Traditional curricula are known to go through a systemic stagnation during a period of rapid changes which implies that they cannot adjust to changing societal and epistemic demands. The paper outlines a substantive theory created as a way to resolve the issue of a new paradigm called the Thinking and Learning Curriculum (TLC). This study employs a grounded theory design which amalgamates the data gotten from the in-depth interviews with sixteen experts from different areas such as curriculum studies, philosophy, and computer engineering. The emergent theory attributes the TLC with characteristics of a self-governing, complex adaptive life form with an internal metacognitive core that endows the system with self-regulation and intelligent decision-making functions. The system of this nature is backed up by a heavily integrated neuro-technical infrastructure where AI is the cognitive partner and the system is located in a decentralized, democratic socio-managerial ecosystem. The research paper concludes by showing the TLC model as an innovative and robust theoretical framework to transcend the static, object-based portrayal of curriculum, thus, proposing a 21st-century education-field ontology that is dynamic, agent-based.

The integration of Electronic and Computer Engineering (ECE) and Artificial Intelligence (AI) has become a key force driving transformations in multiple fields, especially in industries such as healthcare, autonomous driving, and the Internet of Things (IoT). With the continuous advancements in AI technologies such as machine learning, deep learning, computer vision, and natural language processing, the combination of AI and ECE is crucial for developing intelligent systems that can enhance operational efficiency and expand functionalities. This paper aims to explore the synergistic effect between Electronic and Computer Engineering (ECE) and Artificial Intelligence (AI), with a focus on analyzing their key technologies, application domains, and future development potential. Through literature reviews, case analyses, and industry examples, this paper examines the key challenges and opportunities in the integration of AI and ECE. The research findings indicate that although significant progress has been made in the integration of the two in fields such as autonomous driving, intelligent healthcare, and the Internet of Things, many challenges still exist in hardware implementation, algorithm optimization, and data processing. Finally, this paper discusses the future prospects of the integration of ECE and AI, and emphasizes the necessity of further research and development to unleash the full potential of these technologies.

Model-aware Deep Learning (DL) accelerators surpass generic ones in terms of performance and efficiency. These model-aware accelerators typically comprise multiple dedicated Compute Engines (CEs) to handle the varying computational characteristics of the operations within a DL model. Multiple-CE accelerators usually target Field-Programmable Gate Arrays (FPGAs), as FPGAs’ reconfigurability enables tailoring the CEs architectures to the varying computational characteristics of the model operations. The continuous evolution of DL models and their use in application domains with diverse optimization objectives, including low latency, high throughput, and energy efficiency, makes it challenging to identify highly optimized multiple-CE accelerator architectures. The design space of multiple-CE accelerators is vast, and the state of the art explores only limited parts of this space, which hinders the identification of accelerators with high performance and efficiency. To address this challenge, we propose a framework for exploring the design space of FPGA-based multiple-CE accelerators (MC E xplorer). MC E xplorer comprises a set of single and multiobjective optimization heuristics that target throughput, latency, energy efficiency, and trade-offs among these metrics. MC E xplorer searches for optimized multiple-CE accelerator architectures given a DL model, a hardware resource budget, and a single or multiple objectives. MC E xplorer explores a space beyond that explored in the literature by not restricting the accelerator inter-CE arrangements and exploring distinct configurations of individual CEs. We evaluate MC E xplorer with various DL models and hardware resource budgets. The evaluation shows that by exploring a search space beyond that in the literature, MC E xplorer identifies highly optimized multiple-CE accelerators. These accelerators achieve up to 2.8 × throughput improvement, 2.1 × speedup, and \(45\% \) energy reduction compared to the state of the art. Moreover, the evaluation demonstrates that broad space exploration is key to identifying multiple-CE accelerators with the best performance-efficiency trade-offs. MC E xplorer code is available at https://github.com/fqararyah/MCExplorer.

