohammed Seyam

   In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 2, Milan, Italy, Oct 2024

   Abs DOI

   Capstone courses are an integral part of undergraduate and postgraduate degrees in the computing fields. 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 primarily focused on the experiences of the students. The perspectives of instructors who teach these capstone courses has not been explored much. However, an instructor’s motivation and expectancy can have a significant effect on a capstone course quality. In this working group, we plan to use 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 will be examined through a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across multiple continents. This global perspective will be used to develop a guiding framework on the different pedagogical approaches that can be used to enhance engagement and motivation for both staff and students in computing courses.

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

   Amanda Ross and Sara Hooshangi

   In 2024 ASEE Annual Conference & Exposition, Jun 2024

   https://peer.asee.org/46404

   Abs DOI

   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.

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

   Sverrir Thorgeirsson, Theo B. Weidmann, and Sara Hooshangi

   In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2, Portland, OR, USA, Jun 2024

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   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.

2023

1. Modeling Women’s Elective Choices in Computing

   Steven Bradley, Miranda C. Parker, Rukiye Altin, Lecia Barker, Sara Hooshangi, Thom Kunkeler, Ruth G. Lennon, Fiona McNeill, Julià Minguillón, Jack Parkinson, Svetlana Peltsverger, and Naaz Sibia

   In Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education, Turku, Finland, Jun 2023

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   Evidence-based strategies suggest ways to reduce the gender gap in computing. For example, elective classes are valuable in enabling students to choose in which directions to expand their computing knowledge in areas aligned with their interests. The availability of electives of interest may also make computing programs of study more meaningful to women. However, research on which elective computing topics are more appealing to women is often class or institution specific. In this study, we investigate differences in enrollment within undergraduate-level elective classes in computing to study differences between women and men. The study combined data from nine institutions from both Western Europe and North America and included 272 different classes with 49,710 student enrollments. These classes were encoded using ACM curriculum guidelines and combined with the enrollment data to build a hierarchical statistical model of factors affecting student choice. Our model shows which elective topics are less popular with all students (including fundamentals of programming languages and parallel and distributed computing), and which elective topics are more popular with women students (including mathematical and statistical foundations, human computer interaction and society, ethics, and professionalism). Understanding which classes appeal to different students can help departments gain insight of student choices and develop programs accordingly. Additionally, these choices can also help departments explore whether some students are less likely to choose certain classes than others, indicating potential barriers to participation in computing.

2. A workshop for and by disciplinary STEM faculty: Learn how to design high-quality qualitative educational research!

   John R. Morelock, Michelle Jarvie-Eggart, Heather Chenette, Sara Hooshangi, Betsy Chesnutt, Sarah Wilson, Azadeh Bolhari, Kirsten Dodson, Iglika Pavlova, and Rebecca Reck

   Proceedings Frontiers in Education Conference, Jun 2023

   Abs

   The purpose of this workshop is to introduce instructional and disciplinary tenure-track STEM faculty to high-quality qualitative research design in order to explore their intellectual curiosity around STEM education. We will do so using the ProQual approach, a methodologically unencumbered and widely accessible way of thinking about qualitative research design that was deployed and refined over the last three years as part of the NSF-funded ProQual Institute for Research Methods. This workshop will be conducted by STEM faculty who have graduated from the ProQual Institute, who are culturally sensitive to the challenges faced by disciplinary STEM faculty. Leveraging a propagation model of effecting academic change, these workshop leaders will also serve as a community of practice to help workshop participants move their educational research ideas forward during and after the workshop.

3. Developers talking about code quality

   Jürgen Börstler, Kwabena E Bennin, Sara Hooshangi, Johan Jeuring, Hieke Keuning, Carsten Kleiner, Bonnie MacKellar, Rodrigo Duran, Harald Störrle, Daniel Toll, and others

   Empirical Software Engineering, Jun 2023

   Abs DOI

   There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.

