Analyzing CAD competence with univariate and multivariate learning curve models

  • Authors:
  • Ramsey F. Hamade;Mohamad Y. Jaber;Sverker Sikström

  • Affiliations:
  • Department of Mechanical Engineering, American University of Beirut (AUB), P.O. Box 11-0236, Riad El-Solh, Beirut 1107 2020, Lebanon;Department of Mechanical & Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, Ont., Canada M5B 2K3;Lund University Cognitive Science (LUCS), Lund University, Sweden

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2009

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Abstract

Understanding how learning occurs, and what improves or impedes the learning process is of importance to academicians and practitioners; however, empirical research on validating learning curves is sparse. This paper contributes to this line of research by collecting and analyzing CAD (computer-aided design) procedural and cognitive performance data for novice trainees during 16-weeks of training. The declarative performance is measured by time, and the procedural performance by the number of features used to construct a design part. These data were analyzed using declarative or procedural performance separately as predictors (univariate), or a combination of declarative or procedural predictors (multivariate). Furthermore, a method to separate the declarative and procedural components from learning curve data is suggested.