Explore the vast range of titles available.

In conventional ancient Chinese poetry learning, students tend to be under-motivated and fail to understand many aspects of poetry. As generative artificial intelligence (GAI) has been applied to education, image-GAI (iGAI) provides great opportunities for students to generate visualized images based on their descriptions of poems, and to situate students in a context similar to what a poem describes. In addition, the progressive prompt is a strategy that can progressively provide students with clues and guidance in technology-enhanced learning environments. Hence, this study proposed a progressive prompts-based image-GAI (PP-iGAI) approach to support students' ancient Chinese poetry learning. To evaluate its effectiveness, the present study employed a quasi-experiment design and recruited 80 fifth-grade elementary school students to engage in one of two conditions: one class was assigned as the experimental group and adopted the PP-iGAI approach, while the other class was assigned as the control group and used the conventional prompt-based iGAI (C-iGAI) approach. The results revealed that the PP-iGAI approach could better promote students' learning achievement, extrinsic motivation, problem-solving awareness, critical thinking, and learning performance. In addition, no significant differences were found in the two groups' cognitive load. Moreover, the results of the interview disclosed the learning perceptions and experiences of both groups. Accordingly, the present study can provide a reference not only for ancient Chinese poetry learning but also for the application of GAI in educational fields for future research.

Background In the digital age, fostering young children’s computational thinking (CT) and executive functions (EFs) through programming has emerged as a significant research issue. While unplugged programming activities are commonly adopted in preschools, robot programming activities have recently gained attention for the potential to enhance both CT and EFs. Preschoolers are at a pivotal stage for developing CT and EFs. However, there is a dearth of empirical evidence comparing robot programming and unplugged programming activities on preschoolers’ CT and EFs development. Therefore, the current research designed a randomized controlled trial to compare the impact of robot programming and unplugged programming activities on 198 5- to 6 year-old preschoolers’ CT and EFs (including inhibition, working memory, and cognitive flexibility). Children were randomly allocated to either the robot programming group, the unplugged programming group, or the business-as-usual control group. Results After a 12-week intervention, results revealed that: (1) the robot programming and unplugged programming groups both outperformed the conventional kindergarten group in CT, with the robot programming group having superior effects in CT over time; (2) the robot programming group outperformed the unplugged programming and conventional kindergarten group on inhibition, working memory, and cognitive flexibility of EFs over time; and (3) most preschoolers in the robot programming group had positive perceptions of programmable robots. Conclusions The present research demonstrated that robot programming had a more significant and sustained impact on preschoolers’ CT and EFs than unplugged programming and conventional kindergarten activities. Accordingly, these findings offered valuable implications for introducing effective programming activities to develop preschoolers’ CT and EFs.

ObjectivesTo develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.DesignA nationally representative retrospective study.SettingRepresentative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.ParticipantsRepresentative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measuresIn-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).ResultsA total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.ConclusionsThe models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.