Chapter 1 - Introduction
Computing Concepts for LIKES
Table of Contents
I.
II.
III.
IV.
V.
Introduction
Managing editor(s): Weiguo Fan, Steven D. Sheetz
Editorial board: Ghaleb Abdulla (LLNL), Peter Henderson (Butler U.), Harry Hochheiser (Towson
U.), Barbara Zimmerman (Villanova)
A. General structure
1. Acknowledgements: contributors, sponsors, collaborating partners
2. Target audience; Motivation, challenges; Purpose, goals, objectives
3. Supplementary materials outside this book; Structure of this book
B. Computing areas connected most frequently with Other Disciplines
1. Content: data, information, and knowledge - including their representation and
management, and including data structures, databases, document collections, and
knowledge management
2. Processing: algorithms, analysis, problem solving, programming, work flows, and
software engineering
3. Interaction/visualization: HCI, interfaces, graphics, games, visualization, and virtual
environments
4. Modeling and simulation
Performing Arts
Managing editor: Christine M. Nass; Editorial board: TBD
Coverage: Dance, Music, Theatre, Visual Arts
Languages and Humanities
Managing editor: Trudi J. Abel; Editorial board: Valerie Barr (Union College), Judith Kirkpatrick
(English)
Coverage: English, History, Languages, Linguistics, Literature, Philosophy, Religion
Natural Sciences
Managing editor: Robert M. Panoff; Editorial board: E. Hambrusch (Purdue), Jessen Havill
(Denison U.), Christoph M. Hoffman (Purdue)
Coverage: Biological Sciences, Chemistry, Earth Sciences, Physics
A. Biological Sciences - Editorial board: Anne Boettcher (U. S. Alabama), Bruce Kirchoff (UNC
Greensboro), Jamie Kneitel (CSU Sacramento)
B. Chemistry - Editorial board: Clyde Metz (College of Charleston)
C. Earth Sciences: Ecology, Environmental Science, Geology - Editorial board: Denny
Fernandez del Viso (U. Puerto Rico, Humacao), Patricia A. Medvick (N. New Mexico
College)
D. Physics - Editorial board: Bruce Mason (U. Oklahoma)
Social Sciences
Managing editor: Andrea L. Kavanaugh; Editorial board: Joyce Rudinsky (UNC-CH)
Coverage: Anthropology, Archaeology, Communications, Economics, Gender studies,
Geography, Political Science, Psychology, Sociology
A. Anthropology and Archaeology - Editorial board: Samuel Collins (Towson U.), Edward A.
Chapter 1 - Introduction
VI.
VII.
VIII.
Computing Concepts for LIKES
Fox
B. Political Science - Editorial board: Craig L. Brians (Virginia Tech)
C. Psychology - Editorial board: Steven D. Sheetz
Professional and Other Disciplines
A. Business - Editorial board: Wingyan Chung
B. Education - Editorial board: Colleen Lewis (UC Berkeley)
Future Work
A. Dissemination
B. Broader engagement
Appendices
A. Workshop Reports - Editors: Wingyan Chung, Edward Carr, Robert E. Beck
B. Evaluation Report - Managing Editor: Barabara Wildemuth
Chapter 1 - Introduction
Computing Concepts for LIKES
The LIKES project has produced a wealth of information relating to the computing concepts
necessary for range of disciplines. This document combines the results of the four LIKES
Workshops, as well as any follow-on efforts towards building a modular LIKES curriculum to
enable computational thinking (CT) throughout society. This reference source will serve as an
on-line repository and knowledge base for LIKES efforts and course materials.
Figure 1 shows that meeting the needs of the Knowledge Society of today and the future are at
the center of our needs as a nation and global society. Surrounding the Knowledge Society core
are the myriad of computing concepts that enable it, if they are understood and applied. The
application disciplines (outer ring) shown in Figure 1 connect with the vast array of problems
that can be impacted through the application of computing. These disciplines have been
developed to study the human society, to understand the natural world, and to address the
needs of people. Professionals and scholars working in these disciplines have developed many
strategies and techniques that leverage the power of computing and IT to solve their problems.
