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