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The application of information and communications technology (ICT) in higher educational institutions has led to the transformation of the environment from digital to smart. The assessment of smart learning environments will highlight the advantages and disadvantages of its construction results and help establish a sustainable space for students' personalized study. However, the construction level and application effect are difficult to judge thoroughly, so a comprehensive evaluation method is needed. By rethinking the structure of a smart learning environment (the physical space, resource space and social space), this paper uses analytic hierarchy process-fuzzy comprehensive evaluation (AHP-FCE) and genetic algorithm-back propagation (GA-BP) neural network algorithm methods to assess the environment, with the aim of determining their scope of application and providing suggestions for updating the environment construction process. The questionnaire results of 300 students at Central China Normal University (CCNU) were analyzed with an evaluation index system that was collected by expert scoring. The results showed that the AHP-FCE model can simultaneously obtain multiple results but can be influenced by subjective factors, whereas the GA-BP-based model can make the evaluation process easier and improve fault tolerance. The results also indicated that the classrooms need to be modified in terms of the perception infrastructure and resource modules to provide students with a more suitable and comfortable learning space. We hope the study can provide a reference and inspiration for the construction and assessment of smart learning environments.
Dai et al. (Fri,) studied this question.