Inferring the Student Social Loafing State in Collaborative Learning with a Hidden Markov Model: A Case on Slack

Xi Zhang, Shan Jiang, Yihang Cheng

Abstract: With the increasingly prevailing usage of Information and Communication technologies (ICT) in collaborative learning, students can cooperate with others online easily, in spite of the restriction of time and location. Social loafing, a common phenomenon in collaborative work, has negative effect on team performance, especially on the individual's knowledge sharing behavior. In recent years, there are also some researches pointing out that social loafing is a kind of hidden and unobservable behavior. In this study, we propose a research model based on the stimulus-organism-response (S-O-R) framework and build a hidden Markov model (HMM) to infer the student's unobservable social loafing state. We collect real world behavior data from an online collaborative course from Nov 11th 2016 to Dec 21th 2016.The dataset includes more than 1200 knowledge sharing records from 150 students on Slack. Our research is expected to contribute in both academic study and managerial implications on how to set up a collaborative team.