This community service activity aimed to enhance English pronunciation skills and establish a consistent speaking habit among students of SMKN 3 Palangka Raya. Thirty-six students of class X TKJ (Computer and Network Engineering) participated in a one-month program using thematic sentence templates, repetitive pronunciation drills, and self-reflection through voice recording feedback. The implementation employed a participatory and descriptive-qualitative approach, involving active collaboration between facilitators, teachers, and students throughout all stages. Weekly themes included self-introduction, daily activities, hobbies, and opinions to create a contextual and meaningful learning situation relevant to students’ daily lives. The results showed noticeable improvement in pronunciation clarity, increased awareness of pronunciation errors, and higher confidence in speaking English during classroom activities. However, aspects such as fluency and vocabulary variation remained limited due to the short duration of the program. Overall, the pronunciation habituation strategy proved effective in fostering awareness, comfort, and sustainable speaking habits when applied consistently over a longer period.

In our technologically globalized world, higher education teachers need to adjust themselves to modern teaching techniques to help students learn easily and be innovative. Integration of Information, Communication, and Technology (ICT) assists teachers in meeting the demand for technology-based teaching and learning tools. Innovative teaching tools are facilities that are currently replacing traditional teaching methods. In the fields of computer science and engineering, information and communication technology (ICT) innovative teaching approaches are regarded as one of the most crucial components for advanced teaching and learning. Teaching in Higher education is facilitated by a variety of software programs, apps, and information management systems. Innovative teaching encourages students’ engagement and cultivates the spirit of creativity. For engineering students to succeed in their fields of study, they need to have intrinsic motivation toward learning. Educators who are teaching computer science and engineering courses are required to be creative and use innovative teaching approaches to engage every student. The purpose of this paper is to share strategic knowledge with fellow lecturers in the field of computer sciences and engineering on how to effectively teach and engage students using various tools that are available on the internet and at our disposal. The focus of this paper was on innovative teaching strategies that students prefer to use in their studies. Findings from the study highlighted that most of the students preferred practical teaching methods and industry projects. Evidence was gathered from students enrolled under the Faculty of Engineering and Technology from Eswatini, Botswana, and Namibia campuses using a purposive sampling.

This study investigates the significant impact of ICT education from the Higher Secondary Certificate (HSC) on Bangladeshi students' progress in tertiary STEM fields. Through utilizing a comprehensive examination of demographic profiles, proficiency assessments, facility rating systems, and satisfaction measures, this study determines the complex relationships between HSC-level ICT education as well as success in STEM areas at the university level. Data were collected through an online survey from 244 students enrolled in Computer Science and Engineering (CSE), Software Engineering (SWE), and Information Technology Management (ITM) departments. The results highlight how many different factors have significant effects on students' first-semester SGPA. Several variables, including prior ICT knowledge on data handling from college, quality of instruction provided by the college ICT teacher, and HSC-level ICT course grade, have strong relationships with student performance at the university level. This study illustrates the positive impact of improved instructional materials and teacher-led projects on strong skill development, a phenomenon that will increase overall satisfaction among learners. Although geographical location, gender, and college type have been explored, it does not appear that they have significantly affected ICT course grades directly. Instead, instructional components and techniques for improving skills become important factors in determining students' academic performance. The study not only finds significant relationships but also promotes curriculum improvements with a focus towards ICT education technique optimization. With an aim of improving instructional methods and curriculum design, these observations provide governments and other individuals within education with suggestions that are applicable. The study highlights how important it is to effectively utilize ICT education in order to encourage overall STEM development in Bangladesh's educational system.

The rapid shift toward digital learning environments in higher education, particularly within technology-focused disciplines such as computer engineering, has redefined how students interact, collaborate, and exchange information. While this transition brings enhanced flexibility and resource accessibility, it also introduces new vulnerabilities related to information security and ethical use of digital platforms. This paper presents a structured methodology for cultivating a culture of secure information exchange among computer engineering students in digital learning environments. The study is driven by the recognition that digital literacy alone is insufficient students must also develop awareness, attitudes, and behaviors that prioritize information safety, responsible sharing, and ethical collaboration. The proposed methodology is grounded in a multidisciplinary approach that includes digital pedagogy, cybersecurity education, behavioral science, and peer learning strategies. It combines formal instruction on secure communication tools and protocols with experiential learning activities such as simulations, gamified scenarios, and collaborative assignments that require secure practices. The methodology was implemented in a controlled academic setting with third-year computer engineering students. It involved a four-phase cycle: (1) baseline assessment of students’ security awareness and sharing habits; (2) delivery of instructional content and interactive workshops on secure communication and information ethics; (3) integration of security-conscious practices in collaborative coursework and projects; and (4) post-intervention evaluation through surveys, peer reviews, and repository audits. Findings from the intervention indicate significant improvements in students’ understanding of cybersecurity principles, increased use of secure sharing tools (e.g., Git with multi-factor authentication), and a notable shift in attitudes towards digital responsibility. The majority of students reported heightened sensitivity to access permissions, password hygiene, and potential data exposure risks during online collaboration. Peer interactions also reflected improved norms around responsible information handling. This study concludes that embedding security culture into the educational process - not as an add-on but as a core component of academic and technical activities - can foster sustainable behavioral change. The article contributes to the growing body of literature on digital learning and student cybersecurity by offering a practical, scalable model that educators in engineering and related fields can adapt to their institutional contexts. Future research is recommended to explore the long-term effects of such methodologies and their applicability across other disciplines in digital education.

