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Student Success Framework

OSU fosters student success through a proactive approach that supports the whole student. Institutional Research and Analytics (IRA) collects, analyzes, and distributes data regarding student activities in order to provide campus partners with tools to evaluate the effectiveness of their programs and interventions.  


In order to promote student success in an ethical and transparent manner, IRA has adopted the EDUCAUSE Student Success Framework, which identifies four areas for institutions to address to ensure institutional readiness to support student success: 

In addition, we've provided a list of resources to help promote student success.

Preparedness: Literacy and Infrastructure

OSU’s Mission: Building on its land-grant heritage, Oklahoma State University promotes learning, advances knowledge, enriches lives, and stimulates economic development through teaching, research, extension, outreach and creative activities. 


IRA seeks to support OSU’s mission by coordinating the collection and analysis of data and providing accurate and contextualized reporting to promote data-informed decision-making to advance the goals of the institution and system. OSU has several data governance and privacy policies that provide a strong foundation for data initiatives. OSU has also invested in our Information Technology department, which is robust and supports the secure IRA data warehouse. This environment has allowed IRA to establish mature data coordination between multiple systems. 


Trust is paramount to the work that we do. Users must trust the data, the systems, and the staff who manage them. IRA works with departments across campus to bring relevant data together and to deliver useful reporting. Many users at OSU do not have formal data training but must use data frequently when making decisions related to their programs. IRA offers assistance understanding and interpreting data and can provide support to programs and departments in developing data-related processes. In this way, we build relationships across campus with end users, data owners, and decision makers. 


As the availability of reliable and useful data increases, IRA looks forward to providing formal training for users. To begin, we are creating a Data Literacy website to provide a shared frame of reference and common language to discuss data-related concepts in general and at OSU, specifically. We hope to increase campus understanding of digital literacy and best practices. With more informed users, we also hope to identify areas that can benefit from changes or expansion, and thereby improve existing processes and services. Through incremental growth, we will improve the quality of data collected and increase the use of data to inform decisions in meaningful ways. 


By expanding the base of users who are familiar with the services of IRA, we hope to identify partners for pilot programs and to set future goals with stakeholders. We will continue to incorporate feedback and perspectives from stakeholders. 

Outcomes: Defining and Measuring Success

While student success is ultimately determined by the achievement of a student’s individual goals, IRA assesses OSU’s ability to support student success for all students with these measurable metrics:  
Student successis defined as the attainment of one success marker in at least three of the following realms in a given semester: academic achievement, persistence, attainment of learning outcomes, acquisition of skills and competencies, physical and/or mental wellness, and career preparation. 

Student Success Map


Specific examples for each realm include: 

Student Success Realm Examples of Metrics
Academic Achievement GPA, Grades, Fellowships, Scholarships
Satisfaction Survey of Instruction, Survey of Advisement, UAT Surveys
Acquisition of Skills & Competencies Comprehensive Learner Record (CLR), UAT Certification Tests, Undergraduate Research, Grad College 360 Professional Development program, ITA Exam, Leadership Certificate 
Physical and Mental Wellness Participation in Wellness Programming, Intramural/Club Sports, Gym Swipe-in
Attainment of Learning Outcomes Testing for Upper-Level Course Placement, Testing for Admittance/Continuation in Major
Career Preparation Internship/Co-op, On-Campus Job, Meeting with Career Counselor, Attendance at Hiring Event, Job Attainment Rates
Persistence Graduation, Retention, Academic Standing

For example, IRA would consider a student in good academic standing who participates in Wellness programming and has an on-campus job to be a success because they have met a marker for three different realms. 


While other activities and interactions, such as off-campus employment, may also support student success, these are not included in the model as they are not tracked by IRA. However, IRA is eager to partner with departments and colleges to expand the analysis that we can provide by identifying and integrating additional data sources. 
Different members of the OSU community (administrators vs instructors vs students) may have different expectations of student success and use other metrics to measure it. IRA seeks to provide the appropriate level of detail and context for decision-makers across campus. We intend to provide ongoing analysis into the specific realms above as well as the overall student experience to determine what benchmarks are characteristic of successful students. 

