ATTITUDES AND PREFERENCES TOWARDS THE FEATURES OF ADAPTIVE LEARNING PROGRAMS FOR PROFESSIONAL DEVELOPMENT IN THE LOCAL CONSTRUCTION INDUSTRY

Abstract

In recent years, professional upskilling has come to the forefront of national conversation in Singapore. Construction industry professionals looking to stay relevant have been taking part in the large variety of professional development courses being offered today. At the same time, an emerging technology in education that has been gaining traction is adaptive learning, an educational method that utilises artificial intelligence, item response theories and machine learning to create personalised learning materials and activities that are customised to each individual learner. In a fast paced, technologically advanced country like Singapore, professional development should take the next step and integrate adaptive learning into courses for adult built environment professionals. This study examines the attitudes and preferences of Singapore construction professionals towards five features of adaptive learning programs: Knowledge Unit Recommender, Adaptive Quizzes, Forum Recommender, Personalised Feedback and Automatic Teaming. The study uses qualitative research in the form of interviews and focus group discussions to understand the many positive and negative opinions construction professionals have on these features. The key findings of the study showed that professionals had an overall positive attitude towards the features. Most preferred the adaptive quizzes, followed by KU recommender, and personalised feedback, then forum recommender, and least preferred automatic teamin. Respondents liked that adaptive quizzes focus on weaknesses and saves time but may cause inequality in learning. They also liked that the automatic teaming creates diverse teams but disliked the idea of being grouped with weaker learners. Respondents liked using forums to get specific answers to their queries but disliked the low participation and high volume of irrelevant or inaccurate content. They liked the KU recommenders' ability to create a focused and personalised learning path and that it saves time but feared it may be inaccurate. Lastly, they liked the personalised feedback as it provided individual guidance and specific feedback but were not confident that AI could give feedback effectively.