Modelling for Understanding AND for Prediction/Classification--The Power of Neural Networks in Research
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| Title: | Modelling for Understanding AND for Prediction/Classification--The Power of Neural Networks in Research |
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| Language: | English |
| Authors: | Cascallar, Eduardo, Musso, Mariel, Kyndt, Eva |
| Source: | Frontline Learning Research. 2014 2(5):67-81. |
| Availability: | European Association for Research on Learning and Instruction. Peterseliegang 1, Box 1, 3000 Leuven, Belgium. e-mail: info@frontlinelearningresearch.org; Web site: http://journals.sfu.ca/flr/index.php/journal/index |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2014 |
| Document Type: | Journal Articles Reports - Evaluative Information Analyses |
| Descriptors: | Artificial Intelligence, Research Methodology, Prediction, Classification, Mathematical Models |
| ISSN: | 2295-3159 |
| 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. |
| Abstractor: | As Provided |
| Number of References: | 74 |
| Entry Date: | 2016 |
| Accession Number: | EJ1090932 |
| Database: | ERIC |
| 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. |
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| ISSN: | 2295-3159 |
