Multi-dimensional evaluation method of Chinese online teaching effect based on learning behaviour data

In order to overcome the problems of low recall and precision of learning behaviour data, as well as low evaluation accuracy in traditional multi-dimensional evaluation methods for Chinese online teaching effectiveness, a new multi-dimensional evaluation method of Chinese online teaching effect based on learning behaviour data is proposed. Using K-means algorithm and AdaBoost algorithm to mine learning behaviour data, Chinese online learning state recognition is performed based on the mined learning behaviour data and Kalman filtering. Combining the results of Chinese online learning state recognition with Markov chain, multi-dimensional evaluation of Chinese online teaching effectiveness is achieved. The experimental results show that the average recall rate and precision rate of the learning behaviour data of the proposed method are 96.78% and 97.34%, respectively. The accuracy of multi-dimensional evaluation of the effectiveness of Chinese online teaching varies within the range of 93.8% to 97.3%, indicating high accuracy.