4. The Impact of High School Region Socioeconomic Status on Computer Science Student Performance

   Jennifer Alexandra Thompson, Margaret Ellis, and Sara Hooshangi

   In 2023 IEEE Frontiers in Education Conference (FIE), Oct 2023

   Abs DOI

   Research in computing education has been steered towards understanding early indicators of what leads students to succeed in introductory programming courses (CS1). A major finding of these research efforts has been the impact that high school courses and prior programming experience have in predicting success in a post-secondary CS1. However, the socioeconomic status surrounding CS1 students has not been well explored as an indicator of performance. Specifically, a student’s high school socioeconomic status (SES) has not been well investigated in this area, despite the intuition that more socioeconomically advantaged high schools will better prepare students for college computing courses. In this research, we propose a method to examine a student’s prior high school regional socioeconomic status and determine whether this SES has a correlation to their post-secondary CS1 performance. This paper investigates the socioeconomic status of the neighborhood, census tract, and county the high school resides. To understand the socioeconomic statuses of these regions, we utilize multiple socioeconomic indices such as the Area Deprivation Index and the Social Deprivation Index. Some of the factors that create a deprivation index are the housing values of the region, poverty rate, adult educational completion, and household resources. After proposing a method to examine if there are any correlations between a student’s attended high school regional SES and the student’s performance in CS1, we perform a case study using seven years of CS1 student records from our institution. From the 4863 student records we use in this study, our initial findings indicate that students from more advantaged high school regions tend to pass CS1 more frequently across all surrounding region sizes we examined. Since our findings indicate that high school regional socioeconomic status may be a factor in a student’s performance, we argue that future computing education researchers should consider a student’s SES as a demographic factor of course performance in order to advocate for interventions that mitigate this disparity gap.

5. Learn How to Design High-Quality Qualitative Educational Research! – A Workshop for Disciplinary STEM Faculty by Disciplinary STEM Faculty

   John R. Morelock, Michelle Jarvie-Eggart, Heather Chenette, Sara Hooshangi, Betsy Chestnutt, Sarah Wilson, Azadeh Bolhari, Kirsten Dodson, Iglika Pavlova, and Rebecca Reck

   In 2023 IEEE Frontiers in Education Conference (FIE), Oct 2023

   Abs DOI

   The purpose of this workshop-designed for instructional and disciplinary STEM faculty interested in learning about qualitative research-is to (1) introduce participants to high-quality qualitative research design and (2) practice this design process alongside disciplinary STEM faculty to expand their STEM education research abilities and network. We will do so using the ProQual approach, a methodologically unencumbered and widely accessible way of thinking about qualitative research design that was deployed and refined over the last three years as part of the NSF-funded ProQual Institute for Research Methods [1]. This workshop will be conducted by ProQual Institute alumni, who are culturally sensitive to the challenges faced by disciplinary STEM faculty. Leveraging a propagation model of effecting academic change [2], the workshop leaders will serve as a community of practice to help participants move their educational research ideas forward during and after the workshop. In doing so, we strive to further FIE’s mission to create a collaborative, supportive, and inclusive community of educational researchers.

6. A Methodology for Investigating Women’s Module Choices in Computer Science

   Steven Bradley, Miranda C. Parker, Rukiye Altin, Lecia Barker, Sara Hooshangi, Samia Kamal, Thom Kunkeler, Ruth G. Lennon, Fiona McNeill, Julià Minguillón, Jack Parkinson, Svetlana Peltsverger, and Naaz Sibia

   In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2, Turku, Finland, Jul 2023

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   At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center for Women & Information Technology (NCWIT) Engagement Practices framework. One of the report’s recommendations was "Make connections from computing to your students’ lives and interests (Make it Matter) but don’t assume you know what those interests are; find out! " The goal of this 2023 working group is to find out what interests women students by bringing together data from our institutions on undergraduate module enrollment, seeing how they differ for women and men, and what drives those choices. We will code published module content based on ACM curriculum guidelines and combine these data to build a hierarchical statistical model of factors affecting student choice. This model should be able to tell us how interesting or valuable different topics are to women, and to what extent topic affects choice of module - as opposed to other factors such as the instructor, the timetable, or the mode of assessment. Equipped with this knowledge we can advise departments how to focus curriculum development on areas that are of value to women, and hence work towards making the discipline more inclusive.

