Education Data Sciences and the Need for Interpretive Skills
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. • 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. • Data exist inside a social context;
shaped by and shaping that context.
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. • 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. 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?
9. • 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
20. 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
21. 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
22. • 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.
23. • 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.
25. 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
26. 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
39. • Broad fluency with a range of
qualitative/quantitative methods
• Ethics, privacy, and confidentiality (FERPA+)
• Technology acumen and ability to reason from
imperfect evidence
40. 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.
41. 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