Design of personalized teaching resource recommendation system based on wireless communication network

Suling, Song

doi:10.1145/3469213.3472787

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…In a similar study by Ezz and Elshenawy, a multi-class binary classification machine learning technique predicts future college departments. The model aims to determine students’ performance in a specific program in the Faculty of Engineering ( Song 2021 ). Here, the decision on a college program is made based on the predicted performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

Федушко

Ustyianovych²,

Syerov³

2022

J. Intell.

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In this article, we provide an approach to solve the problem of academic specialty selection in higher educational institutions with Ukrainian entrants as our target audience. This concern affects operations at universities or other academic institutions, the labor market, and the availability of in-demand professionals. We propose a decision-making architecture for a recommendation system to assist entrants with specialty selection as a solution. The modeled database is an integral part of the system to provide an in-depth university specialties description. We consider developing an API to consume the data and return predictions to users in our future studies. The exploratory data analysis of the 2021 university admission campaign in Ukraine confirmed our assumptions and revealed valuable insights into the specifics of specialty selection among entrants. We developed a comprehension that most entrants apply for popular but not necessarily in-demand specialties at universities. Our findings on association rules mining show that entrants are able to select alternative specialties adequately. However, it does not lead to successful admission to a desired tuition-free education form in all cases. So, we find it appropriate to deliver better decision-making on specialty selection, thus increasing the likelihood of university admission and professional development based on intelligent algorithms, user behavior analytics, and consultations with academic and career orientation experts. The results will be built into an intelligent virtual entrant’s assistant as a service.

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Section: Literature Reviewmentioning

confidence: 99%