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Philip Piety, John Behrens, Roy Pea
American Education Research Association Annual Meeting
Monday, Apr 29 - 10:35am Parc 5...
• What kind of profession will education
data sciences be?
• What are its ancestor, sister, and adjoining
disciplines?
• W...
• Data exist inside a social context;
shaped by and shaping that context.
• Data exist inside a social context;
shaped by and shaping that context.
• Interpretation is not technical. It is
itself ...
• Data exist inside a social context;
shaped by and shaping that context.
• Interpretation is not technical. It is
itself ...
1. Quantitative shifts in evidentiary artifacts (a
digital ocean) in education
2. Qualitative shifts in educational focus
...
Assessment
Technology
Computing
Technology
Central “Mainframe“
ComputingTabulating Technology
Cloud
Technology
Services
Tr...
• Test scores
• Interim assessments
• In class, formative assessments
• Growth models
• Student collaboration
• Conversati...
Where to Begin?
Influenced by concurrent work with behrens, Mislevy, and DiCerbo for the
Learning Analytics Workgroup.
Structures &
Interrelationships
Influenced by concurrent work with behrens, Mislevy, and DiCerbo for the
Learning Analytic...
Structures &
Interrelationships
Diachronic/Change
Processes
Influenced by concurrent work with behrens, Mislevy, and DiCer...
Structures &
Interrelationships
Diachronic/Change
Processes
Variations in
Affordance
Influenced by concurrent work with be...
1. Reorientation of
center of control
2. Broader focus on
competencies
3. Blended/pers-
onalized learning
Social
Networks
&Teams
Mobile
Technology
Evidence and
Transparency
Institution Focus
Teacher Control
Institutions and Teac...
Social
Networks
&Teams
Mobile
Technology
Evidence and
Transparency
Institution Focus
Teacher Control
Networks and Students...
Cognitive
• Cognitive processes
and strategies
• Knowledge
• Creativity
Intrapersonal
• Intellectual openness
• Work ethic...
Cognitive
• Cognitive processes
and strategies
• Knowledge
• Creativity
Intrapersonal
• Intellectual openness
• Work ethic...
• Blend the best of face-to-
face/online.
• Incorporate interaction and
dynamic material coupled with
metadata and paradat...
• Oriented towards new kinds of education
models while often working with data that
comes from earlier models of education...
Considering Six Adjoining Disciplines
1. Growing interest from
leading universities,
foundations, USED
2. Journals, conferences, &
programs now emerging
3. What...
Statistical
Data
Analysis
Education
Data
Sciences
• Much of the digital ocean is
compatible with statistical
analysis.
• E...
Statistical
Data
Analysis
Education
Data
Sciences
Classroom/
Learning
Technology
• This area is seeing
an explosion in
med...
Statistical
Data
Analysis
Education
Data
Sciences
Classroom/
Learning
Technology
Learning
Sciences
• What does big data
me...
Statistical
Data
Analysis
Education
Data
Sciences
Classroom/
Learning
Technology
Learning
Sciences
Information
Sciences
• ...
Statistical
Data
Analysis
Organization
& Mgmt
Sciences
Education
Data
Sciences
Classroom/
Learning
Technology
Learning
Sci...
Statistical
Data
Analysis
Organization
& Mgmt
Sciences
Education
Data
Sciences
Classroom/
Learning
Technology
Learning
Sci...
The Seventh, Generative Discipline
• Broad fluency with a range of
qualitative/quantitative methods
• Ethics, privacy, and confidentiality (FERPA+)
• Technol...
1. All analytic processes are socially situated and
iterative
2. Data is a mediational tool in an iterative process
of dis...
Philip Piety, John Behrens, Roy Pea
American Education Research Association Annual Meeting
Monday, Apr 29 - 10:35am Parc 5...
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
Education Data Sciences and the Need for Interpretive Skills
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Education Data Sciences and the Need for Interpretive Skills

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AERA 2013 Philip Piety, John Behrens, Roy Pea

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Education Data Sciences and the Need for Interpretive Skills

