Analysis of characteristics and learning effectiveness of MOOC learners in the context of big data

To improve the pass rate and activity of students in exams, this study proposes a method for analysing the characteristics and learning outcomes of MOOC learners based on big data. Firstly, use Kalman filtering technology to process continuous frame images and obtain the behavioural sequence of MOOC learners. Then, ARIMA model is used to construct the MOOC learning behaviour state space, extract and classify the behavioural features of learners. Finally, select the indicators suitable for learning effectiveness analysis, and calculate the comprehensive score by combining fuzzy membership function and fractal evaluation algorithm. The experimental results show that after the application of this method, the pass rate of students in the exam remains between 96.1% and 98.8%, and the highest activity can reach 0.97, indicating that its application effect is good.