Browsing by Author "Hooshangi, Sara"

Now showing 1 - 12 of 12
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    AI in and for K-12 Informatics Education. Life after Generative AI.
    Barendsen, Erik; Lonati, Violetta; Quille, Keith; Altin, Rukiye; Divitini, Monica; Hooshangi, Sara; Karnalim, Oscar; Kiesler, Natalie; Melton, Madison; Suero Montero, Calkin; Morpurgo, Anna (ACM, 2024-12-05)
    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.
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    Factors Influencing Student Performance and Persistence in CS2
    Hooshangi, Sara; Ellis, Margaret; Edwards, Stephen (ACM, 2022-02-22)
    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.
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    High School Socioeconomic Neighborhood Status and CS1 Performance
    Thompson, Jennifer Alexandra; Ellis, Margaret; Hooshangi, Sara (ACM, 2023-03-01)
    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.
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    Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study
    Hooshangi, Sara; Shakil, Asma; Dasgupta, Subhasish; Davis, Karen C.; Farghally, Mohammed; Fitzpatrick, KellyAnn; Gutica, Mirela; Hardt, Ryan; Riddle, Steve; Seyam, Mohammed (ACM, 2025-01-22)
    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 selfreflection 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 semistructured 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.
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    Integration of Practical Computing Skills and Co-curricular Activities in the Curriculum
    Hooshangi, Sara; Buxton, Ryan; Ellis, Margaret (ACM, 2022-07-07)
    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 cocurricular 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.
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    A Methodology for Investigating Women's Module Choices in Computer Science
    Bradley, Steven; Parker, Miranda; Altin, Rukiye; Barker, Lecia; Hooshangi, Sara; Kamal, Samia; Kunkeler, Thom; Lennon, Ruth; McNeill, Fiona; Minguillón, Julià; Parkinson, Jack; Peltsverger, Svetlana; Sibia, Naaz (ACM, 2023-07)
    At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center forWomen & 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.
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    Mixed Method Study of Experiences of Non-Computer Science Majors in Introductory Computer Science Courses
    Parajuli, Khushi (Virginia Tech, 2024-01-04)
    With the unprecedented growth of the Computer Science field, there is an underlying assumption that undergraduate students would naturally gravitate towards Computer Science courses or acquire related skills, irrespective of their career interests. However, this research challenged that assumption, focusing on the experiences and attitudes of Non-Computer Science majors enrolled in Computer Science courses. The objective of this study is to gain a comprehensive understanding of the experiences and attitudes of Non-Computer Science majors taking Computer Science courses. The research questions seek to uncover the factors influencing their engagement in Computer Science. This research employs a mixed-method study, starting with a quantitative phase followed by a qualitative one. Quantitative data is analyzed using factor analysis and inferential statistics, followed by thematic analysis on the qualitative data. The findings reveal that stereotypes associated with the Computer Science field are established as early as high school. These stereotypes, particularly affecting females, sometimes act as barriers, discouraging further pursuit of Computer Science. Addressing these stereotypes becomes crucial for fostering inclusivity in the field. To counteract these stereotypes, it is proposed that Computer Science and its applications should be promoted as early as freshmen year of high school. By introducing students to the field early, we can potentially mitigate the impact of stereotypes and encourage a diverse range of individuals to pursue Computer Science. Further exploration into the experiences of Computer Science majors is recommended to deepen our understanding and inform targeted interventions.
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    Modeling Women's Elective Choices in Computing
    Bradley, Steven; Parker, Miranda; Altin, Rukiye; Barker, Lecia; Hooshangi, Sara; Kunkeler, Thom; Lennon, Ruth; McNeill, Fiona; Minguillón, Julià; Parkinson, Jack; Peltsverger, Svetlana; Sibia, Naaz (ACM, 2023-12-22)
    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.
