E19.2174 Cognitive Science & Educational Technology I Jan L. Plass, NYU, ECT Spring 2010 Overview Visual Visu l Learn rnin ing You are Yo re aske sked to desig sign a visu visualiza lizatio ion for r educa catio ional l purp rpose ses, s, for r exa xamp mple le, to le learn rn about Ja Jan L. Pla Plass ss New Yo York rk Unive iverisit risity • The id ideal l gas s la laws s • The ca carb rbon cycle cycle • Bird Bird mig migra ratio ion pattern rns s • The syst system m of vo votin ing dist istrict ricts s in in the U.S. S. Air Air tra raffic ic co contro rol l • Center for Research and Evaluation of Advanced Technologies in Education • The hist istory ry of the Gulf lf War r Overview Visual Learning Visual Visu l Learn rnin ing Visual Visu l Learn rnin ing Cognitive • Learning from primarily visual materials Variables Cognitive Design • Text only as labels or brief statements • Examples: Visual Environment Visual Load Visual Learning – Gra raphs, s, ch chart rts, s, ma maps, s, networks, rks, pict icture res, s, vid video, anima imatio ion Attitudes, Emotional Design Motivation Visual Learning Visual Learning ion Visual Visu l v. v. Ve Verb rbal l Informa rmatio • Visu Visual l in informa rmatio ion: • analo logous s re repre rese sentatio ions s • inhere in rently ly re rela latio ional l How do Visu Visual l and Ve Verb rbal l Informa rmatio ion dif iffer r fro rom m one another? r? • enco coded simu simult ltaneously sly • Verb Ve rbal l in informa rmatio ion: • discre iscreet unit its s of symb symbolic lic in informa rmatio ion • pro roposit sitio ional l • pro roce cesse ssed se sequentia ially lly
E19.2174 Cognitive Science & Educational Technology I Jan L. Plass, NYU, ECT Spring 2010 Visual Learning Visual Cognitive Load VERBAL STIMULI NONVERBAL STIMULI Dual l Codin ing Cognit itive ive Load Comp mponents s (Sw (Swelle ller, r, 1999) Theory ry • Intrin rinsic sic Load SENSORY SYSTEMS ivio, 1986, 1990) (Paivio (Pa Load related to complexity of the information Element interactivity REPRESENTATIONAL CONNECTIONS • Extra Ext raneous s Load Load pertaining to format and design of the interface Logogens Imagens (presentation mode, modality, temporal & spatial arrangement, representation type) REFERENTIAL • Germa rmane Load CONNECTION Associative Connections S Mental effort expended by learner Associative REFERENTIAL Connections CONNECTION S Intrinsic Extraneous Germane Free Load Load Load VERBAL SYSTEM NONVERBAL SYSTEM Working Memory VERBAL RESPONSES NONVERBAL RESPONSES Visual Cognitive Load Visual Learning Environments Visual Visu l Cognit itive ive Load Visu Visual l Learn rnin ing En Enviro vironme ments • Highly visual learning environments • Cognitive Load for Visual Representations: • Intrin rinsic sic Visu Visual l Load • Examples Visual element interactivity – Simu Simula latio ions, s, virt virtual l world rlds, s, micro microworld rlds, s, game mes s • Ext Extra raneous s Visu Visual l Load Visual format and design of the interface (presentation mode, modality, temporal & spatial arrangement, representation type) Lee, Plass, & Homer (2006) Introduction Introduction Exa Examp mple les s Exa Examp mple les s Ideal Gas Law Odyssey (Oklahoma State Simulation University) Package
E19.2174 Cognitive Science & Educational Technology I Jan L. Plass, NYU, ECT Spring 2010 Introduction Introduction Examp Exa mple les s Exa Examp mple les s Gizmo/ Molecular ExploreLearning Workbench Introduction Introduction Exa Examp mple les s Exa Examp mple les s Schnotz & Rasch (2005) Ideal Gas Laws (NYU Molecules & Minds project, IES) Introduction Overview Visual Visu l Learn rnin ing Examp Exa mple les s Virt Virtual l Pa Patie ient Cognitive (Ab (Abdomin minal l Cognitive Design Variables Exa Exam) m) NYU YU Sch School l of Me Medicin icine Visual Environment Visual Load Visual Learning Attitudes, Emotional Design Motivation
