AI-driven estimation and enhancement of metacognitive monitoring to improve mathematical learning in children

Abstract

Effective learners actively engage with and select their learning processes from metacognitive monitoring, a cornerstone of educational success. Metacognitive monitoring is an essential aspect of effective learning and enables learners to reflect on their thought processes, learning strategies, and knowledge states, thereby regulating their own learning. Enhancing metacognitive monitoring skills has shown substantial educational benefits, as evidenced by existing pedagogical and social research. This is particularly true in mathematics, where precise metacognitive monitoring correlates strongly with improved performance. Effective metacognitive monitoring is essential for enhancing mathematical abilities among learners. Children aged 7 to 11 are in a pivotal developmental phase where metacognitive skills can be significantly shaped. This period is critical for cultivating these skills, which are vital for their future academic and personal growth. In traditional classroom settings, expert teachers encourage students to think about their own learning by asking them reflective questions. Yet, their time is limited, making it difficult to address each student's needs in large classes. With the progress of AI, computer-based learning environments (CBLEs) are increasingly capable of replicating learning support at scale and are well-positioned to tailor support in large, diverse learner populations. However, adapting teacher-driven interventions to CBLEs poses significant challenges. For example, intelligent tutoring systems (ITSs) often provide frequent prompts that can interrupt the natural flow of learning activities and may reduce learners' trust in these systems. Additionally, ITSs require learners to articulate their thoughts during self-reflection, a process that is essential yet complex. These subjective responses are often unreliable. To address these challenges, our work proposes a novel technique that estimates young learners' metacognitive monitoring performance (MMP) by analyzing their spontaneous facial responses, thus aiming to emulate the nuanced approach of expert teachers within digital learning environments. Building upon the prior work about emotion expressed during metacognitive monitoring, we developed the Meta-Facial Expression Interpreter (M-FEI), an approach to estimate MMP through facial cues. It enables real-time estimation and has been proven to outperform an existing conventional method. These conclusions have been derived from a first large user study conducted with 184 children aged 7 to 11 from two provinces in China and from Scotland. An ITS designed to enhance learners' metacognitive monitoring was employed in a second large-scale user study. This compared mathematical learning outcomes between a tailored metacognitive monitoring intervention using M-FEI (condition 1) and, respectively, using the conventional method (condition 2). The study included a total of 215 children aged from 7 to 12. The results showed that children in condition 1 achieved improved learning outcomes and significantly outperformed those in condition 2. This PhD thesis has pioneered an innovative approach for tailoring learning support by a multi-modality deep learning neural network. This approach has the potential to benefit a diverse range of learners for a variety of subjects by providing personalized and effective educational support in improving metacognitive monitoring skills