eprintid: 5683 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/56/83 datestamp: 2023-02-02 23:30:07 lastmod: 2023-07-12 23:31:22 status_changed: 2023-02-02 23:30:07 type: article metadata_visibility: show creators_name: Kaur, Ramneet creators_name: Gupta, Deepali creators_name: Madhukar, Mani creators_name: Singh, Aman creators_name: Abdelhaq, Maha creators_name: Alsaqour, Raed creators_name: Breñosa, Jose creators_name: Goyal, Nitin creators_id: creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: creators_id: creators_id: josemanuel.brenosa@uneatlantico.es creators_id: title: E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: public keywords: education; e-learning; customization; learner profile; MOOCs; user-profiling abstract: Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners’ preferences. date: 2022-10 publication: Electronics volume: 11 number: 20 pagerange: 3354 id_number: doi:10.3390/electronics11203354 refereed: TRUE issn: 2079-9292 official_url: http://doi.org/10.3390/electronics11203354 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Abierto Inglés Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners’ preferences. metadata Kaur, Ramneet; Gupta, Deepali; Madhukar, Mani; Singh, Aman; Abdelhaq, Maha; Alsaqour, Raed; Breñosa, Jose y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2022) E-Learning Environment Based Intelligent Profiling System for Enhancing User Adaptation. Electronics, 11 (20). p. 3354. ISSN 2079-9292 document_url: http://repositorio.uneatlantico.es/id/eprint/5683/1/electronics-11-03354.pdf