Knowledge tracing and learning path optimization are active research fields in education with AI technologies. The purpose of knowledge tracing is to model student's knowledge state of a concept and to predict the percentage of correctly answer a next question. Using the technology of modeling a student's knowledge state, learning path optimization technologies recommend personalized learning path for efficient learning. These two research fields are implemented on learning management systems for individual learning. In this research paper, method of using knowledge tracing and learning path optimization in group learning environment is suggested. Group score prediction model predicts number of students who will answer their next question correctly by utilizing one-dimensional convolution neural network and fully connected layers. The model is adopted in a group score prediction system where instructors utilize the model's output to create a question set corresponding to their strategy and students' responses are used to re-train and evaluate the model.