Unlocking the Potential of Prior Learning Assessments through … · 2018-01-30 · Fall 2010 Fall...
Transcript of Unlocking the Potential of Prior Learning Assessments through … · 2018-01-30 · Fall 2010 Fall...
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Sarah Leibrandt, Lisa Montiel, and Nan Travers
2017 National Institute on the Assessment of Adult Learning
Unlocking the Potential of Prior Learning Assessments through
Better Data
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Multistate Longitudinal Data ExchangeSarah Leibrandt, Ph.D.
Research Analyst
Western Interstate Commission for Higher Education
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Relating practice, policy, and data
Data collection
Data linkages and preparation
Evaluation/AnalysisTranslation to
actionable information
Program/policy adjustments &
behavior changes
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State Data Systems are becoming more robust
• 26 states have full P20W systems, connecting early learning, K-12, postsecondary and workforce data
• 42 states currently link or plan to link postsecondary to workforce data.
Source: SHEEO, Strong Foundations, 2016http://www.sheeo.org/sites/default/files/publications/SHEEO_StrongFoundations2016_FINAL.pdf
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State Data Systems shed light on diversity
Source: SHEEO, Strong Foundations, 2016http://www.sheeo.org/sites/default/files/publications/SHEEO_StrongFoundations2016_FINAL.pdf
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State Data Systems can provide answers…
• Who are our adult learners?
• What are the education outcomes of our adult leaners?
• What are the employment outcomes of our adult learners?
• Data Dashboards allow users to look at student outcomes• Kentucky Center for Education and Workforce Statistics
• Rhode Island Data Hub
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Kentucky Center for Education and Workforce Statistics
Source: https://kcews.ky.gov/
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Rhode Island DataHUB
Source: https://ridatahub.org
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The problem
• State data systems tend to respect state borders
• But students, workers, and employers don’t
• Consumer information, data for policy adjustment, and accountability measures can be based on incomplete data
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For example: there are community colleges near the NY and NJ border
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There are also manyhospitals near the NY and NJ border
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One possible solution: The Multistate Longitudinal Data Exchange
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56% of completers found in Idaho wage
records
Hawaii<1%
Oregon3%
Wash.7%
Those “not found” reduced from 44% to
35%
22% of degree completers not
found in Idaho wage records were found in the other three
states’ data
Notes: 3,158 students who received associate’s or higher award from an institution in Idaho by Dec. 2010 and had a valid SSN
Uncertainty About Employment Outcomes Reduced by 22% in Idaho
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The State University of New York
Lisa Montiel, Ph.D.
Assistant Provost
Office of Institutional Research and Data Analytics
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Role of SUNY System Admn, Office of Institutional Research & Data Analytics• Provide analytic services to SUNY.
• Integrate data across sources.
• Manage SUNY’s information on enrollment, degrees, instructional cost, financial aid, etc.
• Ensure mandatory reporting requirements are met.
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SUNY System & Prior Learning Assessment
• 1976 Memorandum to Presidents on awarding academic credit by evaluation
• Campus level decisions; OIRDA data collection
• Prior learning credits show up as transfer credits
• AP, IB, CLEP, DANTES, military experience, dept. exams, etc = transfer credit
• Is there a difference in student outcomes based on type of prior learning?
