SEA-Supported, CEDS-Aligned, blended, personalized learning, “Big Data” Future...and YOU...
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Transcript of SEA-Supported, CEDS-Aligned, blended, personalized learning, “Big Data” Future...and YOU...
SEA-Supported, CEDS-Aligned, blended, personalized learning, “Big Data” Future...and YOU [Concurrent Session I-A -- Wednesday, February 13, 2013 10:15–11:15 Colonial]
Bill Huennekens, Washington Office of Superintendent of Public InstructionLee Rabbitt, Rhode Island Department of EducationJim Goodell, Quality Information Partners – CEDS K-12 Facilitator
Are you ready for the shift to SEA supported data use by teachers, parents, and students for personalized learning? How about the shift from traditional models of instruction to blended and virtual? How about the shift from seat-time-based-course-scheduling to mastery learning? Are you ready for the shift to micro-standards and mining of big sets of micro-data? Develop a vision for your future role supporting data-for-action at the state or local level in this changing education landscape..
26th Annual Management Information Systems [MIS] Conference
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Session learning objectives • 5 design principles for next generation schools
• 4 Challenges for SEA new role
• CEDS version 3 updates to support personalized learning
• 2 badges YOU can earn
• The 1 most important technology enabler
for mastery-based, student-centric learning
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Post session challenge
?5
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SEA as service provider to support student learning • In WA we need to figure out the role of the SEA in this effort.• Intend to engage stakeholders in a discussion on priorities and
next steps, so we need to know:• What SEA supported data use by teachers, parents, and
students for personalized learning is occurring?• What else are states doing in this domain?
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The Vision in WA
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Rhode Island• Learning Management• Interim Assessment • RTI• Formative Assessment PD System• Data Use PD System • Growth Model Visualization Tool• Virtual Math Modules
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Organizations PeopleRoles
IP Access
LearningResources
Time
ProcessData
CEDS v3 P-20W
Roles include teachers, parents, and students
Data elements to support personalized learning processes
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5 design principles for next generation schools 1. Student Centered2. Mastery-Based / Self-Paced3. High Expectations4. Student Ownership5. Anywhere, Anytime Learning
(blended/virtual)
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Digital learning is… learning facilitated by technology that gives students someelement of control over time, place, path and/or pace.• Time: Learning is no longer restricted to the school day or the school year• Place: Learning is no longer restricted within the walls of a classroom. • Path: Learning is no longer restricted to the pedagogy used by the teacher.• Pace: Learning is no longer restricted to the pace of an entire classroom.(adapted from: http://digitallearningnow.com/wp-content/uploads/2011/10/Roadmap-for-Reform-.pdf)
(from: http://digitallearningnow.com/wp-content/uploads/2011/10/Roadmap-for-Reform-.pdf)12
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Changes at school level for competency-based learning• Roles• Schedule• Practices• Physical environment• Organizational culture
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The 1 most important technology enabler for competency-based education• Formative feedback data• …WHY?
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Traditional scope and sequence…
ClassFixed Inputs: Instruction Plans,Activities, Pedagogy
Variable Outputs:Student Learning is variable
Instructional Decisions
Fixed Inputs: Learning Standards,Curriculum,Scope and Sequence(Time)
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Formative Assessment Process
DescriptiveFeedback
& Scaffolding
Instruction, Activities, Practices
Learning StandardsLearning ProgressionsCurriculum Current Learning GoalsCriteria for SuccessUnit & Lesson Plans
Formative Assessmentreal-time (during instruction) or near-real-time (e.g. daily) measurement of learner mastery of specific competencies
Learner
Formative FeedbackLoop
Determine the nature ofmisunderstandingand adjust
Fixed Inputs:
INPU
TS
OU
TPU
TS
reference
gap
in
unde
rsta
ndin
g
current statusevidence
+
-
Self/peer assessment& shared ownership
Variable Inputs:
Variable Inputs: Student
Learning
(time is the variable)
Fixed Outputs:
CEDS v3
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4 Challenges for SEA new role 1. Bigger data sets2. Scaled Infrastructure3. 24/74. Support
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All images used in this presentation are stock photos used with permission or created by the authors. Release for web use of all photos on file.
“Big Data”• “Big Data” “tends to be operational in nature
and is characterized by large volumes, high velocity, and a variety of formats, including structured, unstructured and semi-structured.”(Soares, Sunil. Big Data Governance An Emerging Imperative. 2012. Page xxii.)
• Business is beginning to realize the benefits of “Big Data” analysis and we need to recognize these benefits as well. CEDS puts our community in a position to do that.
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Micro-transaction data = "Big Data" • Transactional data for mining• Transactional data for instructional feedbackexample:
Time ActionObject
ID / Input text
Tue Aug 23 10:10:01 -0400 2012 Started a problem #
190212
0 mins 11 secs Answered 12
Tue Aug 23 10:10:23 -0400 2012 Started a problem #
190388
0 mins 6 secs Answered 236
Tue Aug 23 10:12:52 -0400 2012 Started a problem #
190303
Data is fictitious.20
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CEDS elements to standardize “big data”
AssessmentItemResponseAssessmentItemResponseId
Value
ScoreValue
RefAssessItemResponseStatusId
RefProficiencyStatusId
AidSetUsed
DescriptiveFeedback
ScaffoldingItemFlag
HintCount
HintIncludedAnswer
Duration
FirstAttemptDuration
StartTime
StartDate
SecurityIssue
AssessmentItemId
AssessmentParticipantSessionId
AssessmentRegistration
AssessmentItem
AssessmentParticipantSession
An entity with information related to a specific response to an assessment item by the person being assessed. The entity includes the response, a score or indication that the response was correct or incorrect, and other information such as response
An entity with information about detailed actions taken by a learner while engaging in learning activities, such as asking for a hint when taking an online formative assessment.
Reference to the assessment item design element for evaluation of the results.
Reference to delivery data for evaluation of results.
LearnerActionAssessmentItemLearnerActionId
AssessmentItemResponseId
RefLearnerActionTypeId
Value
Time
Date
Click-stream data
CEDS v3
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CEDS v3
Micro-data to micro-identifiers
Data Standards for Content: Granular Learning Standards,
Learning ResourcesCulatta, Goodell, Levin, Jay
Concurrent Session VI
9:00–10:00
Thursday, February 14, 20139:00–10:00 – South Carolina
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Infrastructure to scale personalized learning • Services based architectures• Web scale vs. enterprise scale• Open identity management• My data
See: http://www.ofthat.com/enterprise-scale-is-not-web-scale.html
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Synergy between initiatives
Purpose
Approach
Encoding
GIM-CCSS LRMI / Schema.org
Learning Registry
My Student Data Download
Assessment Interoperability Framework, IMS & SIF (“CEDS on the wire”)
Shared Learning Collaborative (inBloom)
Long-term technical implementation of CCSS for assessments, etc.
Search Engine Discovery
Data Exchange (Resources)
Access to Personal Data
Data Interoperability
Shared services for personalized learning
Web publisher Web Crawling Peer Replication
Download Button
Data Transport Open Platform
JSON, HTML HTML JSON, XML Delimited text, XML, Human Readable (XSLT)
XML API Calls, XML, SIF…
Common Education Data Standards (CEDS)Common Vocabulary
Other initiatives to watch: Cross-state data sharing, e.g. SEEDE
CEDS v3
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The “Seat Time” badge.Get a claim ticket before you leave.
The old model: You get credit for showing up…
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Join the LinkedIn group “Data for Learning” to find out how.
Post-session challenge, earn the mastery-learning badge
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