The Unspoken Aspect of Socially Shared Regulation in Collaborative Learning: AI-Driven Learning Analytics Unveiling 'Silent Pauses'

Abstract

ABSTRACT

Socially Shared Regulation (SSRL) contributes to collaborative learning success. Recent advancements in Artificial Intelligence (AI) and Learning Analytics (LA) have enabled examination of this phenomenon’s temporal and cyclical complexities. However, most of these studies focus on students’ verbalised interactions, not accounting for the intertwined ’silent pauses’ that can index learners’ internal cognitive and emotional processes, potentially offering insight into regulation’s core mental processes. To address this gap, we employed AI-driven LA to explore the deliberation tactics among ten triads of secondary students during a face-to-face collaborative task (2,898 events). Discourse was coded for deliberative interactions for SSRL. With the micro-annotation of ‘silent pause’ added, sequences were analysed with the Optimal Matching algorithm, Ward’s Clustering and Lag Sequential Analysis. Three distinct deliberation tactics with different patterns and characteristics involving silent pauses emerged: i) Elaborated deliberation, ii) Coordinated deliberation, and iii) Solitary deliberation. Our findings highlight the role of ‘silent pauses’ in revealing not only the pattern but also the dynamics and characteristics of each deliberative interaction. This study illustrates the potential of AI-driven LA to tap into granular data points that enrich discourse analysis, presenting theoretical, methodological, and practical contributions and implications.