Year: 2024
Type: article
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.
Source: Mathematics
DOI: 10.3390/math12172692
Institution: Beijing Wuzi University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4401992847