Economics
Political
Science
Architecture
Marketing
Biology
Algorithms
HCI
Sociology
Visualization
Geography
Database
Social &
Ethical
Chemistry
Knowledge
Society
Intelligent
Systems
Finance
Systems
Analysis
& Design
Physics
Art
Simulation
Programming
Music
Knowledge
Management
Architecture
History
Psychology
Net-Centricity
Healthcare
Modeling
Communications
Engineering
Library &
Information
Science
English
Figure 1. Knowledge Society core surrounded by enabling computing concepts and other
disciplines
The Living In the KnowlEdge Society (LIKES) Community Building Project (Fox et al., 2009), led
by four sites (Virginia Polytechnic Institute and State University (Virginia Tech, VT), Villanova
University, North Carolina A&T (NC A&T), and Santa Clara University (SCU)) and funded by the
US National Science Foundation (NSF), aims to transform undergraduate computing education
for the 21st century. The vision of LIKES is to build a community that will define the way to
make systemic changes in how computing and IT concepts are taught and applied in both
computing and other fields, thus better preparing the next-generation Knowledge Society
Chapter 1 - Introduction
Computing Concepts for LIKES
builders. Then, these graduates will be well-equipped with the IT competencies and skills
required for the nation’s health, security, and prosperity in the 21st century.
As part of the development of this on-line resource, outlines and initial material were created
for several different “chapters” of reference material. The following is the outline that was
generated for the overall introduction to the digital reference volume.
A. General Structure
This reference source will serve as an on-line repository and knowledge base for LIKES efforts
and course materials.
The mission of this book is to develop curricular guidelines, linked to suitable curricular
materials, connecting computing with other disciplines, that can be deployed nationwide,
and ultimately globally.
The specific goals we plan to achieve include:
Contribute to the development of a global workforce with CT competencies essential to
innovation. These guidelines will lead to modules and courses incorporating CT that will
be studied by nearly all undergraduates, not just those majoring in the computing fields.
Since computing concepts will be learned in context, workers will find it easier to apply
those concepts; they will already conceptually understand what they find in the
workforce, and will have enough experience applying CT in different contexts so that
transfer and analogical reasoning will be straightforward. Those majoring in computing
will have even more exposure and competency.
Increase the number of students developing CT competencies by infusing CT learning
opportunities into undergraduate education in the broad range of disciplines
represented in universities. Note that rather than computer literacy, or familiarity with
popular tools and systems (Word, Excel, Access, PowerPoint, Blackboard, WebCT, Sakai,
Skype, Google, Google Earth, Photoshop, Flash, Premiere, GarageBand, Matlab, …), our
aim is deep understanding of key concepts that enables application in many contexts.
Demonstrate transformative CT-focused undergraduate education models that are
replicable across a variety of institutions. Since we are preparing a national curriculum,
this will serve as a model for nationwide deployment. Since we are working with ACM
and IEEE-CS, this should connect with all of their members and student chapters, as well
as all who read our publications.
To accomplish these goals this book consists of a description of the computing concepts required to
enable CT followed by major sections for the Performing Arts, Languages and Humanities, Natural
Sciences, Social Sciences, and Professional/Other Disciplines. Within these broad sections individual
chapters describe where in the curriculum of that discipline the computing concepts can be included.
Chapter 1 - Introduction
Computing Concepts for LIKES
For example within the Natural Sciences section chapters for Bilogical Sciences, Chemistry, and Earth
Sciences describe the integration of CT into the curricula experienced by these students.
The LIKES project led to four groups of concepts most interesting to those in other disciplines: (1) data,
information, and knowledge; (2) algorithms, analysis, problem solving, programming, work flows, and
software engineering; (3) interaction, interfaces, graphics, games, visualization, and virtual
environments; and (4) modeling and simulation. This virtual book describes how these concepts apply
to each of the disciplines represented.
