Undergraduate Computer Science Students Research Articles

Pathways to Belonging: Understanding How University Entry Routes Shape the Sense of Belonging of Undergraduate Computing Students

Sense of belonging, or belongingness in academia, is an individual’s personal conviction as to their acceptance as a valued member of an academic community. The importance of belongingness lies in correlations with motivation, persistence, mental health and well-being. Prior work has shown that belongingness can be lower in students who are minoritised due to their gender, race, nationality, socio-economic status, religion and/or disability. However, there is limited research on how a student’s university entry route impacts their sense of belonging in Computer Science and Other Science disciplines. In Ireland’s higher education system, university entry is primarily managed through the Central Applications Office (CAO) process, which predominantly serves students transitioning directly from secondary school to university. Additionally, alternative access routes are available for students from socio-economically disadvantaged backgrounds, those with disabilities, and mature students. At University College Dublin (UCD), entry is facilitated through both the traditional direct-entry school leaving route and combined access routes, each serving distinct student demographics. To investigate how these varying entry routes influence students’ sense of belonging, we utilised a survey adapted from the validated ‘Math Sense of Belonging Scale’ to examine the belongingness of undergraduate Science students, including Computer Science students. We examined how the belongingness of students varies by university entry route, and how the belongingness of students in Computer Science varies by university entry route compared to Other Science students. Our results reveal a significant difference in belongingness between students entering university through the direct-entry school leaving route, compared to those who entered via combined access routes (e.g. university admissions schemes for school leavers from socio-economically disadvantaged backgrounds, with disabilities, and mature students). Specifically, within Computer Science, students entering university through the direct-entry school leaving route had significantly higher belongingness than combined access route students. These results provide insight that may help others improve the belongingness of undergraduate Computer Science students.

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.

Enhancing Education for Undergraduate Students of Computer Science through Mentoring: A Big Data Analytics Approach

Enhancing Education for Undergraduate Students of Computer Science through Mentoring: A Big Data Analytics Approach

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.

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.

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.

Exploration and Practice of Project-based Classroom Teaching Reform with the Support of Knowledge Graph: A Case Study of the “Logic Algebra and Digital Circuits” Course

In response to the prominent issues in teaching the Logic Algebra and Digital Circuits course for undergraduate computer science students, such as insufficient student motivation, inadequate support for instructional design and organization, and ineffective project-based teaching, this study explores and implements innovative reforms in project-based classroom teaching supported by a knowledge graph. The reform is carried out by adopting the Outcome-Based Education (OBE) concept to design the course objectives, integrating and optimizing course resources, constructing a comprehensive and rich knowledge graph centered around key concepts, and developing a problem system that covers these key concepts. It establishes an individualized learning path to provide tailored and personalized services for students, creates a holistic learning environment that promotes intrinsic motivation with external support, and implements a 6-3-3 teaching organization strategy based on BOPPPS (Bridge-In, Outcomes, Pre-Assessment, Participatory Learning, Post-Assessment, and Summary). This approach stimulates a student-centered, eco-friendly teaching model through various methods. Through four rounds of teaching reform practice in the Logic Algebra and Digital Circuits course, significant improvements have been observed in student learning enthusiasm and engineering application abilities, and the outcomes have received broad recognition.

Students Unlock the Power of Real Systems: An Experiential Learning in System Software Course

System Software is a fundamental core course for undergraduate students of Computer Science and Engineering. The traditional approach to teaching the System Software course within the School of Computer Science and Engineering lacked a meaningful connection to real-world machine architectures, leading to disinterest and reduced engagement among undergraduate students. This paper introduces an innovative teaching method designed to empower students to grasp the system programs of real systems effectively. Our approach involves effortlessly integrating the delivery of system software content with the Atmel AVR ATmega32 real-time machine, which students have previously encountered in a prior semester. Moreover, this paper provides a detailed examination of the use of a hypothetical machine in traditional teaching methodologies. While this method allowed for a more in-depth exploration of system software concepts, it struggled to establish a practical link to real machine. The novel teaching approach employed in this study adopts a unique method that links all the system software concepts with the practical system program of a real-time machine. This paper also explains how the advances in Technology has played a crucial role in considering real-time machines as examples. And it also discusses the limitations of teaching concepts using only hypothetical machine and concise overview of the chosen real-time machine is provided, followed by the observation of enhanced knowledge of system software concepts through its integration. To measure the effectiveness of the proposed methodology, we also gathered valuable feedback from the students. The course result analysis shows substantial improvement in understanding the concepts, performance and lifelong learning of the students. Keywords—System Software, Atmel AVR ATmega32, Hypothetical machine. Design thinking, Problem solving

