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