TY - GEN
T1 - WEBLORS – A Personalized Web-Based Recommender System
AU - Belghis-Zadeh, Mohammad
AU - Imran, Hazra
AU - Chang, Maiga
AU - Graf, Sabine
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Nowadays, personalization and adaptivity becomes more and more important in most systems. When it comes to education and learning, personalization can provide learners with better learning experiences by considering their needs and characteristics when presenting them with learning materials within courses in learning management systems. One way to provide students with more personal learning materials is to deliver personalized content from the web. However, due to information overload, finding relevant and personalized materials from the web remains a challenging task. This paper presents an adaptive recommender system called WEBLORS that aims at helping learners to overcome the information overload by providing them with additional personalized learning materials from the web to increase their learning and performance. This paper also presents the evaluation of WEBLORS based on its recommender system acceptance using data from 36 participants. The evaluation showed that overall, participants had a positive experience interacting with WEBLORS. They trusted the recommendations and found them helpful to improve learning and performance, and they agreed that they would like to use the system again.
AB - Nowadays, personalization and adaptivity becomes more and more important in most systems. When it comes to education and learning, personalization can provide learners with better learning experiences by considering their needs and characteristics when presenting them with learning materials within courses in learning management systems. One way to provide students with more personal learning materials is to deliver personalized content from the web. However, due to information overload, finding relevant and personalized materials from the web remains a challenging task. This paper presents an adaptive recommender system called WEBLORS that aims at helping learners to overcome the information overload by providing them with additional personalized learning materials from the web to increase their learning and performance. This paper also presents the evaluation of WEBLORS based on its recommender system acceptance using data from 36 participants. The evaluation showed that overall, participants had a positive experience interacting with WEBLORS. They trusted the recommendations and found them helpful to improve learning and performance, and they agreed that they would like to use the system again.
KW - Personalization
KW - Recommender systems
KW - Web mining
UR - http://www.scopus.com/inward/record.url?scp=85076779813&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35758-0_24
DO - 10.1007/978-3-030-35758-0_24
M3 - Published Conference contribution
AN - SCOPUS:85076779813
SN - 9783030357573
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 258
EP - 266
BT - Advances in Web-Based Learning – ICWL 2019 - 18th International Conference, 2019, Proceedings
A2 - Herzog, Michael A.
A2 - Kubincová, Zuzana
A2 - Han, Peng
A2 - Temperini, Marco
T2 - 18th International Conference on Advances in Web-Based Learning, ICWL 2019
Y2 - 23 September 2019 through 25 September 2019
ER -