University Computer Science Research Articles

How self-beliefs, values, and belonging change and relate with performance during introductory computer science

ABSTRACT Background and Context First-year university computer science courses can provide barriers or bridges into CS for students. Understanding student motivation within this context can enable instructors to better support learning. Objective To understand how student motivation changes across the semester and relates to course performance and student background. Method Students were surveyed four times during the course on their motivation for CS and for the course. Differences across groups and time were analyzed, as were links with performance. Findings Starting the course, women reported lower belonging and self-beliefs than men. Student value for CS declined during the course, whereas self-beliefs and belonging increased. Self-beliefs, course importance, and cost predicted final course grades; interest predicted fluctuations in grades across the semester. Implications Intervening to bolster course value and reduce perceptions of course cost may be most fruitful for increasing final grades; fostering interest may be best to improve engagement and within-course performance.

Pencarian Rute Terpendek dari Kampung Duri Kosambi Menuju Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika Jakarta Timur Menggunakan Algoritma A* (A-Star)

Cipta Karya Informatika Computer Science College (STIKOM CKI) is one of the educational institutions that focuses on computer education. Marked by the issuance of a permit on September 5, 2002, in the form of a Decree of the Minister of National Education No.: 204 / D / O / 2002 which legally has two departments of Informatics Engineering and Information Systems that have implemented a competency-based curriculum. The campus is on Jalan Radin Inten II Duren Sawit, East Jakarta. One of the important educational institutions for the local community and students who live around it. However, accessibility to the campus can be a challenge for individuals who come from the Kampung Duri Kosambi area, right in Duri Kosambi Village, Cengkareng District, West Jakarta. To determine the closest route, it has now been widely applied in various navigation applications such as the Global Positioning System, Google Maps, A * Star is one of the optimal route search algorithms. Optimal means that the route produced is the best and most complete route, the algorithm can achieve the expected goal. In addition, the purpose of this study is to provide information about the location of the STIKOM CKI campus for the nearest route so that it can help users to choose the fastest route to get to the location and at the same time provide information on the nearest route based on the results of the A*Star algorithm calculations applied in this study.

Fostering student competencies and perceptions through artificial intelligence of things educational platform

The growing demand for artificial intelligence (AI) skills across various sectors has enhanced AI-focused careers and shaped academic exploration in educational institutions. These institutions have been actively developing teaching methods that enhance practical AI applications, particularly through integrating AI with the Internet of Things (IoT), leading to the emergence of the Artificial Intelligence of Things (AIoT). This convergence promises significant advancements in AI education, addressing gaps in structured learning methods for AIoT. This study explored AIoT's application in Smart Farming (SF) and its potential to enrich AI education and sectoral advancements. The AIoT platform was designed for SF simulations, integrating environmental sensing, AI processing, and user-friendly outputs. This platform was implemented with 40 first-year computer science university students in Thailand using a one-group pre-posttest design. This approach transformed theoretical AI concepts into experiential learning through interactive activities, demonstrating AIoT's capability to increase AI conceptual understanding, trigger AI competencies, and promote positive learning perceptions. Therefore, this study presented the results as indicative of the AIoT platform's potential benefits, emphasizing the need for further robust experimental research. This study contributes to educational technology discussions by suggesting improvements in AIoT platform effectiveness and highlighting areas for future investigation.

Significant Differences Between the IQ Scores of Mathematics Students based on Regression Analysis and Factorial Experimental Design

