Evaluation of Comprehensive Services of an Online Learning Platform Based on Artificial Intelligence

Abstract

With the college students having increasing needs for learning diversified knowledge and skills, online learning platforms for essential qualities enhancement have emerged one by one. However, due to lack of feedbacks from students, the comprehensive service quality of these platforms varies greatly. Therefore, it has certain practical significance to study how to evaluate the comprehensive services of online learning platforms. The current evaluation models are not suitable for online learning platforms, nor have they fully considered the dynamic and subjective feedbacks of students about their experience. To this end, this paper takes an online music education platform as an example and studies the evaluation on the comprehensive services of the online learning platform. First, the overall architecture design of an online learning platform for essential qualities enhancement was displayed, and the teacher-student interaction mode for the comprehensive services of the online learning platform was identified and analyzed. Then, the derivation process of the evaluation model was presented, the evaluation indicator system for the comprehensive service quality of the online learning platform constructed, and the comprehensive service evaluation model for the online learning platform consisting of the hierarchical model and the rough set-neural network evaluation model established. The experimental results verified the effectiveness of the constructed indicator system and evaluation model.

Online Teaching Quality Evaluation: Entropy TOPSIS and Grouped Regression Model

“…He, Y. et al [16] demonstrated that content and activity designs of network teaching can effectively improve learners' learning satisfaction, while object and evaluation designs could not. Yang, P. et al [17] established a comprehensive service Online Teaching Quality Evaluation: Entropy TOPSIS and Grouped Regression Model quality evaluation model of online learning that was composed of hierarchical and rough set-neural network evaluation to verify the validity of the built index system. A careful literature review revealed that many studies have applied the method of combining qualitative and quantitative analyses during the evaluation of university or online teaching quality.…”