AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement

Abstract

Improving learning outcomes is always one of the key objectives of learning analytics (LA) and educational data mining (EDM). In recent years, many Massive Open Online Courses (MOOC) have been deployed and making it easier to collect learners' data for further analysis. Naturally, leveraging AI to process such kind of big data becomes one of the main research streams to support education. In this paper, we collected data and defined student learning patterns by leveraging online courses on Python programming and we then verified if their learning performance was influenced by different learning patterns and interventions. We designed the intervention process, explored the impact of final learning outcomes, and analyze Self-Regulated Learning (SRL) abilities. From the experimental results, we share the learning outcomes and the difference in SRL with detailed explanation based on different groups.

Preface to Volume 3: The Potential of One Tablet Per Student for Supporting Individualized Learning and Collaborative Learning

“…Yang et al [1] explored learning analytics and educational data mining, focusing on improving learning outcomes. By analyzing data from MOOCs, they examined student learning patterns and interventions, revealing their impact on outcomes and Self-Regulated Learning (SRL) abilities.…”