In this research, data on general intelligence and emotional intelligence scores were analyzed and the difference between them was studied in terms of their application to students of the Mathematics Department in the four stages in the College of Computer Science and Mathematics at the University of Kufa for the academic year 2023-2024. Four statistical methods were used. The first was multiple regression analysis to study the extent to which each student's mathematics score was affected on the general intelligence and emotional intelligence tests (Y), by the student's gender (X1) and his type of residence (X2). The second statistical method is to analyze the design of a factorial experiment to study the presence or absence of significant differences between the grades of students in the four stages by classifying the students in the four stages into males and females, as well as studying the significant difference in the scores of the two intelligence tests (general and emotional) for students in the Mathematics Department in general and divided according to gender. requester. The third method of analysis was to analyze the design of a completely randomized experiment to study the significant differences in students’ intelligence scores between the four academic levels in the mathematics department. If there is a significant difference in the results of analyzing the data in the above methods, then we use the fourth statistical method in this research, which is the analysis of the least significant difference test to study the differences between each two groups separately. The results of the data analysis were numerous, the most important of which is that the intelligence scores in the two tests (general intelligence and emotional intelligence) do not depend on nor are affected by the student’s gender or type of residence. In addition, intelligence scores were not affected for the four educational levels.

Computational Thinking Measurement of CS University Students

The measurement of computational thinking ability among computer science (CS) university students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of computational thinking in CS students. Utilising two key instruments, namely, the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of computational thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and programme specialisations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to the measurement of variations in students’ computational thinking and programming skills by gender, university level, and location. By shedding light on the significance of computational thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.

Does Every Computer Scientist Need to Know Formal Methods?

We focus on the integration of Formal Methods as mandatory theme in any Computer Science University curriculum. In particular, when considering the ACM Curriculum for Computer Science, the inclusion of Formal Methods as a mandatory Knowledge Area needs arguing for why and how does every computer science graduate benefit from such knowledge. We do not agree with the sentence “While there is a belief that formal methods are important and they are growing in importance, we cannot state that every computer science graduate will need to use formal methods in their career.” We argue that formal methods are and have to be an integral part of every computer science curriculum. Just as not all graduates will need to know how to work with databases either, it is still important for students to have a basic understanding of how data is stored and managed efficiently. The same way, students have to understand why and how methods work, what their formal background is, and how they are justified. No engineer should be ignorant of the foundations of their subject and the formal methods based on these. In this paper, we aim to highlight why every computer scientist needs to be familiar with formal methods. We argue that education in formal methods plays a key role by shaping students' programming mindset, fostering an appreciation for underlying principles, and encouraging the practice of thoughtful program design and justification, rather than simply writing programs without reflection and deeper understanding. Since integrating formal methods into the computer science curriculum is not a straightforward process, we explore the additional question: what are the trade-offs between one dedicated knowledge area of formal methods in a computer science curriculum versus having formal methods scattered across all knowledge areas? Solving problems while designing software and software-intensive systems demands an understanding of what is required, followed by a specification and formalizing a solution in a programming language. How to do this systematically and correctly on solid grounds is exactly the supported by Formal Methods.

Research on The Mixed Teaching Mode of Python Language Programming Course

With the development of the Internet era, big data, artificial intelligence (AI), and other fields of innovation and change, Python has become the most popular programming tool in the digital field because of its simple grammar and convenience for students to learn. It is also one of the important skills that college computer science students need to master in the face of market demand changes. The degree of mastery of the Python language programming course directly affects students’ ability to use computers to analyze and solve problems To improve the teaching effectiveness of the “Python Language Programming” course, this paper mainly explores the integrated and blended teaching mode of said course.

Comprehensible Extraction of Knowledge Bases for Learning Agents in Games

AbstractThis dissertation abstract summarizes results of the thesis “Comprehensible Knowledge Base Extraction for Learning Agents - Practical Challenges and Applications in Games” (accepted as dissertation at the Department of Computer Science of TU Dortmund University, Germany). The thesis presents approaches that allow for the automated creation of knowledge bases from agent behavior learned in the context of games. The aims are twofold: (1) The creation of human-readable knowledge that can provide insights into what an agent learned, and (2) the investigation of how learning agents themselves can benefit from incorporating these approaches into their learning processes. Applications are presented, e.g., in the context of general video game playing. Moreover, an outlook on the InteKRator toolbox is provided which implements the most essential approaches in a more general context for the potential use in other domains (e.g. in medical informatics).