E19.2174 Cognitive Science & Educational Technology I Jan L. Plass, NYU, ECT Spring 2010 Visual Learning Cognitive Design Factors Cognit itive ive Desig sign Fact ctors rs Gro roup Discu iscussio ssion (3 (3-4 -4 st students, s, 15min min) ) • Representation of information (Information Design) Discu iscuss ss Desig sign Prin Princip ciple les s that in incre crease se the effect ctive iveness ss of visu visual l • Instructional Approach (Interaction Design) repre re rese sentatio ions s for r le learn rnin ing (An (Anima imatio ions s and Simu Simula latio ions)? s)? • Interactivity (Interaction Design) –L –List ist and Discu iscuss ss prin rincip ciple les s fro rom m the assig ssigned re readin ing les • Function of Visuals (in support of cognitive processes) –F –Fin ind and discu iscuss ss exa xamp mple • Scaffolds • Feedback • Narrative structure Cognitive Design Factors Research Materials ions Repre rese sentatio ion of Informa rmatio ion (Se (Semio miotics) ics) Chemist mistry ry Simu Simula latio Ideal Gas Law Which ich mo mode of re rela latio ionsh ship ip between sig signs s and their ir re refere rents s best st • facilit cilitates s le learn rnin ing? • Ico con: Most basic representation, relies on physical resemblance to convey meaning • Symbol: Symb l: Abstract, arbitrary, relies on social conventions for meaning (Peirce, 1956) Quest stio ion of Intere rest st: • Comparison of Iconic v. Symbolic representations Research Materials Results: Representation ions Chemist mistry ry Simu Simula latio Repre rese sentatio ion of Informa rmatio ion (Se (Semio miotics) ics) Does s addin ing ico icons s facilit cilitate le learn rnin ing in in ch chemist mistry ry simu simula latio ions? s? • Ideal Gas Law • Study with 93 11th grade students in a NYC high school: • Adding icons increased recall • Ico cons s esp specia cially lly help lped le learn rners rs wit ith lo low prio rior r kn knowle ledge (Lee, Plass, & Homer, 2006; Plass et al., 2007)
E19.2174 Cognitive Science & Educational Technology I Jan L. Plass, NYU, ECT Spring 2010 Visual Design of Simulations Visual Design of Simulations Inst stru ruct ctio ional l Ap Appro roach ch: Leve vel l of Learn rner r Contro rol Inst stru ruct ctio ional l Ap Appro roach ch: Leve vel l of Learn rner r Contro rol Which ich in inst stru ruct ctio ional l appro roach ch best st facilit cilitates s le learn rnin ing? Which ich in inst stru ruct ctio ional l appro roach ch best st facilit cilitates s le learn rnin ing? Consid sider: r: • Comparison of direct instruction v. guided exploration • Difficulty of content: Intrinsic Cognitive Load • Complexity of interactions: Extraneous Load In other words: • Educational goals / Cognitive Function of materials • Comparison of Worked-out example (Animation) v. Exploration • Learner characteristics (Simulation) Option Or, in even different terms: • Direct instruction v. guided exploration • Kirschner, Sweller, & Clark (2006) v. Everybody Else Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching Research Materials Research Materials ions Chemist mistry ry Simu Simula latio Worke rked-o -out le Exa Examp mple • Kinetic Theory • Ideal Gas Law of Heat Results: Instructional Format Cognitive Design Factors Resu sult lts: s: Simu Simula latio ion (e (exp xplo lora ratory) ry) vs. vs. An Anima imatio ion (w (worke rked-o -out) Examp Exa mple le Leve vel l of Does s abilit ility y to ma manip ipula late para rame meters rs facilit cilitate le learn rnin ing? Intera ract ctivit ivity y • Study with 93 11th grade students 4.0 in a NYC high school: 3.0 • For comprehension: Simulation Direct 2.0 Exploration > Instruction Mean M.C. Posttest 1.0 0.0 Worked Example Explore Wo rked Out/Explore (Plass et al., 2007)
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