A Machine Learning Approach for Advisors to Discover in Higher Education

Abstract

Abstract In higher education, an institute thesis is under the supervision of a scholar with a comparable research interest. An appropriate match has to be made between the topics of scholars and the area of scholar expertise and skills. The determination of the final thesis supervisor is an important factor in the work of the scholar thesis. A system is needed for the information of the scholar and scholar matching for the determination of the supervisors. As currently, the HEC supervisor directory is there, but it is not up to date. There are a lot of supervisor recommendation systems like the cosine similarity technique uses keywords from a text as a metric to calculate the similarity of two scholars in different categories and many others. But theses recommendation system is working on the parameter of supervisor personality matching, criteria and sub-criteria of advisor system and some other demission’s relevance, connectedness, and quality. But, they do not focus on the similarity matching of their area of interest, w.r.t their publications and citations. Thus, a system is needed for the recommendation of the supervisor matching the relevancy according to their domain area, number of citations and publications. This paper explores a system of “SRAF” that assists scholars in finding the best-matched scholar. This proposed framework is different from the existing models because the dataset implemented has some additional features through which it matches the appropriate supervisor like number of area of specializations, publications, and citations. The framework matches the area of interest recommended w.r.t supervisors’ publications and citations to find the best supervisors according to the scholars’ research topic. The success of scholar research not only determined by the supervisor chosen but is also greatly influenced by the performance of the scholar and intellect, aspects which in turn affect higher education.