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Turning Experiments into Objects: The Cognitive Processes Involved in the Design of a Lab‐on‐a‐Chip Device
Author(s) -
Aurigemma Joshua,
Chandrasekharan Sanjay,
Nersessian Nancy J.,
Newstetter Wendy
Publication year - 2013
Publication title -
journal of engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.896
H-Index - 108
eISSN - 2168-9830
pISSN - 1069-4730
DOI - 10.1002/jee.20003
Subject(s) - computer science , artifact (error) , coding (social sciences) , human–computer interaction , process (computing) , cognition , salient , engineering design process , cognitive load , artificial intelligence , software engineering , engineering , psychology , programming language , mechanical engineering , statistics , mathematics , neuroscience
Background Increasingly, modern engineers are designing miniature devices that integrate complex activities, actions, processes, or operations involving many steps, persons, and equipment; a good example is a microfluidic lab‐on‐a‐chip device. The design of such devices is cognitively demanding and requires the generation of multiple model representations, which are used in an iterative fashion to analyze and improve prototype designs. Purpose This study addresses two questions: How are various representations and prototypes used in the iterative design and building of a microfluidic lab‐on‐a‐chip device in a systems biology laboratory? In this design process, what cognitive functions did the representations serve? Design/Method This case study employed mixed methods. We utilized the standard ethnographic methods of participant observation, open‐ended interviewing of participants, and artifact collection in an integrated systems biology research lab. Data were analyzed using open and axial coding. We also used cognitive‐historical analysis to collect and analyze data from traditional historical sources (publications, grant proposals, laboratory notebooks, and technological artifacts) to recover how the salient representational, methodological, and reasoning practices were developed and used by the researchers. Results The device design involved complex interactions among mental models, computational models, and building and testing prototypes; tagging and visualizations were used to query and validate the prototypes as well as the computational models; all these were integrated to meet stringent experimental and fabrication constraints. Integration takes place across many different kinds of representations. The building of external representations helped not just to off‐load cognitive load but also to add detail and constraints to the mental model. Conclusions Representational fluency and flexibility are required to manage the complexity of modern bioengineered devices. To support the development of such fluency and flexibility, engineering students must understand the function and the use of such representations, an instructional goal that has implication for new models of learning.