Unpacking capabilities underlying design (thinking) process

Engineering graduates must know how to frame and solve non-routine problems. While design classes explicitly teach problem framing and solving, it is lacking throughout much of the rest of the engineering curriculum and is often relegated to capstone classes at the end of the students’ educational experience. This paper explores problem framing and solving through the lens of experiential learning theory. It captures core problem framing and solving approaches from critical, design and systems thinking and concludes with a table of learning outcomes that might be drawn upon in designing an engineering curriculum that more fully develops the problem framing and solving capabilities of its students

MODELING INFORMATION NEEDS IN ENGINEERING DATABASES USING TACIT KNOWLEDGE

Online resources of engineering design information are a critical resource for practicing engineers. These online resources often contain references and content associated with technical memos, journal articles and “white papers ” of prior engineering projects. However, filtering this stream of information to find the right information appropriate to an engineering issue and the engineer is a time-consuming task. The focus of this research lies in ascertaining tacit knowledge to model the information needs of the users of an engineering information system. It is proposed that the combination of reading time and the semantics of documents accessed by users reflect their tacit knowledge. By combining the computational text analysis tool of Latent Semantic Analysis with analyses of on-line user transaction logs, we introduce the technique of Latent Interest Analysis (LIA) to model information needs based on tacit knowledge. Information needs are modeled by a vector equation consisting of a linear combination of the user’s queries and prior documents downloaded, scaled by the reading time of each document to measure the degree of relevance. A validation study of the LIA model revealed a higher correlation between predicted and actual information needs for our model in comparison to models lacking scaling by reading time and a representation of the semantics of prior accessed documents. The technique was incorporated into a digital library to recommend engineering education materials to users