مقاله انگلیسی رایگان در مورد حل مسئله در بازی شبیه سازی پزشکی – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله چگونه دانش قبلی بر عملکرد حل مسئله در یک بازی شبیه سازی پزشکی تأثیر می گذارد: استفاده از لاگ های بازی و ردیابی چشمی
عنوان انگلیسی مقاله How prior knowledge affects problem-solving performance in a medical simulation game: Using game-logs and eye-tracking
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۰ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۵٫۸۷۶ در سال ۲۰۱۸
شاخص H_index ۱۳۷ در سال ۲۰۱۹
شاخص SJR ۱٫۷۱۱ در سال ۲۰۱۸
شناسه ISSN ۰۷۴۷-۵۶۳۲
شاخص Quartile (چارک) Q1 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط علوم تربیتی
گرایش های مرتبط تکنولوژی آموزشی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس نقش کامپیوتر در رفتار انسان – Computers in Human Behavior
دانشگاه  School of Health Professions Education, Maastricht University, 6200, MD, Maastricht, the Netherlands
کلمات کلیدی ارزیابی، دانش قبلی، بازی شبیه سازی، بازی جدی، بار شناختی، ردیابی چشمی
کلمات کلیدی انگلیسی Assessment، Prior knowledge، Simulation game، Serious game، Cognitive load، Eye-tracking
شناسه دیجیتال – doi
https://doi.org/10.1016/j.chb.2019.05.035
کد محصول  E13653
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱٫ Introduction
۲٫ Method
۳٫ Results
۴٫ Discussion
Acknowledgements
Appendix. The HMM score computation
References

 

بخشی از متن مقاله:
Abstract

Computer-based simulation games provide an environment to train complex problem-solving skills. Yet, it is largely unknown how the in-game performance of learners varies with different levels of prior knowledge. Based on theories of complex-skill acquisition (e.g., 4C/ID), we derive four performance aspects that prior knowledge may affect: (1) systematicity in approach, (2) accuracy in visual attention and motor reactions, (3) speed in performance, and (4) cognitive load. This study aims to empirically test whether prior knowledge affects these four aspects of performance in a medical simulation game for resuscitation skills training. Participants were 24 medical professionals (experts, with high prior knowledge) and 22 medical students (novices, with low prior knowledge). After pre-training, they all played one scenario, during which game-logs and eye-movements were collected. A cognitive-load questionnaire ensued. During game play, experts demonstrated a more systematic approach, higher accuracy in visual selection and motor reaction, and a higher performance speed than novices. Their reported levels of cognitive load were lower. These results indicate that prior knowledge has a substantial impact on performance in simulation games, opening up the possibility of using our measures for performance assessment.

Introduction

Computer-based simulation games (CBSG) are effective learning environments for complex skills. As simulations, they approximately replicate the complexity of real-life situations (Koivisto, Niemi, Multisilta, & Eriksson, 2017). As computer games, they provide a package of problems that are causally connected, based on learners’ interaction with the game (Kiili, 2005). In this simulated problem-solving environment, learners can train specific professional skills in areas such as aviation, business management, and medicine (Dankbaar et al., 2016; De Freitas, 2006; Hernández-Lara, Perera-Lluna, & SerradellLópez, 2019). However, CBSGs face a challenge in that the performance of a learner in the game is difficult to assess via traditional measurements such as achievement tests (Kang, Liu, & Qu, 2017). This challenge is mainly due to the open-ended nature of CBSGs (Squire, 2008), which allows for a large number of different behaviors. Therefore, recent research has focused on tracking users’ in-game behaviors by looking at game data such as serious game analytics (Kang et al., 2017; Loh, Sheng, & Ifenthaler, 2015; Wallner & Kriglstein, 2013). These studies identified several limitations: Data analysis without involving educational theoretical principles often fails to fully account for students’ performance (Kang et al., 2017), game-logs without translation into high-level meaningful actions can yield confounding information (Zhou, Xu, Nesbit, & Winne, 2010), some important factors such as timing cannot be explained by analyzing sequences of events only (Clark, Martinez-Garza, Biswas, Luecht, & Sengupta, 2012), and empirical studies about how game data can be informative for performance assessment are scarce (Hou, 2015; Kang et al., 2017).

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