A Bayesian-based knowledge tracing model for improving safety training outcomes in construction: An adaptive learning framework

Sheng Xu , Manfang Sun , Weili Fang , Ke Chen , Hanbin Luo , Patrick X.W. Zou

Abstract

Safety training plays a pivotal role in effectively reducing unsafe behaviors in the construction industry. Despite the numerous effort to improve safety training, existing studies lacked consideration of trainees' different learning characters, nor did they adapt the suitable training materials to trainees' mastery of safety knowledge. Against this contextual backdrop, this research proposes a Bayesian-based Knowledge Tracing (BKT) model to recommend personalized training materials according to trainees's learning progress. The proposed BKT model tracks and predicts trainees' performance with their cognitive characters, abilities, and historical training records. It also adjusts trainee's probability of mastering safety knowledge concepts for determining future training sessions. In this way, the safety training is adaptive and personalized, thus more effective. An exploration study is conducted in the laboratory environment to validate the effectiveness and feasibility of the proposed BKT model. The research results showed that 83.33% of the respondents felt gained safety knowledge after the training, and 66.67% of the respondents affirmed the effectiveness of the BKT model-based training system in improving the effectiveness of training. The results demonstrated that the proposed model performed well at educating safety knowledge and reducing unsafe behavior.

https://www.sciencedirect.com/science/article/pii/S266616592200045X