The Engineering Genome Project (TUES Type I)
Edward Berger (PI), Walter Heinecke, Aaron Bloomfield, Sherry Lake
University of Virginia
Project Summary
About ten years ago, a group of music performers, experts, and enthusiasts came together with the goal
of creating “the most comprehensive analysis of music ever.” They defined hundreds of musical
attributes (’genes’) that contain the granular, essential information about a particular piece of music.
They then set about categorizing individual songs according to the taxonomy they had developed. So
the Music Genome is a collection of digital assets (the songs) tagged with highly granular, descriptive
attributes (the genes), and organized into a searchable database. The result is Pandora–the Internet
radio station that allows users to probe the Music Genome and create playlists based upon keyword
searches. Pandora interrogates the Music Genome to create a playlist of songs that are genomically
related – the songs are "close neighbors" in the genome.
Now imagine that we create a similar structure not for music, but for the whole of engineering
knowledge. This Engineering Genome (EG) would include multimedia learning objects (examples:
video and audio files, Matlab .m files, java applets, etc.), tagged with appropriate attributes and
organized into a searchable database, and it would allow learners to interrogate its contents and
explore the underlying relationships among the individual bits of engineering knowledge. When users
interrogate the engineering genome, they will be presented with multimedia learning objects that are
not organized by hierarchy, but rather based upon genomic similarity . The EG intentionally bridges
traditional content silos (i.e., classes in the curriculum) and enables the learner to visualize and
comprehend the elusive and subtle relationships among engineering concepts that, on the surface,
appear to be unrelated (or perhaps only tangentially related).
The Genome Ontology
For the purposes of this research project, an
ontology is composed of several parts: (i) a
structured, explicit description of a knowledge
domain that indicates relationships among classes
of objects in that domain, (ii) properties or traits
(which we will call “genes”) that describe various
attributes of those classes of objects, and (iii)
individual instances (in our case, multimedia
learning elements) that are classified according to
the set of classes and traits from items (i) and (ii). As
a concrete example, consider a multimedia file (say,
a movie) that is a video solution to a particular
problem. In the ontology, this file is an “individual”
whose position in the ontology (and relationship to
other areas of the knowledge domain) is defined by
the class(es) and traits that describe it.
an example in natural language
the object type is a video solution from the
course Statics; the file format is .mov; the
dynamic status in the problem is equilibrium;
it focuses on the application area trusses; the
equation type within the solution is algebraic;
the coordinate system is 2D Cartesian, nonrotating system with a fixed origin; the
equation solution approach is an analytical
method from mathematics domain linear
algebra; the units are SI; the discretization
approach is to use free body diagrams; the
derivation approach for the free body
diagrams is the method of sections; the
learning outcomes targeted are ABET (a) and
course learning outcome 1.c as stated on the
syllabus; and so forth...
The User Experience
Overarching Vision
The Genome is, at its core, an ontology that exposes both hierarchical and other types of relationships
between engineering content knowledge. The EG serves multimedia learning objects to users. Media
files are expertly-coded with rich metadata according to the engineering
ontology, and search can take many forms including keyword and
Supporting Taxonomies
more rich "genomic" searches. The front-end allows users to
(Section 4.1)
interrogate the Genome database, see search results, and
use the multimedia content provided to them.
HigherEd 2.0 Media Library
(Section 4.1)
- lectures, videos, simulations
- tag-rich
- linked to Genome entries
The Engineering Genome
the universal taxonomy of engineering knowledge
back end
front end
The Engineering Genome Project
infinite series
My Favorites
Audio
Video
Slideshows
Simulations
Books
PDF Documents
Saved Searches
infinite series
integrals
vectors
My Profile
My Schedule
@ Preferences
Now Playing
Sequence/Title
MATH 1010--Calculus I
infinite series lecture
radius of convergence
Fourier series derivation
Fourier series example
power series example
Type
Lecture
Lecture
Lecture
Application
Homework
Quicktime
PDF
Quicktime
PDF
Simulation
12:45/14 MB
---/758 KB
8:36/10 MB
---/860 KB
---/240 KB
This lecture show the derivation of an...
The radius of convergence of a power series...
Fourier series are among the most common...
This example shows an approximation to a...
Use this simulation to explore power series...
MECH 2010--Dynamics
Fourier series solution to...
Fourier series derivation for...
Application
Lecture
Quicktime
PDF
14:23/16 MB
---/1.2 MB
This application example shows a Fourier...
This lecture shows the derivation of a...
MECH 3050--Automatic Control
power series application in...
radius of convergence calc...
Application
Lecture
Simulation ---/350 KB
Quicktime 4:55/6 MB
This simulation allows you to filter a signal...
This video shows the derviation of the...
PDF
Quicktime
Solutions to vibration problems sometimes...
This video shows a technique to estimate...
MECH 4020--Mechanical Vibration
Fourier series solution to...
Application
Series approximation to solution... Homework
Media Type Time/Size
---/780 KB
13:10/15 MB
Similarity Description
Algorithm Calibration
Media Type
text-based
multimedia
Content Type
derivations
applications
Share
Relation to Class
my class
downstream classes
Genomic Similarity
less similar
very similar
Refresh
Rate it!
the taxonomy of engineering knowledge
- a category/tag-rich relational database
- expert construction
- Section 4.2
Local Institution Curriculum
(Section 4.1)
- course mapping onto Genome
- learning outcomes
- syllabus and other resources
a user-oriented query engine
- robust search/browse capabilities
- easy-to-use user interface
- Section 4.3
The Learners
engineering students at four universities
On-Going Efforts
Digital Content Categories
The faculty working on this project have created thousands multimedia learning objects used for
asynchronous teaching and learning. These include lectures, problem solution videos, simulations,
and case studies. Students have also authored content to share with their peers. As a result, the
Genome library will contain files of the following types: video (ex: .mov), audio (ex: .mp3), simulation
(ex: Matlab .m), text (ex: .pdf), and potentially many others (ex: Livescribe pen flash-type format).
student podcast contest
The user interface plays an important role in Genome functionality. The vision for the front-end is shown
in the mock-up above (based upon the iTunes interface). Users specify account settings, their media
library, saved searches, and the like on the left-side pane. The search and results window provides results
sorted by course topic, media type, time/file size, or relevance to the search term. Importantly, the sliderbased search algorithm calibration area allows users to control how they interrogate the Genome, and
what type of results are produced. The viewing window allows users to consume the media files offered
to them via search. The front-end available for beta-test will be a simpler version of this, but we expect
the production version to use the best examples from commercial software and web services (such as
Amazon), including user ratings and user-defined tags.
lecture podcasts
video problem solutions
To date, we have defined much of the
engineering mechanics genome, including
dozens of specific genes within the overall
ontology. We have collated the thousands of
multimedia files we have available, and we
have begun to commit them to the Genome
according to the developed ontology. We
have developed both the software back-end to
store the information, and the v 1.0 front-end
to facilitate user interaction and search. We
continue to refine search algorithms, including
keyword, "facet" (i.e., trait), and genomic (i.e.,
nearest-(genomic)-neighbor) searches. We
expect to beta-test the Genome in Spring
2013, including usability studies with students,
robustness tests, and general user satisfaction.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Acknowledgments
The authors gratefully acknowledge the
support of the National Science Foundation
through the CCLI program and awards DUE0717820 and DUE-1123037.
Contact Information
Dr. Edward Berger
Associate Dean for Undergraduate Programs
School of Engineering and Applied Science
University of Virginia
berger@virginia.edu
H gherEd 2.0
http://highered20.wordpress.com
http://highered20.wordpress.com/genome