文章基本信息

标题：Modelling for Prediction vs. Modelling for Understanding
本地全文：下载
作者：Michael Schneider ; Peter Edelsbrunner
期刊名称：Frontline Learning Research
电子版ISSN：2295-3159
出版年度：2013
卷号：1
期号：2
DOI：10.14786/flr.v1i2.74
语种：English
出版社：EARLI
摘要：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.