  1. 1. Philip Piety, John Behrens, Roy Pea American Education Research Association Annual Meeting Monday, Apr 29 - 10:35am Parc 55 San Francisco / Divisadero Room
  2. 2. • What kind of profession will education data sciences be? • What are its ancestor, sister, and adjoining disciplines? • Which kinds of skills and dispositions are important for preparing future practitioners and scholars?
  3. 3. • Data exist inside a social context; shaped by and shaping that context.
  4. 4. • Data exist inside a social context; shaped by and shaping that context. • Interpretation is not technical. It is itself socially situated with goals, predispositions/ biases, and norms.
  5. 5. • Data exist inside a social context; shaped by and shaping that context. • Interpretation is not technical. It is itself socially situated with goals, predispositions/ biases, and norms. • Professional communities have developed valuable ways to reason from imperfect evidence. We can leverage/translate them to this new sociotechnical terrain.
  6. 6. 1. Quantitative shifts in evidentiary artifacts (a digital ocean) in education 2. Qualitative shifts in educational focus 3. Some contributing/relevant disciplines 4. Interpretive skills, how education data scientists should approach data analysis?
  7. 7. 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
  8. 8. • 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
  9. 9. Where to Begin?
  10. 10. Influenced by concurrent work with behrens, Mislevy, and DiCerbo for the Learning Analytics Workgroup.
  11. 11. Structures & Interrelationships Influenced by concurrent work with behrens, Mislevy, and DiCerbo for the Learning Analytics Workgroup.
  12. 12. Structures & Interrelationships Diachronic/Change Processes Influenced by concurrent work with behrens, Mislevy, and DiCerbo for the Learning Analytics Workgroup.
  13. 13. Structures & Interrelationships Diachronic/Change Processes Variations in Affordance Influenced by concurrent work with behrens, Mislevy, and DiCerbo for the Learning Analytics Workgroup.
  14. 14. 1. Reorientation of center of control 2. Broader focus on competencies 3. Blended/pers- onalized learning
  15. 15. Social Networks &Teams Mobile Technology Evidence and Transparency Institution Focus Teacher Control Institutions and Teachers
  16. 16. Social Networks &Teams Mobile Technology Evidence and Transparency Institution Focus Teacher Control Networks and Students Social NetworksLearning Networks Learning Communi ties. Expert Sources Open Ed. Resources Families Institutions and Teachers Related to the Education Data Movement
  17. 17. 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
  18. 18. 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
  19. 19. • 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.
  20. 20. • Oriented towards new kinds of education models while often working with data that comes from earlier models of education. • Not only producing evidence (data jocks), but also change agents. • Will be need to be innovators and draw off of different kinds of disciplines.
  21. 21. Considering Six Adjoining Disciplines
  22. 22. 1. Growing interest from leading universities, foundations, USED 2. Journals, conferences, & programs now emerging 3. What is the disciplinary focus? What counts as rigor and success? From where are faculty? Education Data Sciences
  23. 23. Statistical Data Analysis Education Data Sciences • Much of the digital ocean is compatible with statistical analysis. • Exploratory data analysis (ex: Tukey with satellite data in 70s asked many questions that are being asked today about “big data” • Already established (entrenched) in Education power structures • Can produce strong claims
  24. 24. Statistical Data Analysis Education Data Sciences Classroom/ Learning Technology • This area is seeing an explosion in media for: • Inquiry • Communication • Construction • Expression • This is where the data we want most often come from…
  25. 25. Statistical Data Analysis Education Data Sciences Classroom/ Learning Technology Learning Sciences • What does big data mean for socio- technical multimodal learning? • Socio-cultural and cognitive theories influence/informed by data technologies • A design science for education practice
  26. 26. Statistical Data Analysis Education Data Sciences Classroom/ Learning Technology Learning Sciences Information Sciences • Data visualizations and HCI • Info. architectures that undergird data systems • Codes, classifications • Boundary objects • In schools, media centers evolving with data specialists
  27. 27. Statistical Data Analysis Organization & Mgmt Sciences Education Data Sciences Classroom/ Learning Technology Learning Sciences Information Sciences • Education full of designed processes • Blended learning models essentially re-structuring of org. practices • Inter-organizational functions changing: • States-districts • Special education
  28. 28. Statistical Data Analysis Organization & Mgmt Sciences Education Data Sciences Classroom/ Learning Technology Learning Sciences Information Sciences Decision Sciences • Established field uses large bodies of data to support org. decisions • As volume/quality of education data increase, more situations where decision sciences can be applied emerging
  29. 29. The Seventh, Generative Discipline
  30. 30. • Broad fluency with a range of qualitative/quantitative methods • Ethics, privacy, and confidentiality (FERPA+) • Technology acumen and ability to reason from imperfect evidence
  31. 31. 1. All analytic processes are socially situated and iterative 2. Data is a mediational tool in an iterative process of discovery 3. Data is an imperfect lens for context and for interactions within that context 4. Organizational/systems thinking helps expand the reach of Education data science 5. Ethical as well as legal considerations are important.
  32. 32. Philip Piety, John Behrens, Roy Pea American Education Research Association Annual Meeting Monday, Apr 29 - 10:35am Parc 55 San Francisco / Divisadero Room Contact: ppiety@edinfoconnections.com

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