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LEADS

LEADS. The 2 nd Annual Meeting of the Learning Environments Across Disciplines Research Partnership (LEADS 2013). U SING A UGMENTED R EALITY A PPLICATIONS TO F OSTER L EARNING AND E NGAGEMENT OF H ISTORY . Kevin Kee , Brock University Eric Poitras , McGill University

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LEADS

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  1. LEADS The 2nd Annual Meeting of the Learning Environments Across Disciplines Research Partnership (LEADS 2013) USING AUGMENTED REALITY APPLICATIONS TO FOSTER LEARNING AND ENGAGEMENT OF HISTORY Kevin Kee, Brock University Eric Poitras, McGill University Thursday May 2, 2013

  2. LEADS partnership Kevin Kee, Canadian Research Chair Brock University Susanne Lajoie, Canadian Research Chair McGill University Thomas Madej CEO Furi Enterprises Peter Seixas, Canadian Research Chair The University of British Columbia

  3. Augmented reality (AR) in education • Definition – Augmented Reality (AR) • A situation in which a real world contextisdynamicallyoverlaidwithcoherent location or context sensitive virtual information (Klopfer & Squire, 2008); alsoknown as Location- or Place-basedaugmented reality (Squire & Jan, 2007) • Why AR in education? • To promotelearning and engagement (Dunleavy et al., 2009; Dede, 2009) • Wu et al. (2013) outline the following affordances of augmented reality in education: • Emphasizinglocations • Emphasizing the task

  4. Location-based mobile AR applications “Aided by your virtual tour guide, ISAAC, you have access to instructional content in the form of photos, documents, and videos” “Use onboard applications to reveal messages hidden in ancient maps, compare pictures from the past to the real objects in the present, and decode puzzles that were carved into buildings or the ground.” http://www.ihistorytours.com/

  5. Augmented reality (AR) in education • Key Findings in Learning withAugmented Reality (AR) • Motivation, engagement, and learning are affected by immersed digital resources in authentic settings (e.g., Palm devices in Participatory Simulations; Klopfer & Squire, 2008) • High student motivation and engagement due to role-playing and collaboration (e.g., Alien Contact!; Dunleavy, Dede, & Mitchell, 2009) • May lead to focusingexclusively on devicefeatures & cognitive overload

  6. Augmented reality (AR) in education • Challenges and Issues Addressed in the ProposedResearch Program • Paucity of research on creators of AR usingauthoringtoolsinside the classroom (as an example, see the Neighbourhood Game Design project; Mathews, 2010; Mathews & Squire, 2009) • The changes in historicalthinkingskills and emotionsexhibited by users of location-based AR applications warrants the use of embeddedassessmentmechanisms to adapt instruction (as in Shute & Zapata-Rivera, 2012)

  7. Theoretical framework of learning: Benchmarks of historical thinking (Seixas, 2004, 2011) • Peter Seixas (2006) identifiedseveralkeyhistoricalthinkingskillsthat are critical to gain deeperunderstanding of historical concepts and events: • Establishhistoricalsignificance(whywe care, today, about the event in question) • Use primary source evidence(how to find, select, contextualize, and interprethistorical sources to make an argument) • Identifycontinuity and change (what has changed and what has remained the same about the lives of people in the past and today?) • Analyze cause and consequence(how and why certain conditions and actions led to the occurrence of an event) • Takehistorical perspectives (understanding the past , withitsdifferent social, cultural, intellectual, and emotionalcontexts) • Understand the moral dimension of historicalinterpretations(how differentinterpretations of the pastreflectdifferent moral stances today)

  8. Theoretical framework of engagement: Control-Value Theory of Emotions (Pekrun, 2006, 2010) Reinhard Pekrun (2010) outlinedseveral types of emotionsthat are experiencedwhenstudentsevaluateactivities or their relevant outcomesagainst standards of quality. Table 1 labels suchexperiences as eitherpositive or negative(pleasant or unpleasantexperiences) as well as activating or deactivating(physiologicalresponse of arousal vs. relaxation). Table 1. Achievementemotions

  9. Research Questions • Whatthinkingskills and emotions do the users and creators of mobile AR applications experience in the context of performinginquiries in a collaborative learningenvironment? • How canwe best recognize expressions of thinkingskills and emotions in users and creators of mobile AR applications? • To identify the frequencyand characteristics as well as recognize the type of thinkingskills and emotionsexhibited in speech turnsduring a conversation in the context of the classroom/guided tour.

