Abstract:

Addressing the challenge of teachers' difficulty in effectively utilizing educational big data to Implement differentiated teaching decisions, methods for differentiated teaching decision-making and implementation is studied based on student learning behavior data. Firstly, an input indicator set that can fully describe students' learning behavior is constructed, and data on each student's learning behavior during the online course teaching process is collected. Based on the collected learning behavior data, training samples are constructed and students' online learning effectiveness is manually evaluated to obtain the corresponding label values for the training samples. Then, using the constructed training samples and the evaluated label values, the convolutional neural network is trained to approximate the mapping relationship between student behavior data and evaluation results. A well-trained network can automatically provide differentiated assessment results based on different students' learning behaviors. Based on the evaluation results of each student and combined with their teaching experience, teachers can develop differentiated intervention strategies for different students. Finally, the implementation effect of the intervention strategy is examined in detail, and dynamic adjustments are made to the intervention strategy based on the actual situation during the implementation process. Empirical research results have shown that compared to traditional teaching decision-making methods, teaching decision-making methods based on online learning behavior data are more significant in improving students' academic performance. The findings reveal that the implementation of differentiated teaching decisions based on student learning behavior data is effective and feasible. The research provides support for teachers in analyzing learning behavior data and adjusting corresponding teaching decisions.

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