Evaluation of College English Online Education Course Teaching Effectiveness Based on Deep Learning Algorithms

In the burgeoning landscape of higher education, the effective evaluation of online English teaching outcomes has become increasingly pivotal. This paper focuses on the benefits of employing long short-term memory (LSTM) networks for such evaluations, capitalizing on their strength in processing time-series data and identifying long-term learning patterns. We detail a structured approach using LSTM for assessing the quality of online English education, encompassing data collection, preprocessing, model training, and analysis. The innovative aspect of our methodology lies in its application of LSTM to interpret complex student interaction data, providing a more nuanced understanding of educational effectiveness. Experimental results affirm the LSTM model’s capability to offer accurate and meaningful insights into teaching effectiveness, marking a significant advancement in the application of deep learning techniques in educational assessment.