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    Relationship Between Help-seeking Behaviour of CS Undergraduate Students and Academic Performance
    Cho, Eunoh (Virginia Tech, 2022-06-21)
    Computer Science students need to understand the mechanism of programming systems that involve computation, automation, and information. Computer scientists need to know how to design and analyze a problem and solve it with an algorithm. We study students' behaviors in CS education to find out patterns of those who need help. Several behaviors are examined: Time Management, Incremental development, Self-checking, Persistence, and Planning. Help-seeking, when done correctly, is known as a good strategy related to self-regulated learning. This behavior includes online searching, coming to office hours for help from instructional staff, and asking instructors and peers publicly on online forums. Some of these sources of help can be tracked more easily than others. We present efforts to collect and analyze data related to the help-seeking behavior of students in a second-semester programming course. The goal of this work is to establish mechanisms that will permit us to collect sufficient data from a variety of sources so that we can determine what help-seeking behavior patterns are associated with successful course outcomes. Our current data collection efforts are tied in part to the effects of the COVID-19 pandemic, which caused courses to be taught online during our data collection period that normally would be taught face-to-face. Data includes logs of viewing or posting questions to the online forum system Piazza, office hour visit logs, Zoom logs, and grades from the Canvas LMS. We present initial analysis such as comparing course grades with the number of times students received help from instructional staff both in office hours and online forum Piazza.
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    Replication and Expansion Study on Factors Influencing Student Performance in CS2
    Ellis, Margaret; Hooshangi, Sara (ACM, 2023-03-02)
    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.
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    Self-Regulated Learning Skills Research in Computer Science: The State of the Field
    Domino, Molly Rebecca (Virginia Tech, 2024-08-21)
    Academic success requires not only taking in content, but also understanding how to learn best. Self Regulated Learning (SRL) is process by which humans regulate their thinking, emotions, and behavior. It broadly describes the process of knowing (or learning) how to learn. Education research has found Self-Regulated Learning to be a key predictor of academic success along with other constructs like motivation and self-efficacy. It may be particularly critical in learning to program at the post-secondary level. Studies have shown that students benefit greatly from targeted instruction in these skills. Teaching students how to better self-regulate is both important and valuable for Computer Science students. The solution here may seem straightforward: educators should give instruction on self-regulation skills. However, there are a number of skills that encompass a student's proficiency with self-regulate; including time management, problem decomposition, and reflection. Self regulation also tends to be a highly cognitive and internal process making it difficult to observe directly, let alone measure. Which skills should be prioritized for targeted instruction? How could we empirically measure those skills? What limitations should we keep in mind when making such decisions? Within this dissertation, I will seek to address these questions. In order to get an idea of what skills the Computing Education Research community should be prioritizing, my co-authors and I conducted two studies. First, a Delphi Process study that expanded the field by gaining an understanding of what SRL skills CS post-secondary educators value most. This gave a more firm view of what skills were most important for CS students. Second, a systematic literature review to examine what skills had been studied within the Computing Education Research community. Ultimately, I created a finalized list of 12 SRL skills that appear to be particularly important to CS education. This list also includes behaviors an outside observer could use as indicators of the presence or absence of SRL. After creating this list, I then considered how best to measure these each of these 12 skills. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong inferences about a student's internal state empirically. They also allow for measurement of students at greater scale and through automated means, making them advantageous for large classes. For my third publication, I then set about identifying a set of data traces for these skills taking a theory-first approach. I also make the case that CS is well situated to make great gains in trace-based approaches as they make use of a whole ecosystem of data sources. This is important as it is currently common for studies to utilize just one.
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    Teaching Command Line and Git Skills Using Exercises with Interactive Visualizations
    Buxton, Ryan Todd (Virginia Tech, 2023-01-05)
    Command line and version control skills are vital to computer science students during their education and as they enter the software industry. These skills are commonly taught to undergraduate students via traditional lecturing methods and brief hands-on activities. Many students struggle with learning the Git version control system because they are not familiar with the command line, or they do not understand how Git works internally. Recent research highlights the effectiveness of using interactive visualizations to teach computer science concepts. Thus, we developed novel command line and Git exercises with interactive visualizations. These exercises integrate with learning management systems to automate grading. We tested the effectiveness of the exercises in a CS2 course at a large research institution by conducting pre-assessments before and post-assessments after the students completed the exercises. We found that students performed significantly better on both the command line and Git post-assessments than on the pre-assessments. Furthermore, we found that students with less experience with the command line and Git achieved a significantly greater improvement from the pre-assessments to the post-assessments. Additionally, we found that students with different levels of command line and Git experience did not perform differently on the exercises. Therefore, the exercises provide a novel tool for teaching command line and Git concepts to undergraduate computer science students with any level of command line and Git experience.