TY - GEN
T1 - Multi-label Search Model for Open Educational Resources Based on Learning Purpose
AU - Villalba-Condori, Klinge
AU - Tejada-Ortega, Lushianna
AU - Vera-Sancho, Julio
AU - Mamani-Calcina, Jorge
AU - Vera-Vasquez, Cesar
AU - Cardona-Reyes, Héctor
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The Open Educational Resources (OER) repositories store a large quantity and variety of data from multiple sources, and that present relevant and useful information for the student training process, improving student performance thanks to more up-to-date and didactic educational content, this generates the need for a more specialized Learning Objects (LO) search system. In this research work, a multi-label search platform is proposed based on the learning purpose that unifies the information from different OER in a single platform, using an information extraction process based on Web Scraping techniques and generating a selection of one of them. Or multiple tags, based on the SOLO taxonomy, using an MLP-based multi-tag classifier. As a result, we obtained the improvement of the most specialized search and content according to what teachers really need, up to 85% accuracy of the resources they needed and approval of the use of the platform up to 78.57%.
AB - The Open Educational Resources (OER) repositories store a large quantity and variety of data from multiple sources, and that present relevant and useful information for the student training process, improving student performance thanks to more up-to-date and didactic educational content, this generates the need for a more specialized Learning Objects (LO) search system. In this research work, a multi-label search platform is proposed based on the learning purpose that unifies the information from different OER in a single platform, using an information extraction process based on Web Scraping techniques and generating a selection of one of them. Or multiple tags, based on the SOLO taxonomy, using an MLP-based multi-tag classifier. As a result, we obtained the improvement of the most specialized search and content according to what teachers really need, up to 85% accuracy of the resources they needed and approval of the use of the platform up to 78.57%.
KW - Machine learning
KW - Open educational resources
KW - Scrapy
UR - http://www.scopus.com/inward/record.url?scp=85147985608&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-24709-5_10
DO - 10.1007/978-3-031-24709-5_10
M3 - Conference contribution
AN - SCOPUS:85147985608
SN - 9783031247088
T3 - Communications in Computer and Information Science
SP - 130
EP - 145
BT - Human-Computer Interaction - 8th Iberoamerican Workshop, HCI-COLLAB 2022, Revised Selected Papers
A2 - Agredo-Delgado, Vanessa
A2 - Ruiz, Pablo H.
A2 - Agredo-Delgado, Vanessa
A2 - Ruiz, Pablo H.
A2 - Correa-Madrigal, Omar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th Ibero-American Workshop on Human-Computer Interaction, HCI-COLLAB 2022
Y2 - 13 October 2022 through 15 October 2022
ER -