Graduate school recommender system: Assisting admission seekers to apply for graduate studies in appropriate graduate schools

Hasan, Mahamudul; Ahmed, Shamsuddin; Abdullah, Deen Md.; Rahman, Md. Lutfor

doi:10.1109/iciev.2016.7760053

Cited by 26 publications

(15 citation statements)

References 18 publications

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“…Recommender systems are getting popular nowadays. The graduate recommender system is proposed by [5] to assist students in getting admission in appropriate graduate schools. The K-Nearest Neighbor (KNN) algorithm is applied to recommend particular graduate school to the admission seeker.…”

Section: A Submission Of the Papermentioning

confidence: 99%

Assessment of Affecting Factors for Higher Education Admission Process

Patel,

Trivedi

2019

IJEAT

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Education is the most important aspect of human life. Today, many streams are available to study. It has created a huge opportunity for a student to enroll in any course by fulfilling definite criteria. The courses are managed by various institutes, which are affiliated with particular University. In some courses, students have the choice to enroll without fulfilling any academic criteria. Some students are choosy in joining particular institute or course. Their choice for University or Institute depends upon specific parameters they might have predefined. In this paper, we have studied various parameters, which can influence the decision of a student to take admission in a particular course or institute. To extract essential parameters, we have conducted a survey among students who are studying in different courses and institutes. It is observed from the survey that Job Placement and Good Teaching are very effective parameters in influencing student admission decision-making process. It also reveals that sometimes, the institute is not important when a student is interested in a particular course. This research also guides the institutes or Universities to work upon such parameters to increase the enrollment of students in different courses. To validate this research, the survey from 208 students representing different institutes of India, is collected and analyzed. After analyzing the data collected from the survey, the influential parameters are decided. A graph-based approach is applied to identify the relationship between the varieties of parameters. The results obtained from the graph justify that individual parameters do not affect at large scale in student admission decision-making process while the association of multiple parameters may influence their admission decision-making process.

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Section: A Submission Of the Papermentioning

confidence: 99%

Assessment of Affecting Factors for Higher Education Admission Process

Patel,

Trivedi

2019

IJEAT

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“…[49], [ Collaborative filtering [32], [18], [19], [53], [33], [38], [36], [ [32], [17], [14], [46], [6], [12], [30], [62], [34], [7], [31] Web history / navigations / clicks [43], [37]…”

Section: Purposementioning

confidence: 99%

A Systematic Review of Recommender Systems in Education

Vaidhehi¹,

Suchithra²

2018

IJET

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Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.

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“…This applies to choices one can make while attending higher education, such as what college major to take and what electives to follow (Dwivedi and Roshni, 2017;Khoja and Shetty, 2017;Obeid et al, 2018), as well as to the decision of attending a university or another higher education institution. Whereas the former has been the topic of various recommender system and learning analytics approaches [cf., Hasan et al (2016)], universities are rarely featured in personalized approaches (Rivera et al, 2018). This is arguably surprising, because a significant proportion of students attending higher education in G20 countries is not native to those countries (OECD, 2013)even though most prospective students opt for institutions that are close to home, thus based on proximity (Simões and Soares, 2010;White and Lee, 2020).…”

Section: Introductionmentioning

confidence: 99%

Developing and Evaluating a University Recommender System

Elahi

Starke

Ioini

et al. 2022

Front. Artif. Intell.

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A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.

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Graduate school recommender system: Assisting admission seekers to apply for graduate studies in appropriate graduate schools

Cited by 26 publications

References 18 publications

Assessment of Affecting Factors for Higher Education Admission Process

Assessment of Affecting Factors for Higher Education Admission Process

A Systematic Review of Recommender Systems in Education

Developing and Evaluating a University Recommender System

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