Interdisciplinary Skills for the 21st Century Learner EDUCAUSE – May 6, 2008 John Gibson, Oris Friesen, & Florence Martin Maricopa Community Colleges, Arizona Agenda • • • • • • Project Origins Introduction to DVL DVL Modules Instructional Design & Assessment Excel Module Example YOUR Ideas: DVL’s Potential Uses? 2 Project Origins • 3-year, $600,000 National Science Foundation Advanced Technological Education Grant • Original Partnership between Brown University & Maricopa Community Colleges • Final Year – Focus on Dissemination • GOAL: Introduce Digital Visual Literacy modules into introductory community college courses: – – – – Educate teachers Use DVL modules in existing courses Perform assessment Disseminate results 3 What is DVL? 4 • DVL is a set of vital interdisciplinary skills that enable students and teachers to function in an increasingly digital and visual workplace • DVL builds on previous Visual Literacy efforts, but integrates new research material, as well • Examples: • Visual Language: Global Communication for the 21st Century (Robert E. Horn, 1999) • Visual Thinking (Rudolf Arnheim, 2004) • Visual Literacy: Learn to See, See to Learn (Lynell Burmark, 2002) What Interdisciplinary Skills? • The ability to… 1. Critically evaluate digital visual materials (2D and 3D, static and moving) 2. Make decisions using digital visual representations of data and ideas 3. Use computers to create effective visual communications • Across the curriculum! 5 6 Why? Images Are Everywhere! • Graphical interfaces make easy computing possible • Photorealistic CGI (computer generated images) for movies and simulations • Large data sets can be visualized (weather, etc.) • Visualization lets doctors look inside your body • Industrial design depends on CAD (computer-aided design) • Simulations affect most areas of science, from nanotechnology to biology and beyond A Historic Trend • Images used to be displayed in only a few places, such as churches, but are now ubiquitous • Television was introduced only 60 years ago • By HS graduation US children will have spent more time in front of the TV than in the classroom (AACAP) 7 Computer Technology Has Accelerated this Change 1900: Kodak’s “brownie” camera 1900s (early): first movie theaters 1980s bulky, low quality VCR a “wow” item 1984: ATT breakup 1990: color printers ~$10K 19th century studio camera, with bellows for focusing 8 2006: Kodak sells no film cameras 2006: desktop movie making 2006 tiny iPOD has HD video 2006: cell phones have megapixel+ camera, video, even scanners 2006: given as bank “gifts” Bell Howell Keychain Digital Camera 9 Skill Sets Are Changing • You can’t believe everything you see • Role of design in business is widely recognized • Accelerating demand for graduates with visual, holistic thinking Daniel Pink’s agriculture to industrial age to knowledge work to age of conceptual thinking From “A Whole New Mind: Why Right-Brainers Will Rule The Future” by Daniel Pink, 2006 Profound Implications… “If students aren’t taught the language of … images, shouldn’t they be considered as illiterate as if they left college without being able to read or write?” (George Lucas) Think about the “literacy” we teach… Convergence, 21st Century Skills, & “Complex Tacit Interactions” Is the 4th R a V? 10 11 An Integration of Disciplines! • Academics – Sciences – Humanities – Business • Application areas (medicine, military, etc.) • Little conversation between practitioners in these fields Contributing Disciplines 12 1. Visual Culture: Critical discussion of visual materials and history, including topics in art history, media culture, visual history, and philosophy. 