An AI-Application-Oriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning

Abstract

In-class teaching evaluation, which is utilized to assess the process and effect of both teachers’ teaching and students’ learning in a classroom environment, plays an increasingly crucial role in supervising and promoting education quality. With the rapid development of artificial intelligence (AI) technology, the concept of smart education has been constantly improved and gradually penetrated into all aspects of education application. Considering the dominant position of classroom teaching in elementary and undergraduate education, the introduction of AI technology into in-class teaching evaluation has become a research hotspot. In this paper, we propose a statistical modeling and ensemble learning-based comprehensive model, which is oriented towards in-class teaching evaluation by using AI technologies such as computer vision (CV) and intelligent speech recognition (ISR). Firstly, we present an index system including a set of teaching evaluation indicators combining traditional assessment scales with new values derived from CV and ISR-based AI analysis. Next, we design a comprehensive in-class teaching evaluation model by using both the analytic hierarchy process-entropy weight (AHP-EW) and AdaBoost-based ensemble learning (AdaBoost-EL) methods. Experiments not only demonstrate that the two modules in the model are respectively applicable to the calculation of indicators with different characteristics, but also verify the performance of the proposed model for AI-based in-class teaching evaluation. In this comprehensive in-class evaluation model, for students’ concentration and participation, ensemble learning module is chosen with less root mean square error (RMSE) of 8.318 and 9.375. In addition, teachers’ media usage and teachers’ type evaluated by statistical modeling module approach higher accuracy with 0.905 and 0.815. Instead, the ensemble learning approaches the accuracy of 0.73 in evaluating teachers’ style, which performs better than the statistical modeling module with the accuracy of 0.69.

Evaluation Method of Classroom Teaching Effect Under Intelligent Teaching Mode

“…As can be seen from Table 6 , by comparing with the identification results of reference [ 9 ] and reference [ 10 ], it can be seen that the identification result of this method is the best. In terms of learning speed, the time consumed by this method is significantly less than that of reference [ 9 ] and reference [ 10 ].…”

“…Through the divided training set and test set, this paper determines the optimal parameter comparison results of reference [ 9 ] university teaching quality evaluation method based on da-bp algorithm, reference [ 10 ] establishing classroom teaching evaluation model dominated by artificial intelligence application based on statistical model and integrated learning. The Gaussian kernel function was selected as the kernel function, and the kernel parameter and the classifier penalty parameter were selected by the tenfold cross verification method.…”

“…The evaluation index score is used as the input of DA-BP and the comprehensive score of College Teaching quality is used as the output of DA-BP. This method can evaluate the classroom teaching quality, However, the consideration is relatively single, and there is no in-depth comprehensive analysis and evaluation Guo et al proposed a statistical modeling for classroom teaching evaluation and a comprehensive model based on integrated learning by using artificial intelligence technologies such as computer vision and intelligent speech recognition [ 10 ]. This method can evaluate the quality of classroom teaching in colleges and universities, but its cost is too high, so it is not suitable for popularization and application.…”

The Teaching Design Methods Under Educational Psychology Based on Deep Learning and Artificial Intelligence

“…At present, AI technology and deep learning algorithm have been widely used in various industries of society, including medical treatment, transportation, manufacturing, and education. The AI technology, statistical modeling, and integrated learning are combined and applied to the classroom teaching evaluation, and the results prove the accuracy of teaching evaluation under this mode is higher and shows a good prospect (Guo et al, 2021 ). Seren and Özcan ( 2021 ) also proposed that the main significance of the application of AI in the education industry is to promote the learning adaptability of students, so as to improve the learning enthusiasm of students.…”

Design and Implementation of an IoT-Based Smart Classroom Incubator

“…Examination and model proposition studies measure, observe, or locally control physical conditions using different methods [1,[31][32][33]. Furthermore, experimental studies conducted in classroom environments ignored the effect of physical classroom conditions [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. In learning environments, the main purpose is learning efficiency.…”