ABSTRACT Discrete Mathematics is an important and challenging course for computer science and engineering students. It includes topics, such as logic, sets, proofs, number theory, graphs, trees, computation, relations, functions, and basic algorithmic concepts. These topics require strong analytical reasoning and consistent effort. As a result, many students find this course challenging to perform well. The aim of this study is to predict student performance in a Discrete Mathematics course at a reputed private university located in Bangladesh. Data were collected from both course instructors and students during the spring and summer semester of 2025. Instructors provided academic records, such as attendance, quizzes, assignments, and midterm scores. Students provided additional information, which included daily study time, subject interests, and use of learning platforms. The final data set included records for 240 students. K ‐means clustering with the Davies–Bouldin method was used to group similar students. Then, four machine learning (ML) models were trained and tested: Support Vector Machine (SVM), Decision Tree, K ‐Nearest Neighbors, and Naïve Bayes. The models were implemented using Python's scikit‐learn library, with stratified sampling and fivefold cross‐validation. Among the models, SVM achieved the highest accuracy of 96% after parameter tuning. Naïve Bayes had the lowest accuracy due to the assumption of feature independence. Key predictors of performance included mean score, attendance, and daily study hours. Findings show that ML can help instructors identify at‐risk students early, provide focused academic support, and improve learning outcomes. While the results are promising, the study is limited by sample size and does not include psychological or emotional factors. Future work will explore larger data sets and apply interpretable Artificial Intelligence techniques for better model transparency.

ABSTRACT The teaching of parallel programming in undergraduate engineering programs poses challenges related to high cognitive load and limited student engagement. This study presents a pedagogical strategy aimed at facilitating meaningful learning through a reduction in problem domain complexity and active learning techniques. The proposed approach was implemented in a core course on multiprocessor programming in an undergraduate Computer Engineering degree. Three well‐known problem patterns were selected to guide students through different parallel implementations (OpenMP, PThreads, and MPI). This problem reduction strategy enabled scaffolded learning experiences while minimizing the cognitive barriers typically associated with high‐performance computing education. The approach was designed to promote student motivation and autonomy through guided discovery, hands‐on sessions, and peer interaction. Results from student feedback and course outcomes suggest that this methodology improved comprehension, confidence, and engagement. The article discusses the implications of using reduced problem domains and active learning for teaching parallelism in engineering education, and proposes a replicable framework for similar contexts.

Data warehousing has become an essential tool in modern organizations driven by increasing business complexity and technological advancements. Organizations collect vast amounts of data from multiple sources that require efficient storage and analysis solutions. This research paper examines the role of data warehousing in decision making, its integration with emerging technologies, and its growing impact on various industries. Research significance: This research is significant as it highlights the transformative role of data warehousing in decision-making across industries. By improving data quality, accessibility, and integration, data warehouses enhance business intelligence and operational efficiency. The study provides valuable insights into leveraging data warehousing technologies, addressing challenges, and fostering innovation in data-driven environments. Alternatives taken as Secure Data Pool Engine, Privacy-Aware Data Warehouse, Intelligent Resource Cloud for Privacy, Confidential Data Processing Hub and Adaptive Privacy Compute Engine. Evaluation Parameters taken as Wind resources, Construction and maintenance conditions, nautical environmental influence and Provincial financial subsidies. The results show that Intelligent Resource Cloud for Privacy received the highest ranking, whereas Secure Data Pool Engine received the lowest ranking. Intelligent Resource.

