Migrating a Recommendation System to Cloud Using ML Workflow

Chahal, Dheeraj; Ojha, Ravi; Choudhury, Sharod Roy; Nambiar, Manoj

doi:10.1145/3375555.3384423

Cited by 9 publications

(2 citation statements)

References 2 publications

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“…The following problems have been studied in selected PRISMA research papers: Higher education quality assurance ( Alakbarov 2021 ; Allayarova 2019 ; Asare et al 2021 ); Knowledge-based recommender system ( Barabash et al 2021 ; Barón et al 2015 ; Bin-Noor et al 2021 ; Brunello and Wruuck 2021 ); E-learning and distance learning ( Bukralia et al 2015 ; Burman et al 2021 ; Casselman 2021 ; Chahal et al 2020 ); Inter-professional education ( Cheng 2017 ); Network course recommendation system ( Cinquin et al 2019 ; Dhar and Jodder 2020 ; Dolgikh 2021 ; Ehimwenma and Krishnamoorthy 2021 ); Modern information communication technologies in the higher education sector ( El Gourari et al 2021 ; Elahi et al 2022 ; Ellyatt 2021 ; Elumalai et al 2019 ); Transformation of education during the COVID-19 pandemic ( Erridge 2006 ; Ezz and Elshenawy 2020 ; Fedushko and Ustyianovych 2020 ); Digitalization in higher education ( Gao et al 2021 ; Garay-Jiménez et al 2021 ; Gil et al 2019 ); Personalized recommendation system for learning resources ( Habib et al 2021 ; Ibrahim et al 2019 ; Karan and Asgari 2021 ; Khan and Ramzan 2018 ; Khosravi et al 2021 ); Artificial intelligence techniques for education tasks solving ( Kim Rosemary et al 2014 ; ...…”

Section: Literature Reviewmentioning

confidence: 99%

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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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