Tang, HengtaoArslan, OkanXing, WanliKamalı-Arslantaş, Tuğba2022-06-202022-06-2020231042-1726https:/dx.doi.org/10.1007/s12528-022-09318-1https://hdl.handle.net/20.500.12451/9436SciVal Topics Metrics Abstract Socially shared metacognition is important for effective collaborative problem solving in virtual laboratory settings, A holistic account of socially shared metacognition in virtual laboratory settings is needed to advance our understanding, but previous studies have only focused on the isolated effect of each dimension on problem solving. This study thus applied learning analytics techniques to develop a comprehensive understanding of socially shared metacognition during collaborative problem solving in virtual laboratories. We manually coded 126 collaborative problem-solving scenarios in a virtual physics laboratory and then employed K-Means clustering analysis to identify patterns of socially shared metacognition. Four clusters were discovered. Statistical analysis was performed to investigate how the clusters were associated with the outcome of collaborative problem solving and also how they related to the difficulty level of problems. The findings of this study provided theoretical implications to advance the understanding of socially shared metacognition in virtual laboratory settings and also practical implications to foster effective collaborative problem solving in those settings.eninfo:eu-repo/semantics/embargoedAccessCollaborative Problem SolvingEpisodesLearning AnalyticsSocially Shared MetacognitionVirtual LaboratoryArticle----10.1007/s12528-022-09318-1Q1WOS:000797283600001Q1