School Of Computer Science Research Articles

Changing focus: making sustainability a major theme in existing university modules

In this article we report some practical experiences of integrating education for sustainable development into established university teaching modules. The 2021 guidance on Education for Sustainable Development from the UK’s standards bodies QAA and Advance HE is an important and urgent motivation for introducing sustainability into university courses. We take as our context a first-year introductory module in Chemical Products and Process in the School of Chemistry and Chemical Engineering and a second-year module in Software Engineering and Systems Development in the School of Electronics, Electrical Engineering and Computer Science, both at Queen’s University Belfast. We outline some of the challenges of adding new themes to existing courses. We comment on ways of presenting themes of sustainable development alongside existing module content, and we indicate the type of work students produced. We identify approaches that resulted in good outcomes, and outline changes we have made with a view to improved future outcomes.

An Interview with Martin Robillard - 2024 SIGSOFT Awardee

Martin Robillard received the 2024 SIGSOFT Influential Educator Award for his significant contributions to hands-on software design education, including a textbook (Introduction to Software Design with Java) and a learner-focused software modeling tool (JetUML). He is currently a Professor in the School of Computer Science at McGill University, Montréal, Canada, he is a Distinguished Member of the ACM and a Fellow of the Alexander von Humboldt Foundation. He received his Ph.D. in Computer Science from the University of British Columbia. His research interests fall within the area of software engineering, focussing on human-centred software development. He was awarded six ACM SIGSOFT Distinguished Paper Awards for his work on recommendation systems, software traceability, and software documentation. Recently, he has been working on software documentation generation, test suite quality, and information privacy.

Making Computer Science Accessible through Universal Design for Learning in Inclusive Education

The field of technology and computer science (CS) is developing dynamically. Just as anyone can learn computers at any age, students with special educational needs (SEN) also aspire to acquire IT (information technology) knowledge on an equal footing with all other students. However, one of the obstacles facing students with SEN is the lack of educational materials and programs for CS in secondary schools. The authors have designed teaching materials and assignments that promote inclusion. This study aims to evaluate the impact of teaching resources developed based on universal design for learning (UDL) to make the school’s CS course accessible to all students. The experiment involved 16 students and five teachers. For 8 weeks, students studied computer science using training materials based on UDL. Assessment of knowledge outcome indicators, particularly programming skills, was conducted before and after the experiment. After studying computer science through specific tasks, the interviewees demonstrated a higher level of assimilation of the subject, as indicated by the subsequent test results (mean = 12.13, standard deviation = 1.20), compared to the pre-experimental test (mean = 8.94, standard deviation = 1.12). The study demonstrated that using special UDLbased tasks to teach CS makes it more accessible and has a positive impact on students with special educational needs.

Outsiders Within: How Do Black Girls Fit into Computer Science for All?

Objectives. At the K–12 level, “CS for All” initiatives across the country strive to increase equitable access to and participation in computer science (CS). However, there are many open questions about the implementation and effectiveness of these initiatives, including the extent to which exposing young people to CS early on can shape their longer-term CS interest and engagement. In this article, we examine CS participation among 6th–8th-grade Black girls and assess whether CS participation during middle school shapes CS interest and engagement during their first year of high school. We focus on Black girls in the hopes of developing a more nuanced and rigorous understanding of computing experiences at the intersection of racism and sexism in this field. Participants. The focal group of students in this study are 6th–8th-grade Black girls from New York City. We employ a comparative lens in this article, contextualizing the CS experiences and outcomes of Black girls to Latinas, Asian, and White girls, as well as Black boys. Study Method. We primarily rely on quantitative data for this article, applying a critical lens to our analyses and interpretation. More specifically, we conduct descriptive analyses of course-taking patterns as well as survey data that focus on student attitudes and beliefs about CS. We then carry out inferential analyses of students’ administrative records examining how, if at all, middle school CS participation is related to high school outcomes for Black girls. We employ a comparative lens and rely on qualitative data to make sense of our results. Findings. We find that, troublingly, Black girls in the district are disproportionately less likely to receive CS instruction in middle school. Black girls are also less likely than Black boys, Latinas, and White girls to feel that they belong in CS. However, Black girls in CS courses report similar levels of engagement, family, and peer support, as well as value for CS relative to other students in the district. Finally, we find that participation in CS courses in middle school does not increase the likelihood that Black girls will select high schools that offer CS courses or take a CS course during their first year of high school. Conclusions. Our findings suggest that to increase equitable access and participation in CS, it is not enough to simply expose students to CS coursework. We call for sustained attention to the experiences that Black girls have in their CS classes as well as broader structural barriers that might shape CS course-taking.

