Abstract The Student Industrial Work Experience Scheme (SIWES) is a crucial component of technical and vocational education in Nigerian institutions, designed to bridge the gap between classroom learning and industry practice. However, traditional SIW

Authors

  • O. Oladipo Yaba College of Technology, Nigeria
  • O. S. Ojo University of Ilesa, Ilesa, Nigeria
  • Y. E. Ogunwale University of Ilesa, Ilesa, Nigeria
  • J.O. Adigun Yaba College of Technology, Nigeria
  • O. A. Oyinloye University of Ilesa, Ilesa, Nigeria

Keywords:

Classification algorithm, Data mining, Decision tree, Naïve Bayes, k-nearest neighbour

Abstract

Abstract The Student Industrial Work Experience Scheme (SIWES) is a crucial component of technical and vocational education in Nigerian institutions, designed to bridge the gap between classroom learning and industry practice. However, traditional SIWES logbooks, maintained in paper format, pose challenges such as limited accessibility, data loss, and difficulty in leveraging documented experiences for analytics and grading. This study presents the design and development of a Cloud-Based Logbook-as-a-Service (LaaS) for SIWES, enabling real-time documentation, retrieval, and analysis of students’ industrial experiences. The system has an integrated assessment module to grade student performance based on logged activities and feedback from industry supervisors. This study provides a system that fosters digital transformation on SIWES documentation and grading processes by providing a centralized, scalable, and accessible platform, this innovation enhances the efficiency of SIWES evaluation and fosters data-driven decision-making for academic and industry stakeholders. To assess the usability of the developed platform, the system was evaluated based on user experience and task completion time. The overall system performance, measured using these metrics, indicated positive outcomes.

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Published

2026-06-13