Publication: Hybrid Of Optimized Random Forest And Extreme Gradient Boosting For Online Learning Style Classification
| dc.contributor.author | Haziqah Shamsudin | |
| dc.date.accessioned | 2024-09-20T03:17:38Z | |
| dc.date.available | 2024-09-20T03:17:38Z | |
| dc.date.issued | 2019-03 | |
| dc.description.abstract | Educational Data Mining (EDM) have raised a lot of attention among researchers since the last few decades. EDM is used to gain more insight into the behavior of learners by building models based on data collected from learning tools which result in improving learning system to be more personalized and adaptive. Learning style of specific users in the online learning system is determined based on their interaction and behaviour towards the system. Felder-Silverman’s learning style model is the most common online learning theory used in determining the learning style. Initially, in determining the users’ learning styles, users are asked to fill in the questionnaires which is designed to learn their learning style at the end of the learning sessions. However, this method is time consuming and the result are not reliable due to the human factors behavior. Thus, the researchers started to study the learning style by using an automated approach in which the activity log files are collected in order to understand the interactivity behaviour of the users with the system. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/20507 | |
| dc.language.iso | en | |
| dc.subject | Gradient Boosting | |
| dc.subject | Online Learning | |
| dc.title | Hybrid Of Optimized Random Forest And Extreme Gradient Boosting For Online Learning Style Classification | |
| dc.type | Resource Types::text::thesis::master thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |