Undergraduate Computer Science Research Articles

Harnessing Generative Artificial Intelligence for Digital Literacy Innovation: A Comparative Study between Early Childhood Education and Computer Science Undergraduates

Harnessing Generative Artificial Intelligence for Digital Literacy Innovation: A Comparative Study between Early Childhood Education and Computer Science Undergraduates

Perceptions and Aspirations of Undergraduate Computer Science Students Towards Generative AI: A Qualitative Inquiry

This article presents a comprehensive study conducted during the spring semester of 2024, aimed at exploring undergraduate computer science students’ perceptions, awareness, and understanding of generative artificial intelligence (GAI) tools within the context of their Artificial Intelligence (AI) courses. The research methodology employed qualitative techniques, including human-subject research and focus groups, to delve into students’ insights on the evolution of AI as delineated in the seminal textbook by Russell and Norvig. The study-initiated discussions on the historical development of AI, prompting students to reflect on the aspects that intrigued them the most, and to identify which historical concepts and methodologies, perhaps even those not directly covered in their curriculum, piqued their interest. Furthermore, the dialogue encompassed the learning methodologies highlighted in the textbook, seeking students’ feedback on the strategies that have been most effective for mastering complex AI theories and their practical applications. Interdisciplinary applications of AI were also discussed, encouraging students to contemplate AI’s role beyond the realm of computer science and its potential to foster innovative solutions across various fields. Finally, the conversation shifted towards students’ personal goals and aspirations in AI, urging them to consider how their perspectives have evolved in light of technological advancements, societal needs, and ethical considerations. The findings underscore a notable gap in students’ awareness of AI’s history and its current capabilities, indicating a need for educational strategies that not only deepen understanding but also foster a broader appreciation of AI’s potential. This study contributes valuable insights into enhancing AI education and encouraging interdisciplinary innovation among the next generation of computer scientists.

Metric cake shop: A serious game for supporting education on ISO/IEC/IEEE 15939:2017 – Systems and software engineering – Measurement process in the context of an undergraduate software engineering course

The educational use of serious games has increased in recent years and their pedagogical benefits have been widely documented, especially for teaching specialized knowledge on Software Engineering at undergraduate level. The results achieved by several studies in this area show that it has been possible to increase the student interest and motivation in learning complex topics that tend to be difficult to understand when knowledge is imparted through traditional lectures, as is the case with software process standards. In this regard, the ISO/IEC/IEEE 15939:2017 standard describes a software measurement process, one of the main topics that must be addressed in any curriculum for undergraduate degree programs in Software Engineering. However, despite their relevance in industry, many students are leaving university without basic software measurement skills. With the aim of identifying serious games for teaching the software measurement process at undergraduate level, a Systematic Literature Review was conducted in order to collect, classify and analyze information which enabled the authors of this study to propose a serious game that improves the understanding and education on the ISO/IEC/IEEE 15939:2017 standard. The results from an empirical evaluation involving Computer Science undergraduates provided evidence that positive learning experiences occurred when playing the created serious game.

Evaluating ChatGPT-4 Vision on Brazil’s National Undergraduate Computer Science Exam

Evaluating ChatGPT-4 Vision on Brazil’s National Undergraduate Computer Science Exam

Key Determinants Influencing Computer Science Undergraduates in Fee-Levying Higher Education Institute Choices in Sri Lanka

Key Determinants Influencing Computer Science Undergraduates in Fee-Levying Higher Education Institute Choices in Sri Lanka

What’s cost got to do with it? Cost belief trajectories of undergraduate computer science students

Students want to learn computer science due to its usefulness for future careers, however they often meet challenges in introductory courses. In the increasingly digital world, it is important to understand some important psychological consequences of such challenges: perceived costs of pursuing computer science. This study thus investigated semester-long trajectories of four cost perceptions (effort, opportunity, psychological, and emotional) and their relations to achievement, major intentions, and career intentions (N = 831). All cost beliefs showed average increases, although with nuanced differences in levels and slopes, and the four costs differentially predicted student outcomes. Interestingly, the intercept of psychological cost negatively predicted final course grades while positively predicting major and career intentions. Women reported steeper increases in cost perceptions compared to men. The findings highlight the differential functioning of cost perceptions, with implications for the importance of targeting different aspects of cost perceptions to mitigate students’ barriers to success in computer science.

