A Conceptual Framework for Self-Regulated Learning and Assessment in Pre-service Teacher Education

Abstract

Learning Analytics (LA) is a growing trend used to understand how students manage their learning with self-regulation to achieve learning objectives by combining their aptitude with classroom engagement. These involve collecting, analyzing, and reporting student data to optimize learning processes and achievement. By leveraging student data, LA empowers higher education to enhance teaching effectiveness, personalize learning experiences, and address educational challenges. This study investigates the role of LA in supporting Self-Regulated Learning (SRL) among undergraduate pre-service teachers. By analyzing online trace data, we aimed to identify indicators of effective SRL strategies and develop a conceptual framework for assessing SRL. Our systematic review of literature from 2013 to 2023 examined existing research on LA and SRL in higher education. Results indicate that LA can provide valuable insights into students' learning behaviors, enabling the identification of distinct learner profiles. By prioritizing educational measurements aligned with specific SRL stages, we propose to enhance teaching and learning practices for pre-service teachers.

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Author Information
Apichaya Khwankaew, King Mongkut's Institute of Technology Ladkrabang, Thailand
Jirarat Sitthiworachart, King Mongkut's Institute of Technology Ladkrabang, Thailand