Educational recommender systems and their application in lifelong learning

Dascălu, Maria-Iuliana; Bodea, Constanţa-Nicoleta; Mihailescu, Monica N.; Tanase, Elena Alice; Pablos, Patricia Ordóñez de

doi:10.1080/0144929x.2015.1128977

Cited by 49 publications

(22 citation statements)

References 24 publications

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“…As it may be seen in the next references along with this article, according to authors , today's technology is capable of improving students’ skills towards employability through e‐learning. It consists of multiple skill‐set service providers such as learning centers, recruitment companies, institutions, and private training centers, etc.…”

Section: Introductionmentioning

confidence: 88%

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Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

Sood

Singh

2019

Comp Applic In Engineering

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In the present scenario, the state of engineering education worldwide is facing a big challenge of the employability of graduates. Cloud computing technology has changed the educational landscape by allowing educators and administrators to shift to the cloud for making actionable decisions. In this paper, a smart learning framework is proposed where universities/colleges can advocate the management of employability enhancement and start skill-set enhancement courses through e-learning. This proposed framework monitors the academic/skill data of students to classify their employability at the early stage of graduation. In addition, an algorithm is proposed for skill-set assessment to find various clusters of students who lack in required skill-set. The predicted clusters of students can be offered opportunities to improve their required skill-set through e-learning. Further, we used the design science research methodology for conducting online courses. Moreover, the prediction of resource usage of the proposed e-learning framework is calculated to improve its effectiveness. In our experiment, results show that our proposed hybrid classier achieves 96.45% accuracy of classification. The prediction of resource usage offers better opportunities for adaptive resource elasticity. The results depict that by introducing the proposed framework in engineering education; more effective decisions can be made to improve the employability and overall growth of students. K E Y W O R D S employability enhancement, fog/cloud computing, optical network, resource usage pattern prediction, skill-set assessment, smart learning 1 | INTRODUCTION Universities/colleges are under great pressure to make the youth ready for jobs since the market demands for skilled workers. The education system throughout the world requires a smart system that can be monitored, controlled, quantitatively measured, and continuously improved with the collaboration of universities/ learning centers to enhance students' employability potential during their early years of graduation. Students' employability depends on a variety of features, such as marks obtained in high school, diploma, graduation, etc., extra technical competence, or other relevant information [29]. The information collected from various resources regarding their features is to be accurate, well-timed and precise.As it may be seen in the next references along with this article, according to authors [1,3,4,10,35,37,38,40,41], today's technology is capable of improving students' skills towards employability through e-learning. It consists of multiple skill-set service providers such as learning centers, recruitment companies, institutions, and private training centers, etc. It provides a personally customized and effective educational environment that reduces the gap between students' skill-set and industry demand at multiple employability parameters, such as aptitude, reasoning, communication, technical/professional certifications, etc.The potential of cloud computing encourages education syste...

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Section: Introductionmentioning

confidence: 88%

“…He examined 48 students to compare corner cave system with a head mounted display system. Dascalu et al presented a survey on educational recommender and proposed an ontology‐based educational recommendation to support new learning paradigms.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

Sood

Singh

2019

Comp Applic In Engineering

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“…The purpose of developing and implementing recommendation systems in the e-learning environment is to optimize the selection of educational resources from a variety of available ones based on the analysis of the quality of educational resources and user perception (Sobecki & Tomczak, 2010). To make the LMS personalized, a recommender system can be integrated into the LMS to suggest training material based on information, relevant to a specific student (Dascalu et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

Intelligent Support System for Personalized Online Learning

Seredkina

2020

BRAIN

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The current conditions of the COVID-19 pandemic have required universities to transfer educational processes in the online environment. eLearning systems provide educational institutions and students with the opportunity to effectively organize the educational process and share knowledge. They provide each student with freedom of access to information and flexibility of the learning process. The student can individually determine the duration and sequence of courses by changing the trajectory of the educational process following their needs. In the context of the pandemic, students and teachers have to optimize their work over the Internet. This requires more extended personalization of the learning process. Intelligent technologies allow you to construct personalized learning paths for each student, varying methods, forms, and speed of learning. This study presents the architecture of the e-learning support system for the selection of online resources and for including them in the student's learning path. The system developed as a set of personal agents and services that interact based on a set of interconnected ontological models. Ontologies provide a more adequate representation of online resources and compatibility of the user request format with descriptions of training resources from different developers. The system recommends training modules based on current requests and user characteristics that match their profile. The system dynamically updates the knowledge base user characteristics, thereby increasing the effectiveness of recommendations.

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“…In education, according to [10], who cites (Chen and Pu 2014; Chen 2011; Ozok, Fan, and Norcio 2010) RS are becoming useful and popular in different areas, in the educational field RS had been reported in several articles regarding to documentation management (Weng and Chang 2008), complementary materials for courses (Bobadilla et al 2013; Hsu 2008), select e-courses (Hsu 2008; Bodea, Dascalu, and Lipai 2012). All these researchers suggest different ways to match the user's profiles with the data to be recommend, allowing the user to have proper suggestions according with their learning manner [10].…”

Section: State Of Art and Related Workmentioning

confidence: 99%

Toward a Collective Intelligence Recommender System for Education

Meza¹,

Vaca²,

Simó³

et al. 2017

EDULEARN Proceedings

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The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning?. This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out:  It improves student motivation, as it helps you discover new content of interest in an easy way.  It saves time in the search and classification of teaching material of interest.  It fosters specialized reading, inspires competence as a means of learning.  It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filtering Contents used supports the design of models and strategies of collective intelligence in Higher Education.

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Educational recommender systems and their application in lifelong learning

Cited by 49 publications

References 24 publications

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

Optical fog‐assisted smart learning framework to enhance students’ employability in engineering education

Intelligent Support System for Personalized Online Learning

Toward a Collective Intelligence Recommender System for Education

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