Using ML to Predict User Satisfaction with ICT Technology for Educational Institution Administration

Abstract

Effective and efficient use of information and communication technology (ICT) systems in the administration of educational organisations is crucial to optimise their performance. Earlier research on the identification and analysis of ICT users’ satisfaction with administration tasks in education is limited and inconclusive, as they focus on using ICT for nonadministrative tasks. To address this gap, this study employs Artificial Intelligence (AI) and machine learning (ML) in conjunction with a survey technique to predict the satisfaction of ICT users. In doing so, it provides an insight into the key factors that impact users’ satisfaction with the ICT administrative systems. The results reveal that AI and ML models predict ICT user satisfaction with an accuracy of 94%, and identify the specific ICT features, such as usability, privacy, security, and Information Technology (IT) support as key determinants of satisfaction. The ability to predict user satisfaction is important as it allows organisations to make data-driven decisions on improving their ICT systems to better meet the needs and expectations of users, maximising labour effort while minimising resources, and identifying potential issues earlier. The findings of this study have important implications for the use of ML in improving the administration of educational institutions and providing valuable insights for decision-makers and developers.

Keywords

• ICT
• data mining
• user satisfaction
• AI
• ML
• explainable artificial intelligence (XAI)