Background: The increasing prevalence for public safety threats necessitates the development of intelligent surveillance systems that are capable of real-time weapon detection. These conventional deep learning models achieve high accuracy but are mostly computationally intensive, limiting their deployment on edge devices. The deep learning models have proven to be great, particularly the, you only look once version (YOLOv8) algorithm when applied on Raspberry Pi 5 and web camera. Aims: This research proposes a lightweight and optimized YOLOv8-based deep neural network, specifically tailored for deployment on the Raspberry Pi 5. Study Design: The model is designed using raspberry pi, USB web camera with Google colab baseline. Place and Duration of Study: Sample: Department of Electrical and Electronics Engineering, Joseph Sarwuan Tarka University Makurdi, Nigeria and Department of Computer Engineering Technology, Federal Polytechnic Wannune Nigeria, between June 2024 and January 2025. Methodology: The study used a dataset comprising custom images of six (6) classes and corresponding annotations and labeling specifically tailored for weapon detection. The datasets were captured using USB web camera on raspberry pi 5 using some algorithms of the API and terminal of the raspberry pi5 to have the web camera capture the images in a saved folder. The images were pre-processed and augmented as necessary using algorithms to launch labelimg on raspberry pi 5 platforms to annotate and label the images. Because of the memory and the available TPU on google colab, a google drive was created to upload the datasets and had the model trained using the google colab. The datasets were split on a ratio of 70%, 20% and 10% on train, validate and test respectively with a yaml file of train and val on a class of six (6). It was then proceeded to train the YOLOv8 model, a state-of-the-art object detection architecture, on this dataset. The model was trained, validated and tested using standard evaluation metrics such as precision, recall, and mean Average Precision (MAP). Next, the model (best.pt) was deployed on raspberry pi 5 using an algorithm developed as a user-friendly interface to test the images on the model with the aid of the USB web camera. Results: The dataset after annotation and label was uploaded to google drive and google colab was used for the training of the model after downloading and installing ultralytics and all the dependencies. A yaml file was created with the train and val datasets with their respective paths. The study also shows the result samples of train/box, losses metrics: recall, precision, validation/box, losses and metrices/mAP at _0.5 and metricsmAP_05:0,95 with result showing experimental validation demonstrates that the optimized YOLOv8 model achieves real-time inference at over (axe = 97 %, daga = 97 %, handgun = 93 %, knife = 97 %, matchet = 99 % and picer = 99 %) with a mean average precision (mAP) of 96 % accuracy. Conclusion: The implementation of YOLOv8 for weapon detection on the Raspberry Pi 5 represents a significant advancement in both deep learning algorithms and edge device capabilities. YOLOv8’s improvement over previous versions and the enhanced computational power of the Raspberry Pi 5 provides a robust platform for real-time weapon detection. Future research should continue to address the challenges of deploying advanced models on edge devices and explore innovative solutions to enhance detection accuracy and efficiency.

ABSTRACT Program Outcomes (POs) are critical for engineering program accreditation, yet traditional evaluation methods often lack objectivity, consistency, and timely feedback. While machine learning (ML) has been applied to predict general student success, its use for predicting PO attainment levels from early academic data remains underexplored. This study introduces an integrated framework for computer engineering programs, combining a systematic PO assessment model with ML‐driven prediction. The assessment model quantifies PO attainment rates (POAR) from weighted course assessments, mappings between Course Learning Outcomes (CLOs) and POs, CLO‐assessment relationships, and student grades. Using these POARs, various ML techniques were trained on historical data from 327 graduates, utilizing their grades from 25 early‐semester courses and graduation POARs. Our findings demonstrate that POARs can be successfully predicted from this early data, achieving a mean absolute percentage error around 5%. Consequently, this study presents a scalable and objective tool that (1) provides a systematic framework for POAR measurement; (2) offers an effective ML model for predicting graduation POARs of students; and (3) delivers data‐driven insights for proactive student support, timely interventions, and evidence‐based curriculum optimization, thereby supporting continuous program improvement and accreditation efforts.

The development of technology in the digital era requires the readiness of Vocational High School students, especially in the field of technology and information systems, to optimize the use of technology in the learning process. This challenge is related to students’ academic engagement, which is influenced by technology self-efficacy and learning motivation. This study aims to analyze the relationship between technology self-efficacy and academic engagement, as well as to examine the mediating role of learning motivation in this relationship. This study employs a non-experimental quantitative approach with a correlational design involving students majoring in Network and Application Information Systems, Computer and Network Engineering, and Software Engineering at SMK X Jakarta. The instruments used include the School Engagement Measure, the Computer User Self-Efficacy Scale, and the Academic Motivation Scale. Data collection was conducted online, and data analysis was carried out using SEM through JASP and SmartPLS software. The results of the analysis show that technology self-efficacy has a significant effect on academic engagement, both directly and indirectly through learning motivation as a partial mediator. These findings confirm that technology self-efficacy and learning motivation are key factors in enhancing the academic engagement of vocational school students. The practical implications of this research encourage schools and educators to design digital-based training and intervention programs aimed at improving technology self-efficacy and learning motivation, so that the academic engagement of vocational school students can be optimized in facing the challenges of the digital-era workforce.

Background: Emotion regulation (ER) difficulties are closely linked to maladaptive coping strategies, including impulsive and emotional eating, which undermine health and well-being in young adults. Technical university students are particularly vulnerable due to factors such as a high academic workload, sedentary behavior, and performance-related stress. This study evaluated the effects of a four-week structured physical activity intervention on ER and eating behaviors among engineering students. Methods: Seventy first- and second-year computer science and engineering students (40 males and 30 females, aged 19–25 years) from Politehnica University of Bucharest participated in the study. The intervention included three weekly supervised training sessions and a daily step count requirement (≥6000 steps), verified via weekly smartphone submissions. Pre- and post-intervention assessments employed the Difficulties in Emotion Regulation Scale (DERS-36) and the Adult Eating Behavior Questionnaire (AEBQ-35). Data were analyzed using Kolmogorov–Smirnov tests, Wilcoxon signed-rank tests, and paired-sample t-tests. Results: Significant improvements were observed in five ER domains—non-acceptance of emotional responses, goal-directed behavior, impulse control, access to regulation strategies, and emotional clarity (all p < 0.01). No change occurred in emotional awareness (p > 0.05). Eating behaviors (restrained, emotional, and external eating) showed no significant differences pre- and post-intervention (all p > 0.05). Conclusions: A short-term, structured physical activity program enhanced emotion regulation capacities but did not alter eating behaviors in the short run. These findings highlight the feasibility of embedding low-cost, exercise-based modules into higher education to strengthen students’ psychological resilience. Longer and multimodal interventions may be required to produce measurable changes in eating behaviors.

Libraries are essential in academic work as they expose people to structured, easily procurable information. However, the majority of schools, including Bhayangkara University Surabaya, still face challenges in managing and storing library information because local or manual systems are substandard. The goal of this project is to deploy and test the effectiveness of Google Cloud Computing technologies, such as Google Cloud Storage, Google Cloud SQL, and Google Compute Engine, on a website-based library information system. We adopted a quantitative approach by performing experiments and system testing, i.e., black-box testing, access speed testing, and heavy load resistance testing. The result of the implementation is massive benefits, including a response time of 2 seconds on average, stability with 500 users at the same time, and storage efficiency at just 30% of the original size. Other colleges can have an example that they can use to make a change to a cloud-based digital library from this research. This also helps create digital library information systems that are technology-centered and dependable.

Next-generation sequencing (NGS) technologies offer detailed and inexpensive identification of the genetic structure of living organisms. The massive data volume necessitates the utilization of advanced computational resources for analyses. However, the rapid accumulation of data and the urgent need for analysis tools have caused the development of imperfect software solutions. Given their immense potential in clinical applications and the recent reproducibility crisis discussions in science and technology, these tools must be thoroughly examined. Typically, NGS data analysis tools are benchmarked under homogeneous conditions, with well-trained personnel and ideal hardware and data environments. However, in the real world, these analyses are done under heterogeneous conditions in terms of computing environments and experience levels. This difference is mostly overlooked, therefore studies that examine NGS workflows generated under various conditions would be highly valuable. Moreover, a detailed assessment of the difficulties faced by the trainees would allow for improved educational programs for better NGS analysis training. Considering these needs, we designed an elective undergraduate bioinformatics course project for computer engineering students at Istanbul Technical University. Students were tasked to perform and compare 12 different somatic variant calling pipelines on the recently published SEQC2 dataset. Upon examining the results, we have realized that despite seeming correct, the final variant lists created by different student groups display a high level of heterogeneity. Notably, the operating systems and installation methods were the most influential factors in variant-calling performance. Here, we present detailed evaluations of our case study and provide insights for better bioinformatics training.

