Abstract
Educational data mining has been widely used to predict student performance and establish intervention strategies to improve that performance. Most studies have implemented machine learning algorithms for interventions but the use of data mining in appraising student performance in learning software is obscure. Furthermore, some of the studies that have explored the use of machine learning in predicting student performance in software learning have only used Random Forest, and as such, this study used the same dataset to implement 7 other algorithms and establish the most efficient. The study used two different sets of data and established that Neural Network was the most efficient with regards to the first dataset although Random Forest was the most efficient with regards to the second dataset. Both the NN graphics and RF tree diagram are presented, and the predictions from the two models also compared.
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Acknowledgment
We are grateful to the entire SETAP project team and we appreciate Professor D. Petkovic of San Francisco State University, Prof. Rainer Todtenhoefer of Fulda University, and Professor Shihong Huang of Florida Atlantic University for their role in the project and for sharing the data with UCI Machine Learning Repository.
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Alloghani, M., Al-Jumeily, D., Baker, T., Hussain, A., Mustafina, J., Aljaaf, A.J. (2019). Applications of Machine Learning Techniques for Software Engineering Learning and Early Prediction of Students’ Performance. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_19
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DOI: https://doi.org/10.1007/978-981-13-3441-2_19
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