Journal ArticleDOI
Modelling for Prediction vs. Modelling for Understanding: Commentary on Musso et al. (2013)
Peter A. Edelsbrunner,Michael Schneider +1 more
- Vol. 1, Iss: 2, pp 99-101
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TLDR
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.read more
Citations
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Proceedings ArticleDOI
A hybrid model combining neural networks and decision tree for comprehension detection.
TL;DR: This paper investigates the use of a hybrid model comprising multiple artificial neural networks with a final C4.5 decision tree classifier to investigate the potential of explaining the classification decision through production rules and the significant tree size questions the rule transparency to a human.
JournalDOI
Modelling for understanding AND for prediction/classification - the power of neural networks in research
TL;DR: Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy, with a perspective on its place among other predictive approaches.
Journal ArticleDOI
Prospects and Challenges of Using Machine Learning for Academic Forecasting
Edeh Michael Onyema,Khalid K. Almuzaini,Fergus U. Onu,Devvret Verma,Ugboaja Samuel Gregory,Monika Puttaramaiah,Rockson Kwasi Afriyie +6 more
TL;DR: It is suggested that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.
DissertationDOI
Domain-General and Domain-Specific Scientific Thinking in Childhood: Measurement and Educational Interplay
TL;DR: Zusammenfassung et al. as mentioned in this paper discussed psychometric issues in Research on Scientific Reasoning and proposed a methodological approach to find the root cause of such issues.
Journal ArticleDOI
Using New Models to Analyze Complex Regularities of the World
Petri Nokelainen,Tomi Silander +1 more
TL;DR: In this paper, the authors discuss issues related to model fitting, comparison of classification accuracy of generative and discriminative models, and two (or more) cultures of data modeling.
References
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Journal ArticleDOI
Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks
TL;DR: In this article, the authors used cognitive and non-cognitive measures of students, together with background information, in order to design predictive models of student performance using artificial neural networks (ANN).
BookDOI
Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior
TL;DR: The book traces the development of this methodology and demonstrates how it opens up new ways of thinking about traditional problems, and academic researchers will gain a design template for studying both the linear and non-linear elements of a given problem, and thus enhance their own research.
Journal ArticleDOI
Artificial neural networks modeling gene-environment interaction
TL;DR: This study shows that neural networks are a promising approach for analyzing gene-environment interactions, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, and provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.