- author = "Kaiyan Si and Junmin Ye and Shuang Yu and Xinghan Yin and Wen Ren and Sheng Luo and Gang Zhao",
- title = "Analysis and Prediction of Factors Affecting Student Grades in Online Collaborative Learning Using Genetic Programming-based Approach",
- booktitle = "2024 4th International Conference on Educational Technology (ICET)",
- year = "2024",
- pages = "185--189",
- month = sep,
- keywords = "genetic algorithms, genetic programming, Accuracy, Machine learning algorithms, Federated learning, Collaboration, Predictive models, Prediction algorithms, Feature extraction, Genetics, Data models, Data mining, performance prediction, educational data mining, GP, computer-supported collaborative learning",
-
DOI = "
doi:10.1109/ICET62460.2024.10869115",
- abstract = "Computer-supported collaborative learning (CSCL) has been widely adopted as an instructional method that uses computer technology to support and facilitate cooperation and collaboration in the learning process. In order to achieve high-quality collaborative learning and provide personalized instruction to students in a CSCL environment, it is necessary to analyse collaborative interaction characteristics and predict student performance. There are still some limitations to the existing studies due to the underuse of data on the student collaboration process and the poor interpretability of the models used. Therefore, this study fully mines students' collaborative session data, extracts relevant collaborative interaction features, and then constructs a student performance prediction model using the GP algorithm. Finally, we explored the accuracy of the GP model and validated it using another online collaborative course. The study showed that the GP prediction model was more accurate and stable than other machine learning methods, and the validation results in another course showed a high degree of consistency between student grades in the course and the model's predictions. In addition, the analysis results revealed that the number of statements, length of statements, and positive and negative emotions were the key factors affecting student grades in the CSCL environment. The findings help provide useful insights for further optimising curriculum design and instructional interventions.",
-
notes = "Also known as \cite{10869115}
School of Computer Science, Central China Normal University, Wuhan, China",