What does the phrase “diverse students” mean? An exploration of CS teachers’ ideas of race, culture, and community in their classrooms

ABSTRACT Background and Context Recent research suggests that there is work to be done in overcoming color-evasive ideologies in Computer Science (CS) education. In particular, we have limited insight into how to support white teachers in using Culturally Responsive Computing (CRC) approaches. This paper addresses this gap in the literature by analyzing interviews with white high school CS teachers through the lens of hegemonic whiteness. Objective We ask two questions 1) What are the gaps between these teachers’ views on culture, community, and responsiveness, and how CRC envisions the interplay of these three elements? 2) How can we use teachers’ understandings to reconceptualize presenting CRC to them in a manner that may best benefit their students? Method We use semi-structured interviews of nine teachers until data saturation. We use in-vivo and values coding in the first round and code-weaving in the second round to come up with emergent themes. Findings Our findings show that these teachers did one or more of the following: 1) had dynamic but content-agnostic views about culture in the classroom; 2) focused on community connections with academia, industry, and/or parents; 3) framed the challenges of implementing CRC through a deficit lens; and 4) valued students’ individuality, but were essentialist about student culture. Implications We have implications for practitioners, e.g. to have professional developments that allow teachers to name and challenge white supremacy in CS. We also have implications for researchers, e.g. to investigate ways in which white students and teachers can benefit from anti-racist CS education.

Robot Ethics

Robot Ethics

Robot Ethics

Robot Ethics

The Perceptions and Experiences of In-Service Teachers in a Computer Science Professional Development Program

This research aimed to investigate the perceptions and experiences of in-service teachers participating in a professional development (PD) program focused on computer science (CS). The main research question explored the teachers’ perceptions of their experience in the CSPD program, while sub-research questions examined the challenges encountered and the program’s impact on their capacities to teach CS in elementary schools. The study adopted an interpretivist paradigm and employed a qualitative research approach to understand the subjective meanings and hidden factors underlying teachers’ experiences. Data collection involved observations, reflection essays, and a semi-structured focus group interview. The data analysis was guided by the community of practice elements. The findings revealed prerequisite challenges faced by the teachers, such as the need to develop self-directed learning and research skills. Additionally, the PD program was found to enhance teachers’ knowledge, skills, and confidence in teaching CS. It also fostered changes in their beliefs and self-efficacy. Challenges in the pre-implementation and implementation stages were also revealed, including conflicting perspectives, limited supervisor support, and passive learning and teaching. These findings provide valuable insights that can contribute to the design of effective PD initiatives in CS education and promote sustainable education practices.

Delivering computing module for the large part-time software development class from pre- to post-pandemic: An online learning experience

