A Personality Matching-Aided Approach for Supervisor Recommendation (Research-in-Progress)

Zhang, Mingyu; Sun, Jun; Ma, Jian; Wu, Tailai; Liu, Zhiying

doi:10.1109/hicss.2016.90

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Zhang et al [6] presented a Research Analytics Framework for Education (RAF-E), this being a student centric method for finding and recommending supervisors for new postgraduate students, considering different metrics from 3 dimensions: relevance, connectivity, and quality. Zhang et al [7] proposed a personality-matching aided approach for supervisor recommendation based on their previous work [6], which integrates objective measurements (relevance, connectivity, and quality) and subjective personality matching, to get a list of supervisors to recommend.…”

Section: Related Workmentioning

confidence: 99%

“…Some existing works utilize a combination of both subjective and objective measures of different criteria but also have missed important aspects, such as the professor's collaboration network, and do not utilize important details about other potentially relevant criteria, such as citations of papers, recent publications, and research grant details (grant amount, duration, role, etc.) [6] [7].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

Hasan¹,

Schwartz²

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Selection of a suitable Ph.D. supervisor is a very important step in a student's career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student's research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor's involvement and no subjective measures are employed.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%