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QLearn: Towards a framework for smart learning environments

The new theory of education suggests that learner should be in the center of the learning process and the instructor playing an advising and facilitating role. Building smart learning environments supported by e-learning platforms is an important area of research. The rapid and continuous developmen...

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Detalles Bibliográficos
Autores principales: Şerban, Camelia, Ioan, Lungu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531983/
https://www.ncbi.nlm.nih.gov/pubmed/33042312
http://dx.doi.org/10.1016/j.procs.2020.09.273
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author Şerban, Camelia
Ioan, Lungu
author_facet Şerban, Camelia
Ioan, Lungu
author_sort Şerban, Camelia
collection PubMed
description The new theory of education suggests that learner should be in the center of the learning process and the instructor playing an advising and facilitating role. Building smart learning environments supported by e-learning platforms is an important area of research. The rapid and continuous development of technology that has brought new learning skills for students forces the educational system to enter into a new era. This change is further justified by some unprecedented events that force students to learn remotely. QLearn is an e-learning platform developed as a web based application which provides quizzes for students enrolled at Advanced Programming Methods course from Babeş Bolyai University (Romania) as part of their formative and summative assessment. The existing set of quizzes has been proposed by students throughout the last three iterations of the course in a collaborative manner with course instructor, and this data set is expanding continuously with every new generation of students. QLearn is a smart learning environment offering the students valuable feedback and a good preparation for the exam. Some metrics that quantify the coverage rate of the course syllabus attained by students or their understanding level of knowledge are provided by QLearn. The Artificial Intelligent component of QLearn application uses these measures to make predictions for students’ outcomes at the exam, to find out which topics need to be practised more and to recommend learning plans according to students’ individual needs. The contribution of the paper is therefore twofold. Firstly, we propose a new learning design, based on students’ involvement in a collaborative manner. The second contribution of the paper is QLearn, a software application that provides support (implementation) for the proposed learning process design. Not only does QLearn platform provide a smart learning environment for students, but it also ensures the knowledge transfer from instructors to students in an efficient and effective way.
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spelling pubmed-75319832020-10-05 QLearn: Towards a framework for smart learning environments Şerban, Camelia Ioan, Lungu Procedia Comput Sci Article The new theory of education suggests that learner should be in the center of the learning process and the instructor playing an advising and facilitating role. Building smart learning environments supported by e-learning platforms is an important area of research. The rapid and continuous development of technology that has brought new learning skills for students forces the educational system to enter into a new era. This change is further justified by some unprecedented events that force students to learn remotely. QLearn is an e-learning platform developed as a web based application which provides quizzes for students enrolled at Advanced Programming Methods course from Babeş Bolyai University (Romania) as part of their formative and summative assessment. The existing set of quizzes has been proposed by students throughout the last three iterations of the course in a collaborative manner with course instructor, and this data set is expanding continuously with every new generation of students. QLearn is a smart learning environment offering the students valuable feedback and a good preparation for the exam. Some metrics that quantify the coverage rate of the course syllabus attained by students or their understanding level of knowledge are provided by QLearn. The Artificial Intelligent component of QLearn application uses these measures to make predictions for students’ outcomes at the exam, to find out which topics need to be practised more and to recommend learning plans according to students’ individual needs. The contribution of the paper is therefore twofold. Firstly, we propose a new learning design, based on students’ involvement in a collaborative manner. The second contribution of the paper is QLearn, a software application that provides support (implementation) for the proposed learning process design. Not only does QLearn platform provide a smart learning environment for students, but it also ensures the knowledge transfer from instructors to students in an efficient and effective way. The Author(s). Published by Elsevier B.V. 2020 2020-10-02 /pmc/articles/PMC7531983/ /pubmed/33042312 http://dx.doi.org/10.1016/j.procs.2020.09.273 Text en © 2020 The Author(s). Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Şerban, Camelia
Ioan, Lungu
QLearn: Towards a framework for smart learning environments
title QLearn: Towards a framework for smart learning environments
title_full QLearn: Towards a framework for smart learning environments
title_fullStr QLearn: Towards a framework for smart learning environments
title_full_unstemmed QLearn: Towards a framework for smart learning environments
title_short QLearn: Towards a framework for smart learning environments
title_sort qlearn: towards a framework for smart learning environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531983/
https://www.ncbi.nlm.nih.gov/pubmed/33042312
http://dx.doi.org/10.1016/j.procs.2020.09.273
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