A research analytics framework‐supported recommendation approach for supervisor selection

Zhang, Mingyu; Ma, Jian; Liu, Zhiying; Sun, Jianshan; Silva, Thushari P.

doi:10.1111/bjet.12244

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…We used Average Rate (AR) [6] and Normalized Discounted Cumulative Gain (NDCG) [6] as the Table 2 reports AR values of the user ratings for both retrieval models in the three different settings. It can be easily observed that for both retrieval models, the Custom setting of criteria/subcriteria performs better, and between the TF/IDF and BM25 algorithms, in most cases, the BM25 based algorithm performs better.…”

Section: Discussionmentioning

confidence: 99%

“…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: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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“…The model emphasizes on the fact that when a student with specific set of skills is successful in a course then another student with similar set of skills will have higher success rate in the said course. In 2016, Zhang et al have presented a comprehensive novel approach based on research analytics framework for finding and recommending supervisors for new students. The system integrates multiple measurements from three dimensions, that is, relevance, connectivity, and quality to generate more satisfactory recommendations.…”

Section: Related Workmentioning

confidence: 99%

Student career path recommendation in engineering stream based on three‐dimensional model

Verma

Sood

Kalra

2017

Comp Applic In Engineering

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In the current scenario, students remain in dilemma to choose right career path in their lives. Many students have chosen their career path without receiving proper guidance from suitable professionals or services. This can potentially cause mismatch among personality, interest, and skill of students. Relative to this context, this paper proposes a comprehensive student‐centric recommendation system based on research analytics framework for choosing best career path. More specifically, this system utilizes the multiple measurements from three‐dimensional model, that is, preference, fuzzy logic, and influence. By integrating these measurements with the relative weighted set generated using Analysis Hierarchical Process (AHP) decision system, a desire score of student related to each career oriented engineering stream is computed. The results of user based evaluation demonstrates that our proposed approach generates more satisfactory career based path recommendations as compared to other baseline methods.

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“…Research analytics Zhang et al (2015) present a research analytics framework for matching doctoral students and supervisors. The analytics use multiple measurements on three dimensions (relevance, connectivity and quality) to do the matching.…”

Section: Data Display and Data Visualisation: Making The Data Analysesmentioning

confidence: 99%

Big Data and Educational Research

Beneito-Montagut¹

2017

The BERA/SAGE Handbook of Educational Research: Two Volume Set

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Big data and data analytics offer the promise to enhance teaching and learning, improve educational research and progress education governance. This chapter aims to contribute to the conceptual and methodological understanding of big data and analytics within educational research. It describes the opportunities and challenges that big data and analytics bring to education as well as critically explore the perils of applying a data driven approach to education. Despite the claimed value of the increasing amounts of large and complex data sets and the growing interest in making sense of them there is still limited knowledge on big data and educational research. Over the last decades, the developments on information and communication technologies are reshaping teaching and learning and the governance of education. A broad variety of online behaviours and transactional data is (or can be) now stored and tracked. Its analysis could provide meaningful insights to enhance teaching and learning processes, to make better management decisions and to evaluate progresses-of individuals and education systems. This chapter starts by defining big data and the sources and artefacts collect, generate and display data. In doing so it explores aspects related to data ownership and researchers' access to big data. It then assesses the value of big data for educational research by critically considering the stages involved in the use of big data, providing examples of recent educational research using big data.

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A research analytics framework‐supported recommendation approach for supervisor selection

Cited by 15 publications

References 31 publications

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

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

Student career path recommendation in engineering stream based on three‐dimensional model

Big Data and Educational Research

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