  10. Pilot Testing at McGill University McTavish Reservoir Location-Based AR Reservoir and Pump House Textual Sources Pictorial Sources

  11. Research Conditions • Between-Group Conditions • Users of the Locationatordevelopmentplatform. Groups of students & instructor in the humanitiescomputing class atBrockUniversity. • Users of Queenston 1812 application. Groups of visitors & tour guide atQueenstonHeights. • Users of Niagara 1812 application. Groups of visitors & tour guide at Niagara-on-the-lake. • Within-Group Conditions • Time of measurement • Pre-Test • Before/AfterEach Class Activity/Tour Location • DuringEach Class Activity/Tour Location • Post-Test http://www.brocku.ca/brock-news/?tag=digital-humanities http://www.mrc.ca/mrc_projects/brocks-monument-restoration-queenston-heights-ontario/ http://www.trailcanada.com/ontario/niagara_on_the_lake/

  12. Research Methodology: Quasi-Experimental Design Off-LineMeasures Pre-Test Self-Report and Task Performance Measures On-LineMeasures Niagara 1812 Users Tour Location (i) Queenston 1812 Users Tour Location (i) Locationatordevelopmentplatform user Class Activity (i) Self-Report & Question/Answer Script List Self-Report & Question/Answer Script List Self-Report & Question/Answer Script List Niagara 1812 Users Tour Location (i+1) Queenston 1812 Users Tour Location (i+1) Locationatordevelopmentplatform user Class Activity (i+1) Post-test Self-Report and Task Performance Measures Figure 1. Pre- and Post-Test Design withThree Independent Conditions withAssessments of Changes in Learning and Engagement Over Time

  13. Off-Line Measure of Learning: Historical Thinking Task Performance A historicalthinkingrubricthatassesses the Benchmarks of historicalthinking (Duncan, 2012) wasadapted for the purposes of thisstudy. Table 2. Sample of historicalthinkingassessmenttasks

  14. Off-Line Measure of Emotions: Achievement Emotions Questionnaire • The AchievementEmotions Questionnaire (AEQ; Pekrun et al., 2011) wasadapted for the purposes of thisstudy (e.g., I look forward to myhumanitiescomputing class, adaptedfrommath class) • Class-Related 15-item for users of the Locationatorplatform – Before, During, and After versions • Tour-related 15-item questionnaire for users of Queenston and Niagara 1812 – Before, During, and After versions

  15. On-Line Measures of Learning & Emotions Used in Class Activities/Tour Locations Behavioralanalysis Time: 11:50:31 – Isaac Log View Time: 11:50:29 – Isaac Deliver Message #5 Time: 11:50:35 – Isaac MapView Time: 11:50:40 – View Panel #10 Figure 3. Example of Log-File Entries for User Interactions with ISAAC

  16. On-Line Measures of Learning & Emotions Used in Class Activities/Tour Locations Speech analysis Prosody Pitch Energy Duration Spectrum Spectral Figure 4. Screenshot of the Speech Filling System Interface

  17. On-Line Measures of Learning & Emotions Used in Class Activities/Tour Locations Discourseanalysis TextPre-Processing Tokenization Case Transformation Token Replacement FilterTokens FilterStopwords Generate N-Grams Figure 5. Screenshot of the RapidMiner Interface

  18. Phase 1: Research Hypotheses • Phase 1: Descriptive Modelling • Describe expressions of historicalthinkingskills and emotionsduring class-activities/guided tour locations • Hypothesis 1. Learners’ conversations reflecttheir efforts to use primary source evidence and express outcome-relatedemotionswhilecreating mobile AR applications. • Hypothesis 2. Learners’ conversations exhibit the significance of historicalevents as well as manifestactivity-relatedemotionswhileusing mobile AR applications. • Hypothesis 3. The resultsobtained in 1 and 2 willbecorroborated by the data collectedthrough self-report and task performance measures of learning and engagement.

  19. Phase 2: Research Hypotheses • Phase 2: PredictiveModelling • Recognize expression of historicalthinkingskills and emotions on the basis of behavioral, utterance, and audio features • Hypothesis 4. The use of a Support Vector Machine classifier results in more accurate classifications as opposed to other alternatives (Rule Induction, DecisionTree, NaiveBayse) whilemanipulating the type of kernel (dot, radial, polynomial, neural) • Hypothesis 5. The use of a featureselectionalgorithm in #1 (i.e., forwardselection, backwardelimination, and genetic) results in more accurate classifications • Hypothesis 6. The use of a combination of feature types in #1 (i.e., behavioral, utterance, and audio) afterfeatureselectionresults in more accurate classifications.