IntroductionCovid-19 pandemic brought dramatic changes to higher education settings, particularly for curriculum delivery, moving quickly to online learning. This paper discusses teaching experience covering practices of technology-enhanced learning for the MSc Computing Foundations module (20 CATS) for a large class studying part-time Software Engineering course at the School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen's University Belfast (QUB) during 2019–2022 academic years. We compare on-campus learning with the abrupt shift to online learning during the pandemic and with sustainable online learning a year later. The objective of this study is to answer how part-time software development students perceived their technology-enhanced learning experience from pre- to post-pandemic and to evaluate the impact of the shift to online learning for the part-time class MethodsThis study is based on data collected during 2019/2020, 2020/2021, 2021/2022 academic years. Methodology types employed in this study include online observation with statistics collected from the virtual learning environment (VLE) Canvas, quantitative analysis, individual student surveys on teaching techniques and module content ResultsThis study provided an effective online teaching method for Computer Foundation module, reviewing the impact of different curriculum items and online educational activities starting with content delivery – both synchronous and asynchronous – and moving on to VLE Canvas discussion forums, ungraded formative quizzes, in-term formative assessment in the form of mock exam and, finally, to online summative assessment delivered on VLE Canvas. We investigate positive and negative aspects of technology-enhanced learning from pre- to post-pandemic according to part-time adult students studying the MSc software development program and focus on how this effective learning environment contributes to education practice with a view what developments are worth retaining post-pandemic and what did not work well. Analysis of the data from individual student surveys on teaching techniques and module content for the Computing Foundations module allowed us to conclude that students perceived very positive their technology-enhanced learning experience after shifting to online learning. We also found out that сhanging the module delivery format (from face-to-face to online) did not affect the results of the students’ performance. Conclusion and implicationsAdaptation of the MSc Computing Foundation module to a new model of distance learning has proved to be successful, so we can conclude that this delivery format is appropriate for this target audience. This study explored the effectiveness of the pedagogical approach while also gaining valuable insights into the software development student experience of learning in the VLE. The findings from this study may contribute to developing effective teaching practices in software engineering education and adult learning, and improve the preparation of future software professionals in the IT industry.

The Effectiveness of Using Virtual and Augmented Reality Technologies for Teaching Computer Science in Schools

Currently, using new technologies in education provides great opportunities for schools. This research aims to determine the effectiveness of using Virtual reality and Augmented reality technologies for teaching Computer science in schools. Furthermore, using Virtual and Augmented Reality in teaching computer science has been proposed as a pedagogical approach. The article theoretically analyzes the use of virtual reality and augmented reality technologies, and determines the effectiveness of practical applications of Virtual reality created in the Unity environment, as well as Augmented reality created in the Vuforia environment for teaching Computer science at school with the non-parametric method of Pearson’s chi-squared. Workbooks and digital educational materials for teaching computer science have been produced in the pedagogical model’s methodological section. The participants of the research were students from schools in the Republic of Kazakhstan, whose total number was 151. They were divided into two groups: a control one and an experimental one. For teaching students in the control group, only traditional educational materials were used, while students in the experimental groups took part in the experiment involving the use of Virtual and Augmented reality applications. At the end of the research, both groups were assigned a test according to three motivational, content, and technical categories, which enabled them to compare the results. As a result, the effectiveness of using virtual reality and augmented reality to teach Computer Science to students at school was determined with the non-parametric method of Pearson’s chi-squared. Compared to traditional teaching methods, research has demonstrated that virtual and augmented reality helps students learn computer skills better. This has led to improvements in motivation, content comprehension, and technical proficiency in computer science education in schools.

МЕТОДИКА СМЕШАННОГО ОБУЧЕНИЯ ШКОЛЬНОМУ КУРСУ ИНФОРМАТИКИ С ПРИМЕНЕНИЕМ ПОДХОДА CLIL

The article is devoted to the problem of optimizing the teaching of a Computer Science course in high school and is aimed at demonstrating the methodology for using blended learning technology in a school Computer Science course using the CLIL approach. A number of blended learning models were studied and the Flipped Classroom model was chosen for our research work. According to the “Flipped Classroom” model, students learn new material and complete assignments using the Internet at home, consolidate and practice the material they have covered during face-to-face learning in the classroom. In addition, given that there is currently a strong demand for a CLIL approach based on a trilingual policy aimed at mastering Kazakh, Russian and English, in a blended learning environment there are opportunities to apply the CLIL approach in a high school computer science course. The article shows the stages of CLIL classes, the actions in which are revealed using specific examples. The results of the study led to the conclusion that the use of two different tools in a school Computer Science course, consisting of the Flipped Classroom and CLIL methods used in educational activities, will help cover voluminous material in English, create conditions for independent study, and facilitate the process of monitoring knowledge for the teacher students and get students interested in learning the subject.

