Teaching Engineering Design with Digital Fabrication: Imagining, Creating, and Refining Ideas

Analyzing Engineering Design through the Lens of Computation

Journal of Learning Analytics ◽

10.18608/jla.2014.12.8 ◽

2014 ◽

Vol 1 (2) ◽

pp. 151-186 ◽

Cited By ~ 22

Author(s):

Paulo Blikstein

Keyword(s):

Education Research ◽

Engineering Design ◽

Learning Analytics ◽

Design Patterns ◽

Educational Data Mining ◽

Digital Fabrication ◽

Video Data ◽

Human Cognition ◽

Maker Movement ◽

K 12

Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some domains for which pure computational analysis is currently infeasible. One such area, which is particularly important today, is open-ended, hands-on, engineering design tasks. These open-ended tasks are becoming increasingly prevalent in both K–12 and post-secondary learning institutions, as educators are adopting this approach in order to teach students real-world science and engineering skills (e.g., the “Maker Movement”). This paper highlights findings from a combined human–computer analysis of students as they complete a short engineering design task. The study uncovers novel insights and serves to advance the field’s understanding of engineering design patterns. More specifically, this paper uses machine learning on hand-coded video data to identify general patterns in engineering design and develop a fine-grained representation of how experience relates to engineering practices. Finally, the paper concludes with ideas on how the specific findings from this study can be used to improve engineering education and the nascent field of “making” and digital fabrication in education. We also discuss how human–computer collaborative analyses can grow the learning analytics community and make learning analytics more central to education research.

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Resolution and measurement in the scanning electron microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100127256 ◽

1987 ◽

Vol 45 ◽

pp. 534-537 ◽

Cited By ~ 2

Author(s):

Michael T. Postek

Keyword(s):

Electron Microscope ◽

Scanning Electron Microscope ◽

Engineering Design ◽

Resolving Power ◽

Practical Applications ◽

Instrument Resolution ◽

Accurate Definition ◽

Definition Of ◽

Scanning Electron ◽

Better Than

The term ultimate resolution or resolving power is the very best performance that can be obtained from a scanning electron microscope (SEM) given the optimum instrumental conditions and sample. However, as it relates to SEM users, the conventional definitions of this figure are ambiguous. The numbers quoted for the resolution of an instrument are not only theoretically derived, but are also verified through the direct measurement of images on micrographs. However, the samples commonly used for this purpose are specifically optimized for the measurement of instrument resolution and are most often not typical of the sample used in practical applications.SEM RESOLUTION. Some instruments resolve better than others either due to engineering design or other reasons. There is no definitively accurate definition of how to quantify instrument resolution and its measurement in the SEM.

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