Enhancing Self-Regulated Learning With Artificial Intelligence-Powered Learning Analytics

Introduction

With recent advances in digital technologies, self-regulated learning (SRL) and learning analytics (LA) have become two essential areas of focus in education. SRL is the ability to regulate one’s learning process metacognitively (involving planning, goal setting, organization, self-monitoring, and evaluation), motivationally (drawing upon intrinsic interest during learning), and behaviorally (utilizing adaptive adjustments) (Zimmerman, 1990). LA refers to the active use of past and real-time data to support and enhance learning outcomes (Siemens, 2012). SRL encompasses planning, monitoring, evaluating, and adapting learning strategies to achieve academic goals. LA complements SRL by providing insights into students’ behaviors, interactions, and performance within educational contexts. LA provides a wealth of information about students’ learning patterns, preferences, and progress that can be used to personalize instruction, support individual learning goals, and facilitate metacognitive development. LA leverages data analysis to create a feedback loop, helping students understand how they engage with the learning material and identify areas where improvements can be made. Through LA, educators and policymakers can better understand how students learn and identify ways to optimize teaching and learning practices to enhance student success. For example, LA can analyze how students engage with the online course materials and identify areas of difficulty. In response to this LA analysis, the instructor can provide personalized resources and support to help students overcome their challenges. As a result, students can gain a better understanding of the course material, aligning with their learning goals and improving their overall performance. This scenario illustrates how LA can personalize instruction and support individual learning goals by leveraging data analysis and creating a feedback loop.

In recent years, artificial intelligence (AI) has led to the emergence of AI-powered LA tools, which have the potential to support SRL. Integration of AI into LA enhances this symbiotic relationship between LA and SRL. AI can support LA by providing more sophisticated and accurate analyses of student data, as well as by automating certain aspects of the analytics process. AI can autonomously learn patterns and associations in data, and this feature can be leveraged to enhance the effectiveness of LA (Alam, 2021). AI can automatically capture and analyze a wealth of data related to students’ SRL behaviors, including their study habits, progress, and even their emotional or metacognitive states. As a result, cutting-edge techniques, such as AI, in learning analytics have gained popularity in recent years (Alam, 2021). By combining AI’s capabilities with LA’s mechanisms, we can gain the power to offer personalized recommendations and support based on students’ specific learning profiles. This integration streamlines the process of adapting instruction to meet individual needs, ultimately fostering more effective SRL and improving overall learning outcomes. For example, an AI-powered LA can adjust the difficulty level and content of learning materials based on students’ performance to ensure that they are challenged but not overwhelmed. This can help the students develop better self-regulation skills and improve their learning outcomes.

In this chapter, we embark on an exploration of the dynamic synergy between AI and LA and their role as catalysts for SRL. The complete potential of SRL becomes a formidable challenge in the absence of the insights and support delivered by LA. The gap lies between the benefits of SRL and the challenges learners face in harnessing these benefits. It is a challenge to realize that, without an intelligent and data-driven framework, these benefits may remain underutilized or even inaccessible. This chapter aims to address this challenge by providing a comprehensive exploration of how AI-powered LA can bridge this gap and empower learners. The integration of AI-powered LA becomes not just advantageous but a necessity, particularly in an educational landscape increasingly reliant on data-driven decision-making and personalized learning experiences. This chapter provides an overview of the role of AI-powered LA in supporting SRL and explores the benefits and ethical considerations associated with the use of AI-powered LA for SRL support. The chapter includes the following sections: theoretical background, AI-powered LA for SRL support, and ethical considerations and challenges.