Computer Science Research Articles

Education for technologies and ICT in compulsory education in Romania. Comparative analyses, the need for reconsiderations and equal opportunities

The research analyses the situation of education in Romania regarding the presence in the official documents of the school subjects that form competences for the technological field and for ICT. The framework plans for compulsory education at the primary, secondary and lower cycle of high school were analysed. The conclusions show the fact that many of the official school documents are older than 10 years and they were developed and approved by the Ministry of Education without considering continuity and a unitary vision. If in secondary education the situation is somewhat better, in all the analysed profiles, in primary education the curricular area "Technologies" is underrepresented, although equal opportunities are offered to students from all majors. The high school operates framework plans approved in 2009, which means that there is no continuity in the study of some subjects, ICT/Computer science being present only in certain specializations. The situation is analysed in the classes belonging to mass education, the one intended for national minorities and from specializations such as: mathematics, computer science, technological, arts, music, theology, pedagogy, social sciences, sports, military, public security. A series of research directions and priorities are proposed for a better presence of technology and ICT education in the training of students for the society of the future.

ALSort – A Software Instrument for Learning Sorting Algorithms

Finding methods to teach sorting algorithms is a challenge for most Computer Science teachers, but also for students who are open to learn and search for information. Because nowadays students are very receptive to educational software, we want to introduce our instrument named ALSort – a software designed for teachers and students interested in the instructional process of sorting algorithms. ALSort is an educational software based on gamification and modelling. It is built on six stages of challenges related to the six levels of learning from the Revised Bloom Taxonomy and the version from this paper is a prototype.

Performance Comparison of AI Platforms in Solving Computer Science Problems

In the rapidly evolving landscape of artificial intelligence (AI), its significance and accelerated development are undeniable. AI has emerged as a cornerstone technology with profound implications across various domains, driving innovation and reshaping the way we approach complex problems. Particularly, the utilization of AI in coding tasks has garnered substantial attention, given its potential to streamline development processes and enhance the efficiency of software engineering practices. Against this backdrop, this paper presents a detailed comparative analysis of four different AI platforms, namely ChatGPT, Gemini, Blackbox, and Microsoft Copilot, in addressing key challenges within the realm of computer science, spanning natural language processing, image processing, and cybersecurity. The study focuses on leveraging the C++ programming language to develop solutions for these multifaceted problems across the aforementioned platforms. Each platform's outputs are meticulously evaluated on various parameters including accuracy, execution time, code size, and time complexity to provide a comprehensive understanding of their performance. Furthermore, an iterative optimization methodology is employed, entailing three rounds of refinement for the code produced by each platform, with the resultant outputs subjected to comparative analysis in each iteration. Through this rigorous approach, the paper not only elucidates the efficacy of different AI platforms in addressing diverse computational challenges but also underscores the iterative enhancement process on AI platforms for refining code quality and performance across multiple domains within computer science.

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

AbstractIn 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.

Digital Empowerment of Women: The Role of the Internet in an Online University Setting

This study explores the impact of digital empowerment on female students in online university settings, focusing on internet connectivity, digital tools, and barriers to success. Using a survey distributed through Google Forms, we collected responses from 120 female participants across four faculties: Medical, Economics, Computer Science, and Education. The purpose was to assess how reliable internet access influences academic performance and engagement, evaluate the effectiveness of digital resources for professional skill development, and identify obstacles affecting women's participation. Results revealed that reliable internet access significantly enhances academic performance and engagement. Digital tools were highly rated for their effectiveness in skill development and career advancement. However, technological limitations and socio-economic factors were identified as major barriers. The findings suggest that while online universities offer valuable opportunities, addressing technological and socio-economic challenges is crucial for maximizing their benefits. Recommendations include improving internet infrastructure and providing targeted support to overcome socio-economic barriers. Future research should explore the longitudinal effects of digital empowerment initiatives and their impact on long-term academic and career outcomes for women.

