Abstract
In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring.
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