Analysis: Understanding Data Analytics

Institutional Research and Analytics uses several types and methods of data analysis to provide decision-making support to users across campus. The following information is modified from the EDUCAUSE Framework for Student Success Analytics and Intellispot Data Use Infographic to help explain how we can use existing and new data to answer questions about the past and make informed choices for the future. 


There are two types of data that can be analyzed: 

    • Quantitative Data: involves specific numbers and provides a count or a measure; this data often already exists or is faster to acquire and analyze 
    • Qualitative Data: non-numerical by nature; involves observations and stories; can provide context and richer understanding for quantitative data or stand on its own 

Data Analytics is the “interpretation of information in context, typically through use of statistical measures, data models, reports, and dashboards” (Oklahoma Information Services Glossary of Data-Related Terms).


There are four main types of analytics: 

    • Descriptive Analytics: provides hindsight to explain what happened in the past and can help identify trends and areas in need of further exploration and diagnosis 
    • Diagnostic Analytics: may be used initially to begin to understand the problem and can inform next steps in evaluation 
    • Predictive Analytics: uses historical data to create a data model, which may help fill in gaps in available data and forecast and predict future outcomes 
    • Prescriptive Analytics: helps determine an optimal course of action, which is achieved through considering all factors using large quantities of historical data, and yields recommendations for next steps and action 

Below are some a few examples of analytical methods and algorithms and how they are used: 

    • Descriptive Analysis: used to answer questions such as  “Are students who are engaged on campus retained at a higher rate?” or  “Do students who live on campus their first year have different GPAs than students who do not live on campus?”
    • Regression Analysis: used to analyze several factors to determine their effect on a desired outcome; one example is the ELPA model for incoming freshmen to identify areas of remediation
    • Decision Trees: used to rank factors by the degree of importance for progressively smaller populations; can help generate cut-off values when developing interventions, such as a minimum or maximum GPA 

Analytics in education can be categorized into functional areas which reflect their scope. Below are examples of how OSU provides analytics to the university community. 

Type Uses Examples How End Users May Access at OSU
Institutional Analytics
Using aggregate data, describes students and their outcomes at the macro/institutional level. Used for the purposes of understanding institution-level outcomes and trends.
Retention and graduation rates; application data; demographics including race, ethnicity, and socioeconomic status; student engagement records 
Cowboy Data Round-Up  
Academic Analytics
Describes academic outcomes within an institution. Used for the purposes of optimizing academic offerings and programs.
Grades, major, student characteristics, and admission/prior learning information  Entry Level Placement Assessment (ELPA) Report


Course Roster + Dashboard provides instructors with aggregate data on the students in their course sections. 
Other IRA Dashboards: Academic Program Changes, Academic Program Review, Major Analytics 
Learning Analytics Focuses on the learner rather than the institutional outcome. Used for understanding and optimizing learning and the environments in which learning occurs. Course-level data, including course performance such as grades, but more granular than typical academic measures  The Teacher role in Canvas allows access to data within a specific course such as the amount of time a student spent watching videos or the number of responses to discussion board post. 
Student Success Analytics Describes the integration of data-informed practices that consider students and their diverse contexts to influence decisions that affect student experiences and outcomes 
Analyzes data such as application, GPA, major, transfer work, on campus employment, course registration and completion, financial aid, and engagement 
Othot Institutional Score (coming soon) in Slate for Faculty and Staff

Decisions: Ethics and Principles

IRA follows several guidelines to help ensure that we make ethical decisions:

AIR Statement of Ethical PrinciplesOSU Core ValuesOSU’s Commitment to Diversity, Equity and Inclusion Statement


We are also informed by other statements and principles: 

Council for the Advancement of Standards in Higher Education (CAS) Statement of Shared Ethical Principles

Asilomar Convention

Global Guidelines: Ethics in Learning Analytics

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