College of Automation Engineering, Shanghai University of Electric Power, Shanghai, China.
PLoS One. 2024 Sep 6;19(9):e0303613. doi: 10.1371/journal.pone.0303613. eCollection 2024.
Nowadays, colleges and universities focus on the assessment model for considering educational offers, suitable environments, and circumstances for students' growth, as well as the influence of Teaching Quality (TQ) and the applicability of the skills promoted by teaching to life. Teaching excellence is an important evaluation metric at the university level, but it is challenging to determine it accurately due to its wide range of influencing factors. Fuzzy and Deep Learning (DL) approaches must be could to build an assessment model that can precisely measure the teaching qualities to enhance accuracy. Combining fuzzy logic and DL can provide a powerful approach for assessing the influencing factors of college and university teaching effects by implementing the Sequential Intuitionistic Fuzzy (SIF) assisted Long Short-Term Memory (LSTM) model proposed. Sequential Intuitionistic Fuzzy (SIF) can be used sets to assess factors that affect teaching quality to enhance teaching methods and raise the standard of education. LSTM model to create a predictive model that can pinpoint the primary factors that influence teaching quality and forecast outcomes in the future using those influencing factors for academic growth. The enhancement of the SIF-LSTM model for assessing the influencing factors of teaching quality is proved by the accuracy of 98.4%, Mean Square Error (MSE) of 0.028%, Tucker Lewis Index (TLI) measure for all influencing factors and entropy measure of non-membership and membership degree correlation of factors related to quality in teaching by various dimensional measures. The effectiveness of the proposed model is validated by implementing data sources with a set of 60+ teachers' and students' open-ended questionnaire surveys from a university.
如今，高校注重教育投入评估模式、适合学生成长的环境和条件，以及教学质量（TQ）的影响和教学所推广技能对生活的适用性。教学卓越是大学层面的重要评价指标，但由于影响因素广泛，准确确定教学卓越水平具有挑战性。必须采用模糊和深度学习（DL）方法来构建评估模型，以提高准确性，精确衡量教学质量。通过实施提出的顺序直觉模糊（SIF）辅助长短期记忆（LSTM）模型，可以结合模糊逻辑和 DL 为评估影响高校教学效果的因素提供一种强大的方法。顺序直觉模糊（SIF）可以用于评估影响教学质量的因素，以增强教学方法并提高教育水平。LSTM 模型创建一个预测模型，可以使用这些影响因素确定影响教学质量的主要因素，并预测未来的结果，从而促进学术发展。通过各种维度的措施，SIF-LSTM 模型评估教学质量影响因素的准确性为 98.4%，均方误差（MSE）为 0.028%，Tucker Lewis 指数（TLI）测量所有影响因素和非隶属度和隶属度相关因素的熵测量度来证明其有效性。通过实施来自一所大学的 60 多位教师和学生的开放式问卷调查数据集来验证该模型的有效性。
