Higher Education Environment Monitoring and Quality Assessment Model Using Big Data Analysis and Deep Learning

Abstract

A university exists to help students develop their skills. The goal of university development is to raise the standard of personnel training, and the core component of university development is teaching quality. Whether the level of practical training can as soon meet the requirements of enterprise employment. For responding to the demands of professions and occupations, it is essential. Teaching quality evaluation is a crucial cornerstone for ensuring the quality of instruction. Universities and colleges should therefore concentrate on evaluating instruction. Schools and colleges can more rapidly and thoroughly comprehend how their off-campus cooperative adult education programmes are running while also enhancing the efficacy and impartiality of their quality assessment by utilising educational data mining and learning analysis technology. Currently, issues with backward evaluation instruments, a single evaluation topic, and easy evaluation methods exist when evaluating the quality of schooling. Big data technology is used to create a higher vocational education environment monitoring and quality evaluation system that offers new and varied approaches to evaluate teaching quality. The technique for evaluating the quality of schooling is expanded upon in this research using various big data mining technologies. The improved collaborative filtering algorithm’s mean absolute difference is approximately 18.23% when the data set is larger. In conclusion, when applied to big data sets, the technique in this work performs with greater accuracy than the conventional collaborative filtering algorithm. The scoring matrix becomes denser as there are more scoring items in the model. In turn, this results in a more accurate similarity calculation at the beginning of the programme, albeit the similarity calculation error increases as the scoring matrix becomes denser.