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Tan, Seng Chee
Preferred name
Tan, Seng Chee
Email
sengchee.tan@nie.edu.sg
Department
Office of Graduate Studies and Professional Learning (GPL)
Learning Sciences and Assessment (LSA)
ORCID
2 results
Now showing 1 - 2 of 2
- PublicationOpen AccessMENTOR – Intelligent mobile online peer tutoring application for face-to-face and remote peer tutoring(2019-12-02)
;Chung, Sheng-HungE-learning platforms have been increasingly adopted by universities to extend and enhance learning. However, the literature review has shown that limited research has been conducted on the effects of electronic peer tutoring on student learning. Correspondingly, there is a lack of a suite of technological affordances to facilitate online peer tutoring sessions and appointments remotely. This paper describes the development of a novel smartphone app – Mobile Education Networked Tutoring On Request (MENTOR) – to facilitate face-to-face and remote peer tutoring. The MENTOR app aims to predict the tutoring needs of students using tutor-tutee matching, provides coordination of face-to-face tutoring sessions via the use of smartphones’ location data and online operation of remote tutoring sessions.283 274 - PublicationOpen AccessHolistic design of a mobile peer tutoring application based on learning and user needs analysis(2020)
; ;Chung, Sheng-Hung; ; Wong, Wai HoeResearch has shown that peer tutoring at the university level could improve students’ performance and, enhance their motivation and learning, increase self-determination and learner autonomy, and reinforce conceptual knowledge by providing opportunities for reapplication of concepts. This paper describes the development of a mobile peer tutoring application – Mobile Education Networked Tutoring On Request (MENTOR). We start with a review of the literature to identify the relevant affordances that this mobile app should possess. In addition, questionnaires were administered with students studying in higher education to understand the needs of peer tutoring with tutors and tutees. The findings of the survey data showed that a majority of the students are receptive to peer tutoring and found it to be a user-friendly and intuitive method of mobile peer tutoring. One feature of MENTOR is the tutor-tutee matching – tutees are individually paired with tutors by using predictive modeling based on student data. Tutor-tutee matching can be efficiently accomplished via MENTOR mobile application by granting tutees the choice of tutors based on mutual tutor-tutee availabilities, students' background and tutor ratings. The other main features of the mobile peer tutoring application, such as online peer tutoring are presented in this study. The study contributes to the application of learning sciences and learning technologies to provide a holistic design for supporting student peer tutoring at the university level.92 272