PROACTIVE COPING DITINJAU DARI SELF EFFICACY PADA MAHASISWA YANG SEDANG MENYUSUN SKRIPSI DI PROGRAM STUDI SISTEM INFORMASI FAKULTAS TEKNOLOGI DAN ILMU KOMPUTER UNIVERSITAS PRIMA INDONESIA MEDAN

ABSTRACT This study aims to find out relationship between self efficacy and proactive coping on students are writing essay in study program information systems faculty of technology computer science in University of Prima Indonesia Medan. The hypothesis of this study states that there is a positive correlation between self efficacy and proactive coping, assuming that the higher self efficacy, the higher will proactive coping be and conversely the lower the self efficacy the lower will proactive coping be. The subjects of this study were 101 students at Faculty of Technology and Computer Science University of Prima Indonesia selected by purposive sampling method. Data were obtained from a scale to measure self efficacy and proactive coping. The calculation was performed by means of testing requirements analysis (assumption), which consists of normality test for distribution and linearity test for relationships. The data analysis used is Product Moment Correlation with SPSS 17 for Windows. The results of data analysis showed that the correlation coefficient was 0.506 with p 0.000 (p <0.05). It shows that there is a positive relationship between self efficacy and proactive coping. These results indicate that the contribution of a given variable of self efficacy to proactive coping is at 26 percent, while the remaining 74 percent is influenced by other factors not examined. From these results, it is concluded that the hypothesis stating that there is a positive correlation between self efficacy and proactive coping is acceptable. Keywords: Self Efficacy, Proactive Coping

Bob or Bot: Exploring ChatGPT's Answers to University Computer Science Assessment

Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a dual-anonymous “quality assurance” marking exercise across four end-of-module assessments across a distance university computer science (CS) curriculum. Each marker received five ChatGPT-generated scripts alongside 10 student scripts. A total of 90 scripts were marked; every ChatGPT-generated script for the undergraduate modules received at least a passing grade (>40%), with all of the introductory module CS1 scripts receiving a distinction (>85%). None of the ChatGPT-taught postgraduate scripts received a passing grade (>50%). We also present the results of interviewing the markers and of running our sample scripts through a GPT-2 detector and the TurnItIn AI detector, which both identified every ChatGPT-generated script but differed in the number of false positives. As such, we contribute a baseline understanding of how the public release of generative AI is likely to significantly impact quality assurance processes. Our analysis demonstrates that in most cases, across a range of question formats, topics, and study levels, ChatGPT is at least capable of producing adequate answers for undergraduate assessment.

LLM and RAG Powered Chatbot for the College of Computer Science and Mathematics at the University of Mosul

This paper presents a proposal for the design and implementation of a chatbot for the website of the College of Computer Science and Mathematics at the University of Mosul. The chatbot uses a Large Language Model (LLM) supported by Retrieval Augmented Generation (RAG) technology, the first of its kind at the university. This innovative approach to designing the chatbot combines the strengths of pre-trained language models and RAG technology, enabling the chatbot to generate informative and accurate responses to a wide range of user inquiries. The chatbot is designed to provide immediate support and act as a virtual assistant for students, faculty, staff and visitors to the college website. It covers various topics related to the College of Computer Science and Mathematics at the University of Mosul, including faculty profiles, research activities, events, academic programs, admission requirements and other college-related matters.

Exploring the Interplay of Commonplaces: Activity Theory, Postman's Five Things, and Learner/Teacher Needs in San Bernardino Valley College's Computer Science Education

Exploring the Interplay of Commonplaces: Activity Theory, Postman's Five Things, and Learner/Teacher Needs in San Bernardino Valley College's Computer Science Education

Exploration of Innovative Development and Talent Digital Cultivation Path of Computer Specialties in Colleges and Universities

Abstract In the face of globalization and the rapid evolution of information technology, higher education, particularly in computer science, encounters both challenges and opportunities. This paper investigates strategies for fostering innovation and digital competency among university computer science students. Through an analysis of teaching methodologies across various institutions, student development, and industry collaborations, we propose a comprehensive educational framework. We evaluate and refine our student innovation ability assessment system by employing both quantitative and qualitative approaches, including the entropy weight method and gray correlation analysis. By updating curriculum content, prioritizing hands-on learning, and enhancing industry partnerships, we demonstrate a marked improvement in students’ programming and practical skills—showing 20% and 25% increases, respectively. Our findings advocate for a holistic revamp of teaching approaches, course material, and university-enterprise collaborations, aligning computer science education with the demands of the digital age.