A major component of this project is to develop and distribute curricular materials to support the
development of CT across broad range of disciplines. These materials are to be implemented through
the definition of modules. Each module consists of lectures, assignments, references, and grading
guidelines. Table 1 shows the elements of a module that are to be developed. Thus the general
structure of the book is from broad areas to individual disciplines with modules for each of the 4 areas
of computing. Distributing modules widely within disciplines and transposing outstanding modules
across disciplines has the potential to infuse CT into university education.
Table 1. Educational module template
http://curric.dlib.vt.edu/~dlcurric/Template.2008-10-03.pdf
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Module name
Scope
Learning objectives
Level of effort needed (In-class and out-of-class time needed for students)
Relationships with other modules (Pre- and post-requisite modules listed)
Prerequisite knowledge/skills required (Completion optional)
Introductory remedial instruction (Completion optional)
Body of knowledge (Theory + practice; an outline that will be used as the basis for
lectures)
Resources (Reading materials and other educational resources for both students and
instructors)
Exercises/learning activities
Evaluation of learning objective achievement (e.g., grading criteria)
Glossary
Additional useful links
Contributors (authors and reviewers of the module)
Chapter 1 - Introduction
Computing Concepts for LIKES
B. Computing areas connected most frequently with Other Disciplines
The LIKES project identified four clusters of computing concepts (see also related discussion in
Figure 4):
1. Data, information, and knowledge―including their representation and management,
and including data structures, databases, document collections, and knowledge
management
2. Algorithms, analysis, problem solving, programming, work flows, and software
engineering
3. Interaction, interfaces, graphics, games, visualization, and virtual environments
4. Modeling and simulation
The computing concepts developed from the LIKES workshop are consistent with the computing
concepts defined by the ACM. Table 2 shows the fit of the computing concepts from the LIKES project
and the ACM computing classification system. The first column shows the four clusters of computing
concepts. The second column shows some ACM categories relating to the broader concept. The third
column identifies, in a representative set of other disciplines, where the corresponding computing
concepts apply. This reveals the many contexts in other disciplines where computational thinking is
especially appropriate, and suggests the range of areas of computing that need to be understood.
Table 2. Four computing concept categories mapped to ACM Classification System as well as
their application examples.
Computing
Concepts
Algorithms,
processing,
analysis,
work flow,
programming
languages
ACM Classification
Examples
D. Software
Architecture: Generative systems
- D.3. Programming Languages
Geography (GIS): Correction algorithm for remotelysensed images
F. Theory of Computation
- F.2. Analysis of Algorithms and
Complexity
Problem
Business: Risk analysis, Excel scenario analysis;
financial data mining; mining Web data for
business intelligence; classification algorithms for
market segmentation; text classification &
summarization algorithms
Communication: Applying graph algorithms to
communication pattern analysis
Data,
information,
knowledge
E. Data
- E.1. Data Structures
- E.2. Data Storage and Representation
- E.3. Data Encryption
- E.4. Coding and Information Theory
H. Information Systems
- H.1. Models and Principles
-- H.1.1. Systems and Information Theory
Architecture: Energy, lighting, cost and symbol
libraries
Geography (GIS): Vector, raster, networks, meta
data; GIS database design and development, SQL
queries
Business: Market basket analysis; customer
satisfaction data processing; user-generated Web
Chapter 1 - Introduction
Computing Concepts for LIKES
- H.2. Database Management
- H. 3. Information Storage and Retrieval
content analysis; enterprise feedback management
Communication: Social interaction data
Graphics,
visualization,
games,
HCI
Modeling,
Simulation
H. Information Systems
- H.5. Information Interfaces and
Presentation
-- H.5.2. User Interfaces (D.2.2., H.1.2., I.3.6.)