Design of Dental Materials for CAD/CAM by Computational Science and AI

Design of Dental Materials for CAD/CAM by Computational Science and AI

The Role of IT and Computer Science Education in Uzbekistan: The Impact of UNESCO Initiatives

The Role of IT and Computer Science Education in Uzbekistan: The Impact of UNESCO Initiatives

An examination of Emotional Intelligence disparities between first-year B.Sc. Students choosing Physical Education and BCA students: a Comparative Analysis

This research paper explores the dimensions of Emotional Intelligence (EI) among first-year college students in two distinct academic disciplines: Physical Education within the Bachelor of Science (B.Sc.) program and Computer Applications (BCA). Recognizing the potential impact of educational choices on emotional competencies, the study investigates whether students in these fields exhibit nuanced differences in EI. The focus arises from the divergent curricular emphases, with Physical Education emphasizing physical wellness, teamwork, and leadership, while BCA delves into the complexities of computer science. Emotional Intelligence is contextualized as a crucial factor in academic, personal, and professional realms, encompassing self-awareness, self-regulation, empathy, motivation, and social skills. The objectives involve both analysis and comparison of EI between the two groups of students. The research employed a survey method with random sampling of 40 male students from Holkar Science College, Indore, aged 18-21. Emotional Intelligence was measured using the Emotional Intelligence Scale (EIS) developed by Anukul Hyde, Sanjyot Pethe, and Upindar Dhar (2005). Descriptive statistics and the Independent T-test were applied for analysis. Results: reveal no significant differences in the ten factors of Emotional Intelligence between B.Sc. students in Physical Education and those pursuing BCA. The study concludes that, based on the statistical analysis, the chosen academic path does not lead to a significant variance in Emotional Intelligence levels among these first-year students. This finding contributes to our understanding of how different academic trajectories may not necessarily shape distinct emotional competencies during this crucial phase of higher education.

Development and Usability Testing of a Virtual Reality Game for Learning Computational Thinking

Virtual reality (VR) is an innovative technology that immerses users within its environment, holding significant potential across various sectors, including education. Developing a VR-based application in the form of a game for educational purposes necessitates a usability evaluation. This study aims to develop and evaluate the usability of a VR game, CT Saber, designed explicitly for learning computational thinking (CT). The name CT Saber is inspired by the popular VR game Beat Saber, with 'CT' denoting computational thinking to emphasize the educational focus of the game. The research employed a Design and Development Research (DDR) approach, encompassing four stages: analysis & definition, design, development, and evaluation. To assess the usability of CT Saber, pilot testing was carried out involving 36 participants (24 male, 12 female) of computer science students aged between 19-22 years. The USE Questionnaire framework was used to evaluate usability, consisting of four variables: usefulness, ease of use, ease of learning, and satisfaction. The evaluation revealed that CT Saber is categorized as 'acceptable' from dichotomous and conventional academic grading perspectives. Similarly, a multiple linear regression analysis confirmed that the independent variables (usefulness, ease of use, and ease of learning) together significantly (p < 0.01) influence the dependent variable (satisfaction). However, only usefulness significantly influences satisfaction. Responses to open-ended questions in the questionnaire also indicated predominantly positive feedback from most participants. Consequently, it can be concluded that the CT Saber VR game developed in this study successfully meets the established criteria for usability.

Upscaling Current Data Caching and Prefetching Strategies for Online Databases in Nigeria

This study investigated upscaling current data caching and prefetching strategies for online databases in Nigeria, with a focus on practical implications. The research design adopted for this study was the descriptive survey. The population comprised of all undergraduate’s library students in public tertiary institutions in Ekiti State. A simple random sampling technique was adopted to select 200 library students from Ekiti State University in the study area. The instrument used for data collection was a structured 4 Likert type questionnaire. The questionnaire was distributed to the respondents to find out the effectiveness of caching and prefetching techniques on online databases. The instrument was both face and content validated by two experts from the department of Library studies in Ekiti State University, Ado-Ekiti State. The reliability of the instrument was ensured using Pearson Product Moment Correlation formula which yielded a coefficient of 0.99. The data collected were analyzed using descriptive statistics such as mean and standard deviation. The result showed that the current caching and prefetching techniques employed in online databases are highly effective; the different access patterns have effect on the effectiveness of caching and prefetching techniques in online databases and there are impacts of cache coherence mechanisms on the efficiency of caching and prefetching techniques in online databases. It was therefore recommended that the inclusion of caching and prefetching in curriculum is important across all educational level in Nigeria, with a clear emphasis on the practical implications. In addition, caching and perfecting have come under fire for focusing mostly on computer science.