2. Art and Design: Basic concepts in 2D, 3D, and time3. based visual art and design Vision Science: Basic concepts in neuroscience, perception, and cognitive science 4. Computer Graphics and Visualization (CS): 5. Basic concepts in the science of computer graphics Image Economy: The economic implications of creating, distributing, purchasing, exchanging visual (largely digital) materials Best Delivered As “Modules” • FREE!!! • Strengthens Textbooks – with supplementary materials • • • • • • Complete Instructional Design Small, flexible, & tested Downloadable from website You can adapt original files! Can be used in any course 12 modules a beginning… 13 14 Beginning Modules Introduction to Digital Visual Literacy Anne Spalter – Brown University Practical Visual Copyright John Gibson – Glendale Community College Visual Rhetoric for Blog Janet Brooks – Glendale Community College Visual Dialog in Ecommerce Linda Offenberg – Phoenix College Graphics Literacy Gail Korkames – Phoenix College 3D Graphics John Roberts – Glendale Community College Practical Visual Copyright 15 • Laws balancing financial gain and freedom of use • Protecting and licensing your images • How to find images and assess their copyright status • Resolving workplace Issues BLOG Module • How do we “read” images? Similarities with text. • How is visual meaning created? • Synergy between text and images in establishing an explanation or a line of argument. 16 Visual Dialog in E-commerce • New business models and strategies inspired by digital visual communication… – Kodak Easy Share, flickr, etc. • Compare company product photos with user uploaded ones on Amazon You can now add a picture of a product to Amazon… 17 18 Graphics Literacy Module • Overview of basic graphics terms, concepts, and tools • An introduction to basic skills using MS Paint • Designed to promote student awareness of the technology and talent involved in creating computer graphics 19 3D Graphics Module • What is 3D graphics? • How are businesses taking advantage? MS Office Modules Kay Gaisford – Mesa Community College Visual Display of Information Using Word • 2003 • 2007 Visual Display of Information Using PowerPoint • 2003 • 2007 Influencing Decisions with Charts – Excel • 2003 • 2007 20 Word and PowerPoint 21 • Better visual design in Word and PowerPoint • Addresses basic design principles and vocabulary Better Design Through 1. Contrast 2. Repetition 3. Alignment 4. Proximity Excel Charts and Graphs 22 • Excel can make your numbers look pretty. But is the result meaningful? • How to analyze and create… Two Excel Charts Made From One Set of Data, but They Convey Very Different Messages • Pyramid shape makes top portion look smaller than the data it represents • 3-D effect make it difficult to accurately read the numbers DVL Brochure (outside) 23 DVL Brochure (inside) 24 Development of the Instructor Guides 25 Instructor Guide Sample Pages Instructor Guide Summary Presentation with Instructor Notes 26 Grading Rubric ADDIE Process in DVL • • • • • Analysis Design Development Implementation Evaluation Instructor Guide Summary Templates 27 28 Instructional Design Matrix – Using Excel 2007 to Design Charts that Influence and Inform Goal: The student will Demonstrate knowledge of the power of visual representations of numerical data. Be able to determine which type of chart best supports accurate, effective communication of data. Analyze and interpret numeric data by creating charts. Analyze charts to determine accuracy of data representation. Objectives Information/Examples Practice with Feedback Assessment 1. Describe the difference in ease of comprehension between the numerical data and the visual representation of that data. Excel-2007-DVL-Presentation.ppt Excel-2007-Chart-Types-andDesign.doc In class discussion Excel-2007-QuizWithAnswers.doc 2. Explain reasons for the widespread use of charts and graphs in popular media as well as business communications. Excel-2007-DVL-Presentation.ppt Excel-2007-Chart-Types-andDesign.doc In class discussion Excel-2007-QuizWithAnswers.doc 3. Select appropriate chart type that will best visually represent the data based on the chart type rubric. Excel-2007-DVL-Presentation.ppt Excel-2007-Chart-Types-andDesign.doc In class discussion Projects 1 and 2 Excel-2007-Project1.doc Excel-2007-Project2.doc Excel-2007-QuizWithAnswers.doc 4. Describe criteria for displaying and formatting chart elements. Excel-2007-DVL-Presentation.ppt Excel-2007-Chart-Types-andDesign.doc In class discussion