Mining Smart Learning Analytics Data Using Ensemble Classifiers
Samina Kausar,Solomon Sunday Oyelere,Y K Salal,Sadiq Hussain,Mehmet Akif Cifci,Slavoljub Hilcenko,Muhammad Shahid Iqbal,Zhu Wenhao,Xu Huahu +8 more
TL;DR: In this study, a SLA dataset was explored and advanced ensemble techniques were applied for the classification task, and Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively.
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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
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References
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
TL;DR: In this article, the authors compared the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5 and found that in situations with little or no classification noise, randomization is competitive with bagging but not as accurate as boosting.
•Journal Article
Penetrating the Fog: Analytics in Learning and Education.
George Siemens,Phil Long +1 more
1.4K
A research framework of smart education
TL;DR: A four-tier framework of smart pedagogies and ten key features of smart learning environments are proposed for foster smart learners who need master knowledge and skills of the 21st century learning.
Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective
TL;DR: The definition and criteria of smartlearning environments are presented from the perspective of context-aware ubiquitous learning and a framework is presented to address the design and development considerations of smart learning environments to support both online and real-world learning activities.
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