Educational Technologies
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Presentation by Dr Jason Zagami to the Queensland Society for Information Technology in Education (QSITE) conference on 1 October 2013 on the Sunshine Coast, Queensland. Zagami, J. (2013, October). Educational Technologies. Presentation presented at the Queensland Society for Information Technology in Education Conference, Sunshine Coast, Australia. Retrieved from http://www.slideshare.net/j.zagami/educational-technologies-26715895
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Educational Technologies
- 1. Educational Technologies What is coming next? Tuesday, 1 October 13
- 2. Dr Jason Zagami jason.zagami.info Tuesday, 1 October 13
- 3. Lecturer Griffith University School of Education and Professional Studies Gold Coast Tuesday, 1 October 13
- 4. Educational Technologies • Clouds / Virtual Labs / 3D Printing / Publishing • Online / Mobile / VWorlds / AR / VR / Wearable • Tablets / Surfaces / Gestures / Neural Interfaces • Gaming / Gamification / Badging Technological Innovation Tuesday, 1 October 13
- 5. Lee Shulman Tuesday, 1 October 13
- 9. Pedagogical Content Knowledge (PCK) Content Knowledge (CK) Pedagogical Knowledge (PK) Technological Knowledge (TK) Context Tuesday, 1 October 13
- 10. Matthew Koehler Punya Mishra Tuesday, 1 October 13
- 11. Pedagogical Content Knowledge (PCK) Content Knowledge (CK) Pedagogical Knowledge (PK) Technological Knowledge (TK) Context Tuesday, 1 October 13
- 17. Technology/Pedagogy/ Learning Analytics • Boundaries of school based learning • Ethics of crossing these boundaries • Implications for life long learning • Individual vs aggregated analysis Pedagogical Diversity Tuesday, 1 October 13
- 19. Cloud Computing Technological Innovation Tuesday, 1 October 13
- 20. Cloud Computing Technological Innovation Tuesday, 1 October 13
- 21. Cloud Computing Technological Innovation Currently 42% of schools 35% of K-12 IT Budgets, Savings of 20% Tuesday, 1 October 13
- 22. Cloud Computing Technological Innovation • Collaboration • Conferencing • File storage • Virtualization • Processing Power • Scalability • Desktop - Laptop - Tablet - Mobile • Private cloud computing Tuesday, 1 October 13
- 23. Cloud Computing Technological Innovation Tuesday, 1 October 13
- 24. Cloud Computing Pedagogical Diversity • Multiple Authors • Activity Trails (Time and Content) • Autosave • Ownership (Content) • Ownership (Environment) Tuesday, 1 October 13
- 25. Virtual Laboratories • Students can make mistakes and repeat experiments • Record and replay experiments to analyse for areas of improvement and acknowledge excellence • Often developed and linked with current research labs • Laboratory/Workshops work not limited to a schools physical labs/workshops Pedagogical Diversity Tuesday, 1 October 13
- 27. Virtual Laboratories Technological Innovation Tuesday, 1 October 13
- 28. 3D Printing Technological Innovation Tuesday, 1 October 13
- 29. 3D Printing Technological Innovation Tuesday, 1 October 13
- 30. 3D Printing Technological Innovation Tuesday, 1 October 13
- 31. 3D Printing Technological Innovation Tuesday, 1 October 13
- 32. 3D Printing Technological Innovation Tuesday, 1 October 13
- 34. 3D Printing diy.org Pedagogical Diversity Tuesday, 1 October 13
- 42. Online Learning • Canada (Compulsory Offering) • USA (Compulsory Completions) • Khan Academy (4300 micro lectures) • Computer Education ACS report Technological Innovation Tuesday, 1 October 13
- 43. Online Learning Pedagogical Diversity Tuesday, 1 October 13
- 44. LMS’s vs PLN’s • Teacher/Institution Centred vs Learner Centred • Blackboard/Moodle vs Social Media • SCORM -> Tin Can API • Platform Reliance Technological Innovation Tuesday, 1 October 13
- 45. LMS’s vs PLN’s Pedagogical Diversity Tuesday, 1 October 13
