Unpacking Socially Shared Metacognitive Regulation (SSMR) Strategies in a Simulation-based Learning Environment

<p dir="ltr">Recent reforms in accounting education emphasize alternative instructional approaches to equip students with Industry 4.0 soft skills, such as problem-solving, adaptability, teamwork, and communication (Silva et al., 2021; World Economic Forum, 2016). Simulation-based learning tools like ProBanker, enhance these skills by engaging learners in real-world tasks that promote collaboration and metacognitive strategies (Kim et al., 2009; Roland & Buchana, 2014). While research on game-based learning often focuses on cognitive outcomes, metacognitive outcomes, particularly socially shared metacognitive regulation (SSMR), remain underexplored (Hadwin et al., 2017; Jarvela et al., 2019). This study addresses this gap by examining the SSMR strategies undergraduate students employ during weekly discussions over four weeks, while interacting with ProBanker, examining trends in orienting, planning, monitoring, and evaluation. 15 accountancy undergraduates from a Singapore-based university participated in this study as part of the "Financial Institutions and Markets" module. Organized into three groups of five, they used the ProBanker simulation to act as bank managers, making strategic financial decisions weekly. Metacognitive regulation (MR) prompts guided strategy development, such as "What trading strategies did your team use?" and "How did your team check if the trading strategies used were effective?". Weekly team discussions were audio-recorded, transcribed, and coded using a framework adapted from De Backer et al. (2015). MR strategies were identified, categorized as individually oriented or socially shared, and coded, achieving an acceptable minimum interrater agreement of 75% (McHugh, 2012). All groups utilized a full range of SSMR strategies (task analysis, content orientation, interim planning, comprehension monitoring (CM), progress monitoring, and evaluation of learning outcomes and processes (ELOP)) during the four weeks. CM was the most frequently used, highlighting the importance of summarizing and elaborating to understand strategies, consistent with De Backer et al. (2015). ELOP increased over time, reflecting a growing focus on evaluating processes and outcomes. Group differences emerged: Group 2 started with the highest ELOP, likely due to trial strategy evaluations guiding their adoption of SSMR, while Groups 1 and 3 began lower. This suggests that prior experiences could have influenced how teams engaged in SSMR. This study offers valuable insights into SSMR strategies in collaborative learning. CM emerged as the dominant strategy, emphasizing the need for targeted support in active listening, questioning, and collective notetaking. Group 2's higher use of ELOP suggests that trial simulations shaped their strategies, highlighting the importance of prior experiences in SSMR development. Educators should design environments that support CM and foster evaluation skills. Generative AI-drive real-time feedback and adaptive support could deepen metacognitive abilities, such as navigating complex tasks (Tankelevitch et al., 2024). Future research should explore CM strategies: impact, early ELOP's influence on long-term outcomes like deep understanding and team performance, and mediating factors like team cohesion and task complexity.</p>