Data Mining of Online Teaching Evaluation Based on Deep Learning

Fenghua Qi, Yuxuan Gao (), Meiling Wang, Tao Jiang and Zhenhuan Li
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Fenghua Qi: School of Statistics and Data Science, Beijing Wuzi University, Beijing 101149, China
Yuxuan Gao: School of Statistics and Data Science, Beijing Wuzi University, Beijing 101149, China
Meiling Wang: School of Statistics and Data Science, Beijing Wuzi University, Beijing 101149, China
Tao Jiang: School of Statistics and Data Science, Beijing Wuzi University, Beijing 101149, China
Zhenhuan Li: School of Statistics and Data Science, Beijing Wuzi University, Beijing 101149, China

Mathematics, 2024, vol. 12, issue 17, 1-19

Abstract: With the unprecedented growth of the Internet, online evaluations of teaching have emerged as a pivotal tool in assessing the quality of university education. Leveraging data mining technology, we can extract invaluable insights from these evaluations, offering a robust scientific foundation for enhancing both teaching quality and administrative oversight. This study utilizes teaching evaluation data from a mathematics course at a university in Beijing to propose a comprehensive data mining framework covering both subjective and objective evaluations. The raw data are first cleaned, annotated, and preprocessed. Subsequently, for subjective evaluation data, a model combining Bidirectional Encoder Representations from Transformers (BERT) pre-trained models and Long Short-Term Memory (LSTM) networks is constructed to predict sentiment tendencies, achieving an accuracy of 92.76% and validating the model’s effectiveness. For objective evaluation data, the Apriori algorithm is employed to mine association rules, from which meaningful rules are selected for analysis. This research effectively explores teaching evaluation data, providing technical support for enhancing teaching quality and devising educational reform initiatives.

Keywords: Apriori; BERT; data mining; LSTM; sentiment analysis; teaching evaluation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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