Rethinking Undergraduate Computer Science Education: Using the 4Es Heuristic to Center Students in an Introductory Computer Science Course

Rethinking Undergraduate Computer Science Education: Using the 4Es Heuristic to Center Students in an Introductory Computer Science Course

Understanding College Students’ Satisfaction With ChatGPT: An Exploratory And Predictive Machine Learning Approach Using Feature Engineering

Artificial Intelligence (AI) technologies are continually improving and becoming more pervasive in many facets of our lives. ChatGPT is one such cutting-edge artificial intelligence application, and it has received a lot of worldwide media attention, specifically from educationists, technologists, and learners. It is imperative to understand and evaluate the impact of ChatGPT on computer science students as it directly and holistically influences them. A quantitative instrumental case study explores ChatGPT’s impact on early adopters in education. A survey of undergraduate computer science students at a state university of Delhi was conducted to get insight into their opinion on adopting this revolutionising technology for their education, career, and overall satisfaction. An end-to-end data science approach is applied to encompass exploratory and predictive modelling with feature engineering solutions. Results reveal the most influential features contributing to students’ satisfaction in adopting ChatGPT for their day-to-day chores concerning their social life, education, and career. The Linear Support Vector classifier, a machine learning algorithm for predicting the satisfaction or dissatisfaction in students’ shows an accuracy score of 72.73% and 97.72%, respectively. The AUC for this multiclass prediction model is convincing and is 0.74, 0.71, and 0.96 for satisfied, neutral, and dissatisfied classes, respectively.

“Hear” and “Play” Students Misconceptions on Concurrent Programming using Sonic Pi

Concurrency is a complex to learn topic that is becoming more and more relevant, such that many undergraduate Computer Science curricula are introducing it in introductory programming courses. This paper investigates the combined use of Sonic Pi and Team-Based Learning to mitigate the difficulties in early exposure to concurrency. Sonic Pi, a domain-specific music language, provides great support for “playing” with concurrency and “hearing” common problems such as data races and lack of synchronization among different concurrent threads. More specifically, the paper focuses on students’ misconceptions regarding concurrency in Sonic Pi, and compares them to those arising in traditional concurrent programming languages. In addition, it preliminarily explores knowledge transfer from Sonic Pi to C/C++. The approach has been applied in two teaching experiments with undergraduate students in our University involving 184 participants. Our investigations bring out the need to address misconceptions through targeted interventions for a clear understanding of concurrent programming concepts. Sonic Pi’s simplified abstraction and domain-specific flavor has demonstrated to be effective, especially for first-year students.

Fostering the development of computer science graduate employability through agile projects

ABSTRACT This article presents the usage of Integrated Course Design (ICD) in the design and evaluation of applying agile methodologies within an undergraduate module of study to foster the development of computer science students employability skills. Undergraduate programs of computer science typically follow traditional educational methods which can lead to students unable to connect knowledge learned in class to actual situations and students are often assessed individually, whereas collaborative group projects are more akin to industry practice. The teaching experience reported gives students the opportunity to relate concepts learnt in class to a practical group-based project. Students must meet the requirements of a ‘client’ who will provide feedback and additional challenges for students while following the Agile framework SCRUM. Positive student feedback and module grades 7.70% higher than the department average over a four year period indicates the teaching structure and assessment presented is an effective method to foster the development of technical and soft skills of undergraduate computer science students.

"Not my Priority:" Ethics and the Boundaries of Computer Science Identities in Undergraduate CS Education

Researchers in the CSCW community have long problematized the separation of social and ethical considerations from design work. Despite increasing attention to tech ethics and ethics education, however, computer scientists' sense of ethical responsibility remains of concern. This paper offers insights on how this boundary between tech and ethics is maintained and reinforced for students as they develop their identities as computer scientists. Drawing on interviews with eight undergraduate computer science (CS) students at McGill University, we explore the role that ethics play in the legitimate peripheral participation of students inside and outside their formal education. We found that while individual opinions on the importance of ethics varied, students agreed that ethics are not valued or rewarded in their education, extracurriculars, or future work prospects. We describe how placing ethics outside the boundary of computing acts as a form of occupational closure, excluding both important multidisciplinary work and marginalized bodies. We argue that in order to promote ethical practice in the design of CSCW systems, we must make it in the interest of future designers to learn socially grounded ethics. This requires that designers, researchers, and future employers actively reshape the boundaries of computing by asserting social and ethical considerations as values of computing and design.

