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Abstract Computational thinking is acknowledged as an essential competency for everyone to learn. However, teachers find it challenging to implement the existing learning approaches in K-12 settings because the existing approaches often focus on teaching computing concepts and skills (i.e., programming skills) rather than on helping students develop their computational thinking competency—a competency that can be used across disciplinary boundaries in accordance with curriculum requirements. To address this need, the current study investigated how game-based learning influenced middle school students’ learning processes, particularly on the development of computational thinking competency, self-efficacy toward computational thinking, and engagement during gameplay. Additionally, the study examined how these outcomes were moderated by individual differences. We observed evidence that the gaming experience influenced students’ computational thinking self-efficacy, but not computational thinking competency or game-based engagement. Compared to age (grade) and prior gaming experience, gender tended to play a more important role in moderating students’ computational thinking competency, self-efficacy toward computational thinking competency, and game-based engagement. Implications and possible directions for future research regarding using game-based learning to enhance computational thinking competency are discussed. 

Abstract Study ObjectivesEvaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. MethodsChildren (n = 82, 61% male, 43.9% black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. Three-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin’s concordance correlation coefficient (CCC), mean absolute error (MAE), and mean absolute percent error (MAPE) assessed agreement with ECG estimated heart rate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. ResultsThe developed scripts are available on Github. For the GT9X, CCC was poor at −0.11 and MAE and MAPE were high at 16.8 (SD = 14.2) beats/minute and 20.4% (SD = 18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD = 9.9) beats/minute and 7.3% (SD = 10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. ConclusionsRaw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e. hardware, software, etc.) of the GT9X’s poor performance.