- 46. MOOCs • cMOOC vs xMOOC • Global market and commercialisation • MOOC’s vs Communities of Practice • Publishers/BB Pedagogical Diversity Tuesday, 1 October 13
- 47. Learning Analytics Social Networks Adapting Pedagogical Practice Learning Analytics Tuesday, 1 October 13
- 48. Learning Analytics Wolfram|Alpha Analytics for Facebook Learning Analytics Tuesday, 1 October 13
- 50. Automated Feedback • aka Assessment • Targeted/Dynamic Assessment • Peer Assessments • Essay Grading • Feedback Analysis Learning Analytics Tuesday, 1 October 13
- 51. Publishing • ePub • iBooks -> OSX • CC SA and closed LMS’s • Teacher published texts • Publishing companies / Academic publishing • Integration of texts with LMS/LA access/ quizzes Technological Innovation Tuesday, 1 October 13
- 52. Publishing • Reading time • Reading rate • Eye tracking • Pupil dilation Learning Analytics Tuesday, 1 October 13
- 53. Open Content Pedagogical Diversity Tuesday, 1 October 13
- 54. Open Content Pedagogical Diversity Tuesday, 1 October 13
- 57. Open Learning Pedagogical Diversity Tuesday, 1 October 13
- 58. Open Learning Pedagogical Diversity Tuesday, 1 October 13
- 64. Mobile • Price points <$50 Tablets and netbooks • Realistically ~$100 • Ubiquitous processing and connectivity • Magazines, books, all electronic devices • Video/Audio/Location monitoring Technological Innovation Tuesday, 1 October 13
- 66. Apps • Small applications, low cost • Learning objects ($$$$ TLF/ESA) • SCORM -> Tin Can API • Purchasing and licencing models • Micro-transactions Technological Innovation Tuesday, 1 October 13
- 67. Micro Purchases Pedagogical Diversity Tuesday, 1 October 13
- 68. Meganets • Closed Meganets (App stores) but developers cross networks • Closing the internet (RSS, country filtering/ tariff protections) • Discriminatory pricing • Educational restrictions (sector LMS’s / ESA -> country restrictions) • HTML5 (reduced reliance on Apps) Technological Innovation Tuesday, 1 October 13
- 70. Health Monitoring Learning Analytics Tuesday, 1 October 13
- 71. Remote Health Monitoring Learning Analytics Tuesday, 1 October 13
- 73. Improvement Monitoring Learning Analytics Tuesday, 1 October 13
- 74. Attention/Stress Monitoring Learning Analytics Tuesday, 1 October 13
- 75. RFDI Tracking Learning Analytics Tuesday, 1 October 13
- 76. Activity Monitoring Learning Analytics Tuesday, 1 October 13
- 78. Learning Analytics • Prediction (in which data is used to predict future performance); • Clustering (identify similarities in students/tasks); • Relationship mining (between students, teachers, concepts, etc.); • Modeling (creating a model and using this for further prediction or analysis); and • Distillation of data for human judgment (the most common use by teachers using statistics and visualisations). Learning Analytics Tuesday, 1 October 13
- 79. Learning Analytics • Extracting and analysing data from learning management systems; • Building an analytics matrix that incorporates data from multiple sources (social media, LMS, student information systems, etc); • Profile or model development of individual learners (across the analytics matrix); • Predictive analytics: determining at-risk learners; • Automated intervention and adaptive analytics: i.e. the learner model should be updated rapidly to reflect near real-time learner success and activity so that decisions are not made on out-dated models; Learning Analytics Tuesday, 1 October 13
- 80. Learning Analytics • Development of "intelligent curriculum" where learning content is semantically defined; • Personalisation and adaptation of learning based on intelligent curriculum where content, activities, and social connections can be presented to each learner based on their profile or existing knowledge; and • Advanced assessment: comparing learner profiles with architecture of knowledge in a domain for grading or assessment. Learning Analytics Tuesday, 1 October 13
