Modelling for understanding AND for prediction/classification - the power of neural networks in research
Main Article Content
Abstract
Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current research and several advances have improved the model building and the interpretation of the resulting neural network models. What is needed is the courage and open-mindedness to actually explore new paths and rigorously apply new methodologies which can perhaps, sometimes unexpectedly, provide new conceptualisations and tools for theoretical advancement and practical applied research. This is particularly true in the fields of educational science and social sciences, where the complexity of the problems to be solved requires the exploration of proven methods and new methods, the latter usually not among the common arsenal of tools of neither practitioners nor researchers in these fields. This response will enrich the understanding of the predictive systems methodology proposed by the authors and clarify the application of the procedure, as well as give a perspective on its place among other predictive approaches.
Article Details
How to Cite
Cascallar, E., Musso, M. F., Kyndt, E., & Dochy, F. (2015). Modelling for understanding AND for prediction/classification - the power of neural networks in research. Frontline Learning Research, 2(5), 67–81. https://doi.org/10.14786/flr.v2i5.135
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References
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Kyndt, E., Musso, M., Cascallar, E., & Dochy, F. (2015, in press). Predicting academic performance: The role of cognition, motivation and learning approaches. A neural network analysis. In V. Donche & S. De Maeyer (Eds.), Methodological challenges in research on student learning. Antwerp, Belgium: Garant.
Laguna, M., & Marti, R. (2002). Neural network prediction in a system for optimizing simulations. IIE Transactions, 34, 273-282. doi: 10.1080/07408170208928869
Lee, C., Rey, T., Mentele, J., & Garver, M. (2005). Structured neural network techniques for modeling loyalty and profitability. Proceedings of the Thirtieth Annual SAS® Users Group International Conference. Cary, NC: SAS Institute Inc.
Lei, P. W., & Qiong Wu, Q. (2007). Introduction to structural equation modelling: Issues and practical considerations. Items – Instructional Topics in Educational Measurement - Fall 2007, NCME Instructional Module, 33-43.
Lek, S., Belaud, A., Baran, P., Dimopoulos, I., & Delacoste, M. (1996). Role of some environmental variables in trout abundance models using neural networks. Aquat. Living Resour, 9, 23-29. doi: 10.1051/alr:1996004
Luft, C. D. B., Gomes, J. S., Priori, D., & Takase, E. (2013). Using online cognitive tasks to predict mathematics low school achievement. Computers & Education, 67, 219-228. doi: 10.1016/j.compedu.2013.04.001
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Musso, M. F., & Cascallar, E. C. (2009a). New approaches for improved quality in educational assessments: Using automated predictive systems in reading and mathematics. Journal of Problems of Education in the 21st Century, 17, 134-151.
Musso, M. F. & Cascallar, E. C. (2009b).Predictive systems using artificial neural networks: An introduction to concepts and applications in education and social sciences. In M. C. Richaud & J. E. Moreno (Eds.). Research in behavioural sciences (Volume I), (pp. 433-459). Buenos Aires, Argentina: CIIPME/CONICET.
Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2012). Predicting mathematical performance: The effect of cognitive processes and self-regulation factors. Education Research International. Vol 2012, Article ID 250719, 13 pages. doi: 10.1155/2012/250719
Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1, 42-71. doi: 10.14786/flr.v1i1.13
Musso, M. F., Boekaerts, M., Segers, M., & Cascallar, E. C. (in preparation). A comparative analysis of the prediction of student academic performance.
Neal, W., & Wurst, J. (2001). Advances in market segmentation. Marketing Research, 13, 14-18.
Nguyen, N., & Cripps, A. (2001). Predicting housing value: A comparison of multiple regression and artificial neural networks. Journal of Real Estate Research, 22, 313-336.
Nokelainen, P. & Silander, T. (2014). Using New Models to Analyse True Complex Regularities of the World: Commentary on Musso et al. (2013). Frontiers in Psychology, 3, 78-82. doi: .org/10.14786/flr.v2i1.107.
Olden, J. D., & Jackson, D. A. (2002). Illuminating the ''black box'': a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154, 135-150. doi: 10.1016/S0304-3800(02)00064-9
Olden, J. D., Joy, M. K. & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178, 389-397. doi: 10.1016/j.ecolmodel.2004.03.013
Orre, R., Lansner, A., Bate, A., & Lindquist, M. (2000). Bayesian neural networks with confidence estimations applied to data mining. Computational Statistics & Data Analysis, 34, 473-493. doi: 10.1016/S0167-9473(99)00114-0
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