Mining Smart Learning Analytics Data Using Ensemble Classifiers
Samina Kausar, Solomon Sunday Oyelere, Yass Khudheir Salal, Sadiq Hussain, Mehmet Akif Çifçi, Slavoljub Hilĉenko, Muhammad Shahid Iqbal, Zhu Wenhao, Huahu Xu
Abstract
Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse which reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively and contentedly. Smart Learning (SL) despite not being a new concept describing learning methods in the digital age- has caught attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievements. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, a SLA dataset was explored and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively.