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The massive open online course (MOOC) is a large-scale open online course teaching method with many advantages such as abundant resources, open access, social interaction, and learning freedom. However, there are also flaws such as low completion rate, lack of personalized consultation, mechanical inspection, and unity. In order to resolve these shortcomings of MOOC, this study proposes a hybrid course model consisting of MOOC and small private online courses (SPOC) based on machine learning. This integrates deterministic rules into our machine learning pipeline in a variety of ways, gradually adding rules as data preprocessing steps and then using object-oriented programming (OOP) to generate novel ML model classes. Finally, it includes data in all deterministic rules through a hybrid model so that we can train it like any other machine learning model. Through experimental analysis, it can be observed that the MOOC + SPOC hybrid teaching mode can effectively integrate MOOC, SPOC, and physical classrooms and make full use of the advantages of the three to provide learners with a unified interactive interface for implementation.
Yingying Lou (Wed,) studied this question.