A COMPARISON OF THE UTILITY OF VARIOUS NEURAL NETWORK MODELS IN IMPROVING EDUCATION AND DESIGNING LEARNING PATHS
Keywords:
Neural Network, Recommendation Algorithm, ANN, Machine LearningAbstract
Neural networks are self-improving computational systems used for prediction. Artificial Neural Networks (ANNs) computationally process information in a way that is similar to the human brain. There are myriad existing prediction models that can be used for various purposes, and this report aims to identify the predictive model most useful in the realm of education. It is taken into account that different students require different types of media to learn most effectively. In this project, different predictive models are compared to one another in their effectiveness specifically in predicting learning performance in certain subjects. Additionally, various activations (i.e., tanh, sigmoid, identity) and filtering methods (i.e., content-based, collaborative, and hybrid) are compared. These findings are then used to describe a possible recommendation algorithm to improve education by creating learning paths.
References
I. Krogh, A. What are artificial neural networks? Nat Biotechnol 26, 195–197 (2008). https://doi.org/10.1038/nbt1386
II. Mohammadzaheri, M., Chen, L., Ghaffari, A., & Willison, J. (2009). A combination of linear and nonlinear activation functions in neural networks for modeling a de-superheater. Simulation Modelling Practice and Theory, 17(2), 398-407.
III. Karlik, B., & Olgac, A. V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4), 111-122.
IV. Desmos Graphing Calculator, https://www.desmos.com/calculator
V. Kalman, B. L., & Kwasny, S. C. (1992, June). Why tanh: choosing a sigmoidal function. In [Proceedings 1992] IJCNN International Joint Conference on Neural Networks (Vol. 4, pp. 578-581). IEEE.
VI. Li, Q., & Kim, B. M. (2003, July). An approach for combining content-based and collaborative filters. In Proceedings of the sixth international workshop on Information Retrieval with Asian languages (pp. 17-24).
VII. Samy Abu Naser, Ihab Zaqout, Mahmoud Abu Ghosh, Rasha Atallah, Eman Alajrami, Predicting Student Performance Using Artificial Neural Network: in the Faculty of Engineering and Information Technology, International Journal of Hybrid Information Technology Vol.8, No.2 (2015), pp.221-228 http://dx.doi.org/10.14257/ijhit.2015.8.2.20
VIII. Shaobo Huang, Ning Fang, Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models, Computers & Education, Volume 61, 2013, Pages 133-145, ISSN 0360-1315, https://doi.org/10.1016/j.compedu.2012.08.015. (https://www.sciencedirect.com/science/article/pii/S0360131512002102)
IX. Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2020). Transfer Learning from Deep Neural Networks for Predicting Student Performance. Applied Sciences, 10(6), 2145. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app10062145
X. T. Wang and A. Mitrovic, "Using neural networks to predict student's performance," International Conference on Computers in Education, 2002. Proceedings., Auckland, New Zealand, 2002, pp. 969-973 vol.2, doi: 10.1109/CIE.2002.1186127.
XI. Yağcı, Ali & Çevik, Mustafa. (2017). Predictions of academic achievements of vocational and technical high school students with artificial neural networks in science courses (physics, chemistry and biology) in Turkey and measures to be taken for their failures. SHS Web of Conferences. 37. 01057. 10.1051/shsconf/20173701057.
XII. Suthaharan, S., & Suthaharan, S. (2016). Support vector machine. Machine learning models and algorithms for big data classification: thinking with examples for effective learning, 207-235.
XIII. Samuelowicz, K. (1987). Learning problems of overseas students: Two sides of a story. Higher Education Research and Development, 6(2), 121-133.
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