A Learning Analytics Approach to Monitoring the Quality of Online One-to-One Tutoring

Abstract

One-to-one online tutoring provided by human tutors can improve students’ learning outcomes. However, monitoring the quality of such tutoring is a significant challenge. In this paper, we propose a learning analytics approach for monitoring online one-to-one tutoring quality. The approach analyses teacher behaviours and classifies tutoring sessions into those that are effective and those that are not effective. More specifically, we use sequential behaviour pattern mining to analyse tutoring sessions using the CM-SPAM algorithm and classify tutoring sessions into effective and less effective using the J-48 and JRIP decision tree classifiers. To show the feasibility of the approach, we analysed data from 2250 minutes of online one-to-one primary Maths tutoring sessions with 44 tutors from 8 schools. The results showed that the approach can classify tutors’ effectiveness with high accuracy (F measures of 0.89 and 0.98 were achieved). The results also showed that effective tutors present significantly more frequent hint provision and proactive planning behaviours than their less effective colleagues in these online one-to-one sessions. Furthermore, effective tutors sequence their monitoring actions with appropriate pauses and initiations of students’ self-correction behaviours. We conclude that the proposed approach is feasible to monitor the quality of online one-to-one primary Maths tutoring sessions.