Big Data, Analytics and the Future of Learning
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The document discusses the transformative impact of big data and computational technologies on learning and educational practices, highlighting future strategies for integrating data-informed approaches and innovative learning models at Curtin University. It emphasizes the importance of developing digital learning ecosystems, fostering computational skills, and creating flexible learning environments to enhance student engagement and collaboration. The document also raises ethical considerations regarding data collection and its implications for privacy and ownership.
Big Data, Analytics and the Future of Learning
- 1. Big Data, Analytics and the Future of Learning Kim Flintoff DLTV Digicon 2016 July 2016, Melbourne
- 2. Abstract Einstein's ideas were pivotal in shifting the way we think about the physical world - from the Newtonian to the Quantum models - in turn this changed the way we think about the world and allowed us to develop new ways of engaging with the world. We are at a similar juncture now with the Information Age and the global Internet of Everything. The development of computational technologies allows us to think about and to make meaning from data about the world in what might be called the age of algorithms and computational thinking. Data science, conducted with global computational resources changes the way we think about, define and solve problems; and in turn, the ways in which we learn. An age of creativity for research teams working in partnership with computational resources may be upon us, extending data science impacts across all fields. Einstein Schrödinger Gödel Bohr
- 3. Presentation Timeline Building a Digital Learning Ecosystem Learning Analytics Data- Informed Learning and Teaching Data Ethics, Privacy and Security DiscussionNew forms of Credentials
- 4. TL@C is a university-wide strategy which will apply innovative thinking to our learning and teaching. It will enable us to: •evolve a contemporary student experience, •align our graduate outcomes with the expectations of employers, and •remain highly competitive in a dynamic global market. Transforming Learning Learning Futures Learning at Curtin is "on your terms"! Personal, collaborative, innovative, richly interactive, flexible and, above all, global.
- 5. Curtin Converged Learning Futures The new Curtin model for Teaching and Learning is Curtin Converged, a mix of traditional lectures, flipped-classes, technology enriched environments and distributed learning techniques (Massive Online Open Courses (MOOCs) and high-end video and 3-D technologies) that actively engage students whether they attend classes on campus or from another location.
- 6. Digital Learning Strategy Learning Futures The purpose of this strategy is to re-imagine the provision of digitally delivered learning focussing on transformed processes, innovative recognised credentialing and the adaptive business models needed to implement change. The scenarios highlight the need for a digitally mature organisation with staff equipped with the necessary skills to lead, create and facilitate digital learning in all modes.
- 7. Digital Learning @ Curtin Learning Futures The strategy is based on eight transformational pillars:
- 11. Big Data and Computational Capacity Institute for Computation, CISCO IoT Innovation Hub, Pawsey Supercomputing Centre Computation is an increasingly important activity in most disciplines, leading to new fields such as computational science and engineering, computational humanities, computational finance and many others. Universities have found it difficult to bring these inherently cross-discipline subjects into the curriculum. By bringing together the leading computational researchers at Curtin, the institute is looking to build the computational skills across the entire university community. BIG DATA ANALYTICS -- SIMULATION – VISUALISATION – MODELLING AND OPTIMISATION -- EDUCATION BIG DATA ANALYTICS -- SIMULATION – VISUALISATION – MODELLING AND OPTIMISATION -- EDUCATION
- 12. 03Developing digitally capable learning spaces From collaborative learning spaces to next generation learning spaces. Curtin has redeveloped a number of its existing teaching and learning spaces with the goal to transform learning at the University. These vibrant new spaces have been designed to increase student engagement, foster collaboration between staff and students and provide flexible, technology-rich environments which support active student learning.
- 13. Timeline for development 01 Write your subtitle in this line Flexible Learning Policy and Procedures Blackboard OLAS The Move Online 2008 eLearning Advisory Team Curtin Online Open Universities Australia Centre for eLearning 2009 eScholars Unit Outline Builder Innovation 2010
- 14. Course redesign HIVE ART 2014 Blackboard Analytics Curtin Challenge Platform Transformation 2013 ECHO 360 Curtin Teaching and Learning iPad User Group Restructure 2012 Blackboard 9.1 Blackboard Collaborate Managed Hosting Infrastructure 2011
- 15. Greater Curtin 2030 1,000,000 Learners 2017 elsie Curtin Institute for Computation Consolidation 2016 The Agency edX Consortium Review 2015 Education and Innovation Social and Cultural Networks Urbanisation Business and Research Medical School Insights Centre for Learning Analytics
- 18. Campus Quest Learning Futures Games and gamification as data streams in the learning analytics ecosystem.
- 19. Curtin Challenge Learning Futures Welcome to Curtin Challenge, where you can develop your skills, build your networks, and shape your future. Challenge is a fun and interactive way to learn, and is just one of the many ways Curtin University is transforming your University experience.
- 21. Curtin Challenge Learning Futures Aggregation of data allows rich insights to emerge and more unique and personalised solutions
- 22. Learning Futures Smart Classrooms of the Future
- 23. Learning Futures Smart Classrooms of the Future
- 24. Learning Futures Smart Classrooms of the Future – The Agency
- 25. Learning Futures Smart Classrooms of the Future – The HIVE
- 28. Educational Data Mining From “what was” to “what could be” “Goals of EDM: 1. Predicting students’ future learning behavior by creating student models that incorporate such detailed information as students’ knowledge, motivation, metacognition, and attitudes; 2. Discovering or improving domain models that characterize the content to be learned and optimal instructional sequences; 3. Studying the effects of different kinds of pedagogical support that can be provided by learning software; and 4. Advancing scientific knowledge about learning and learners through building computational models that incorporate models of the student, the domain, and the
- 29. Ethics and data collection Ethics, security and privacy Key questions Who is allowed to collect data? What can they do with it? What limits should be in place? Who owns data? What data should be public domain? At what point does tracking become stalking? At what point does data hoarding become a Photo credit: We are Anonymous - https://www.flickr.com/photos/equinoxefr/6856903841/
- 31. Value Difficulty Information Optimization Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Hindsight Insight Foresight What happened? Why did it happen? What will happen? What should I do? Future Focussed Analytics From “what was” to “what could be”
- 32. Personalisation at scale Learning Futures Curtin University has embarked on an ambitious project to expand its educational offerings to the world - aiming to engage one million learners across the globe by 2017.
- 33. Digital Enhancements to Assessment Learning Futures 1. An authentic learning experience involving digital media, with 2. Embedded continuous unobtrusive measures of performance, learning and knowledge, which 3. Creates highly detailed, high resolution data records which can be computationally analyzed and displayed so that 4. Learners and teachers can immediately utilize the information to improve learning. Gibson, D. C., & Webb, M. (2013). Assessment as, for and of 21st century learning. In International Summit on ICT in Education (p. 17). Torun: EDUsummIT 2013. Retrieved from http://www.edusummit.nl/fileadmin/contentelementen/kennisnet/EDUSummIT/Documenten/2013/6_WCCE_2013- _Educational_Assessment_supported_by_IT_1_.pdf
- 34. Good ByeSee you next time, have nice day
Editor's Notes
- #3: http://tiny.cc/data-kf Square Kilometre Array / Murchison Widefield Array
- #7: To shift into a data driven paradigm the context needs a high level of digital currency.
- #24: Similar models can be applied to epidemiology, global sustainability issues, social media, etc. By combining real world data for learning and learning analytics for feedback and assessment we believe we can establish a highly responsive, personalized and scalable ecosystem for education.