Software Development for Comprehensive Assessment of English Online Teaching Quality in Universities Based on Data Mining
DOI:
https://doi.org/10.3991/ijet.v18i02.35525Keywords:
K-modes algorithm, Feed-forward neural network, Web-based teaching, Comprehensive quality evaluationAbstract
In the context of big data in education, the education industry is combined with information technology to form an online teaching model. In order to analyze the actual effectiveness of online teaching methods, a comprehensive evaluation model of online teaching quality is developed based on data mining. The model is divided into two parts: an improved K-modes algorithm to evaluate English teachers’ “teaching” and a feed-forward neural network to evaluate students’ “learning”. The improved K-modes algorithm cleansed, analyzed, and mined the teaching data, and improved the calculation of cosine similarity to establish a model for evaluating teachers’ teaching status, the neural network model has more excellent index results, where the average error is 0.98, within 1, so the neural network model has a smaller error result. The combined model has a strong feasibility for the comprehensive evaluation of English online teaching quality.
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