7. Replication and Expansion Study on Factors Influencing Student Performance in CS2

   Margaret Ellis and Sara Hooshangi

   In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, Toronto ON, Canada, Mar 2023

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   While many studies have focused on students’ performance in CS1 courses, research related to the performance and persistence of students in CS2 classes is not as widely performed. In this work, we will extend our previous work to examine students’ performance in CS2. We examined a data set that spanned over seven years on more than 5300 student records. In addition to typical factors studied by others (i.e. gender, race, CS1 performance), our work also took into account the relationship between various CS1 pathways to CS2, student major, and the number of previous college CS courses (including transfer credits) and student performance in CS2. CS1 grade is a good indicator of performance in CS2. Gender was not a significant factor in determining performance in CS2 and undeclared engineering majors stood out as high performers. CS majors passed the course at higher rates than other majors. Our large data set allowed for more granular analysis according to race and ethnicity and additional access to students’ underserved status. Race and ethnicity had a significant correlation with performance, and so did the underserved status. Our large data set confirmed some of the findings of our previous work, while providing some new insight.

8. High School Socioeconomic Neighborhood Status and CS1 Performance

   Jennifer Alexandra Thompson, Margaret Ellis, and Sara Hooshangi

   In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2, Toronto ON, Canada, Mar 2023

   Abs DOI

   CS1 student success rates are a longstanding issue in the computer science community. Indicators of performance prior to CS1 continue to be investigated in research, especially concerning prior programming and math courses taken at the high school level. This study aims to take a look at students’ high school socioeconomic neighborhood status and determines whether there is a correlation to CS1 performance. Specifically, we examine the Area Deprivation Index (ADI) of the high schools that CS1 students attended and the passing rates in CS1 based on the socioeconomic status of these high schools. The goal is to compare the performance of students from socioeconomic disadvantaged high schools to students from advantaged high schools. In this research, we find that students from the top 15% high schools ADI percentile pass CS1 at a higher rate with a significant difference.

2022

1. Integration of Practical Computing Skills and Co-curricular Activities in the Curriculum

   Sara Hooshangi, Ryan Buxton, and Margaret Ellis

   In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1, Dublin, Ireland, Mar 2022

   Abs DOI

   Participation in co-curricular activities, such as hackathons, coding clubs, and undergraduate research has been shown to have a positive impact on the retention, persistence, and sense of belonging of students in the Computer Science (CS) field. In this paper, we will present the result of a study to assess the impact of integrating co-curricular activities and practical skills into the undergraduate CS curriculum. More than 500 senior CS students were surveyed over a span of four semesters about their comfort level, use of practical skills, and their experience in a sophomore-level required course which was redesigned a few years ago. The new course introduced practical skills such as version control, SQL, command line tools, and web development as a way to better engage the students and prepare them for co-curricular computing experiences. Our data analysis provides insight about when and where students use practical skills, how students feel about co-curricular activities, and the positive impact of the course redesign on the overall student experience.

2. Factors Influencing Student Performance and Persistence in CS2

   Sara Hooshangi, Margaret Ellis, and Stephen H. Edwards

   In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1, Providence, RI, USA, Mar 2022

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   Performance in CS1 and introductory CS courses has been an area of active research in the CS education research community for more than four decades, but studies related to student performance in CS2 are not as widely available. Past studies have examined the impact of CS1 grade, prior math preparation, and other factors such as homework, test, and project grades, on the overall performance in CS2. In this work, we will build upon the existing research related to CS2 performance with an emphasis on a few factors that have not been previously considered for this course. In addition to typical factors studied by others (i.e. gender, race, CS1 performance), our work also takes into account the impact of various CS1 pathways to CS2 and the number of previous college CS courses (including transfer credits) on student performance in CS2. We also look into both persistence, by distinguishing students who stay in the course versus those who drop from the class before the mid-semester drop deadline, and performance. Gender and race were not significant factors in determining performance in CS2 but undeclared engineering majors stood out as high performers and students’ CS pathway leading to CS2 was also significant. Notably, students with CS1 transfer credit had significantly lower pass rates. Students with only 1 previous CS course credit were less likely to drop or not pass the course.

