Research On Evaluation Of Smart Learning Environment In Universities Based On Multi-Space Integration

With the rapid development of Internet of things,big data,artificial intelligence and other emerging information technology and its in-depth application in education and teaching,the learning environment has already begun to transform from a digital to a smart one,and the smart learning environment has emerged.As a pilot demonstration in response to the reform of the learning environment,universities invested capital and human resources heavily in the construction of their own smart learning environment;however,the construction level and application effects are difficult to judge reasonably.Therefore,it is of great significance both theoretically and practically to evaluate and study the smart learning environment of universities with multi-space integration.Firstly,an introduction to the concepts and research status of the smart learning environment and learning space was shown.Based on the perspective of multi-space fusion,a smart learning environment model with holistic,smart,open and ecological characteristics was proposed,a detailed explanation of its constituent,i.e.physical space,resource space and social space and an analysis of the mutual interactions among them were provided.Then put forward the design principles of smart learning environment and carry out case analysis to provide a reference for the construction of smart learning environment in Central China Normal University.Secondly,the three major spatial elements of the smart learning environment were used as the first-level indicators of the evaluation index system.By referring to relevant literature and combining with the actual needs of teachers and students,28 second-level indicators have been initially constructed.After rounds of expert consultation,a smart learning environment evaluation indicator system with 3 first-level indicators and 26 second-level indicators finally came into being.Lastly,a questionnaire,grounded on the evaluation indicator system,was designed to collect sample data.Factor analysis was adopted to reduce the indicators,and the evaluation model was established in conjunction with the BP neural network algorithm.The parameters of the BP neural network were optimized using genetic algorithms.Take Central China Normal University as the example,it carried out an empirical analysis of the evaluation model.According to the prediction results of the model,the smart learning environment evaluation results of Central China Normal University were obtained.The effectiveness of the model was verified through comparing it with the traditional evaluation model of combined with analytic hierarchy process and fuzzy comprehensive evaluation.The research results showed that the evaluation level of the smart learning environment of Central China Normal University was good and still had room for improvement.The evaluation model based on factor analysis and BP neural network better circumvented the influence of human evaluation of traditional evaluation models,and was more in line with the smart learning environment actual demands.The above-mentioned research results have theoretically enriched the study of the smart learning environment and offered reference value for the follow-up improvement of the smart learning environment in practice.