The identification of a neurological disorder known as autism spectrum disorder (ASD) is essential and vital for improving the quality of life and providing appropriate medical care for those with autism. Good health and well-being are essential for individuals with autism, just like anyone else. In the last decade, numerous machine learning (ML) and deep learning (DL) based techniques and methods were used for Autism Disorder Detection (ASD) with the help of magnetic resonance images (MRI). The performance of this technique is susceptible to poor feature representation, redundant features, complexity of DL frameworks, and poor visual quality of the images. This paper presents ASDD based on a parallel Deep Convolution Neural Network (PDCNN). It includes image enhancement, feature extraction, feature selection, deep feature representation, and ASDD. It presents an improved double-stage Gaussian Weiner Filtering scheme to minimize blur, contrast, and uneven illumination in some images. Further, it offers the shape and texture feature extraction of functional MRI (fMRI) with gray level co-occurrence matrix (GLCM), local binary pattern (LBP), and histogram of oriented gradient (HOG), and local directional pattern (LDP). Afterward, an improved teaching-learning-based scheme is utilized to select prominent features to minimize the computational intricacy of the PDCNN. The outcomes of the system are validated on the ABIDE-I dataset.
{"title":"Autism spectrum disorder detection using parallel DCNN with improved teaching learning optimization feature selection scheme","authors":"Triveni Dhamale;Sheetal Bhandari;Varsha Harpale;Pramod Sakhi;Kiran Napte;Anurag Mahajan","doi":"10.23919/SAIEE.2025.11090064","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090064","url":null,"abstract":"The identification of a neurological disorder known as autism spectrum disorder (ASD) is essential and vital for improving the quality of life and providing appropriate medical care for those with autism. Good health and well-being are essential for individuals with autism, just like anyone else. In the last decade, numerous machine learning (ML) and deep learning (DL) based techniques and methods were used for Autism Disorder Detection (ASD) with the help of magnetic resonance images (MRI). The performance of this technique is susceptible to poor feature representation, redundant features, complexity of DL frameworks, and poor visual quality of the images. This paper presents ASDD based on a parallel Deep Convolution Neural Network (PDCNN). It includes image enhancement, feature extraction, feature selection, deep feature representation, and ASDD. It presents an improved double-stage Gaussian Weiner Filtering scheme to minimize blur, contrast, and uneven illumination in some images. Further, it offers the shape and texture feature extraction of functional MRI (fMRI) with gray level co-occurrence matrix (GLCM), local binary pattern (LBP), and histogram of oriented gradient (HOG), and local directional pattern (LDP). Afterward, an improved teaching-learning-based scheme is utilized to select prominent features to minimize the computational intricacy of the PDCNN. The outcomes of the system are validated on the ABIDE-I dataset.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"89-100"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.23919/SAIEE.2025.11090062
Esmeralda Mukon;Karen S. Garner
This paper presents an optimization study for a grid-connected hybrid energy system combining wind, solar PV, and a battery energy storage system (BESS) for hydrogen production. To address the intermittency of wind and solar resources, the grid compensates for insufficient energy to meet the electrolyzer load demand, while excess or curtailed energy is stored in the BESS to enhance reliability. The study employs a constrained multi-objective non-dominated genetic algorithm within the Python-based Pymoo framework. The optimization identifies an ideal grid-connected hybrid energy system with minimized electricity costs and maximized efficiency at high reliability. Subsequently, the BESS is optimized to reduce storage and electricity costs while maintaining reliability. The optimized BESS is successfully integrated into the hybrid system. Cost of electricity and reliability are assessed based on time-of-use tariffs and loss of power supply probability, respectively. Using a 2 MW proton exchange membrane electrolyzer, the study achieves a highly efficient hybrid system with the BESS applied to six Renewable Energy Development Zones in South Africa. Including the BESS reduces electricity costs, improves reliability, and lowers curtailment ratios by 40–66%.
