Designing Personalized Learning Environments — The Role of Learning Analytics

Abstract

Learning analytics, as a rapidly evolving field, offers an encouraging approach with the aim of understanding, optimizing and enhancing learning process. Learners have the capabilities to interact with the learning analytics system through adequate user interface. Such systems enables various features such as learning recommendations, visualizations, reminders, rating and self-assessments possibilities. This paper proposes a framework for learning analytics aimed to improve personalized learning environments, encouraging the learner’s skills to monitor, adapt, and improve their own learning. It is an attempt to articulate the characterizing properties that reveals the association between learning analytics and personalized learning environment. In order to verify data analysis approaches and to determine the validity and accuracy of a learning analytics, and its corresponding to learning profiles, a case study was performed. The findings indicate that educational data for learning analytics are context specific and variables carry different meanings and can have different implications on learning success prediction.

Implementation of Data Analytics and Machine Learning in Thailand Education Sector

“…It indicates that big data analytics can enhance the efficiency of education sectors that rely on statistical data due to the requirements of digitalization during the COVID-19 pandemic. In addition to predicting the performance of an educational system using data analytics, categorizing datasets by specific groups of students is essential to optimizing a personalized learning scheme [13]. Machine learning (ML) transforms learning and research patterns in problem identification and group-specific performance [14].…”