ADAM: Advisory agents modeling system to enhance student-supervisor decision making

Mosharraf, Maedeh; Taghiyareh, Fattaneh; Soleimani, Aliakbar; Orooji, Fatemeh

doi:10.1109/ictee.2012.6477236

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…These parameters are GPA, undergraduate university, research interest, passed courses and specific skills [7]. The studies show that supervisors tend to work with the students who share high compatibility with them [7,9]. Maedeh develops an advisory agents modeling system in University of Tehran to enhance the decision making of student-supervisor selection [9].…”

Section: Student-supervisor Selectionmentioning

confidence: 99%

“…The studies show that supervisors tend to work with the students who share high compatibility with them [7,9]. Maedeh develops an advisory agents modeling system in University of Tehran to enhance the decision making of student-supervisor selection [9]. Additionally, researchers propose an approach based on genetic algorithm for studentsupervisor assignment [7].…”

Section: Student-supervisor Selectionmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Recommendation Service for Student-Selection on Research Social Network: Bridging the Gap Between Students and Supervisors—Research-in-Progress

Zhang¹,

Sun²

2019

dtssehs

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Student-selection is of critical importance for research supervisors in the higher education environment. On account of the information asymmetry, it poses a significant challenge for supervisors to find the most appropriate students. Current studies are limited to the context of one university. They are not suitable in web 2.0 era which are inundated with vast online information. This article proposes an intelligent approach with the help of recommendation system techniques which have emerged in both research social network to provide services for scholars and e-commerce applications to serve for consumers. Furthermore, the method distinguishes a supervisor according to his co-author network firstly. Then, it applies respective recommendation strategy to provide student recommendation services for the target supervisor. A prototype is implemented on Scholarmate, which is a research social network website emphasizing communication between students and supervisors. The evaluation of our proposed method will be completed in the future.

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Section: Student-supervisor Selectionmentioning

confidence: 99%

Section: Student-supervisor Selectionmentioning

confidence: 99%

An Intelligent Recommendation Service for Student-Selection on Research Social Network: Bridging the Gap Between Students and Supervisors—Research-in-Progress

Zhang¹,

Sun²

2019

dtssehs

View full text Add to dashboard Cite

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“…Multiple methods have been proposed for supervisor selection (Mosharraf & Taghiyareh, ; Mosharraf, Taghiyareh, Soleimani & Orooji, ), such as multi‐criteria decision making (MCDM) (Datta, Beriha, Patnaik & Mahapatra, ), analytical hierarchy process (AHP) (Ray & Marakas, ) and analytic network process (ANP) (Momeni, Samimi, Afshari, Maleki & Mohammadi, ). In order to make a sensible decision, Datta et al () developed a strategic approach based on the MCDM method.…”

Section: Literature Reviewmentioning

confidence: 99%

A research analytics framework‐supported recommendation approach for supervisor selection

Zhang¹,

Ma²,

Liu³

et al. 2015

Brit J Educational Tech

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Identifying a suitable supervisor for a new research student is vitally important for his or her academic career. Current information overload and information disorientation have posed significant challenges for new students. Existing research for supervisor identification focuses on quality assessment of candidates, but ignores indirect relevance with candidate supervisors' previous students, social network connections and their thinking styles. This paper presents a comprehensive student-centric approach based on research analytics framework for finding and recommending supervisors for new students. In particular, it integrates multiple measurements from three dimensions, ie, relevance, connectivity and quality. A prototype system was developed to support student-supervisor recommendations on a research social network platform (ie, www.ScholarMate.com). The results of user-based evaluations demonstrate that our proposed approach generates more satisfactory recommendations as compared with that of all baseline methods.

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An Approach to Designing the Educational Intelligent Analytical System

Globa

Novogrudska

Popova

2021

Current Trends in Communication and Information Technologies

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ADAM: Advisory agents modeling system to enhance student-supervisor decision making

Cited by 8 publications

References 10 publications

An Intelligent Recommendation Service for Student-Selection on Research Social Network: Bridging the Gap Between Students and Supervisors—Research-in-Progress

An Intelligent Recommendation Service for Student-Selection on Research Social Network: Bridging the Gap Between Students and Supervisors—Research-in-Progress

A research analytics framework‐supported recommendation approach for supervisor selection

An Approach to Designing the Educational Intelligent Analytical System

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