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

Multi-Scale Class Attention Network for Diabetes Retinopathy Grading

Article Multi-Scale Class Attention Network for Diabetes Retinopathy Grading Hongyu Chen 1, Ronghua Wu 1, Chen Tao 1, Wenjing Xu 1, Hongzhe Liu 3, Cheng Xu 3, and Muwei Jian 1,2,* 1 School of Information Science and Technology, Linyi University, 276000, China 2 School of Computer Science and Technology, Shandong University of Finance and Economics, 250014, China 3 Beijing Key Laboratory of Information Service Engineering, 100101, Beijing, China * Correspondence: jianmuweihk@163.com Received: 1 June 2023 Accepted: 21 July 2023 Abstract: Diabetes retinopathy (DR) is a universal eye disease, which brings irreversible blindness risks to patients in severe cases. Due to the scarcity of professional ophthalmologists, it has become increasingly important to develop computer-aided diagnostic systems for DR grading diagnosis. However, the current mainstream deep learning methods face challenges in accurately classifying the severity of DR, making it difficult for them to provide a reliable reference for clinicians. To tackle this problem, we propose two novel modules to improve the accuracy of DR classification. Specifically, we design a multi-scale feature extraction module to capture tiny lesions in fundus images and differentiate similar lesions simultaneously. In addition, we develop a class attention module to alleviate the adverse impact of intra-class similarity on DR grading. Experimental results show that our proposed modules attain significant performance improvement on the APTOS2019 blind detection dataset, with accuracy and quadratic weighted Kappa metrics achieving 95.98% and 97.12%, respectively.

Career path prediction using XGBoost Model and students’ academic results

This paper proposes an approach for constructing a system for career prediction by applying the eXtreme Gradient Boosting (XGBoost) Decision Tree model to the academic results of Ho Chi Minh International University’s School of Computer Science and Engineering graduates in the past 5 years. Initially, the dataset is cleaned up and normalized to be usable for the prediction algorithm with the help of Python 3 programming language. It is then split into 2 subsets: one for training (80 percent) and the other for testing (20 percent). After that, the algorithm uses the training subset to build the classification model. Finally, the testing subset is loaded into the model to predict each student’s career path based on the respective inputs and hyper-parameters tuning is employed to boost the model’s accuracy. By utilizing this solution, the problem related to predicting students’ future career paths based on their performance throughout their years studying at the university can be adequately addressed and handled.

The online teaching design of software engineering courses to alleviate social anxiety among college students

BackgroundThe software engineering course is essential for cultivating the abilities of computer major college students. It is an introductory course for software majors. The design purpose of the online software engineering course is to improve students’ teamwork and hands-on abilities in certain aspects. Social anxiety disorder is a mental disorder characterized by fear of speaking or communicating in public. Nowadays, it is widely distributed among young people worldwide.Subjects and MethodsThis study focuses on 30 college students with social anxiety disorder from the School of Computer Science and Technology of the University of Electronic Science and Technology of China. The main direction of improvement is to improve team collaboration and hands-on skills and their software engineering courses. A 3-month experimental period is used, using the Anxiety Self Rating Scale (SAS). The social status of the tested college students is observed, and the survey results are statistically analyzed using Amos software.ResultsFrom the experimental results, it can be seen that team collaboration has a good effect on improving the symptoms of social disorders, with 70% of the tested college students experiencing varying degrees of improvement in their social skills, and another 30% are more resistant to exploring social media.ConclusionsThe focus of online teaching design for software engineering courses should be on innovation and time, which can alleviate the social anxiety disorder of college students and effectively improve their social skills.

Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification

Article Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification Yanchao Bi, Yuyan Yin, Xinfeng Liu *, Xiushan Nie, Chenxi Zou, and Junbiao Du 1 School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China * Correspondence: liuxinfeng18@sdjzu.edu.cn Received: 17 June 2023 Accepted: 13 September 2023 Published: 26 September 2023 Abstract: Traffic detection devices can cause accuracy degradation over time. Considering problems such as time-consuming and laborious manual statistics, high misdetection probabilities, and model tracking failures, there is an urgent need to develop a deep learning model (which can stably achieve detection accuracy over 90%) to evaluate whether the device accuracy still satisfies the requirements or not. In this study, based on dual-granularity classification, a real-time traffic flow statistics method is proposed to address the above problems. The method is divided into two stages. The first stage uses YOLOv5 to acquire all the motorized and non-motorized vehicles appearing in the scene. The second stage uses EfficientNet to acquire the motorized vehicles obtained in the previous stage and classify such vehicles into six categories. Through this dual-granularity classification, the considered problem is simplified and the probability of false detection is reduced significantly. To correlate the front and back frames of the video, vehicle tracking is implemented using DeepSORT, and vehicle re-identification is implemented in conjunction with the ResNet50 model to improve the tracking accuracy. The experimental results show that the method used in this study solves the problems of misdetection and tracking effectively. Moreover, the proposed method achieves 98.7% real-time statistical accuracy by combining the two-lane counting method.