Prototype Analysis in Hopfield Networks with Hebbian Learning.

We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show that this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states, alleviating capacity woes. This process has similarities to prototype learning in human cognition. We provide a substantial literature review of prototype learning in associative memories, covering contributions from psychology, statistical physics, and computer science. We analyze prototype formation from a theoretical perspective and derive a stability condition for these states based on the number of examples of the prototype presented for learning, the noise in those examples, and the number of nonexample states presented. The stability condition is used to construct a probability of stability for a prototype state as the factors of stability change. We also note similarities to traditional network analysis, allowing us to find a prototype capacity. We corroborate these expectations of prototype formation with experiments using a simple Hopfield network with standard Hebbian learning. We extend our experiments to a Hopfield network trained on data with multiple prototypes and find the network is capable of stabilizing multiple prototypes concurrently. We measure the basins of attraction of the multiple prototype states, finding attractor strength grows with the number of examples and the agreement of examples. We link the stability and dominance of prototype states to the energy profile of these states, particularly when comparing the profile shape to target states or other spurious states.

A Framework for Universality in Physics, Computer Science, and Beyond

A Framework for Universality in Physics, Computer Science, and Beyond

Psychomatics-A Multidisciplinary Framework for Understanding Artificial Minds.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

A computational materials science paradigm for a Course-based Undergraduate Research Experience (CURE)

Abstract Course-based Undergraduate Research Experiences (CUREs) bring the excitement of research into the classroom to improve learning and the sense of belonging in the field. They can reach more students, earlier in their studies, than typical undergraduate research. Key aspects are: students learn and use research methods, give input into the project, generate new research data, and analyze it to draw conclusions that are not known beforehand. CUREs are common in other fields but have been rare in materials science and engineering. I propose a paradigm for computational material science CUREs, enabled by web-based simulation tools from nanoHUB.org that require minimal computational skills. After preparatory exercises, students each calculate part of a set of closely related materials, following a defined protocol to contribute to a novel class dataset which they analyze, and also calculate an additional property of their choice. This approach has been used successfully in several class projects. Graphical abstract

Examining the Effects of Peer-Led Team Learning as a Support for Community College Transfer Students’ STEM Achievement

Peer-led Team Learning (PLTL) is a model of instruction and learning that has been used to address low success rates in various science, technology, engineering, and mathematics (STEM) courses and other fields in higher education. (1) While research conveying the efficacy of this model of instruction remains mixed, this project sought to add to the body of knowledge communicating the benefits of PLTL for STEM achievement. This study examined the experiences of STEM community college transfer students’ engagement in PLTL within their major course of study when a suite of supports was provided. (2) Specifically, study participants were enrolled in degree programs at a four-year rural, regional university in the engineering, computer science, and mathematics (ECSM) fields and were provided social, financial, and academic assistance. Qualitative analysis of the data revealed that with a PLTL component, community college transfer students were more likely to participate, engage, and perform better in coursework given the additional supports. (3) Study participants indicated the community of learners created through PLTL was a primary motivator for continued engagement and progress towards ECSM degree attainment. (4) Implications for practice extend to engagement and retention efforts of STEM students from the community college population

Higher-Order Cellular Automata Generated Symmetry-Protected Topological Phases and Detection Through Multi Point Strange Correlators