- 81. Learning Analytics • Data can uncover problems that might otherwise remain invisible • Data can convince people of the need for change • Data can confirm or discredit assumptions about students and school practices • Data can get to the root cause of problems, pinpoint areas where change is most needed, and guide resource allocation Learning Analytics Tuesday, 1 October 13
- 82. Learning Analytics • Data can prevent reliance on standardised tests. • Data can help evaluate program effectiveness and keep the focus on student learning • Data can prevent one-size-fits-all solutions • Data can help address accountability questions • Data can build a culture of inquiry and improvement Learning Analytics Tuesday, 1 October 13
- 83. Surfaces • Screen real-estate • Multiple monitors Technological Innovation 20 Tuesday, 1 October 13
- 84. Surfaces • Screen real-estate • Multiple monitors Technological Innovation Tuesday, 1 October 13
- 86. Interactive Surfaces Technological Innovation Tuesday, 1 October 13
- 87. Technological Innovation Location based learning Tuesday, 1 October 13
- 88. Technological Innovation Location based learning Tuesday, 1 October 13
- 90. Technological Innovation Augmented Reality Tuesday, 1 October 13
- 91. Technological Innovation Augmented Reality Tuesday, 1 October 13
- 92. Augmented Reality Technological Innovation Tuesday, 1 October 13
- 93. 0:22 Augmented Reality Technological Innovation Tuesday, 1 October 13
- 94. 0:17 Augmented Reality Technological Innovation Tuesday, 1 October 13
- 95. 1:30 Augmented Reality Technological Innovation Tuesday, 1 October 13
- 96. 0:40 Augmented Reality Technological Innovation Tuesday, 1 October 13
- 97. Augmented Classrooms Technological Innovation Tuesday, 1 October 13
- 99. Wearable Cameras Technological Innovation Tuesday, 1 October 13
- 100. Google Glass Technological Innovation Tuesday, 1 October 13
- 101. Technological Innovation Google Glass Tuesday, 1 October 13
- 103. Virtual Reality Technological Innovation Tuesday, 1 October 13
- 104. Virtual Reality Technological Innovation Tuesday, 1 October 13
- 105. Technological Innovation Interactive Projections Tuesday, 1 October 13
- 106. Technological Innovation Interactive Projections Tuesday, 1 October 13
- 107. Technological Innovation Motion Capture Tuesday, 1 October 13
- 108. Technological Innovation Motion Capture Tuesday, 1 October 13
- 109. Technological Innovation Motion Capture Tuesday, 1 October 13
- 110. Technological InnovationMotion Capture Tuesday, 1 October 13
- 111. Technological InnovationMotion Capture Tuesday, 1 October 13
- 112. Holographic Telepresence Technological Innovation Tuesday, 1 October 13
- 117. Neural Interfaces Technological Innovation Tuesday, 1 October 13
- 118. Neural Interfaces Technological Innovation Tuesday, 1 October 13
- 119. Neural Interfaces Technological Innovation Tuesday, 1 October 13
- 120. Neural Interfaces Technological Innovation Tuesday, 1 October 13
- 122. Brain Waves Delta 1-4hz Theta 4-7hz Alpha 7-13hz Beta 13-30hz Technological Innovation Tuesday, 1 October 13
- 123. Switch in reality Wake -> Sleep Theta 4-7hz Alpha 7-13hz Sleep -> Wake Technological Innovation Tuesday, 1 October 13
- 124. Synaptic Pruning Technological Innovation Tuesday, 1 October 13
- 125. Interactive Art Technological Innovation Tuesday, 1 October 13
- 126. Neural Interfaces Technological Innovation Tuesday, 1 October 13
- 127. Neural Interfaces Technological Innovation Tuesday, 1 October 13
- 128. Neural Interfaces Technological Innovation 60 Tuesday, 1 October 13
- 129. Griffith University Dr Jason Zagami jason.zagami.info Tuesday, 1 October 13