I. Computing Methodologies
- I.3. Computer Graphics
-- I.3.4. Graphics Utilities
-- I.3.7. Three-Dimensional Graphics and
Realism
K. Computing Milieux
- K.8. Personal Computing
-- K.8.0. General (Games)
I. Computing Methodologies
- I.6. Simulation and Modeling
-- I.6.2. Simulation Languages
-- I.6.3. Applications
-- I.6.4. Model Validation and Analysis
-- I.6.5. Model Development
-- I.6.6. Simulation Output Analysis
-- I.6.7. Simulation Support Systems
-- I.6.8. Types of Simulation
Architecture: Animation, presentation
Geography (GIS): Cartography, scene visualization;
Finding Malaria hotspots
Business: Business intelligence system dashboard;
animation in online advertisements; BI
visualization
Communication: visualizing online communities;
mapping Facebook friends; plotting and
aggregating network data; studying media richness
in CMC
Architecture: Thermal simulation, 3D modeling,
building information models
Geography (GIS): GeoSim educational modules,
simulation for long-term effects of infrastructure
development; vehicle traffic modeling in urban
area; modeling soil erodability and land habitability
Business: Virtual business experience (computerized
business simulations and exercises) for human
resource training; stock trading simulation; EBay
auction simulation; inventory modeling; supply
chain modeling
Communication: modeling social network formation
and evolution
The LIKES project identified three themes of computing concepts to be supported and a potential
ordering of concepts within a curriculum. The first theme is the Social Context Theme which provides
students with the most important and easiest to learn concepts. The Problem Solving Theme also was
identified by participants in the LIKES workshops. The final theme is the Data/Knowledge Theme, which
includes the influence of Database on Knowledge Representation concepts in both group maps. HCI is
included in this theme; since both groups see it as not requiring learning a previous concept.
Curriculum Step 1
Curriculum Step 2
Curriculum Step 3
Social Theme (required)
Networking
IP,Privacy,Civil Lib
Social Context
Problem Solving Theme
Curriculum Step 4
Chapter 1 - Introduction
Basic logic
Computing Concepts for LIKES
Programming
Algorithms/Prob.
Computer Literacy
Data/Knowledge Theme
HCI
Database
Knowledge Rep
Modeling/Sim.
Table 3. Potential ordering of computing concepts in a general curriculum
Each of the clusters of computing areas are presented in the following sections.
1.0 Data, information, and knowledge
This is what was done as a direct result of the first NSF-funded LIKES grant. We have
successfully organized 4 workshops thusfar: SCU, NCA&T, VT, and Villanova. Each of the
workshop covered different disciplines.
Computing
concepts
Definition
Data structure
(graph, trees)
A data structure is any data representation and its associated
operations. Common examples include stacks, queues, binary search
trees, B-trees, hash tables, etc. Data structures can be the
implementation of mathematical constructs with well-defined
properties, such as graphs and trees.
Knowledge
representation,
Information
retrieval and
storage
This area includes the capture, digitization, representation,
organization, transformation, and presentation of information;
algorithms for efficient and effective access and updating of stored
information, data modeling and abstraction, and physical file storage
techniques.
Databases /
Data modeling
This topic includes history and motivation for database systems;
components of database systems; DBMS functions; database
architecture and data independence; data modeling; conceptual models
and object-oriented models.
May need a table here to show some information about the 4 workshops.
Chapter 1 - Introduction
Computing Concepts for LIKES
2.0 Algorithms, analysis, problem solving, programming, work flows, and software
engineering
Computing
concepts
Definition
Logic
Logic is the study of the principles and criteria of valid inference and
demonstration.
Programming
Programming is a process of designing, writing, testing and debugging a
program, which is a series of computer instructions, to achieve tasks
using computers.
A programming language is a programmer’s principal interface with the
computer. Programmers need to understand the different styles of
programming promoted by different languages.
Algorithms /
Problem solving
An algorithm is a method or process used to solve a problem.
