Harnessing process data to enhance learning regulation in individual and collaborative contexts: Implications and challenges in school settings

• In Event: Topics in educational research on schools: smart educational systems, digital technologies in Indian schools, school counselling and harnessing process data

Proposal

Self-regulated learning (SRL) is a foundational skill essential for lifelong success, enabling students to monitor and adapt their strategies to achieve learning goals. In social learning contexts, students not only regulate their own learning but also engage in co- and socially shared regulated learning with peers, teachers, and technological tools (Hadwin et al., 2018). In the age of AI, the ability to effectively regulate learning becomes even more crucial, as it helps students avoid over-reliance on AI tools and ensures opportunities for meaningful learning experiences (Lodge et al., 2023). Despite the growing importance of SRL, there is limited understanding of how SRL is supported, particularly at the policy level. This paper explores recent research and considers the complexities of supporting students to enact SRL in a digital age.
Learning tools and platforms generate rich process data that provide valuable insights into how students regulate their learning over time, enabling educators to offer timely and adaptive support (Kew & Tasir, 2022). However, the collection and use of data raise ethical concerns, particularly regarding student privacy and data security (Murchan & Siddiq, 2021). Furthermore, unequal access to digital tools and infrastructure across schools and regions can exacerbate existing educational disparities. Students in under-resourced environments may not benefit from the advantages of process data. Despite the potential, there is limited research into the ethical challenges of using process data to enhance learning, particularly in relation to fairness, equity, and privacy.
Drawing on theoretical models of learning regulation (Hadwin et al., 2018), educational technology evaluation frameworks (e.g., Saubern et al., 2022), conceptual studies (e.g., Molenaar et al., 2023), and empirical studies from global contexts, this presentation aims to (1) explore the value of process data in understanding learning regulation and transforming educational practices, (2) examine the ethical, privacy, and practical considerations associated with collecting and using process data in school settings, and (3) develop a conceptual framework that leverages process data to monitor and support learning regulation in both individual and collaborative contexts. The framework outlines three key dimensions for the effective use of process data: (1) identifying the types of process data that capture self- and social forms of regulatory processes, (2) determining appropriate timing to leverage this data, and (3) understanding the roles of various stakeholders—students, teachers, and school leaders—in using this data to enhance learning.
This research makes several important contributions. At a policy level, it highlights the need for ethical guidelines around the collection and use of process data, particularly concerning privacy and potential bias of data analytics. Such guidelines will ensure that process data can be collected and effectively utilised to unlock its potential to enhance student learning. The study also emphasises how educators can use data to scaffold SRL, facilitating students to take control of their learning with the support of school leadership. Theoretically, this research introduces a novel framework that contributes to the ongoing discussion about the role of technology, including AI, in education by enhancing learning in both individual and collaborative settings.

Author

• Suijing Yang, Australian Council for Educational Research