Intelligent Teaching Recommendation System for Chinese Language Course in Higher Vocational Education based on Hybrid Deep Learning Networks

The objective of the higher education is to cultivate innovative and applied talents. It focuses on cultivating students’ interests and improving their practical abilities, encouraging students to transform their theoretical knowledge into practical skills. Based on the training objectives of Chinese based vocational education, course design includes two parts: theoretical teaching and practical teaching. Practical teaching focuses on cultivating and examining the students’ mastery of a certain skill. Traditionally, the basic teaching mode is utilized, which suffers from several problems such as non-interactive teaching process, a mismatch of interests with the subject content and feedback delay, which marks its own hurdle in implementing Chinese language-based vocational educational systems as an effective process. To overcome this challenge, this paper proposes the Hybrid Deep learning Model for designing the intelligent teaching recommendation systems (ITRS) for an efficient deployment of Chinese languages for Vocational Education. The proposed framework was evaluated using CMRC-2018 Chinese Datasets, and performance metrics such as accuracy, precision, recall, specificity and F1-score are calculated. To establish the superiority of the model, performance of the suggested model was compared with the other learning models. Results demonstrate the performance of hybrid machine learning model has produced the accuracy of 99%, precision of 98.7%, recall of 98.6%, specificity of 98.5% and F1-score of 98.9% respectively. The experimental results illustrate that the proposed model has effectively increased students’ interest in learning the Chinese language within the vocational education system.