Artificial Intelligence in the CS2023 Undergraduate Computer Science Curriculum: Rationale and Challenges

Roughly every decade, the ACM and IEEE professional organizations have produced recommendations for the education of undergraduate computer science students. These guidelines are used worldwide by research universities, liberal arts colleges, and community colleges. For the latest 2023 revision of the curriculum, AAAI has collaborated with ACM and IEEE to integrate artificial intelligence more broadly into this new curriculum and to address the issues it raises for students, instructors, practitioners, policy makers, and the general public. This paper describes the development process and rationale that underlie the artificial intelligence components of the CS2023 curriculum, discusses the challenges in curriculum design for such a rapidly advancing field, and examines lessons learned during this three-year process.

Constructing a game engine: A proposed game engine architecture course for undergraduate students

This paper presents a structured series of lessons for teaching undergraduate computer science or game development students how to construct a game engine using C++ and standard graphics APIs such as OpenGL or DirectX. Our proposed course content discusses rendering topics, game object management, scene management, and rigid body physics where learners are tasked to incorporate them into a fully functional prototype scene editor. By incorporating actual coding into the course, students can gain a better understanding of low-level engine features and also hone their programming skills. In addition to the learning content, we provide recommended assessment methods. We include discussions about our teaching experience in handling a game engine architecture course, titled GDENG03, which 36 students have passed. Our paper stands out from related works by providing a framework for interested educators on how to write a 3D game engine and how to teach the same in class. Based on our actual delivery of GDENG03, all 36 students achieved high scores of >80% out of 100 which demonstrates the effectiveness of our learning content. We received positive student feedback, such as a better appreciation of game engines, while suggestions for course improvement were raised, such as using simpler terminologies and better workload management. Based on student scores and feedback, we can attribute the success of our students’ learning to our inclusion of practical coding examples and discussions, highlighting their relevance to mainstream game engines. The learning materials and accompanying source code can be accessed in the following link: .

Assessing Common Software Vulnerabilities in Undergraduate Computer Science Assignments

As the demand for secure coding education grows, there is a need for improvements in how secure coding is taught and in preparing students to develop more secure software. As time in a Computer Science classroom is finite, educational efforts should be placed on targeting the most common types of vulnerabilities to better prepare students to avoid common security pitfalls in coding. Existing research in this area mainly focuses on developing vulnerability detection tools rather than analyzing the types of commonly produced vulnerabilities by students. Limited research exists in determining common student-produced vulnerabilities, and the available studies differ from the types of vulnerabilities that are researched in vulnerability detection literature. Our research works to further establish the types of vulnerabilities produced by students by using a static analysis tool on assignment code submissions in an undergraduate Programming II (CS2) course. We present our findings on what types of vulnerabilities are commonly produced by students and contrast them with what is commonly researched in the literature. We find there is little overlap between the vulnerability types reported by our study and other studies in the research area. This research has potential implications for secure coding education in a Computer Science curriculum. Further work should be done to establish the contexts in which specific vulnerability types are more likely to be produced and how to best teach students to avoid producing these vulnerabilities.

Undergraduate Computer Science Curricula

First-job readiness versus long-term career preparation.

Influence of Parental Education on Decision-Making Factors for Choosing Fee-levying Higher Education Institutes among Computer Science Undergraduates in Sri Lanka

The choice of a fee-levying higher education institute (HEI) is critical for students as it can significantly impact their future. However, research indicates that the process students follow to select an HEI is complex and is carried out in several stages. Hence parents often become involved in the selection process to help their children make the right choice. With the rising number of fee-levying HEIs, the higher education sector has become highly competitive and institutes invest heavily in strategies to attract not only students but also parents. Furthermore, Sri Lanka’s growing Information and Communication Technology (ICT) sector has resulted in a rise in the enrolments of undergraduates majoring in computer science (CS). Hence, the main objective of this study was to determine if parental education influences the key determinants namely, internal characteristics of students, institute-specific factors, and communication methods used by HEIs, shaping Sri Lankan students’ choice of a fee-levying HEI for CS degrees. The study used a quantitative approach, and data was collected from 510 students studying at eight fee-levying HEIs using the survey method. The results of the study revealed that neither fathers’ nor mothers’ education has an impact on any of the three key determinants. Hence, HEIs can consider this outcome when formulating their student enrolment strategies. Further research is also suggested with samples that include a higher percentage of students having parents with higher education and parents of students from other disciplines besides CS before generalising this outcome to the higher education sector of Sri Lanka.