Sistem Absensi Online Berbasis Website Studi Kasus: Program Studi Ilmu Komputer Universitas Pat Petulai

The Computer Science Study Program at Pat Petulai University is currently still adopting a conventional or manual attendance system, both in implementing and managing attendance data, so there is a need for a website that can manage attendance data and processes online. Based on these problems, this research aims to build a website-based online attendance system that can manage attendance data and processes online without having to do it manually or conventionally. By using qualitative descriptive research methods in this research, it is hoped that it can explain the results of the research that has been carried out. The use of information technology in this attendance was made with PHP, MySQL as a database and Laravel framework. System design using Unifed Modeling Language (UML) and waterfall tools as software development methods. The results of the software were tested in terms of functionality using black box testing. With the website that has been built, it is hoped that it can support the progress of the Pat Petulai University Computer Science Study Program in terms of managing student attendance data.

The Alignment between International and National Academic Accreditations - An Application in Information Systems Bachelor Program at Kingdom of Saudi Arabia

This study proposes a method for consolidating various program accreditations into a unified accreditation process for a single academic program. The primary challenge lies in harmonizing the diverse requirements of multiple accreditations. In the context of academic technology programs in Saudi Arabia, this research focuses on the integration of NCAAA and ABET accreditations, with a specific application to the Information Systems at King Abdulaziz university. The core methodology employed in this study involves applying Bloom's taxonomy of learning to identify the synergies between the accreditation criteria of ABET and NCAAA. To ascertain the alignment of learning outcome measurement based on both accreditations, a questionnaire was administered to faculty members within the College of Computer Science and Information Technology at renowned universities in the Kingdom of Saudi Arabia. This article is intended for researchers specializing in the quality of education in computing and engineering, as well as professionals, developers, and officials with an interest in academic accreditations. Furthermore, this research seeks to establish a foundation for the seamless dissemination of educational content through websites and digital applications, adhering to the standards and principles of academic accreditation.

Metal Surface Defect Detection Based on Metal-YOLOX

Article Metal Surface Defect Detection Based on Metal-YOLOX Xiaoli Yue , Jiandong Chen , and Guoqiang Zhong * 1 College of Computer Science and Technology, Ocean University of China, Qingdao 266000, China * Correspondence: gqzhong@ouc.edu.cn Received: 14 June 2023 Accepted: 13 September 2023 Published: 21 December 2023 Abstract: Due to the limitations of manufacture technologies, working environments and other conditions, metals (such as steel and aluminum) are susceptible to surface defects during the production process. Therefore, defect detection is an indispensable part of metal manufacturing. This paper innovatively proposes a one-stage defect detection model named Metal-YOLOX. Metal-YOLOX addresses the limitations in existing models posed by large variances in defect features and inadequate balance between detection accuracy and efficiency. Firstly, the composite convolution module of Metal-YOLOX integrates texture, dilated and deformable convolutions to filter out irrelevant features and extract effective feature information. Secondly, the feature cross-fusion module (HCNet) alleviates the problem of large dimensional differences in defects. HCNet uses skip connections to establish the connection between the original multi-scale features and the output nodes, and reduces the addition of redundant information. Thirdly, Metal-YOLOX adopts the deep separable convolution and global channel reduction. This lightweight design helps reduce computational complexity. Finally, detailed experiments demonstrate that, in terms of mean average precision, Metal-YOLOX achieves 79.83, 69.14, and 81.22 on the NEU-DET, GC-10 and Aluminum datasets, respectively. Furthermore, Metal-YOLOX dramatically reduces parameter number and computational complexity. The experiments validate that the Metal-YOLOX model improves the detection performance, maintains the detection speed, and meets the real-time requirements.

