A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Li, Qinglong; Kim, Jae-Kyeong

doi:10.3390/app11198993

Cited by 31 publications

(19 citation statements)

References 61 publications

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“…However, such information requires either a big user base, or monitoring users for a long period, in order to draw significant conclusions about the courses taken by the students. For similar reasons the results of our study are not comparable with the results in [41]. Specifically, the authors evaluate their system in terms of hit ratio (HR) and normalized discounted cumulative gain (NDCG).…”

Section: Discussioncontrasting

confidence: 56%

Bridging the Gap between Technological Education and Job Market Requirements through Data Analytics and Decision Support Services

et al. 2022

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In the 21st century, technology evolves extremely fast. The same applies to technology-related professions, mostly in terms of skills requirements. Contradictorily, higher education technological institutions are not always in the position to keep up with the labor market requirements. As a result, some of the skills taught in their courses are oftentimes outdated. From a learner’s perspective, the main goal should be to avoid such outdated courses, as for most university students, the long-term objective is to land a job, where they will utilize the skills they acquired from their studies. On the other hand, from an educational decision maker’s perspective, the most important goal is to keep up with the changes in the labor market, offering courses that will be valuable for the prospective careers of students. The work conducted in the context of this publication aims to bridge the gap between education offered in universities and job market skills’ requirements in technology. Specifically, a skill and course recommender system was developed to help learners select courses that are valuable for the job market, as well as a curriculum design service, which recommends updates to a given curriculum based on the job market needs. Both services are built on top of a text mining service that retrieves job posts from several online sources and performs skill extraction from them based on text analytics techniques. Moreover, a decision support service was developed to facilitate optimal decisions for both learners and education decision makers. All services were evaluated positively by 31 early users.

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Section: Discussioncontrasting

confidence: 56%

Bridging the Gap between Technological Education and Job Market Requirements through Data Analytics and Decision Support Services

et al. 2022

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“…And also improving the latency and scalability of recommender systems with new emerging technology. [34] Deep learning with implicit feedback Learner (Users No. are not mentioned)…”

Section: Learner (1709)mentioning

confidence: 99%

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M.,

Goudar,

Kulkarni

et al. 2024

IEEE Access

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This review delves into using e-learning technology and personalized recommendation systems in education. It examines 60 articles from prominent databases and identifies the different methods used in recommendation systems, such as collaborative and content-based approaches with a recent shift towards machine learning. However, the current personalized recommendation system faces challenges such as a lack of understanding of the content, student discontinuity, language barriers, confusion in selecting study materials, inadequate infrastructure and funding. The review proposes using new digital technologies to address these issues, including Fluxy AI, Twin technology, AI-powered virtual proctoring, and Alter Ego. These technologies can create a dynamic and interactive learning environment, providing tailored learning experiences for students and insights for educators to provide targeted support and guidance. The integration of these technologies can improve individualized learning, increase understanding capacity and enhance the learning experience for students with speech disorders.

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“…Machine learning techniques for education tasks ( Korzh et al 2014 ; Lee and Jung 2021 ; Li and Kim 2021 ; Mansouri et al 2021 );…”

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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A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Cited by 31 publications

References 61 publications

Bridging the Gap between Technological Education and Job Market Requirements through Data Analytics and Decision Support Services

Bridging the Gap between Technological Education and Job Market Requirements through Data Analytics and Decision Support Services

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

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

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