Exploring Learning Analytics In E-Learning: A Comprehensive Analysis of Student Characteristics and Behavior
DOI:
https://doi.org/10.33830/ptjj.v24i2.5055.2023Keywords:
Learning analytics, Exploratory Data Analysis, E-learning, Learning Management SystemAbstract
This article aims to explore learning analytics in e-learning through a comprehensive analysis of student characteristics and behavior. E-learning has become increasingly significant in education, particularly due to the social situation influenced by the pandemic. The Learning Management System (LMS) has become a crucial tool for educators to track and record student learning in e-learning environments. Learning analytics can aid in understanding the context of students, ensuring that they receive a personalized learning experience aligned with learning objectives. However, educators often face challenges in conducting learning analytics for e-learning students, primarily due to the large number of students to analyze and limited data availability. This study seeks to provide a detailed description of e-learning students within the Open and Distance Education (ODE) system. ODE students exhibit high diversity in demographic profiles, learning behaviors, and competency backgrounds. To support this research, we utilize datasets containing student demographic profiles and learning activity data during e-learning sessions. The datasets are obtained from the academic system and LMS log data of Universitas Terbuka. The article employs Exploratory Data Analysis (EDA) and data science approaches as the foundation for predictive and prescriptive analytics of student learning outcomes. Relevant features are extracted from the dataset to build a robust predictive model. The analysis results present patterns and relationships between student characteristics, learning behaviors, and academic achievements. This research aims to provide valuable insights for the development of more effective and personalized e-learning strategies to enhance student learning outcomes in the context of distance education. Moreover, the analysis findings can serve as a basis for informed academic decision-making to improve the quality of e-learning environments.
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