Educational Technologies: What should you be thinking about next?
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Zagami, J. (2013). Educational Technologies: What should you be thinking about next? [Presentation slides]. Retrieved from http://www.slideshare.net/j.zagami/educational-technologies-what-should-you-be-thinking-about-next? Presentation for Teacher Education Industry Advisory Group (TEIAG) by Dr Jason Zagami, 6 August 2013, at the Queensland Academy for Health Sciences, Queensland, Australia.
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Educational Technologies: What should you be thinking about next?
- 1. Educational What should you be thinking about next? Technologies
- 2. Griffith University Dr Jason Zagami jason.zagami.info
- 3. Lecturer Griffith University School of Education and Professional Studies Gold Coast
- 6. ADE Apple Distinguished Educator Google Certified Teacher Australian HP Innovations In Education Mentor
- 7. Queensland Society for Information Technology in Education Immediate Past President Australian Council for Computers in Education Editor Australian Educational Computing Australian College of Educators (Gold Coast Region) President
- 9. Educational Technologies • Clouds / Virtual Labs / 3D Printing / Publishing • Online / Mobile / VWorlds / AR / VR / Wearable • Tablets / Surfaces / Gestures / Neural Interfaces • Gaming / Gamification / Badging Technological Innovation
- 10. Lee Shulman
- 15. Matthew Koehler Punya Mishra
- 22. 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
- 26. Cloud Computing Technological Innovation Currently 42% of schools 35% of K-12 IT Budgets, Savings of 20%
- 27. Cloud Computing Technological Innovation • Collaboration • Conferencing • File storage • Virtualization • Processing Power • Scalability • Desktop - Laptop - Tablet - Mobile • Private cloud computing
- 29. Cloud Computing Pedagogical Diversity • Multiple Authors • Activity Trails (Time and Content) • Autosave • Ownership (Content) • Ownership (Environment)
- 30. 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
- 47. Online Learning • Canada (Compulsory Offering) • USA (Compulsory Completions) • Khan Academy (4300 micro lectures) • Computer Education ACS report Technological Innovation
- 53. LMS’s vs PLN’s • Teacher/Institution Centred vs Learner Centred • Blackboard/Moodle vs Social Media • SCORM -> Tin Can API • Platform Reliance Technological Innovation
- 55. MOOCs • cMOOC vs xMOOC • Global market and commercialisation • MOOC’s vs Communities of Practice • Publishers/BB Pedagogical Diversity
- 56. Learning Analytics Social Networks Adapting Pedagogical Practice Learning Analytics
- 57. Learning Analytics Wolfram|Alpha Analytics for Facebook Learning Analytics
- 59. Automated Feedback • aka Assessment • Targeted/Dynamic Assessment • Peer Assessments • Essay Grading • Feedback Analysis Learning Analytics
- 60. 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
- 61. Publishing • Reading time • Reading rate • Eye tracking • Pupil dilation Learning Analytics
- 76. Mobile • Price points <$50 Tablets and netbooks • Realistically ~$100 • Ubiquitous processing and connectivity • Magazines, books, all electronic devices • Video/Audio/Location monitoring Technological Innovation
- 77. Apps • Small applications, low cost • Learning objects ($$$$ TLF/ESA) • SCORM -> Tin Can API • Purchasing and licencing models • Micro-transactions Technological Innovation
- 79. 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
- 89. 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
- 90. 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
- 91. 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
- 92. 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
- 93. 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
- 94. Surfaces • Screen real-estate • Multiple monitors Technological Innovation 20
- 95. Surfaces • Screen real-estate • Multiple monitors Technological Innovation
- 158. Learning Analytics
- 159. Learning Analytics
- 160. Learning Analytics
- 161. Learning Analytics
- 162. 3 year investigation into the introduction of games into teaching practice Pedagogical Diversity
- 163. • Using Games: Learning and Digital Games • Analysing Games: Critical Analysis of Games • Making Games: Media Literacy and Creativity Pedagogical Diversity
- 178. Brain Waves Delta 1-4hz Theta 4-7hz Alpha 7-13hz Beta 13-30hz Technological Innovation
- 179. Switch in reality Wake -> Sleep Theta 4-7hz Alpha 7-13hz Sleep -> Wake Technological Innovation
- 185. Griffith University Dr Jason Zagami jason.zagami.info