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.

Relationship Between Implicit Intelligence Beliefs and Maladaptive Self-Regulation of Learning

Objectives . Although prior research has uncovered shifts in computer science (CS) students’ implicit beliefs about the nature of their intelligence across time, little research has investigated the factors contributing to these changes. To address this gap, two studies were conducted in which the relationship between ineffective self-regulation of learning experiences and CS students’ implicit intelligence beliefs at different times during the semester was assessed. Participants . Participants for Studies 1 (n = 536) and 2 (n = 222) were undergraduate students enrolled in introductory- and upper-level CS courses at a large, public, Midwestern university. Race-ethnicity information was not collected due to IRB concerns about possible secondary identification of participants from underrepresented groups. Study Method . Participants completed a condensed version of the Implicit Theories of Intelligence Scale [ 16 , 54 ] and the Lack of Regulation Scale from the Student Perceptions of Classroom Knowledge Building scale [ 51 , 53 ] at the beginning (Studies 1 and 2), middle (Study 2), and end (Studies 1 and 2) of semester-long undergraduate CS courses. Survey responses were analyzed using path analyses to investigate how students’ lack of regulation experiences throughout the semester predicted their implicit intelligence beliefs at the beginning (Study 2) and end (Studies 1 and 2) of the semester. Findings . Results from Study 1 indicate that undergraduate CS students come to more strongly believe that their intelligence is a fixed, unchanging entity from the beginning until the end of the semester. Moreover, participants’ responses to the lack of regulation scale were predictive of their implicit intelligence beliefs at the end of the semester. Results from Study 2 indicate that ineffective self-regulation experiences early in the semester enhance CS students’ belief in the unchanging nature of intelligence (i.e., during the first half of the semester). Taken altogether, these findings provide evidence that self-regulation experiences influence students’ beliefs about the malleability of intelligence. Conclusions . Findings align with Bandura's [ 4 ] contention that students’ behaviors and experiences influence their values and beliefs. Students who experienced poor self-regulated learning came to view intelligence as more of a fixed, unalterable entity than their more successfully self-regulated peers. Findings suggest that CS instructors can positively affect student motivation and engagement by embedding self-regulated learning strategy instruction into their courses and helping CS students adopt an incremental-oriented (e.g., growth-oriented) belief system about their intellectual abilities.

Level of consistency between students' self-reported and observed study approaches in flipped classroom courses: How does it influence students' academic learning outcomes?

Using Student Approaches to Learning research as a theoretical framework, the present study used both self-reported and observational log data to understand students' study approaches in a flipped classroom course amongst 143 computer science undergraduate students. Specifically, it aimed to examine: 1) to what extent students' study approaches identified by self-reported and observational log data are consistent with each other; and 2) to what extent students' academic learning outcomes differ between students who showed consistent and inconsistent study approaches by self-reported and observational log data. Using The Revised Study Process Questionnaire, students were clustered as reporting either a Deep or a Surface Study Approach. Using frequencies of students' participation in five online learning activities, they were classified as adopting either an Active or a Passive Study Approach. A 2 x 2 cross-tabulation showed a positive and moderate association between clusters of students' study approaches resulted from two types of data. Amongst students who self-reported a Deep Study Approach, the proportion of students who adopted an Active Study Approach (80.7%) was significantly higher than those who adopted a Passive Study Approach (19.3%). In contrast, of the students who self-reported a Surface Study Approach, the proportion of students who used a Passive Study Approach (51.2%) was significantly higher than those who used an Active Study Approach (48.8%). Furthermore, students who had good study approaches by both self-report and observation did not differ from students who adopted an Active study approach by observation but reported a Surface Study Approach on course grades. Likewise, there was no significant difference in terms of academic learning outcomes between those who had poor study approaches by both self-report and observation and those who adopted Passive study approach by observation but reported a Deep Study Approach. Future studies may consider incorporating some qualitative methods in order to find out possible reasons behind the inconsistencies between self-reported and observed study approaches.