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Seamless Transfer & Degree Works
• System-wide, comprehensive transfer policy (gen ed & required coursework for major)
• Degree Works = web-based auditing software so student can track progress toward degree & how status would be impacted if changed majors or institutions
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Fall 2016 Enrollment of Undergraduate Students Over the Age of 25
First-Time Students Transfer Students All Students
SUNY 6.7% 27.6% 19.1%
2-Year Campuses 10.6% 35.6% 23.8%
4-Year Campuses 1.9% 22.4% 14.0%
Among Students Who Are:
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Example of Educational Outcomes: First-Time Associate Students at 2-Year Campuses
Cohort Type Cohort Term Reporting Term Age Group Total Degrees %
Over 25 yrs. 30.6
All others 40.8
Over 25 yrs. 25.5
All others 34.2
Cohort Type Cohort Term Reporting Term Age Group Total Degrees %
Over 25 yrs. 24.6
All others 19.7
Over 25 yrs. 16.5
All others 13.2
1st-Time, PT Associate
Fall 2010 Fall 2016
Fall 2012 Fall 2016
1st-Time, FT Associate
Fall 2010 Fall 2016
Fall 2012 Fall 2016
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Employment Outcomes: New York State Dept. of Labor Data Exchange• SUNY System Administration MOU with NYS DOL for all of our 64 campuses
• We sent them SSNs of SUNY graduates for the last 10 years
• NYS DOL returns quarterly wage & employer info (sometimes)
• SUNY System compiles aggregate reports for each campus, enforce data security policies, establish procedures for data use, create research roadmap including scope of work, & determine mode of campus access to data
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Employment Outcomes: MLDE
• SUNY partner with WICHE for MLDE participation (without secondary ed & labor)
• About 80% of SUNY graduates working in state
• Downstate area important for multi-state data exchange.
• Online student graduates employment outcomes
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SUNY Empire State College
Nan L. Travers, Ph.D.
Director of the Center for Leadership in Credentialing Learning
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SUNY Empire State College• One of 64 SUNY Institutions; 35 locations across NY, online, and
international
• Established in 1971, focused on the Adult Learner (avg. age 35)
• Associate, Bachelor’s and Master’s Degrees
• About 17,000 undergraduate students and 1200 graduate students
• Every undergraduate student can use prior learning assessment (PLA) towards degree requirements
• Advanced Standing credits – Transfer and PLA (avg. 56 credits for Bachelor’s)
• 50% of graduates use some form of PLA in their degrees
• At any given time, 4 - 5,000 students are working on an individualized PLA portfolio (iPLA)
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Types of Advanced Standing
• Liberal advanced standing policies• Up to 40 credits for associate degree• Up to 93 credits for bachelor’s degree• Can be used to meet any degree requirements
• Many sources of advanced standing learning• Transcripts (avg. 5 per student)• Standardized Exams (e.g., CLEP, AP, DSST, TECEP, UEXCEL, NYU Language)• Military Credits (ACE-all training and occupations)• Professional training, certifications and licenses (ACE, NCCRS, CACE, Empire
State College, and other regionally accredited institutions)• Individualized PLA Portfolio
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Data Needs
• Develop an Advanced Standing document to guide what can be used in a degree• Available credit
• Credit designations (e.g., level, liberal arts, SUNY General Education)
• Degree Design • Degree guidelines (i.e., degree competencies)
• Concentration versus General Learning
• Degree Audit (Degree Works)
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General Issues
• No systematic mechanism to share data from/to other organizations or institutions
• Very few external sources have electronic data to share• Convert transcripts, pdfs, and other sources into data
• Hand enter advanced standing data into student information system
• Homegrown databases• PLA Planner
• Degree Program Planner
• Confusing to students and faculty/advisors as to what can or cannot be used in a degree
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Compliance Issues
• Financial Aid – need for degree plan, class standing, and for cost of attendance
• Military – both prospect and matriculated students need for degree plan and to know what will transfer in for cost of attendance
• Dept. of Labor – both prospect and matriculated students need for degree plan and to know what will transfer in for cost of attendance
• SUNY Policy – need to tell students transfer credits before first payment
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Solutions
• Internally• Articulation tables for all Advanced Standing (transfer and PLA)
• Joint Service Transcript Review of Available Credit (JSTRAC)
• SUNY• Degree Works
• Sharing catalog information
• Updating system-wide PLA policy (original is from 1976)
• Externally• CACE institutions sharing review data
• ACE – initial conversations about data sharing
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Discussion Questions
• Does (and if so, how) your institution or state system distinguish types of advanced standing credits?
• How does your institution or state system evaluate current PLA programs and policies?
• What data do you wish you had that could provide even more insight into adult learner outcomes?
• What issues has your institution or state system had in addressing data needs?
• What successes have you had?
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Contact Us
Sarah Leibrandt, Ph.D., [email protected]
Lisa Montiel, Ph.D., [email protected]
Nan Travers, Ph.D., [email protected]