Background. An increasing number of studies support a mediating influence of personality on video-game preferences and player experiences, and in particular, traits associated with playfulness, such as extraversion. Educational institutions, however, tend to reward serious personality traits, such as conscientiousness. Aim.To discern how students respond to Game-Based Learning (GBL) in the classroom, and to understand if and how conscientiousness mediates GBL, we performed a field study at a leading university of technology in northeast China. Method. In May 2019, 60 bachelor and executive students in public-administration studies consecutively played two digital serious games, TEAMUP (multiplayer) and DEMOCRACY3 (single player). Data accrued through surveys with pregame measurements of personality (conscientiousness), mediating factors (motivation, player experience), learning effectiveness (cognitive and non-cognitive learning), and GBL acceptance. Results. Analysis showed a strong overall learning effect for both games. Conscientiousness significantly related to cognitive learning in both games and noncognitive learning in the multiplayer game only. Conscientiousness also significantly related to player experiences in the multiplayer game. Furthermore, the conscientiousness facet of perfectionism was a dominant factor in player experience and learning. We discuss the findings in light of several aspects around GBL that require more attention and research, especially that, alongside other factors, conscientiousness may be an important dimension to consider in the design and implementation of GBL in education, and GBL can have a positive role in the modernization of education in non-Western countries.
The pelvic structure is complex and the tumor is poorly defined from the surrounding tissues. Finding the exact tumor resection margin based on the surgeon's clinical experience alone is a time‐consuming and difficult task, which is a major factor leading to surgical failure. An accurate method for segmenting pelvic bone tumors is needed. In this paper, a semiautomatic segmentation method for pelvic bone tumors based on CT‐MR multimodal images is presented. The method combines multiple medical prior knowledge and image segmentation algorithms. Finally, the segmentation results are visualized in three dimensions. We tested the proposed method on a collection of 10 cases (97 tumor MR images in total). The segmentation results were compared with the manual annotation of the physicians. On average, our method has an accuracy of 0.9358, a recall of 0.9278, an IOU value of 0.8697, a Dice value of 0.9280, and an AUC value of 0.9632. The average error of the 3D model was within the allowable range of the surgery. The proposed algorithm can accurately segment bone tumors in pelvic MR images regardless of tumor location, size, and other factors. It provides the possibility to assist pelvic bone tumor preservation surgery.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.