This research discusses Association Rules as one of the data mining functions implemented using the Apriori Algorithm. The Institute for Computerization Development (LePKom) is a unit at Gunadarma University that organizes courses and workshops. The course and workshop participants are Gunadarma University students from the following undergraduate and diploma programs: Bachelor of Information Systems (S1-SI), Bachelor of Computer Systems (S1-SK), Diploma in Information Management (D3-MI), Diploma in Computer Engineering (D3-TK), and Bachelor of Informatics Engineering (S1-TI). New students must select three course topics from the six fundamental courses available at the Institute for Computerization Development (LePKom), and from these three choices, students will be assigned one course each semester. Association rules can be generated using the Apriori Algorithm to identify patterns of which course topics are most frequently selected together by new students each semester, as well as to optimize course infrastructure requirements.

Abstract Materials exhibit geometric structures across mesoscopic to microscopic scales, influencing macroscale properties such as appearance, mechanical strength, and thermal behavior. Capturing and modeling these multiscale structures is challenging but essential for computer graphics, engineering, and materials science. We present a framework inspired by hypertexture methods, using implicit surfaces and sphere tracing to synthesize multiscale structures on the fly without precomputation. This framework models volumetric materials with particulate, fibrous, porous, and laminar structures, allowing control over size, shape, density, distribution, and orientation. We enhance structural diversity by superimposing implicit periodic functions while improving computational efficiency. The framework also supports spatially varying particulate media, particle agglomeration, and piling on convex and concave structures, such as rock formations (mesoscale), without explicit simulation. We demonstrate its potential in the appearance modeling of volumetric materials and investigate how spatially varying properties affect the perceived macroscale appearance. As a proof of concept, we show that microstructures created by our framework can be reconstructed from image and distance values defined by implicit surfaces, using both first‐order and gradient‐free optimization methods.

The flipped classroom has been increasingly experienced in various higher and middle level courses. There are interesting results, although there are still few studies on the true benefits inherent to its use. Because it impacts the learning process, it is natural that a good part of these experiences occur in courses in the exact areas, especially in basic disciplines with higher school dropout and retention rates, such as Calculus and Linear Algebra. Thus, experiences that aim at gains in students' learning, should be investigated in order to change the serious situations of school dropout and repetition in these courses. This paper consists of the use of the flipped classroom methodology in the discipline of Linear Algebra of the Computer Engineering course of the Federal Institute of Triangulo Mineiro (IFTM). The results have benefits for learning, as well as indicate factors that may improve the use of this methodology.

The article presents an experimental study on the effectiveness of gamification in the Google Classroom educational platform for developing students' teamwork skills. The research was conducted at Osh State University with 80 first-year Computer Science and Engineering students divided into experimental and control groups. The theoretical foundations of educational process gamification are examined, and a model of a gamified educational environment has been developed and tested, including a system of achievements, team ratings, and multi-component assessment. Methodologically, the study employed a mixed-methods approach combining quantitative assessment through standardized psychometric instruments with qualitative analysis of interviews and focus groups. Statistical analysis included Student's t-tests for between-group comparisons, repeated measures ANOVA for tracking developmental dynamics, multiple regression analysis (R² = 0.67) for determining predictor significance, and structural equation modeling (χ² = 67.35, df = 42, RMSEA = 0.047) to validate causal relationships. Effect sizes (Cohen's d ranging from 1.82 to 2.75) were calculated to assess practical significance. The effectiveness of the model has been experimentally confirmed, showing increased student engagement, improved team interaction, and enhanced digital competences. Research limitations and directions for further model development are identified. Practical recommendations for implementing gamification elements in various educational contexts are presented. The study demonstrates the potential of gamification as a tool for developing soft skills in the context of digital transformation of education.