Abstract:

E-Learning and online education have become inseparable parts of the world’s education systems due to the rapid growth of technology and its positive effects on education quality and accessibility. The social implications caused by the COVID-19 outbreak (e.g., closure of educational institutions) have further contributed to the wide adoption of e-Learning solutions, especially learning management systems (LMSs), which make up the largest segment of the e-Learning market. The effectiveness of online education strongly depends on the learning environment that LMS provides for students, as the most important user group. LMS should sit at the heart of learning strategies to give students an interactive and engaging learning experience. This requires significant research and development (R&D) by service providers to understand students’ needs, learning styles, and learning patterns in order to improve learning environments such that they will be endorsed and welcomed by the students. Artificial intelligence (AI) and machine learning are the most powerful processing technologies that have transformed many sectors, including education, in the past decade. AI and machine learning have huge potentials to aggregate, curate, and personalize the learning experience and content to realize state-of-the-art e-Learning at its finest. In this paper, we first review various scenarios regarding students’ learning experiences in an LMS, which can be improved by data-driven approaches. Then, we present examples of machine learning-based solutions that can effectively extract insights from students’ behaviors to improve and personalize their learning experiences. These solutions are the results of the R&D conducted by the TechClass AI Department to implement such features into the TechClass Next-generation Learning Environment.

Keywords:

e-learning, online education, learning management system, learning environment, artificial intelligence, machine learning, data-driven solutions.