In computer and system sciences, higher-order cellular automata (HOCA) are a type of cellular automata that evolve over multiple time steps and generate complex patterns, which have various applications, such as secret-sharing schemes, data compression, and image encryption. In this paper, we introduce HOCA to quantum many-body physics and construct a series of symmetry-protected topological (SPT) phases of matter, in which symmetries are supported on a great variety of subsystems embbeded in the SPT bulk. We call these phases HOCA-generated SPT (HGSPT) phases. Specifically, we show that HOCA can generate not only well-understood SPTs with symmetries supported on either regular (e.g., linelike subsystems in the two-dimensional cluster model) or fractal subsystems, but also a large class of unexplored SPTs with symmetries supported on more choices of subsystems. One example is that has either fractal and linelike subsystem symmetries simultaneously or two distinct types of fractal symmetries simultaneously. Another example is in which chaotic-looking symmetries are significantly different from and thus cannot reduce to fractal or regular subsystem symmetries. We also introduce a new notation system to characterize HGSPTs. We prove that all possible subsystem symmetries in a square lattice can be locally simulated by an HOCA-generated symmetry. As the usual two-point strange correlators are trivial in most HGSPTs, we find that the nontrivial SPT orders can be detected by what we call . We propose a universal procedure to design the spatial configuration of the multi point strange correlators for a given HGSPT phase. Specifically, we find deep connections between multi point strange correlators and the spurious topological entanglement entropy (STEE), both exhibiting long-range behavior in a short-range entangled state. Our HOCA approaches and multi point strange correlators pave the way for a unified paradigm to design, classify, and detect phases of matter with symmetries supported on a great variety of subsystems, and also provide potential useful perspective in surpassing the computational irreducibility of HOCA in a quantum mechanical way. Published by the American Physical Society 2024

Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy

The 2022 Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy brought together research experts from many domains of social sciences, demography, public policy, statistics, and computer science to address key challenges in the use of the differentially private Census noisy measurement files to support social research and policy decisions.

Exploring social and computer science students’ perceptions of AI integration in (foreign) language instruction

Artificial intelligence (AI) has gained acceptance in the field of education. Nevertheless, existing research on AI in education, particularly in foreign language (FL) learning and teaching, is notably limited in scope and depth. In the present study, we addressed this research gap by investigating social and computer science students' perceptions of the integration and use of AI-based technologies in education, focusing specifically on foreign language teaching. Using an online questionnaire, we analysed factors such as students' field of study, gender differences, and the type of AI used. The questionnaire included statements categorised into thematic clusters, with responses measured on a five-point Likert scale. Statistical analysis, including chi-square tests and Cohen's d, revealed that individuals studying computer science, males, and supporters of generative AI are more likely to use AI tools for educational purposes. They perceive fewer barriers to the integration of AI into FL education. Social science students and women are less likely to use AI tools in FL education and express scepticism about their potential to improve academic outcomes. They tend to be more critical or cautious regarding the role of AI in FL education. They view AI as a valuable tool that enhances the learning experience but, at the same time, recognise the irreplaceable role of human teachers. The study highlights the need for targeted educational initiatives to address gender and disciplinary gaps in AI adoption, promote informed discussions on AI in education, and develop balanced AI integration strategies to improve FL learning. These findings suggest educators and policymakers should implement comprehensive AI training programs and ethical guidelines for responsible AI use in (FL) education.

Challenging Normalized Gendered Racism in Departmental Efforts to Broaden Participation in Computer Science

In recent decades, initiatives to diversify post-secondary educational spaces have blossomed. Many of these “broadening participation” efforts are in STEM undergraduate departments that, historically and presently, predominantly serve white men. Using a raced-gendered theoretical lens, we conducted a narrative analysis of interviews with 55 faculty, staff, students, and administrators at four public universities, individuals who had all been identified as leaders of diversity, equity, and inclusion efforts in their universities’ computer science departments. Our findings across institutions illustrate how their efforts often contravened the stated goals of diversity initiatives, especially for Women of Color, and continued to reproduce white supremacy in computer science. This intersectional inquiry affirms prior scholars’ calls to name and confront how diversity initiatives and their leaders deflect to gender while being race-avoidant, a pattern that functions to further marginalize Women of Color in higher education.

Optimizing network insights: AI-Driven approaches to circulant graph based on Laplacian spectra

The study of Laplacian and signless Laplacian spectra extends across various fields, including theoretical chemistry, computer science, electrical networks, and complex networks, providing critical insights into the structures of real-world networks and enabling the prediction of their structural properties. A key aspect of this study is the spectrum-based analysis of circulant graphs. Through these analyses, important network measures such as mean-first passage time, average path length, spanning trees, and spectral radius are derived. This research enhances our understanding of the relationship between graph spectra and network characteristics, offering a comprehensive perspective on complex networks. Consequently, it supports the ability to make predictions and conduct analyses across a wide range of scientific disciplines.