Gerçek Zamanlı Erişim Haritası
Machine learning based learner modeling for adaptive web-based learning
MetadataTüm öğe kaydını göster
Especially in the first decade of this century, learner adapted interaction and learner modeling are becoming more important in the area of web-based learning systems. The complicated nature of the problem is a serious challenge with vast amount of data available about the learners. Machine learning approaches have been used effectively in both user modeling, and learner modeling implementations. Recent studies on the challenges and solutions about learner modeling are explained in this paper with the proposal of a learner modeling framework to be used in a web-based learning system. The proposed system adopts a hybrid approach combining three machine learning techniques in three stages.
Başlık, yazar, küratör ve konuya göre gösterilen ilgili öğeler.
İmamoğlu, Zeynep (Izmir Institute of Technology, 2019-06)In the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including ...
Gürakın, Çağrı (Izmir Institute of Technology, 2019-07)This study, purposes to explain the development stages and methodology of data classification service that has a text-based adaptable programming interface. One of the successful classification algorithms, XGBoost, was ...
Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological ...