Leveraging Data

Anthony Wagner

ORAHEAD Fall Conference 2024

Disclaimers

• No affiliation with Accessible Information Management LLC (AIM)
• Not a presentation about data security – please consult with relevant authorities at your institution (e.g. Information Technology, Institutional Research)
• FERPA reminder – accessing only data relevant to your role

Acknowledgment

Some content of this presentation is adapted from and inspired by an AHEAD master class by Ann Knettler and Chester Goad in 2023

Presenter Background

• Postsecondary Ed Work Hx
• Chemeketa Community College
• Accommodation Specialist
• Data Analyst
• Student Accessibility Services
• Testing Services
• Education
• Masters in Rehab Counseling
• Bachelors in Biology and Computer Science

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Why Data?

Data can help address common challenges:

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• Understaffed, under resourced
• Challenge of communicating out re: compliance
• Limited institutional awareness of the work we do

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Approaches to Using Data

Data as Communication

Data as Communication – Process

Start with the question or problem

• Why are only some students using their accommodations?
• We are consistently under-budgeted
• How can we get more institutional support to purchase notetaking software?

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Next, work backwards to identify relevant data

• Don’t ask: what data do I have?
• Do ask: what data can I get?

Data as Communication – Example (1)

Problem: requesting funding for a new staff position

• Possible data-based approaches
• Institutional data on staffing levels, relative caseload sizes of other student service areas
• AHEAD benchmark data – biennial surveys of programs and professionals
• Calculate tuition associated with students served annually
• Calculate staff hours devoted to direct service vs. administrative duties and faculty consultation

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Data as Communication – Example (2)

Problem: want more institutional support for student outreach

• Possible data-based approaches
• Survey students served about referral source, awareness of services at time of application
• Gather/compile statistics about percentages of students served nationally and/or at comparable institutions
• Review and compile information from scholarly articles about transition from high school and student awareness of services

Data for Insight

• Gaining greater understanding of services provided
• Guiding revisions to processes/procedures
• Data-informed decision-making

Insights at Chemeketa (1)

Students’ consistency in ‘requesting’ accommodations correlates with modest improvements in GPA and persistence

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• Example
• Carlos and Edwin both start fall 2023 and both attend through spring 2024
• Both are determined eligible for accommodations in the fall. Carlos ‘requests’ his consistently each term, Edwin does not.
• Carlos is likely to have a higher cumulative GPA than Edwin and is also more likely to return fall 2024.

Insights at Chemeketa (2)

Substantial majority of student disengagement through the initial engagement process occurs at the step of providing documentation of disability

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• Initial engagement steps:
• Online application
• Provide documentation of disability
• Initial interview appointment
• Determination of eligibility for accommodations

Insights at Chemeketa (3)

• Institution-wide, persistence roughly correlates inversely with age of matriculation - students tend to persist when they start young
• For at least a subset of students served by Student Accessibility Services, the same trend may not hold true
• More investigation is needed

Ways to Obtain and Work with Data

Readily Available Data

• Scholarly articles
• National statistics
• AHEAD Benchmark and Survey Data
• Institutional data examples:
• Persistence rates
• Average cost of tuition
• Numbers served by other student service departments

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Built-In Reports

• Type and content of reports varies by platform (e.g. AIM, Glean, etc.)
• Often rigid, but sometimes allow for filtering/customization
• Can be more useful in conjunction with exporting to CSV

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Survey Data

• Google Forms, Survey Monkey, etc.
• Built-in reports available
• Survey results can also be compiled into a spreadsheet and/or exported
• Google Forms Survey Example
• Collector Sheet

CSV Export

• Comma Separated Values (CSV)
• Simple formatting convention for text in a file
• ‘Columns’ separated by commas
• ‘Rows’ separated by return carriage (new line)
• Can be used with spreadsheet software
• Can be imported with a database management system (e.g. Microsoft Access)
• Learning to ‘join’ multiple exported files (tables) opens up many possibilities

Favorite CSV Exports in AIM

• Advanced Student Search
• Manage Users -> List Users -> Advanced Student Search -> Click to Expand Advanced Search Panel
• List Case Notes
• Manage Users -> List Case Notes -> Generate Report -> Export Raw Data -> (make selections) -> Search -> (make selections in Additional Fields for Export Area) -> Export to CSV file button
• Student Applications
• Manage Users -> Click to Expand Advance Search Panel -> Customized Search: All Student Applications -> Search -> Export to CSV file
• List Requests
• Manage Accommodations -> List Requests -> Click to Expand Advanced Search Panel -> Accommodation Term: (Select) -> Export to CSV File
• Student Classes
• Manage Accommodations -> List Student Classes -> Click to Expand Advanced Search Panel -> (customized search field has a number of good options) -> Search -> Export to CSV (options: By Student, By Student and Course, by Course (Eligibility Only))
• Equipment Checkout Log
• Manage Equipment -> List Users with Equipment -> Click to Expand Advanced Search Panel -> Export Current Search Result to CSV

Using Spreadsheet Applications

• Microsoft Excel, Google Sheets, etc.
• Getting data with sorting/filtering/mathematical functions only
• Joining tables in Google Sheets - vlookup() function
• SQL queries in Google Sheets - QUERY() function
• Google Sheets Example

Using Database Management Systems

• Microsoft Access, SQL Server, etc.
• Powerful tools for manipulating and disaggregating data
• Oriented toward more advanced users
• Prerequisite knowledge
• Foundational data types
• relational database structure basics
• Server Query Language (SQL) basics

Questions? Comments?

Contact Information

• anthony.wagner@chemeketa.edu
• 503.589.7754