Recommendation Systems for Education: Systematic Review

Urdaneta-Ponte, María Cora; Zorrilla, Amaia Méndez; Ruiz, I.

doi:10.3390/electronics10141611

Cited by 89 publications

(37 citation statements)

References 107 publications

(116 reference statements)

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“…According to many recent studies, the lack of specialists in certain areas causes economic damage to states. This situation is especially critical during the COVID-19 pandemic ( The Economist 2022 ; Tømte et al 2019 ; Tungpantong et al 2021 ; Unified State Electronic Database on Education 2022 ) and the ongoing war in Ukraine and its economic implications ( Urdaneta-Ponte et al 2021 ; Valkanas and Diamandis 2022 ).…”

Section: Resultsmentioning

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: Resultsmentioning

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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“…Information recommendation is a method for students to quickly and effectively screen out the objects that meet their preference characteristics from the mass objects. At present, information recommendation technology is widely used in many fields [ 3 ]. It is an effective way to solve the problem of “information trek” to apply information recommendation technology to online education and realize personalized recommendation of innovation and entrepreneurship education resources in universities in the process of online education.…”

Section: Introductionmentioning

confidence: 99%

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

Geng

2022

Computational Intelligence and Neuroscience

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In the huge number of online university education resources, it is difficult for learners to quickly locate the resources they need, which leads to “information trek.” Traditional information recommendation methods tend to ignore the characteristics of learners, who are the main subjects of education. In order to improve the recommendation accuracy, a recommendation algorithm based on improved collaborative filtering model is proposed in this paper. Firstly, according to the student behavior data, consider the behavior order to create the behavior graph and behavior route. Then, the path of text type is vectorized by the Keras Tokenizer method. Finally, the similarity between multidimensional behavior path vectors is calculated, and path collaborative filtering recommendations are performed for each dimension separately. The MOOC data of a university in China are introduced to experimentally compare the algorithm of the article as well as the control group algorithm. The results show that the proposed algorithm takes better values in evaluation indexes, thus verifying that this algorithm can improve the effectiveness of innovation and entrepreneurship education resources recommendation in universities.

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“…In the systematic review presented by [19], attention should be drawn to the work shown in Table 1 in this area. In some cases, ML is used in RSs to make inferences about how past actions correspond to future outcomes.…”

Section: Introduction and Related Studiesmentioning

confidence: 99%

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

Self Cite

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Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.

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Recommendation Systems for Education: Systematic Review

Cited by 89 publications

References 107 publications

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

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

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

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