Date post: | 21-Jan-2016 |
Category: |
Documents |
Upload: | linette-fay-parrish |
View: | 221 times |
Download: | 0 times |
The next step to simplified SLO reporting
Planning today
A working paper of Lawson, Zentner and Sanchez (2014) sought to quantify the relationship between past enrollments and how these relationships influence future enrollment projections. A longitudinal analysis of historic enrollment data from California K-12 schools was conducted to determine whether K-8 enrollments affect High School enrollments. Data collected from the California Department of Education was aggregated and manipulated to create a new historic enrollment trend measurement that accounted for how previous grades affect the enrollment patterns of future grades. One county’s enrollments (Orange, CA) was selected as the test population. In each study, a Weighted Enrollment Segment Mean (WESM) was used to predict different years’ enrollment numbers. The study yielded 96.87% accuracy in predicting next-year enrollment numbers based off of an oscillating momentum factor derived from weighted enrollment segments for years 1987-2012.When only the predictions from 2000-2012 were accounted for, the accuracy of the model rose to 98.75% with a forecast error of 1.257%. Additionally, when only 2006-2012 projections were accounted for, the model's accuracy was 98.913% with a forecast error of 1.088%. The working paper is seeking to replicate the results across different counties through the advancement of a new modeling structure in 2015.
Id ASMT OREN COUN EDPL FUP S80 S90 S100a 0 0 0 0 0 1 1 1b 0 0 0 0 0 1 1 1c 0 0 0 0 0 0 0 0d 0 0 0 0 0 0 0 0e 0 0 0 0 0 1 1 1f 0 0 0 0 0 0 0 0g 0 0 0 0 0 1 1 1h 0 0 0 0 0 0 0 0i 0 0 0 0 0 1 1 1j 0 0 0 0 0 1 1 1k 1 1 1 0 1 0 0 0l 0 0 0 0 0 1 1 1m 0 0 0 0 0 1 1 1n 1 1 1 0 1 0 0 0o 0 0 0 0 0 0 0 0p 0 0 0 0 0 0 0 0
Step 1• Step 1a• Step 1b• Step 1c• Step 1d• Step 1e• Step 1f• Step 1g
Step 2• Drink• Eat • Sleep• Play • Poop
Step 3• Apple• Cat• Car• Beard
Step 4• Guess• Jump• Butter
Step 5• Eye• Ice tea• Bros• Database
Step 6• Means nothing• Call this • LOL• Text in the meeting• Diaper
Step 7• Step 7 if• Step 8 if• Step 9 if• Step 10 if• Step 11 if• Step 12 if• Step 13 if
Outcomes Assessment
Developing a culture of evidence
Continuous improvement
Moving the needle
Evolution of Reporting Strategies
Limitation in Reporting Strategies
Facts:
Everyone SLO Assessment
Assumptions: 100% of SLOs are reported during the assessment cycle
The interface is easy
Limited training is needed and utilized
There is a clear line from assessment to planning
Survey Findings: Quantitative
39%
50%
11%
Difficult user interface
Lack of training
Lack of information
Why not 100%?
Survey Findings: Qualitative
25%
37%4%
29%
4%Relevency in the data
Limited support for part-time faculty
Contractual issues
Associated benefit
Time to complete SLO assessment
Why not 100%?
Resistance
Do I need to say more…
Case Study
Traditional Progression
Identified Limitation
Faculty Agreement
Implementation
Case Study Findings
2010-2011 2011-2012 2012-2013 2013-2014
31% 25%
86% 100%
Ele
ctro
nic
SLO
Clo
ud
Research Conclusions
The work level stayed the same
Agreements and continuous training drove the process 25% to 86%
User-friendly systems created a shift in reporting to 100% participation
Replication
SLO Cloud Progression
Re-engineering the SLO Cloud focus on the foundational keys of: Access
Usability
Security
Accuracy
Cloud
bit.do/slocloud
Best Practices to Engage Participation
Foster an environment of faculty and staff driven assessment
Draw meaningful linkages to planning
Incentivize the purpose of assessment
Lead by example
SLO Cloud 3.0
Unitary level reporting
Linkage to the database
Federated identity (No more multi-logins)
Feeds SLQ server for real-time dashboard querying
Questions