Building a Learning System for the Basics of Human Skeleton with Cognitive Theory of Multimedia Learning and Augmented Reality

• Main Authors: Pei-Yi Chen, 陳佩怡
• Other Authors: Wey-Wen Jiang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/12690635621911000621

碩士 === 國立臺北護理健康大學 === 資訊管理研究所 === 102 === Due to the development of computer science, applications of information technology on education are increasing, and the computer-assisted instruction has emerged. Utilization of augmented reality (AR) has becomes popular in many fields. Augmented Reality allows virtual information to be combined with real-world scene, which strengthens participants’ cognition and interaction towards and with the physical environment, making learning more interesting and effective, and indeed interactive. In this study, the author followed Mayer’s cognitive theory of multimedia learning (CTML) and developed the system in accordance with his multimedia design principles. This research also adopted Ausubel’s meaningful learning theory to build teaching materials, and let learners link the newly taught contents with their prior knowledge to achieve effective learning. In this study, we took human skeleton as an example to build the proposed system. The system has two modes: the learning mode and the testing mode. The learning mode provides the information about human skeleton. It also shows the associated 3D skeleton models with subtitles and voice. The testing mode adopted the intelligent queue selection strategy, which allows learners to reinforce their learning about unfamiliar subjects by practicing repeatedly. The 5-point Likert Scale was used in a questionnaire to assess the established AR systems. Thirty learners were recruited to use this system and answer the questionnaire. According to the received results, the average satisfactory score of every question for both modes was above 4, and the average score of overall satisfaction was 8.5 out of 10. It implies that the recruited learners favored this system. In the testing mode, they can answer 10 questions correctly within 148 seconds on average. This indicates that users can gain skeletal-related knowledge quickly when use this system. In summary, the study shows that AR technique is suitable for building learning and testing systems. Learners obtained good learning outcomes through this system.