{"title":"Optimization of a grid-connected hybrid energy system with battery storage for hydrogen production in South Africa","authors":"Esmeralda Mukon;Karen S. Garner","doi":"10.23919/SAIEE.2025.11090062","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090062","url":null,"abstract":"This paper presents an optimization study for a grid-connected hybrid energy system combining wind, solar PV, and a battery energy storage system (BESS) for hydrogen production. To address the intermittency of wind and solar resources, the grid compensates for insufficient energy to meet the electrolyzer load demand, while excess or curtailed energy is stored in the BESS to enhance reliability. The study employs a constrained multi-objective non-dominated genetic algorithm within the Python-based Pymoo framework. The optimization identifies an ideal grid-connected hybrid energy system with minimized electricity costs and maximized efficiency at high reliability. Subsequently, the BESS is optimized to reduce storage and electricity costs while maintaining reliability. The optimized BESS is successfully integrated into the hybrid system. Cost of electricity and reliability are assessed based on time-of-use tariffs and loss of power supply probability, respectively. Using a 2 MW proton exchange membrane electrolyzer, the study achieves a highly efficient hybrid system with the BESS applied to six Renewable Energy Development Zones in South Africa. Including the BESS reduces electricity costs, improves reliability, and lowers curtailment ratios by 40–66%.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"125-134"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.23919/SAIEE.2025.11090066
S. Sinha;B. Lacquet;B. T. J. Maharaj;N. Maddali
The SAIEE Africa Research Journal, incorporating the Transactions of the South African Institute of Electrical Engineers (SAIEE), has evolved from a local cornerstone of South African engineering research into a globally recognized publication platform. Since its establishment in 1909, the journal has consistently fostered innovation and academic excellence in electrical engineering and related disciplines. This article summarizes the journal's transformative journey, highlighting its integration into prominent global databases/indices such as IEEE Xplore, Scopus, SciELO SA, DOAJ and WoS. These achievements have amplified its international visibility and impact, as reflected in steadily increasing SCImago Journal Rank (SJR) metrics and the attainment of its first Impact Factor in 2024. The journal's commitment to ethical publishing practices and alignment with global best practices in peer-review have further bolstered its credibility. Key milestones, such as the integration of over a century of archives into IEEE Xplore and the adoption of Open Access Creative Commons licensing, highlight the journal's mission to make African engineering research globally accessible. Additionally, its diverse editorial board and international collaboration highlight its role as a bridge between researchers worldwide.
南非电气工程师学会（SAIEE）的《南非电气工程师学会学报》（Transactions of South African Institute of Electrical Engineers，简称SAIEE）已从南非工程研究的一个地方基石发展成为一个全球公认的出版平台。自1909年成立以来，该杂志一直致力于电气工程及相关学科的创新和学术卓越。本文总结了该期刊的变革历程，重点介绍了其与IEEE Xplore、Scopus、SciELO SA、DOAJ和WoS等全球知名数据库/索引的整合。这些成就扩大了SCImago的国际知名度和影响力，这反映在稳步增长的SCImago期刊排名（SJR）指标和2024年第一个影响因子的实现上。该杂志对道德出版实践的承诺以及与同行评议的全球最佳实践保持一致，进一步增强了其可信度。关键的里程碑，例如将一个多世纪的档案整合到IEEE explore中，以及采用开放获取知识共享许可，突出了该杂志的使命，即使非洲工程研究能够在全球范围内获得。此外，其多样化的编辑委员会和国际合作突出了其作为世界各地研究人员之间桥梁的作用。
{"title":"From local legacy to global impact: The SAIEE Africa research journal's journey through international indices","authors":"S. Sinha;B. Lacquet;B. T. J. Maharaj;N. Maddali","doi":"10.23919/SAIEE.2025.11090066","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090066","url":null,"abstract":"The SAIEE Africa Research Journal, incorporating the Transactions of the South African Institute of Electrical Engineers (SAIEE), has evolved from a local cornerstone of South African engineering research into a globally recognized publication platform. Since its establishment in 1909, the journal has consistently fostered innovation and academic excellence in electrical engineering and related disciplines. This article summarizes the journal's transformative journey, highlighting its integration into prominent global databases/indices such as IEEE Xplore, Scopus, SciELO SA, DOAJ and WoS. These achievements have amplified its international visibility and impact, as reflected in steadily increasing SCImago Journal Rank (SJR) metrics and the attainment of its first Impact Factor in 2024. The journal's commitment to ethical publishing practices and alignment with global best practices in peer-review have further bolstered its credibility. Key milestones, such as the integration of over a century of archives into IEEE Xplore and the adoption of Open Access Creative Commons licensing, highlight the journal's mission to make African engineering research globally accessible. Additionally, its diverse editorial board and international collaboration highlight its role as a bridge between researchers worldwide.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"84-88"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.23919/SAIEE.2025.11090061
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2025.11090061","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090061","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"135-135"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.23919/SAIEE.2025.11090065
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2025.11090065","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090065","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"82-82"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.23919/SAIEE.2025.11090063
Boriane Y. Tchaleu;Alain R. Ndjiongue;Collins A. Leke
The deep learning ability to recognize patterns in data has recently become popular within education. Created in 2014, generative adversarial networks (GANs) are innovative classes of deep learning generative models based on game theory and consist of two players. GANs generate data from scratch using two neural networks: the generator and the discriminator. Since their creation, GANs have been utilized in many applications and have advantages and disadvantages. In light of such a long journey, evaluating the technology is essential as it provides readers with the way forward. To this end, this paper reviews GANs and explores some fundamental challenges that develop during evaluation and training. We also discuss GANs' challenges and elaborate subsequent solutions. Through a single context, we explain the reasoning behind the GAN technology and examine its direction and motivation. We discuss different variants of GANs and real-world application examples, including performance evaluation metrics across various sectors. We consider results obtained recently and highlight ideas for further investigation. This detailed retrospect will give the reader a better understanding of the possible uses of GANs. It will also show how they can help address current issues in a variety of disciplines. Before that, the paper reviews GANs' architectures and network approaches and elaborates on challenges and solutions. The reader is then guided through the literature on the various applications of GANs and the importance of the research interest associated with GANs. As a final step, we suggest the way forward and conclude the review.
