Undergraduates’ participation in faculty-mentored research is becoming an important issue in tertiary education in recent years, and it benefits both undergraduates and faculty members. In reality, many faculty members have research projects that need help from undergraduates, but undergraduates can hardly find the information, which creates information asymmetry problem. Besides, undergraduates lack the experience of doing academic research, the research interest information is incomplete, so they have difficulties in choosing suitable research projects. Thus recommender systems are necessary to facilitate undergraduates’ participation in research projects. Traditional recommendation approaches require relative complete information for decision making, and they can hardly meet the requirements as undergraduates’ research information is incomplete. In this study, we propose a two-stage model that integrates content-based method with collaborative method by leveraging research social networks, where undergraduates are encouraged to connect with faculty members and participate in social network activities, through which research information is collected. The proposed two-stage model alleviates the problems of information asymmetry and incomplete information. The recommender system has been developed in ScholarMate (www.scholarmate.com), and it allows undergraduates to choose suggested research projects.
Liu, Yang; Ma, Jian; Du, Wei; Yang, Chen; and Hua, Zhongsheng, "Supporting Undergraduate Research: Recommending Personalized Research Projects to Undergraduates" (2015). PACIS 2015 Proceedings. 116.