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Technology, Innovation, and Education Presentation to Emerging Technologies Class at George Washington School of Business

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Discusses some of the emerging technologies in education from an innovation perspective.

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Technology, Innovation, and Education Presentation to Emerging Technologies Class at George Washington School of Business

  1. 1. What’s Data Got to Do With It? Technology, Innovation, and Education George Washington University Mar 5, 2014
  2. 2. Technology, Innovation, and Education
  3. 3. Technology, Innovation, and Education
  4. 4. Technology, Innovation, and Education
  5. 5. Technology, Innovation, and Education
  6. 6. Technology, Innovation, and Education
  7. 7. “New” Education Technology
  8. 8. “New” Education Industry http://siia.net/eis/2014/
  9. 9. “New” Education Creativity http://vimeo.com/71053336/
  10. 10. Outline: What’s Data Got to Do With IT? • The Educational Data Movement • My book Assessing The Educational Data Movement • Comparing education and other fields, including business • Quantitative and Qualitative shifts in the field • Modeling education as a sector
  11. 11. THE EDUCATIONAL DATA MOVEMENT
  12. 12. The Educational Data Movement Understanding how the organizational model of education is similar to/different from other fields is key to understanding the educational data movement. 1980 – 1990 - 2000 - 2010 Finance Manufacturing Retail Health Care Education
  13. 13. Data Across Educational Levels Education Level Technologies
  14. 14. Data Across Educational Levels Education Level • National Technologies No federal data system, national organizations/standards
  15. 15. Data Across Educational Levels Education Level • National • State Technologies No federal data system, national organizations/standards State Longitudinal Data Systems (SLDS)
  16. 16. Data Across Educational Levels Education Level • National • State • Districts Technologies No federal data system, national organizations/standards State Longitudinal Data Systems (SLDS) District data warehouses, teacher and principal evaluation systems
  17. 17. Data Across Educational Levels Education Level • National • State • Districts • Schools Technologies No federal data system, national organizations/standards State Longitudinal Data Systems (SLDS) District data warehouses, teacher and principal evaluation systems Dashboards, special education & behavior/discipline tracking systems
  18. 18. Data Across Educational Levels Education Level • National • State • Districts • Schools • Classrooms Technologies No federal data system, national organizations/standards State Longitudinal Data Systems (SLDS) District data warehouses, teacher and principal evaluation systems Dashboards, special education & behavior/discipline tracking systems Interactive content, dashboards and reports, open educational resources
  19. 19. “ASSESSING THE EDUCATIONAL DATA MOVEMENT”
  20. 20. The Educational Data Movement • Compares education to business • Explains why using data for education is both necessary and difficult • Synthesizes different strands of education and organizational research
  21. 21. The Business/Education Dichotomy Journals Conferences Academic Programs Journals Conferences Academic Programs
  22. 22. Balancing Business/Education Views
  23. 23. Business/Education Family View Large Small Size
  24. 24. Business/Education Family View Large Small Size Local Distributed
  25. 25. Business/Education Family View Large Small Size Local Distributed Brick & Mortar Virtual For/Non Profit
  26. 26. Education is Unique • Culture and Human Capital (This is Changing) Historical problem • Legislative Context(The US Constitution) Each state can decide what it wants to do and there are over 17,000 school districts • Learning is Extremely Variable and Context-specific Much less understood than medicine • In The K-12 Space Education is Universal Everyone goes to school and schools must accept everyone
  27. 27. Education Data Has Issues • Human/social creation. Much of educational data is human generated with possibility for error/manipulation. • Measurement imprecision. Educational data can be imprecise, especially assessments of learning. • Comparability challenges. Comparisons across different areas of education is often impacted by context variation. • Fragmentation. The world of educational data is fragmented. There are incomplete/partially adopted technical standards.
  28. 28. QUANTITATIVE AND QUALITATIVE SHIFTS IN THE FIELD
  29. 29. Dramatic Growth in Artifacts Assessment Technology Computing Technology Central “Mainframe“ ComputingTabulating Technology Cloud Technology Services Traditional fixed response, short task assessments Analog Paper-based (Textbooks, worksheets, and manual classroom tools) Classroom Technology Distributed Integrated Assessment Systems Digital Classroom Technology 1850s 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 20101850s 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
  30. 30. Quantitative Shifts in Data • Test scores • Interim assessments • In class, formative assessments • Growth models • Student collaboration • Conversation records from classroom talk and online tools • Student work, including rich and multimodal demonstrations of knowledge and competency (essays, presentations, etc.) • Records of after-school experiences • Records of informal learning • Activity traces from digital media (in school, out of school, etc.) • Demographics • Student-teacher relationships (TSDL) • School improvement plans/goals • Classifications (ex: proficiency groups) • Video records of teaching • Annotated/evaluated records of teaching • Teacher evaluations • Individual Education Plans (IEPs) and personalized learning maps • Geospatial information (mapping and trends) • Attendance and rosters (more important than you think!) • FERPA/privacy blocks
  31. 31. Qualitative Shifts in Education 1. Reorientation of center of control 2. Broader focus on competencies 3. Blended/ personalized learning
  32. 32. Social Networks &Teams Mobile Technology Evidence and Transparency Institution Focus Teacher Control Institutional Reorientation Institutions and Teachers
  33. 33. Social Networks &Teams Mobile Technology Evidence and Transparency Institution Focus Teacher Control Networks and Students Institutional Reorientation Social NetworksLearning Networks Learning Communi ties. Expert Sources Open Ed. Resources Families Institutions and Teachers Related to the Education Data Movement
  34. 34. Emphasis on Broader Competencies Cognitive • Cognitive processes and strategies • Knowledge • Creativity Intrapersonal • Intellectual openness • Work ethic and conscientiousness • Positive core self- evaluation Interpersonal • Teamwork and collaboration • Leadership • Critical thinking • Information literacy • Reasoning • Innovation • Flexibility • Initiative • Appreciation for diversity • Metacognition • Communication • Collaboration • Responsibility • Conflict resolution
  35. 35. Emphasis on Broader Competencies Cognitive • Cognitive processes and strategies • Knowledge • Creativity Intrapersonal • Intellectual openness • Work ethic and conscientiousness • Positive core self- evaluation Interpersonal • Teamwork and collaboration • Leadership DigitalMediation • Critical thinking • Information literacy • Reasoning • Innovation • Flexibility • Initiative • Appreciation for diversity • Metacognition • Communication • Collaboration • Responsibility • Conflict resolution Artifacts
  36. 36. Blended/Personalized Learning • Blend the best of face-to- face/online. • Incorporate interaction and dynamic material coupled with metadata and paradata to enable feedback. • Leverage embedded diagnostic assessments & interactive data visualization tools. • “Learning algorithms” match content/activities/ teaching approaches with learner’s needs. • Connect the in/out of school learning for complete picture of student’s development.
  37. 37. VIEWING EDUCATION AS A SECTOR
  38. 38. Viewing Education as a Sector K-12 Education Post Secondary Professional/Career Jobs Early Childhood
  39. 39. Mapping Innovations to Level/Scale Early Childhood K-12 Post Secondary Continuing/ Career Individuals Cohorts Organizations Systems Scale of Educational Context EducationalLevel(Age)
  40. 40. Mapping Innovations to Level/Scale Early Childhood K-12 Post Secondary Continuing/ Career Individuals Cohorts Organizations Systems Scale of Educational Context EducationalLevel(Age)
  41. 41. What’s Data Got to Do With It? Technology, Innovation, and Education George Washington University Mar 5, 2014

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