IJBTT

Hybrid Learning and Blended Learning in the Perspective of Educational Data Mining and Learning Analytics: A Systematic Literature Reviews

Hybrid Learning and Blended Learning in the Perspective of Educational Data Mining and Learning Analytics: A Systematic Literature Reviews
	© 2023 by IJETT Journal
	Volume-71 Issue-10
	Year of Publication : 2023
	Author : Pratya Nuankaew, Phaisarn Jeefoo, Patchara Nasa-Ngium, Wongpanya S. Nuankaew
	DOI : 10.14445/22315381/IJETT-V71I10P211

How to Cite?

Pratya Nuankaew, Phaisarn Jeefoo, Patchara Nasa-Ngium, Wongpanya S. Nuankaew, "Hybrid Learning and Blended Learning in the Perspective of Educational Data Mining and Learning Analytics: A Systematic Literature Reviews," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 115-132, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P211

Abstract
Rapid societal and technological changes have driven enormous educational developments in learning styles and assessment. Due to the Coronavirus 2019 (COVID-19) epidemic, the world’s education system has recognized distance education’s importance in dealing with and solving asynchronous learning. Consequently, hybrid and blended learning have been proposed to support a learning process through a combination of face-to-face (onsite) and online learning. The learning process and activities arise from a more versatile and flexible teaching strategy than traditional learning patterns; however, the reliance on educational technology results in the dissemination and collection of massive amounts of data called big data. Thus, educational data mining and learning analytics have been applied for statistical and qualitative data analysis in hybrid learning and blended learning. Although these methods can be optimized for distinctive learning formats, the implementation varies depending on course structure and dataset characteristics. Therefore, this study analyzes and reviews different educational data mining and learning analytics methods and applications in hybrid and blended learning aspects.

Keywords
Blended learning, Educational data mining, Hybrid learning, Learning analytics, Learning styles.

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