Feedforward‐ or feedback‐based group regulation guidance in collaborative groups

AbstractBackgroundCollaboration is a crucial concept in learning and has the potential to foster learning. However, the fact that collaborative groups act with a common understanding in a common task brings many difficulties. Therefore, there is a need for group regulation and guidance to support effective group regulation in collaborative learning. On the other hand, the focus should be on the effect of group regulation guidance intervention on the improvement of both individual and group performance, modes of motivational regulation strategies, and the progression of group metacognition.ObjectivesIn this study, group regulation guidance approaches were developed to support the collaborative learning of Computer Science university students. The developed group regulation guidance approach consists of feedback and a feedforward mechanism. The purpose of this study was to examine the effectiveness of these approaches on academic performance, group regulation performance perception, self‐, co‐, and socially shared regulation of intrinsic motivation, and group metacognition levels.MethodsThe study was patterned with a quasi‐experimental design. 65 students participated in the study, and various scales were used to collect data. Data were analysed using paired sample t‐tests, analysis of covariance, and multivariate analysis of covariance.Results and ConclusionAccording to the research results, academic achievement showed an increase in the experimental groups after the intervention compared with the pre‐intervention. The group in which the feedforward‐supported regulation guidance approach was used showed better learning success. In the group using the feedback‐supported regulation guidance approach, better group regulation, motivational regulation strategies, and group metacognition scores were obtained. Modes of motivational regulation strategies and group metacognition scores differed according to the experimental groups.ImplicationsWhile the results provide important clues for teachers and instructional designers, the developed approaches can be used as a framework for different fields.

Evaluating the Impact of Serious Games on Study Skills and Habits

Learning is a constructive process that requires dedication and motivation. Learning games can be used to en-gage learners, by providing opportunities to apply knowledge in a safe and fun environment. We created a peer-quizzing game that allows students to quiz each other on the course material and to playfully engage in good study habits. We set out to explore if learners with better learning skills and habits would be more en-gaged in the game in the context of a first-year computer science university class. We used the "Study Skills and Habits Questionnaire" to study the relationship between study skills and habits (especially time management, concentration, goal setting, and comprehension) and engagement in a competitive version of a peer-quizzing game including a leaderboard. We collected and analysed gameplay data of the students (n=34), such as creating quiz questions and solving the questions created by their peers. The results of the data analysis showed a moderate positive correlation between study skills and habits (time management, concentration, goal setting, and comprehension) and the number of questions answered and a moderate negative correlation with the number of questions created in the game. However, the regression model was not statistically significant in explaining the variation in the dependent variable (questions created). Among the individual predictor variables, only goal setting had a statistically significant positive effect on the number of questions created. Other variables, such as time management, concentration, and comprehension, did not show significant effects on the number of questions created by students.

Participatory Design of Virtual Humans for Mental Health Support Among North American Computer Science Students: Voice, Appearance, and the Similarity-attraction Effect

Virtual humans (VHs) have the potential to support mental wellness among college computer science (CS) students. However, designing effective VHs for counseling purposes requires a clear understanding of students’ demographics, backgrounds, and expectations. To this end, we conducted two user studies with 216 CS students from a major university in North America. In the first study, we explored how students co-designed VHs to support mental wellness conversations and found that the VHs’ demographics, appearance, and voice closely resembled the characteristics of their designers. In the second study, we investigated how the interplay between the VH’s appearance and voice impacted the agent’s effectiveness in promoting CS students’ intentions toward gratitude journaling. Our findings suggest that the active participation of CS students in VH design leads to the creation of agents that closely resemble their designers. Moreover, we found that the interplay between the VH’s appearance and voice impacts the agent’s effectiveness in promoting CS students’ intentions toward mental wellness techniques.

Enhancing Student Performance Prediction via Educational Data Mining on Academic data

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement.