Abstract
The curriculum recommendation strategies are the engines that drive educational organizations. Therefore, this research has three main goals: 1) to explore the context of deploying text mining technology as a curriculum recommendation application, 2) to develop a prototype model for interaction between curriculum coordinators and interested parties, and 3) to evaluate the performance of the prototype model. Research tools are text mining techniques with the genetic algorithm for evolutionary feature weighting optimization and ensemble learning algorithms, including Naïve Bayes (NB), Neural Network (NN), and k-Nearest Neighbor (k-NN). Data collection is 1,592 transactions, with seven classes via the online chat platform of the Department of Information and Communication Technology at the Faculty of Information Technology, Rajabhat Maha Sarakham University. The results showed that the model developed with the majority voting technique had the highest accuracy of 91.65%, averaging 5% higher than that of the single split model. This research has discovered tools and methods to promote and support educational processes in higher education. Therefore, the adoption of text mining technology should be enabled in the education system to communicate with the learners to meet their needs and reduce the duplication of work.
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Acknowledgements
This research project was supported by the Thailand Science Research and Innovation Fund and the University of Phayao (Grant No. FF65-UoE006). The authors would like to thank all of them for their support and collaboration in making this research possible.
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Nuankaew, W.S., Bussaman, S., Nuankaew, P. (2022). Evolutionary Feature Weighting Optimization and Majority Voting Ensemble Learning for Curriculum Recommendation in the Higher Education. In: Surinta, O., Kam Fung Yuen, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2022. Lecture Notes in Computer Science(), vol 13651. Springer, Cham. https://doi.org/10.1007/978-3-031-20992-5_2
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