A Mentor finder based on student preference and learning status for webbased learning systems
Gwo-Dong Chen, Chin-Yeh Wang, National Central Univ., Taiwan ; Chen-Chung Liu, Yuan Ze Univ., Taiwan ; Chih-Kai Chang, Da-Yeh Univ., Taiwan
EdMedia + Innovate Learning, in Norfolk, VA USA ISBN 978-1-880094-42-6 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC
Because students can not interact face to face with each other in an asynchronous learning situation, they are difficult to know who is able to help them. Thus, teachers or teaching assistants should put many efforts in solving students' questions. Besides, students have no chance to learn by teaching others. In this paper, we use existing commercial machine learning tools to construct a mechanism for suggesting peer mentors for students according to their questions and preference. The mechanism will locate peer mentors who not only perform better than the student in the area of the proposed questions but also can possibly provide solutions that can be understood by the student that issues the questions. Experiment data is provided to demonstrate that the mechanism also distributes the mentors equally without putting all the efforts on some particular well perform students..
Chen, G.D., Wang, C.Y., Liu, C.C. & Chang, C.K. (2001). A Mentor finder based on student preference and learning status for webbased learning systems. In C. Montgomerie & J. Viteli (Eds.), Proceedings of ED-MEDIA 2001--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 274-279). Norfolk, VA USA: Association for the Advancement of Computing in Education (AACE).
© 2001 Association for the Advancement of Computing in Education (AACE)