Mapping the Course Objectives and Program Learning Outcomes of Software Engineering Course by Bloom’s Revised Taxonomy

The outcomes of software engineering can have a significant impact on the success of a software project and the satisfaction of its stakeholders. The results of the study provide a clear understanding of how success can be achieved in a program. Bloom’s Revised Taxonomy is a framework used to classify learning outcomes in terms of different levels of complexity and cognitive ability. It can be used to assess the active learning outcomes of a software engineering course. Bloom's Revised Taxonomy can be applied to assess the active learning outcomes of a software engineering course. Assessing the active learning outcomes of a software engineering course using Bloom's Revised Taxonomy, educators can ensure that students are gaining a deep understanding of the material and are able to apply what they have learned in new and innovative ways. The main goal of the survey was to enable computer science students to use real-world software to help them make better decisions. Other objectives of this research include: (1) students having good communication with teachers gives them the opportunity to feel motivated and involved in the software engineering learning process and (2) Improve teaching and learning methods for Software Engineering. This research design is for undergraduate computer science students.

Teaching computer programming: impact of Brown and Wilson's didactical principles

This research studies the effects of the application of didactics to the teaching of computer programming, focusing on programming skills in the Python computer language. The problem arises from the failure and dropout rates of students in computer programming in computer science careers in INACAP and the consequent interest in promoting better learning. The general objective is to study the effects of an innovative methodology, based on Brown and Wilson's didactic principles, on the teaching process of Python programming in computer science students at INACAP. The theoretical framework is based on the didactics of teaching computer programming and the concepts of computational thinking skills of various theoretical references, and in particular on the didactic principles of Brown and Wilson. This research is carried out with a quantitative methodology of explanatory scope and with a quasi-experimental design, with a purposive sample, for the experimental stage the sample will consist of 100 first year undergraduate students of Computer Science, of which 50 will be the experimental group and 50 will be the control group. The hypothesis proposed is that “The students in the experimental group obtain a higher performance when applying Brown and Wilson's didactic principles than the students in the control group who are taught in a traditional way.” The data collection technique used will be a 45-question multiple-choice test. The data analysis will be performed by applying statistical criteria, comparison of means and variances, among others.

The Comparing the Impact of Asynchronous Online Quizzes on Student Learning Outcomes in a Computer Communication and Networking Course

This study investigates the effectiveness of asynchronous online quizzes in improving student learning outcomes in higher education. Specifically, we compare the impact of two teaching methods - Synchronous Lecture and Asynchronous Tutorial pair (SLAT) versus Asynchronous Lecture and Synchronous Tutorial pair (ALST) - in delivering weekly quizzes to 70 undergraduate computer science students. Our results show that the SLAT outperformed the ALST method in enhancing students' academic performance after each learning unit. The findings highlight the potential of asynchronous quizzes as a valuable learning tool, particularly when combined with live lecture classes and asynchronous tutorials. These results have implications for educators looking to implement blended learning models that prioritize student engagement and academic achievement.

Interactive Learning Environment Through Visualization to Promote Creative Thinking Among Non-Major Undergraduate Computer Science Students Learning Computer Programming Subject

This study aimed to investigate whether the experience of learning using a visual interactive learning environment through visualization impacts performance and dimensions of creative potential in terms of creative thinking. Sixteen (16) non-random non-major undergraduate computer science students learning computer programming subjects were exposed to a visual interactive learning environment through visualization in a pre-experimental single group pre-posttest research design. Before and after participants learn using visual interactive learning environment through visualization for six (6) weeks, they must complete three Visual tests and two verbal tasks of the Torrance Tests of Creative Thinking (TTCT). Both descriptive and inferential statistics were used to analyze the data. The study showed significant evidence that the performance and creative level of the students are better after learning using a visual interactive learning environment except for Verbal Fluency and Verbal Flexibility dimensions of Creative thinking. Implications of these findings for future research are discussed.

