Toward Open-Source Cloud-Based Visual Machine Learning Platform: A Human-Interface Usability Study

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Argitaratua izan da:Journal of Information Systems Education vol. 36, no. 4 (Fall 2025), p. 342-352
Egile nagusia: ElSaid, AbdElRahman
Beste egile batzuk: Shi, Yao, Mkaour, Mohamed Wiem, Altalouli, Mahmoud, Tawfik, Anas
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100 1 |a ElSaid, AbdElRahman  |u College of Science & Engineering University of North Carolina Wilmington Wilmington, NC 28403, USA elsaida@uncw.edu 
245 1 |a Toward Open-Source Cloud-Based Visual Machine Learning Platform: A Human-Interface Usability Study 
260 |b EDSIG  |c Fall 2025 
513 |a Journal Article 
520 3 |a The growing integration of artificial intelligence (AI) into everyday life necessitates a transformation in machine learning (ML) education and development practices, empowering end users with domain knowledge to independently design, train, test, and deploy specialized ML models. However, the technical complexity of ML, particularly in areas such as neural networks, presents a significant barrier for those users. To overcome this challenge, it is essential to reduce the cognitive burden associated with coding, algorithm configuration, and system setup. This study introduces an early-stage prototype of an open-source, cloud-based visual ML platform aimed at lowering this barrier. The platform enables users to configure, execute, and monitor ML workflows through an intuitive graphical interface, eliminating the need for programming skills or environment setup. To evaluate the platform's usability and user-friendliness, a user study was conducted involving participants from diverse academic backgrounds. Participants engaged with both visual and command-line versions of the system and completed a structured questionnaire. The results revealed a strong preference for the visual interface, especially among users with limited technical experience. These findings suggest that intuitive, no-code platforms can significantly reduce entry barriers and foster broader engagement with ML in educational settings. 
653 |a Usability 
653 |a Computer science 
653 |a Prototypes 
653 |a Kindness 
653 |a Everyday life 
653 |a Optimization techniques 
653 |a Machine learning 
653 |a Artificial intelligence 
653 |a Barriers 
653 |a Human-computer interaction 
653 |a End users 
653 |a Neural networks 
653 |a Learning 
653 |a Education 
653 |a Open source software 
653 |a Skills 
653 |a Transformation 
653 |a Cloud computing 
653 |a Empowerment 
653 |a Design 
653 |a Architecture 
653 |a Intellectual Disciplines 
653 |a Expertise 
653 |a Experiments 
653 |a Educational Objectives 
653 |a Database Management Systems 
653 |a Resource Allocation 
653 |a Algorithms 
653 |a Educational Quality 
653 |a Editing 
653 |a Structural Elements (Construction) 
653 |a Outcomes of Education 
653 |a Networks 
653 |a Programming 
700 1 |a Shi, Yao  |u Cameron School of Business University of North Carolina Wilmington Wilmington, NC 28403, USA shiy@uncw.edu 
700 1 |a Mkaour, Mohamed Wiem  |u College of Innovation & Technology University of Michigan-Flint Flint, MI 48109, USA mmkaouer@umich.edu 
700 1 |a Altalouli, Mahmoud  |u Department of Education, Languages, and Instructional Design State University of New York Brockport Brockport, NY 14420, USA maltalouli@brockport.edu 
700 1 |a Tawfik, Anas 
773 0 |t Journal of Information Systems Education  |g vol. 36, no. 4 (Fall 2025), p. 342-352 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3272440667/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3272440667/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3272440667/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch