# Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)

TL;DR: It is concluded that ANNs have high potential for theoretical and practical improvements in learning sciences and researchers in the learning sciences should prefer more theory-driven and parsimonious modelling techniques over ANNs whenever possible.

Abstract: 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.

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##### Citations

6 citations

### Cites background from "Modelling for Prediction vs. Modell..."

...[11] observed that the basic problems of communicating how they reach their conclusions in meaningful terms has yet to be solved....

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### Cites background or methods or result from "Modelling for Prediction vs. Modell..."

...Edelsbrunner and Schneider (2013) in their commentary on Musso, Kyndt, Cascallar and Dochy (2013) argue that artificial neural networks (ANNs) should only be used as exploratory modelling techniques, in spite of being powerful statistical modelling tools with demonstrated ability to improve…...

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...The reasons Edelsbrunner and Schneider (2013) argue for their rather strong position are centred on two main arguments: (a) that the output from ANNs cannot be fully translated into a meaningful set of rules because of a lack of accessibility to the input-output relationships, and (b) that there is…...

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...Therefore, contrary to what has been pointed out by Edelsbrunner and Schneider (2013) and quoted by Golino and Gomes (2014), the ANN approach offers the potential to examine the complex relationships amongst its components....

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...Now it 69 | F L R expressed in Edelsbrunner & Schneider (2013)....

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...The second main argument regarding problems associated with the ANN methodology, as claimed by Edelsbrunner and Schneider (2013), has to do with the lack of some statistical parameters in ANNs....

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##### References

27,846 citations

11,971 citations

### "Modelling for Prediction vs. Modell..." refers methods in this paper

...Like ANNs, modern regression techniques can account for non-linear relations (Bates & Watts, 2007) and complex interactions between variables (Aiken & West, 1991)....

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3,063 citations

### "Modelling for Prediction vs. Modell..." refers methods in this paper

...Like ANNs, modern regression techniques can account for non-linear relations (Bates & Watts, 2007) and complex interactions between variables (Aiken & West, 1991)....

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1,164 citations

759 citations