If your Student-Tutor Ratio is Small Groups instead of one-on-one, the composition of these groups will influence session effectiveness. If students are grouped haphazardly, without regard for their academic strengths and struggles, then tutors will find it much more challenging to meet the individual needs of every student in a group.
Students all learn in different ways: some of these differences are obvious, while others are more subtle. However, this seemingly simple truth is surprisingly difficult to internalize in practice. Most learning experiences are designed with only one kind of learning in mind, and thus optimized for only one kind of learner.
The most effective sessions are personalized to meet an individual student’s needs. Student productivity and growth will increase if the tutor can identify the missing or incomplete skills that are holding a student back and focus on those specific skills. Identifying and addressing these skill gaps requires tutors to use both quantitative and qualitative data to shape the content they include and the approach they use during sessions.
A Landscape Analysis outlines the strengths, resources, and needs of a particular community. It provides a framework for designing a service and ensuring that it is embedded directly in the needs of the community.
While tutoring programs vary greatly in the content that is focused on during sessions, tutors should have a standards-aligned, rigorous, and grade-level appropriate curriculum to use during sessions. Having an established curriculum for tutors to follow ensures that tutors’ planning time is spent optimizing implementation and building deep content knowledge, not creating tutoring session plans.
Below, you will find two protocols for reviewing student academic data, one for a frequent post-session review and one for any data collected on an interim or infrequent basis. These protocols follow the same What/So What/Now What format as the Standard Data Review Protocol, but they are tailored with tutor-specific questions to guide data analysis.
Standardizing a Data Review process helps set a clear expectation that the end product of Data Review is not knowledge, but action. Any Data Review Protocol should ensure that raw data is converted into a clear and digestible format before the reflection process so that reviewers can focus their energies on reflecting on the data, rather than synthesizing the data.
Data Review is the process of collecting data, reflecting on it, and distilling it into actionable insights. This process is how you can turn data into knowledge and knowledge into action. Data Review requires going "below the surface" to find root causes for your results (both positive and negative) and planning actionable changes to continue improving your program.
Consult an attorney to ensure program compliance with all federal, state, and local laws.
| Logic Model Elements (Program Outputs and Short Term Impact) | Sub-Area | Measures | Tool | Data Collection Cadence | Performance Expectation |
|---|---|---|---|---|---|