Novel Industry Engagement approaches for academic projects leading to internships, placements, and entrepreneurships

A popular pedagogical approach in engineering education that has been proven to be successful in building problem-solving, design, and collaborative abilities is project-based learning. Giving students freedom to choose the type and scope of the projects they want to work on has apparent advantages but doing so without professional advice or a defined plan of action can lead to confusion, frustration, and unmet expectations. Additionally, engineering schools must give their students the chance to interact with industry during their studies, which can be challenging owing to logistical and time constraints. This paper describes a case study in which undergraduate computer science and computer engineering students met with business professionals to get feedback during the last stage of project implementation. Students presented their concepts to faculty members and industry guests on campus in the form of a project poster. The success of the strategy in raising students' self-efficacy and performance is attested to by internships, employability, and evaluation results, as well as by the satisfaction of the participants. The proposed research continues with suggestions for engineering educators who want to put similar initiatives into practice as well as a brief description of the authors' future goals.

Opinion of Engineering Students on the Use of Live Online Lectures after the Pandemic

The first outbreak of COVID-19 was reported in December 2019 and the disease took the shape of a pandemic in the next few months. Universities around the world imported lessons to their student mostly in online mode in 2020 and 2021. Thirty-five undergraduate computer science students were interviewed about their experience of attending online lectures during the COVID-19 pandemic. A quantitative analysis of their responses revealed that 43% of them felt that they can learn equally well from online and offline lectures, 49% felt that online lectures provide them flexibility which in turn helps them to perform better in academics and 54% felt that professors have improved their online teaching skills since the beginning of the pandemic. Further, a qualitative analysis revealed that students appreciate online lectures for allowing them to access ebooks and digital resources while attending lectures, and making it easier to study topics that require a lot of visualization and ask queries to professors. Consequently, 77% students said that a combination of online and offline lectures may be used in the future with students being allowed to choose how they learn. Alternatively, only online lectures may be scheduled on some days of the week so that students need not travel to the campus on those days. Keywords—COVID-19 pandemic, online lecture, flexible learning, professor-student interaction, content delivery.

Using Discrimination Response Ideation to Uncover Student Attitudes about Diversity and Inclusion in Computer Science

Helping students learn to identify and respond to situations involving discrimination is important, especially in fields like Computer Science where there is evidence of an unwelcoming climate that disproportionately drives underrepresented students out of the field. While students should not be considered responsible for fixing issues around discrimination in their institutions, they do have a role to play. In this paper, we present the results of a study in which 318 undergraduate computer science majors were presented with scenarios of discrimination and asked to identify the issues, rate the severity of the issues, and ideate 3–5 responses to address the described situations. They were also asked to identify which of their responses would likely be most effective in addressing discrimination and which of their responses they would be most likely to use if they were in the situation described in real life. Our results show that while students generally are able to identify various forms of discrimination (sexism, racism, religious discrimination, ethnic discrimination, etc.), any ambiguity in a scenario led to students describing the scenario as less severe and/or as an example of oversensitivity. We also show that students come up with many passive responses to scenarios of discrimination (such as ignoring the situation or wishing it had not happened in the first place). Students in our study were more likely to say they would deploy passive responses in real life, shying away from responses that involve direct confrontation. We observed some differences between student demographic subgroups. Women and BIPOC students in CS tend to think these issues are more severe than men and White and Asian students in CS. Women are more likely to ideate direct confrontation responses and report willingness to use direct confrontation responses in real situations. Our work contributes a methodology for examining student awareness and understanding of diversity issues as well as a demonstration that undergraduate computer science students need help in learning how to address common situations that involve either intentional or unintentional discrimination in an academic environment.