Evaluation of a student-centered online one-to-one tutoring system

ABSTRACT

This paper introduces a system that supports student-centered online one-to-one tutoring and evaluates the practical value of the system by running an experiment with 64 experienced mathematics teachers and 810 students in Grade 7. The experiment lasted for 50 days. A comprehensive evaluation was performed using students’ academic performance before and after usage of the system and the system log files. By classifying the students into active and inactive usage groups, it was determined that active students significantly outperformed inactive students on posttests, but with a small effect size. The results also suggested that high prior knowledge students tended to benefit more from using the system than low prior knowledge students. An explanation for this result was that students with a high level of prior knowledge were more likely to have good-quality interactions with their teachers. Therefore, although some advantages of this type of student-centered online one-to-one tutoring are observed, in this system, both the students and the teachers need to be further facilitated to produce more effective tutoring interactions.

KEYWORDS:

• One-to-one tutoring
• student-centered
• learning analytics
• online learning
• qualitative analysis
• tutoring transcript coding

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 61807004]; Central University Basic Scientific Research Business Expenses Special Funds [grant number 31101200003].

Notes on contributors

Lishan Zhang

Lishan Zhang is an associate professor at Central China Normal University. He received a PhD in Computer Science from Arizona State University. He has published over 20 peer-reviewed academic papers. His research interests include intelligent tutoring systems, student modeling for personalized learning and educational data mining.

Mengqi Pan

Mengqi Pan is a master student majoring in education technology at Beijing normal university. Her research interests include intelligent tutoring systems and teacher professional development in virtual communities.

Shengquan Yu

Shengquan Yu is a Professor at Beijing Normal University. He received a PhD in Educational Technology from Beijing Normal University. His research fields include mobile and ubiquitous learning, ICT and curriculum integration, network learning technology, and education informatization policy. He has published about 100 peer-reviewed academic papers, four popular science books and three scholarly monographs.

Ling Chen

Ling Chen is an associate professor at Beijing Normal University. She received a PhD in Educational Technology from Beijing Normal University. Her research interests include intelligent tutoring systems, teacher professional development in virtual communities and language learning supported by technology.

Jing Zhang

Jing Zhang holds a master degree in education technology at Beijing normal university. Her research interests include intelligent tutoring systems and teacher professional development in virtual communities.