Algorithms are fundamental to computer science and software
engineering. The real-world performance of any software system
depends largely on two things: (1) the algorithms chosen and (2) the
suitability and efficiency of the various layers of implementation.
Software
engineering
Software engineering is the discipline concerned with the application of
theory, knowledge, and practice for effectively and efficiently building
software systems that satisfy the requirements of users and customers.
3.0 Interaction, interfaces, graphics, games, visualization, and virtual environments
Computing
concepts
HumanComputer
Definition
Human-computer interaction is a discipline concerned with the design,
evaluation and implementation of interactive computing systems for
Chapter 1 - Introduction
Computing Concepts for LIKES
Interaction
human use and with the study of major phenomena surrounding them.
It is often regarded as the intersection of computer science, behavioral
sciences, design, and possibly other fields of study.
Graphics /
Visualization
Computer graphics is the art and science of communicating information
using images that are generated and presented through computation.
Visualization: The field of visualization seeks to determine and present
underlying correlated structures and relationships in both scientific
(computational and medical sciences) and more abstract datasets. The
prime objective of the presentation should be to communicate the
information in a dataset so as to enhance understanding.
4.0 Modeling and simulation
Simulation and modeling is an important skills required by many science disciplines such as biology,
environmental sciences. Simulation and modeling will help students to learn concepts like
•
Discrete Events
•
Event-based simulation
•
Modeling with PDEs
•
Dynamic modeling with PDEs or Agent-based modeling
Chapter 1 - Introduction
Computing Concepts for LIKES
APPENDIX – List of Organizations and Individuals for finding Partners
The following is a list of People and Societies that were identified from the computing side as being
potential contributors / collaborators / editors, etc.
People
Heikki Topi – information systems
Data analysis / statistics
Joseph Fair – Kennesaw State University
Lyman Ott
Michael Longnecker – Texas A & M
Information management - Databases
Lois Delcambre
David Maier
Ed Fox
Rick Furuta
Frank Shipman
Michael Nelson
Gary Marchinini
Geneva Henry – Rice University
Graphics / games / visualization / VR
Fred Brooks – UNC-Chapel Hill
James Foley - graphics
Andres VanDam - graphics
Steven Spence – information visualization
Steven Cunningham
Scott Owen – education (graphics) (retired?)
Gaming Programs
Georgia Tech
Purdue
Louisiana-Lafayette
Clemson
Visualization
Conference committee for VisWeek 2009:
Klaus Mueller (Vis)
Raghu Machiraju (Vis)
Chris North (InfoVis)
Jörn Kohlhammer (VAST)
Richard May (VAST)
Stony Brook University
The Ohio State University
Virginia Tech
Fraunhofer IGD
PNNL/NVAC
Chapter 1 - Introduction
Computing Concepts for LIKES
Hanspeter Pfister
Robert Moorhead
John Stasko
Daniel Keim
Larry Rosenblum
David Ebert
Rachael Brady
Harvard University
Mississippi State University
Georgia Institute of Technology
Universität Konstanz
National Science Foundation
Purdue University
Duke University
HCI
Ben Shneiderman
Jack Carroll - PSU
Mary Beth Rosson – PSU
Judy Olson - UCI
Gary Olson - UCI
Simulation and modeling
Averill Law
Osman Balci - VT
Jim Wilson – NCSU
Barry Nelson – Northwestern
Tom Shriver – Michigan (retired?)
Societies
IEEE computer society (IEEE-CS EAB)
IEEE education activities board: Alan Clements
ACM education board – Andrew McGettrick
CSTA – K-12 teachers’ group
SIGCSE
AIS-SIGEd
INFORMS simulation society
Places – Field Test
Villanova
Virginia Tech
NC A&T
Santa Clara University
Texas A&M
Portland State
Purdue?
NJIT?
TCNJ (The College of New Jersey)
Pitt
UNC-Chapel Hill