Main Article Content
Musso et al. (2013) predict students’ academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can mainly be used for exploratory modelling. Moreover, the output generated from ANNs cannot be fully translated into a meaningful set of rules because they store information about input-output relations in a complex, distributed, and implicit way. These problems hamper systematic theory-building as well as communication and justification of model predictions in practical contexts. Modern-day regression techniques, including (Bayesian) structural equation models, have advantages similar to those of ANNs but without the drawbacks. They are able to handle numerous variables, non-linear effects, multi-way interactions, and incomplete data. Thus, researchers in the learning sciences should prefer more theory-driven and parsimonious modelling techniques over ANNs whenever possible.
FLR adopts the Attribution-NonCommercial-NoDerivs Creative Common License (BY-NC-ND). That is, Copyright for articles published in this journal is retained by the authors with, however, first publication rights granted to the journal. By virtue of their appearance in this open access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.
Bates, D. M., & Watts, D. G. (2007). Nonlinear regression analysis and its applications (2nd ed.). Hoboken, NJ: Wiley.
Benitez, J. M., Castro, J. L., & Requena, I. (1997). Are artificial neural networks black boxes? IEEE Transactions on Neural Networks, 8, 1156-1164. doi:10.1109/72.623216
Cortez, P., & Embrechts, M. J. (2013). Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1-17. doi:http://dx.doi.org/10.1016/j.ins.2012.10.039
Günther, F., Pigeot, I., & Bammann, K. (2012). Artificial neural networks modeling gene-environment interaction. BMC Genetics, 13(1), 37. doi:10.1186/1471-2156-13-37
Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. New York: Guilford Press.
Intrator, O., & Intrator, N. (2001). Interpreting neural-network results: A simulation study. Computational Statistics & Data Analysis, 37, 373-393. doi:10.1016/S0167-9473(01)00016-0
Kaplan, D. (1990). Evaluating and modifying covariance structure models: A review and recommendation. Multivariate Behavioral Research, 25, 137-155. doi:10.1207/s15327906mbr2502_1
Luger, G. F. (2009). Artificial intelligence: Structures and strategies for complex problem solving (6th ed.). Boston, MA: Pearson Education.
Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1, 42-71. Retrieved from http://journals.sfu.ca/flr/index.php/journal/article/view/13
Scarborough, D., & Somers, M. J. (2006). Neural networks in organizational research: Applying pattern recognition to the analysis of organizational behavior (pp. 137-144). Washington, DC: American Psychological Association.
Song, X. Y., & Lee, S. Y. (2012). Basic and advanced Bayesian structural equation modeling: With applications in the medical and behavioral sciences. Chichester, UK: John Wiley & Sons.