Toward predicting the performance of novice CAD users based on their profiled technical attributes

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dc.contributor.author Ammouri, A.H.
dc.contributor.author Artail, H.
dc.contributor.author Hamade, R.F.
dc.date.accessioned 2017-01-17T14:01:50Z
dc.date.available 2017-01-17T14:01:50Z
dc.date.copyright 2012 en_US
dc.date.issued 2017-01-17
dc.identifier.issn 0952-1976 en_US
dc.identifier.uri http://hdl.handle.net/10725/5024
dc.description.abstract In previously published research (Hamade et al., 2005, Hamade et al., 2007, Hamade et al., 2009 and Hamade and Artail, 2008) the authors developed a framework for analyzing the technical profiles of novice computer-aided design (CAD) trainees as they set to start training in a formal setting. The research included conducting a questionnaire to establish the trainees’ CAD-relevant technical foundation which served as the basis to statistically correlate this data to other experimental data collected for measuring the trainees’ performance over the duration of training. In this paper, we build on that work and attempt to forecast the performance of these CAD users based on their technical profiled attributes. For this purpose, we utilize three Artificial Neural Networks, ANN, techniques: Feed-Forward Back propagation, Elman Back propagation, and Generalized Regression with their capabilities are compared to those of Simulated Annealing as well as to those of linear regression techniques. Based on their profiled technical attributes, the Generalized regression neural network (GRNN) method is found to be most successful in discriminating the trainees including their predicted initial performance as well as their progress. en_US
dc.language.iso en en_US
dc.title Toward predicting the performance of novice CAD users based on their profiled technical attributes en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SOE en_US
dc.author.idnumber 201306469 en_US
dc.author.department Industrial And Mechanical Engineering en_US
dc.description.embargo N/A en_US
dc.relation.journal Engineering Applications of Artificial Intelligence en_US
dc.journal.volume 25 en_US
dc.journal.issue 3 en_US
dc.article.pages 628-639 en_US
dc.keywords CAD expertise development en_US
dc.keywords Technical attributes en_US
dc.keywords Training en_US
dc.keywords Prediction en_US
dc.keywords Simulated annealing en_US
dc.keywords Neural networks en_US
dc.identifier.doi http://dx.doi.org/10.1016/j.engappai.2012.01.004 en_US
dc.identifier.ctation Hamade, R. F., Ammouri, A. H., & Artail, H. (2012). Toward predicting the performance of novice CAD users based on their profiled technical attributes. Engineering Applications of Artificial Intelligence, 25(3), 628-639. en_US
dc.author.email ali.ammouri@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://www.sciencedirect.com/science/article/pii/S0952197612000085 en_US
dc.author.affiliation Lebanese American University en_US

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