3. Human-Machine Hybrid Decision Making with Applications in Auditing

   Sara Hooshangi and Soheil Sibdari

   Mar 2022

   Abs

   The decision making process in a variety of organizations faces substantial changes, largely as a result of advances in information technology and artificial intelligence (AI). A considerable number of decisions that were traditionally made by humans, are now made by machines. Consequently, many jobs that were held by experts in some fields are now occupied by data scientists who can build AI algorithms. In this paper, we address this change in work environments and suggest an innovative process for hybrid decision making between humans and machines. We focus on the auditing profession, but our method can be used in other human-intensive and critical fields such as healthcare, financial services public sectors, and humanitarian organizations.

2020

1. Cloud Computing Curriculum: Developing Exemplar Modules for General Course Inclusion

   Joshua Adams, Brian Hainey, Laurie White, Derek Foster, Narine Hall, Mark Hills, Sara Hooshangi, Karthik Kuber, Sajid Nazir, Majd Sakr, Lee Stott, and Carmen Taglienti

   In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education, Trondheim, Norway, Mar 2020

   Abs DOI

   The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that deliver practical and hands-on material relevant to this rapidly evolving field. This is especially relevant as cloud services are quickly becoming the standard computing model for many new computational approaches, including advanced subjects such as machine learning and data science. Since cloud computing influences a number of other traditional computing subjects such as networking and data storage, the ability to incorporate specific components of cloud computing teaching content into a variety of courses has also become important. However, a lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts, continues to be a challenge for instructors. As the volume of teaching material that educators and other providers are willing to share increases, it is important that the content is easily discoverable and accurately catalogued. Using appropriate Learning Objectives (LOs) and tags associated with the correct course material contributes toward a reliable and trustworthy source for faculty. This Working Group (WG) has addressed these challenges by 1) creating a canonical list of LOs, based on the LOs included in the Knowledge Areas (KAs) developed by the ITiCSE working groups in 2018 and 2019; 2) searching for existing teaching material, cataloging the material by LO and then combining the LOs into exemplar modules for some of the more common classes that adopt cloud computing; 3) creating sample syllabi for these exemplars following the ACM Computer Science Curricula 2013 approach; and 4) developing a solution for disseminating the materials and keeping them up to date.

2018

1. "I know it when I see it" Perceptions of Code Quality: ITiCSE ’17 Working Group Report

   Jürgen Börstler, Harald Störrle, Daniel Toll, Jelle Assema, Rodrigo Duran, Sara Hooshangi, Johan Jeuring, Hieke Keuning, Carsten Kleiner, and Bonnie MacKellar

   In Proceedings of the 2017 ITiCSE Conference on Working Group Reports, Bologna, Italy, Mar 2018

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   Context. Code quality is a key issue in software development. The ability to develop high quality software is therefore a key learning goal of computing programs. However, there are no universally accepted measures to assess the quality of code and current standards are considered weak. Furthermore, there are many facets to code quality. Defining and explaining the concept of code quality is therefore a challenge faced by many educators.Objectives. In this working group, we investigated code quality as perceived by students, educators, and professional developers, in particular, the differences in their views of code quality and which quality aspects they consider as more or less important. Furthermore, we investigated their sources for information about code quality and its assessment.Methods. We interviewed 34 students, educators and professional developers regarding their perceptions of code quality. For the interviews they brought along code from their own experience to discuss and exemplify code quality.Results. There was no common definition of code quality among or within these groups. Quality was mostly described in terms of indicators that could measure an aspect of code quality. Among these indicators, readability was named most frequently by all groups. The groups showed significant differences in the sources they use for learning about code quality with education ranked lowest in all groups.Conclusions. Code quality should be discussed more thoroughly in educational programs.