Excel-2007-QuizWithAnswers.doc 5. Describe design principles that will help make charts more attractive and effective. Excel-2007-DVL-Presentation.ppt Excel-2007-Chart-Types-andDesign.doc In class discussion Projects 1 and 2 Excel-2007-QuizWithAnswers.doc 29 Knowledge of DVL - based on student perception 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Before DVL Module W or d (N G ra =1 ph 1) ic s (N C =1 op 3) y rig Ec ht om (N m =9 er ce ) (N =6 3) 3D N= 33 ) t( in we rp o Po Ex ce Bl og l( N= 2 =5 2 ) 0) After DVL Module (N Knowledge of DVL on Likert Scale Before & After Modules Rating Scale: 5 = very knowledgeable 4 = Knowledgeable about 3 = Uncertain 2 = Somewhat knowledgeable 1 = not knowledgeable about Charts are Computer Graphics 30 Apply Graphic Design Principles - Contrast ▲ Contrast is the use of differences to create interest, excitement, show importance of different items. Used Auto Sales Drop Off Friday Evenings During Football Season Sales Average Sales September-November 2006 40 35 30 25 20 15 10 5 - ▲ Spot color contrasts with other colors. Market share has improved 12 points since 2003 New Models Boosted Sales beginning in 2006 ▲ Bold columns (color, width) contrast with white background. 33% 23% 24% 25% 2003 2004 2005 2006 35% 2007 Lying With Charts This Charity Keeps Administrative Costs Low Lying with Poor Chart Choices ▲ Misleading charts may result in errors in decision-making ▲ Charts are often designed in ways that hide what the data might tell us, or that distract the reader from quickly discerning the meaning of the chart. Adapted from Charts and Graphs for Microsoft Excel 2007, Bill Jelen, Que Books, 2007. Wedges at the front of a 3-D pie chart appear larger than in the back Administrative Costs Other Expenses Administrative Costs Take a Big Chunk of Funds Raised Administrative Costs Other Expenses 31 a Through the Roof Marketing Costs Are Going Misleading Charts 350 300 250 Using a Chart Type that Obscures the Truth 200 150 100 50 ▲ the Area chart to the right above shows that marketing costs are rising sharply. 0 2003 2004 2005 2006 c This Stacked Area Chart Obscures Cost Trends 1000 800 ▲ The Stacked Area chart to the right obscures this trend 600 400 200 0 ▲ The clustered column chart shows each data point separately so that trends can be examined for all 4 expense areas. 2003 2004 Manufacturing 2005 Marketing R&D 2006 Sales This Clustered Column ChartcShows Data Points Clearly 350 300 250 200 150 100 50 32 Adapted from Charts and Graphs for Microsoft Excel 2007, Bill Jelen, Que Books, 2007. 0 2003 2004 Manufacturing 2005 Marketing 2006 R&D Sales Chart Types – Column The best chart type is the one that gets your message across most effectively ▲ Column charts are good for up to 12 data points. Otherwise, use bar or line chart. ▲ Clustered column chart shows each data point separately (easy to interpret) ▲ 100% Stacked Column chart compares the percentage each value contributes to a total across categories ▲ Cylindars, Cones, and Pyramids are similar to column charts with more dramatic shapes (sometimes distort perception). ▲ 3-D effects use more ink, more space, and distort perception 33 DVL Wiki • DVL Wiki: http://dvl.mc.maricopa.edu 34 DVL – The 4th R is a V! • New skills are needed to succeed in a digital visual workplace • DVL modules can help: – Strategically introduce this emerging field in your classes – Strengthen your current offerings • JOIN US!!! 35 YOUR Ideas: DVL’s Potential Uses? 36 Decision Theatre (Arizona State University) • • • How could you apply Digital Visual Literacy in your curriculum? Do you have any thoughts about future DVL modules and new initiatives? Interest so far: Universities, community colleges, high schools, middle schools, and new partnerships/grants For More Information 37 Digital Visual Literacy Project Maricopa Community Colleges http://dvl.mc.maricopa.edu/ (480) 731-8124 dvl@domail.maricopa.edu This material is based upon work supported by the National Science Foundation Grant No. 0501965. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).