Distributed Computing Column 87 Recent Advances in Multi-Pass Graph Streaming Lower Bounds

Overview. In this Fall edition of the column, we resume our coverage of "hot topics" in areas connected to distributed computing with a column by Sepehr Assadi, who is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. Sepehr's work was highlighted by the PODC community before, as he was the recipient of the 2019 PODC Doctoral Dissertation Award for his work on streaming algorithms, multiparty communication, and massively parallel computation on graphs, and features frequently in the proceedings of mainstream theory conferences such as FOCS, STOC, and SODA. In this column, Sepehr covers some of the recent progress on multi-pass graph streaming lower bounds, which includes a significant amount of his own work. Many thanks to Sepehr for his interesting contribution!

Preface

2023 2nd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence (SIUSAI 2023) was held in Shenzhen, China, on April 21-23, 2023.It aims to bring scientists, researchers, practitioners and industry delegates from academia and industry together to exchange and share their latest research and experiences in the field of Unmanned Systems and Artificial Intelligence. The conference is expected to provide opportunities for advances and collaborations in this field.SIUSAI 2023 is organized by Southwest University of Science and Technology School of Computer Science and Technology, Southwest University of Science and Technology School of Manufacturing Science and Engineering, co-organized by Hohai University, Key Laboratory of Testing for Manufacturing Process, Ministry of Education, Southwest University of Science and Technology, Sichuan Big Data and Intelligent System Engineering Technology Research Center, The International Association of Electrical, Electronic and Energy Engineering (IAEEEE), and supported by CoreShare Academy Exchange Center.The conference contained one day of registration and two full-days program consisting of keynote lectures, oral presentations, video presentations and poster presentations. We would like to express our sincere gratitude to our keynote speakers Pro. Huiyu Zhou from University of Leicester and Prof. Saleh Mobayen from University of Zanjan who shared their newest and outstanding research achievements at the conference.After a couple of months well preparation and hard work, the conference has been held successfully and the proceeding of the 2023 International Symposium on Intelligent Unmanned Systems and Artificial Intelligence (SIUSAI 2023) will be smoothly published soon.On behalf of the conference organizing committee, we would like to show our congratulation and sincere gratitude to all authors, peer reviewers, speakers, oral, Video and poster presenters and listeners. We deeply appreciate their efforts in making the conference an impressive success.List of Committee Members is available in this pdf.

An Interview with Chunyang Chen - 2023 SIGSOFT Awardee

Chunyang Chen received the 2023 SIGSOFT Early Career Researcher Award for outstanding contributions to the study of intelligent software development automation including automated mobile application development, software testing, migration, and accessibility. He received a Ph.D. in computer science from School of Computer Science and Engineering, Nanyang Technological University (Singapore). He is now a senior lecturer in the Faculty of Information Technology of the Monash University (Australia).

Retraction

Retraction: Shengying Yang, Jianfeng Wu, Huibin Qin, Qiangqiang Xie, Zhiwang Xu, Yongzhu Hua Distributed buildings energy storage charging load forecasting method considering parallel deep learning model. J. Concurrency and Computation: Practice and Experience. 2021, 33–12.The above article, published online on 19 Nov 2019 Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5580) has been retracted by agreement between the authors, the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies including unattributed image reproduction and irrelevant citations were found, making the motivation and conclusions of the article untrustworthy. The authors admitted to the shortcoming in the article especially on article writing and references and agreed to retracting the article. Shengying Yang takes responsibility for these mistakes.