Technology, Innovation, and Education Presentation to Emerging Technologies Class at George Washington School of Business
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This document discusses the educational data movement. It begins by comparing how data is used across different levels in education from national to classroom levels. It then analyzes why using data in education is both necessary and difficult due to issues like imprecise measurements and fragmentation. The document also examines quantitative and qualitative shifts in education brought about by more data, including a reorientation around competencies and blended learning. Finally, it explores viewing education as an entire sector and mapping innovations across different educational levels and scales.
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Technology, Innovation, and Education Presentation to Emerging Technologies Class at George Washington School of Business
- 1. What’s Data Got to Do With It? Technology, Innovation, and Education George Washington University Mar 5, 2014
- 2. Technology, Innovation, and Education
- 3. Technology, Innovation, and Education
- 4. Technology, Innovation, and Education
- 5. Technology, Innovation, and Education
- 6. Technology, Innovation, and Education
- 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
- 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. Data Across Educational Levels Education Level Technologies
- 14. Data Across Educational Levels Education Level • National Technologies No federal data system, national organizations/standards
- 15. Data Across Educational Levels Education Level • National • State Technologies No federal data system, national organizations/standards State Longitudinal Data Systems (SLDS)
- 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. 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. 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
- 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. The Business/Education Dichotomy Journals Conferences Academic Programs Journals Conferences Academic Programs
- 25. Business/Education Family View Large Small Size Local Distributed Brick & Mortar Virtual For/Non Profit
- 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. 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. QUANTITATIVE AND QUALITATIVE SHIFTS IN THE FIELD
- 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. 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. Qualitative Shifts in Education 1. Reorientation of center of control 2. Broader focus on competencies 3. Blended/ personalized learning
- 32. Social Networks &Teams Mobile Technology Evidence and Transparency Institution Focus Teacher Control Institutional Reorientation Institutions and Teachers
- 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. 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. 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. 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. VIEWING EDUCATION AS A SECTOR
- 38. Viewing Education as a Sector K-12 Education Post Secondary Professional/Career Jobs Early Childhood
- 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. Mapping Innovations to Level/Scale Early Childhood K-12 Post Secondary Continuing/ Career Individuals Cohorts Organizations Systems Scale of Educational Context EducationalLevel(Age)
- 41. What’s Data Got to Do With It? Technology, Innovation, and Education George Washington University Mar 5, 2014