  20. LEADS Partnership Structure Director Susanne Lajoie AdvisoryCommitee Alan Lesgold Gerhard Fischer Kellogg Booth FrançoysLabonté Theme Leaders Roger Azevedo Reinhard Pekrun Jacqueline Leighton Theme 1 – Cognitively and socially-guidedlearning Kevin Kee Cindy Hmelo-Silver Krista Muis Jeffrey Wiseman Theme 2 – Emotional Engagement and Disengagement Claude Frasson Robert Stupnisky Kevin Lachapelle James Lester Rafael Calvo Ricki Goldman Theme 3 - Innovations in the Science of Assessment EuniceJang Peter Molenaar ValerieShute Collaborator Nathan Hall Project Manager NootanKumar Partners Computer Research Institute of Montreal (CRIM) Graphics, Animation and New Media Design (GRAND) Consortium for Research and Evaluation of Advanced Technologies in Education (CREATE) Group on Educational Medialogy (GEM) Arnold and Blema Steinberg Medical Simulation Centre McGill Centre for Medical Education (McGill MedEd) Health Sciences Education and Research Commons (HSERC) LearnLab Pittsburgh Science of Learning Center (LearnLab) RockyView Schools, Alberta Réseauréussite Montréal Groupe de RechercheInteruniversitaire en TutorielsIntelligents (GRITI) Institute of Medical Health Sciences Education

  21. References Klopfer, E., & Squire, K. (2008). EnvironmentalDetectives – the development of an augmented reality platform for environmental simulations. Education Tech ResearchDev, 56, 203-228. Suqire, K. D., Jan, M. (2007). Mad City Mystery: Developingscientific argumentation skillswith a place-basedaugmented reality game on handheld computers. Journal of Science Education and Technology, 16(1), 5-29 Dunleavy, M., Dede, C., & Mitchell, R. (2009). Affordances and limitations of immersive participatoryaugmented reality simulations for teaching and learning. J Si EducTechnol, 18, 7-22. Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66-69. Wu, H.-K., Lee, S. W.-Y., Chang, H.-Y., & Liang, J.-C. (2013). Currentstatus, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41-49. Mathews, J. M. (2010). Using a studio-basedpedagogy to engage students in the design of mobile-based media. English Teaching: Practice and Critique, 9(1), 87-102. Mathews, J., & Squire, K. (2009). Augmented Reality gaming and game design as a new literacy practice. In K. Tyner (Ed.), Media literacy: New agendas in communication (pp. 209-232). New York, NY: Routledge. Shute, V. J., & Zapata-Rivera, D. (2012). Adaptive educationalsystems. In P. Durlach (Ed.), Adaptive technologies for training and education(pp. 7-27). New York: Cambridge UniversityPress. Seixas, P. (2006). Benchmarks of HistoricalThinking: A Framework for Assessment in Canada. Vancouver: Centre for the Study of HistoricalConsciousness, University of British Columbia. Pekrun, R. (2006). The control-value theory of achievementemotions: Assumptions, corollaries, and implications for educationalresearch and practice. EducPsycholRev, 18, 315-341. Pekrun, R. (2010). Academicemotions. In T. Urdan (Ed.), APA educationalpsychologyhandbook(Vol. 2). Washington, DC: American Psychological Association. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuringemotions in students’ learning and performance: The AchievementEmotions Questionnaire (AEQ). ContemporaryEducationalPsychology, 36(1), 36-48. Duncan, I. (2012). Summative Performance Task. The HistoricalThinking Project. http://historicalthinking.ca/resources/assessment

  22. Mathews, J. M. (2010). Using a studio-basedpedagogy to engage students in the design of mobile-based media. English Teaching: Practice and Critique, 9(1), 87-102. Collaborative design of the AR simulation Preliminary investigation in the community Alpha testing the final AR simulation in the field

  23. Dunleavy, M., Dede, C., & Mitchel, R. (2009). Affordances and Limitations of Immersive ParticipatoryAugmented Reality Simulations for Teaching and Learning. J SciEducTechnol, 18, 7-22. GPS technology to trackstudent real world location Inquiry-based AR simulation « Groups that interview virtualcharacters, collect digital items, solve math problems, language arts, and scientificliteracy puzzles to determinewhyaliens have landed »

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