STUDENT DROPOUT PREDICTION USING MACHINE LEARNING

In a higher education environment, we considered the likelihood of probable dropouts from a first-year undergraduate Computer Science program. In order to achieve this, data from five academic sessions were obtained from the Department of Computer Science, University of Benin, Nigeria. Out of nine hundred and forty seven (947) data obtained, only a total of nine hundred and six (906) was usable after cleaning and preprocessing. Six distinct classifiers including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANN) were modeled for the prediction of student success and dropouts. The performance six were stated to have performed on average at 90.4%, 98.9%, 98.5%, 97.4%, 96.0% and 97.3% respectively. Although there wasn't much of a performance difference between the DT, SVM, and LR, the LR model was chosen for deployment since it performs better than the other two models in terms of F1_score and Recall.

Bolstering the Persistence of Black Students in Undergraduate Computer Science Programs: A Systematic Mapping Study

Background: People who are racialized, gendered, or otherwise minoritized are underrepresented in computing professions in North America. This is reflected in undergraduate computer science (CS) programs, in which students from marginalized backgrounds continue to experience inequities that do not typically affect White cis-men. This is especially true for Black students in general, and Black women in particular, whose experience of systemic, anti-Black racism compromises their ability to persist and thrive in CS education contexts. Objectives: This systematic mapping study endeavours to (1) determine the quantity of existing non-deficit-based studies concerned with the persistence of Black students in undergraduate CS; (2) summarize the findings and recommendations in those studies; and (3) identify areas in which additional studies may be required. We aim to accomplish these objectives by way of two research questions: (RQ1) What factors are associated with Black students’ persistence in undergraduate CS programs?; and (RQ2) What recommendations have been made to further bolster Black students’ persistence in undergraduate CS education programs? Methods: This systematic mapping study was conducted in accordance with PRISMA 2020 and SEGRESS guidelines. Studies were identified by conducting keyword searches in seven databases. Inclusion and exclusion criteria were designed to capture studies illuminating persistence factors for Black students in undergraduate CS programs. To ensure the completeness of our search results, we engaged in snowballing and an expert-based search to identify additional studies of interest. Finally, data were collected from each study to address the research questions outlined above. Results: Using the methods outlined above, we identified 16 empirical studies, including qualitative, quantitative, and mixed-methods studies informed by a range of theoretical frameworks. Based on data collected from the primary studies in our sample, we identified 13 persistence factors across four categories: (I) social capital, networking, & support; (II) career & professional development; (III) pedagogical & programmatic interventions; and (IV) exposure & access. This data-collection process also yielded 26 recommendations across six stakeholder groups: (i) researchers; (ii) colleges and universities; (iii) the computing industry; (iv) K-12 systems and schools; (v) governments; and (vi) parents. Conclusion: This systematic mapping study resulted in the identification of numerous persistence factors for Black students in CS. Crucially, however, these persistence factors allow Black students to persist, but not thrive, in CS. Accordingly, we contend that more needs to be done to address the systemic inequities faced by Black people in general, and Black women in particular, in computing programs and professions. As evidenced by the relatively small number of primary studies captured by this systematic mapping study, there exists an urgent need for additional, asset-based empirical studies involving Black students in CS. In addition to foregrounding the intersectional experiences of Black women in CS, future studies should attend to the currently understudied experiences of Black men.

Examining students’ course trajectories using data mining and visualization approaches

The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.

Computing trust: on writing ‘good’ code in computer science education

ABSTRACT What does it mean to produce trustworthy code for computer scientists? Based primarily on ethnographic fieldwork in an undergraduate computer science program in Singapore, this article explores what it means for computer science students to write ‘good code.’ In doing so, it explores the values that underlie ideas of trust in the computer science discipline. Drawing on the work of Rebecca Bryant, this article shows how, as students learn to become ‘good at’ writing code that is technically functional, aesthetically un-individuated, and decontextually efficient, they also learn to become ‘good’ computer scientists. These standards of good code are distributed across human and nonhuman actors and provide a framework for ‘trustless trust’ in code. That is, while computer science often assumes an omnipresence of mistrust, this article argues that the production of ‘good’ code and ‘good’ computer scientists works to build a system of distrust for computer scientists. At the same time, becoming a good computer scientist is intimately intertwined with students’ selfhoods, undermining the foundation of trustless trust even as the ideal of objectively ‘good’ and trustworthy code cuts this contradiction from view.