Develop a User Behavior Analysis Tool in ETHOL Learning Management System
Students have different learning styles when studying online. Meanwhile, lecturers use the same method for all students who take their online lectures. These different learning styles can affect the level of understanding and the results obtained by students. By knowing student learning styles, lecturers are expected to be able to use the right way in delivering material. In this research, we developed a student behavior analysis feature on self-developed Virtual Learning Environment (VLE) called Enterprise Hybrid Online Learning (ETHOL). Students’ data collected includes data on online activities, personal data, and survey data on student learning styles. User behavior analysis was carried out by dividing into three clusters: average scores, time to collect assignments, and student learning styles. The clustering method used is the Hierarchical K-Means. The results obtained are students who have the habit of collecting assignments on time have higher scores than others. In addition, the lecturer is able to see the results of the analysis of the behavior and learning styles of each student. These results can be used as information in delivering lecture material.
Dongming Xu, Wayne W. Huang , Huaiqing Wangd, Jon Heales a, Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation, Elsevier, Information & Management, Vol. 51, Issue 4, pp.430-44, 2014. DOI: https://doi.org/10.1016/j.im.2014.02.009
Gunathilaka T. M. A. U, Fernando M. S. D, Identification of the Learning Behavior of the Students for Education Personalization, International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), 2017. DOI: https://doi.org/10.1109/I-SMAC.2017.8058372
Ismail El Haddioui, Mohamed Khaldi, Learning Style and Behavior Analysis A Study on the Learning Management System Manhali, International Journal of Computer Applications, Vol. 54, No. 4, 2012. DOI: https://doi.org/10.5120/8877-2862
Wei Zhang, Xujun Huang, Shengming Wang, Jiangbo Shu, Hai Liu, Hao Chen, Student Performance Prediction via Online Learning Behavior Analytics, International Symposium on Educational Technology, 2017. DOI: https://doi.org/10.1109/ISET.2017.43
André Luiz de Brandão Damasceno, Dalai dos Santos Ribeiro, Simone Diniz Junqueira Barbosa, What the Literature and Instructors Say about theAnalysis of Student Interaction Logs onVirtual Learning Environments, IEEE Frontiers in Education Conference (FIE), 2019.
Baolin Yi, Yi Wang, Dujuan Zhang, Hai Liu, Jiangbo Shu, Zhaoli Zhang;Yuegong Lv, Learning Analytics-Based Evaluation Mode for Blended Learning and Its Applications, International Symposium on Educational Technology (ISET), 2017.
Pernanda,S.T., Winarno I., Susanto, D., Yuwono, W, Design and Development of ELearning Mobile Based Interface (Case Study: DOSENJAGA PENS) , International Electronics Seminar EEPIS, 2014.
Hao Li, Libin Wang, Xu Du, Mingyan Zhang, Research on the Strategy of E-Learning Resources Recommendation based on Learning Context, The Sixth International Conference of Educational Innovation through Technology, 2017. DOI: https://doi.org/10.1109/EITT.2017.58
Honghao He, Zhengzhou Zhu, and Qun Guo, A Personalized E-learning Services Recommendation Algorithm Based on User Learning Ability, IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2019.
Harish Anantharaman, Abdullah Mubarak, B.T Shobana, Modelling an Adaptive e-Learning system using LSTM and Random Forest classification, IEEE Conference on e-Learning, e-Management and e-Services (IC3e), 2018. DOI: https://doi.org/10.1109/IC3e.2018.8632646
Shaimaa M. Nafea, François Siewe, Ying He, A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles, International Conference on Innovative Trends in Computer Engineering (ITCE), 2019. DOI: https://doi.org/10.1109/ITCE.2019.8646355
Munyaradzi Maravanyika, Nomusa Dlodlo, Nobert Jere, An adaptive recommender-system based framework for personalised teaching and learning on e-learning platforms, IST-Africa Week Conference (IST-Africa), 2017. DOI: https://doi.org/10.23919/ISTAFRICA.2017.8102297
Yunia Ikawati, M. Udin Harun Al Rasyid, Idris Winarno, Student Behavior Analysis to Detect Learning Styles in Moodle Learning Management System, International Electronics Symposium (IES), 2020. DOI: https://doi.org/10.1109/IES50839.2020.9231567
Ouafae El Aissaoui, Yasser El Madani El Alami, Lahcen Oughdir,Youssouf El Allioui, Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach, International Conference on Intelligent Systems and Computer Vision (ISCV), 2018. DOI: https://doi.org/10.1109/ISACV.2018.8354021
M S Hasibuan, L E Nugroho, PI Santosa, Detecting Learning Style Based on Level of Knowledge, Third International Conference on Informatics and Computing (ICIC), 2018. DOI: https://doi.org/10.1109/IAC.2018.8780435
Johnson et al, Method of And Apparatus for Providing Automatic Detection of User Activity, International Business Machines Corporation CP Command and Utility Reference, Release 1 IBM No. SC24-5519-00, 1994.
K. Alhasan, L. Chen, F. Chen, Mining Learning Styles for Personalised E-Learning, IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovations, 2018 DOI: https://doi.org/10.1109/SmartWorld.2018.00204
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