{"title":"Generative adversarial networks: A comprehensive review and the way forward","authors":"Boriane Y. Tchaleu;Alain R. Ndjiongue;Collins A. Leke","doi":"10.23919/SAIEE.2025.11090063","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.11090063","url":null,"abstract":"The deep learning ability to recognize patterns in data has recently become popular within education. Created in 2014, generative adversarial networks (GANs) are innovative classes of deep learning generative models based on game theory and consist of two players. GANs generate data from scratch using two neural networks: the generator and the discriminator. Since their creation, GANs have been utilized in many applications and have advantages and disadvantages. In light of such a long journey, evaluating the technology is essential as it provides readers with the way forward. To this end, this paper reviews GANs and explores some fundamental challenges that develop during evaluation and training. We also discuss GANs' challenges and elaborate subsequent solutions. Through a single context, we explain the reasoning behind the GAN technology and examine its direction and motivation. We discuss different variants of GANs and real-world application examples, including performance evaluation metrics across various sectors. We consider results obtained recently and highlight ideas for further investigation. This detailed retrospect will give the reader a better understanding of the possible uses of GANs. It will also show how they can help address current issues in a variety of disciplines. Before that, the paper reviews GANs' architectures and network approaches and elaborates on challenges and solutions. The reader is then guided through the literature on the various applications of GANs and the importance of the research interest associated with GANs. As a final step, we suggest the way forward and conclude the review.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 3","pages":"101-124"},"PeriodicalIF":1.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11090063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852573
A. Ferrer-Rojas;B. T. J. Maharaj
The rapid and expansive integration of Internet of Things (IoT) environments across various industrial sectors has led to an unprecedented surge in data generation and management. This exponential growth in data underscores the critical necessity for robust data security methodologies that can effectively safeguard the confidentiality and integrity of information without imposing undue computational burdens. In response to this challenge, numerous studies have sought to leverage Attribute-Based Encryption (ABE) as a means to enable fine-grained access control. Among the ABE variants, Ciphertext Policy ABE (CP-ABE) and bilinear pairings have emerged as popular choices to construct security schemes that strike a balance between robust protection and computational efficiency. Despite the advancements achieved through CP-ABE and bilinear pairings, a prevalent concern arises in the utilization of Linear Secret Sharing Scheme (LSSS) access policies. LSSS policies, while providing a flexible and expressive way to define access controls, can significantly impact the execution time of encryption methods. This study recognizes the importance of addressing this challenge and explores the potential of employing a Key Policy Attribute-Based Encryption (KP-ABE) approach. The primary objective is to mitigate the computational overhead associated with encryption methods, thereby enhancing the efficiency of data security measures within IoT environments. Furthermore, this research delves into the incorporation of Elliptic Curve Cryptography (ECC) to generate cryptographic keys. ECC, known for its strong security properties and computational efficiency, is considered a promising approach to bolster data security while concurrently minimizing computational overhead. By integrating KP-ABE with ECC, this study aims to offer a comprehensive solution that ensures robust security measures within the intricate landscape of IoT environments. Through detailed analysis and empirical investigation, the research endeavors to contribute valuable insights to the ongoing discourse on securing IoT data in a manner that aligns with the dual imperatives of security and computational efficiency.
{"title":"Multiauthority KP-ABE access model with elliptic curve cryptography","authors":"A. Ferrer-Rojas;B. T. J. Maharaj","doi":"10.23919/SAIEE.2025.10852573","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852573","url":null,"abstract":"The rapid and expansive integration of Internet of Things (IoT) environments across various industrial sectors has led to an unprecedented surge in data generation and management. This exponential growth in data underscores the critical necessity for robust data security methodologies that can effectively safeguard the confidentiality and integrity of information without imposing undue computational burdens. In response to this challenge, numerous studies have sought to leverage Attribute-Based Encryption (ABE) as a means to enable fine-grained access control. Among the ABE variants, Ciphertext Policy ABE (CP-ABE) and bilinear pairings have emerged as popular choices to construct security schemes that strike a balance between robust protection and computational efficiency. Despite the advancements achieved through CP-ABE and bilinear pairings, a prevalent concern arises in the utilization of Linear Secret Sharing Scheme (LSSS) access policies. LSSS policies, while providing a flexible and expressive way to define access controls, can significantly impact the execution time of encryption methods. This study recognizes the importance of addressing this challenge and explores the potential of employing a Key Policy Attribute-Based Encryption (KP-ABE) approach. The primary objective is to mitigate the computational overhead associated with encryption methods, thereby enhancing the efficiency of data security measures within IoT environments. Furthermore, this research delves into the incorporation of Elliptic Curve Cryptography (ECC) to generate cryptographic keys. ECC, known for its strong security properties and computational efficiency, is considered a promising approach to bolster data security while concurrently minimizing computational overhead. By integrating KP-ABE with ECC, this study aims to offer a comprehensive solution that ensures robust security measures within the intricate landscape of IoT environments. Through detailed analysis and empirical investigation, the research endeavors to contribute valuable insights to the ongoing discourse on securing IoT data in a manner that aligns with the dual imperatives of security and computational efficiency.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"59-67"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852569
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2025.10852569","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852569","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"44-44"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852565
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2025.10852565","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852565","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"79-79"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.23919/SAIEE.2025.10852572
T. Fu;R. Sun;C. Li;L. Wang
Cultivating students' software design capabilities through effective training has always been a challenge in software education. This paper is aimed at addressing this issue by adopting a multi-scenario case study approach to examine the independent design processes of 23 undergraduate students on an online teaching system. The selected case scenario models include transaction flow diagrams (TFDs), activity diagrams, sequence diagrams, and entity-relationship (ER) diagrams. By analyzing students' behavioral performances and design outcomes, a series of common phenomena are identified. These phenomena encompass common errors, such as overlooking key steps, struggling to distinguish similar data objects, and omitting critical entities or attributes. Common behaviors include offering various solutions, facing challenges in achieving specific design goals due to a lack of prior experience, and experiencing difficulties meeting requirements using prescribed syntax. Common approaches to assist students include providing reference software, adopting teamwork for idea generation, and allowing iterative modifications to improve outcomes. Based on these common phenomena exhibited by students, several recommendations are provided for software educators to enhance the development of students' software design capabilities, which mainly include considering students' prior experience in assignments, providing design references for unfamiliar software, encouraging peer discussions and multiple iterations, and guiding students towards continuous improvement rather than disregarding unconventional outcomes. The common phenomena identified in this paper seamlessly integrate with software design education, reflecting its distinct characteristics. Those common phenomena will help researchers understand student needs and challenges. Additionally, the research design, which analyzes student behaviors based on their software design outcomes, provides a fresh perspective in the field. Furthermore, the conclusions drawn in this paper offer valuable insights for educators who aim to enhance classroom experiences in software design courses.
{"title":"Common phenomena exhibited by students in their individual design processes: A multi-scenario case study on software design education","authors":"T. Fu;R. Sun;C. Li;L. Wang","doi":"10.23919/SAIEE.2025.10852572","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10852572","url":null,"abstract":"Cultivating students' software design capabilities through effective training has always been a challenge in software education. This paper is aimed at addressing this issue by adopting a multi-scenario case study approach to examine the independent design processes of 23 undergraduate students on an online teaching system. The selected case scenario models include transaction flow diagrams (TFDs), activity diagrams, sequence diagrams, and entity-relationship (ER) diagrams. By analyzing students' behavioral performances and design outcomes, a series of common phenomena are identified. These phenomena encompass common errors, such as overlooking key steps, struggling to distinguish similar data objects, and omitting critical entities or attributes. Common behaviors include offering various solutions, facing challenges in achieving specific design goals due to a lack of prior experience, and experiencing difficulties meeting requirements using prescribed syntax. Common approaches to assist students include providing reference software, adopting teamwork for idea generation, and allowing iterative modifications to improve outcomes. Based on these common phenomena exhibited by students, several recommendations are provided for software educators to enhance the development of students' software design capabilities, which mainly include considering students' prior experience in assignments, providing design references for unfamiliar software, encouraging peer discussions and multiple iterations, and guiding students towards continuous improvement rather than disregarding unconventional outcomes. The common phenomena identified in this paper seamlessly integrate with software design education, reflecting its distinct characteristics. Those common phenomena will help researchers understand student needs and challenges. Additionally, the research design, which analyzes student behaviors based on their software design outcomes, provides a fresh perspective in the field. Furthermore, the conclusions drawn in this paper offer valuable insights for educators who aim to enhance classroom experiences in software design courses.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 2","pages":"46-58"},"PeriodicalIF":1.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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