Design and Application of University Intelligent Learning Environment Centered on Improving User Experience

• Published in: Design, User Experience, and Usability. Application Domains pp. 457 - 471
• Main Authors: Liu, Guang, Liu, Zejiang, Lu, Fang, Ye, Qinmei, Liu, Zhen
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Intelligent learning environment; Interactive learning; Learning behavior; Learning environment innovation; Learning space; Learning-centered furniture design; Space interaction; University students; User experience
• ISBN: 3030235378; 9783030235376
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-23538-3_35

With the emergence of new technologies, such as the Internet of things (IoT), big data, cloud computing and mobile communication, smart education has gradually become a new driving force for national education development. China’s Ministry of Education called for the in-depth promotion of “Internet + higher education”, breaking the time-space boundaries and school walls of traditional education. Currently, China’s reconstruction of the intelligent learning environment is mainly focused on the design of smart learning spaces. The difference between the stage of wisdom education and other stages of informationization development is to emphasize the cultivation of learners’ creativity. This directly becomes the main starting point for the design of a new generation of learning spaces. Therefore, the intelligent learning environment will combine the combination of creativity and digital as the key to innovation. However, there is currently a lack of innovation in the improvement of the user experience in an intelligent learning environment. Therefore, this paper focuses on the analysis of the main problems of the current Chinese university learning environment, such as teaching-centered classroom setting unsuitable for classroom interaction, public learning space with low user experience, and lack of teaching decisions supported by big data analysis and learning behavior analysis, and then achieves the best student learning effect through the reconstruction of the smart learning environment centered on improving the use experience, including physical and virtual environments. The use experience-centered smart learning environment ultimately has been evaluated in a case study, which indicates that the environment can effectively improve students’ self-learning and problem-solving abilities.