A framework for negative knowledge to support hybrid geometric modeling education for product engineering. Issue 1 (11th November 2017)

• Record Type: Journal Article
• Authors: Otto, Harald E.; Mandorli, Ferruccio
• Is Part Of: Journal of computational design and engineering. Volume 5:Issue 1(2018)
• Page Start: 80
• Page End: 93
• Publication Date: 2017-11-11
• Subjects: Normative geometric knowledge -- Strategic knowledge -- Negative expertise -- Learning from errors; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1016/j.jcde.2017.11.006
• Languages: English
• ISSNs: 2288-4300

Abstract

Graphical abstract: Abstract: Due to the full integration of CAD systems into modern product development and engineering, the competency to create usable geometric models has become an essential requirement for current CAD users. To avoid serious repercussions for future engineering labor, the focus of CAD education needs to be raised from the teaching of knowledge that is merely aimed at operating a system, to the development of basic strategic knowledge. From a pedagogical point of view, this situation represents a challenging task that requires new, innovative teaching methodologies. These new methodologies must facilitate the development of know-how and cognitive ability to organize domain knowledge within a holistic mental model allowing for accurate perception of the significance of circumstances and the possible consequences of actions. In this paper a new direction for CAD education is presented, based on the integration of traditional teaching methods with an educational approach based on negative knowledge. Analysis of first empirical results of this newly developed and implemented approach showed promising results. Improvements were observed in a better understanding of issues related to the usability of CAD models and an increased capability to recognize critical modeling situations and thus prevent the mistakes typically made by novices. Also, successful autonomous attempts could be observed of recovery from situations caused either by an accumulation of small mistakes or by severe modeling errors, which usually require remedial intervention by academic supervisors.