Building a Logic Model for Your Tutoring Program

Building a Logic Model for Your Tutoring Program

More than ever before, community tutoring programs have the potential to fundamentally transform K12 education. When starting a community tutoring program, it is imperative to maximize your potential based on the resources available to you and the greatest identified needs. By creating a model that outlines your program’s goals, activities, and expected outcomes, you can identify potential challenges and be equipped to evaluate your program’s success. 

An overview of the five key steps you need to take to build such a model is provided below.

Step 1: Needs

Performing a community landscape analysis allows you to evaluate the full context in which you are operating.  In a landscape analysis, data collection and information-gathering activities are used to identify a community’s strengths, resources, and needs.  During this exploration period, your ultimate objective is to clearly define beneficiaries and community needs.

It is important to balance several strategies for collecting intelligence. You must  consider the stakeholders who will contribute to your program and ask the tough questions: 

Once you have compiled information, it is critical to evaluate the community’s strengths, gaps, needs, opportunities, and threats. If there is a clear opportunity, the next step is to share your findings and align with your stakeholders. The elegant part of this last step is that, if done correctly, you will build buy-in for your program.

  1. Do the students need tutoring in the first place?
  2. Does the district or school currently have access to a tutoring program?
  3. What are the strengths of current academic support in the community?

Step 2: Inputs

The next step after completing your needs assessment is to ensure access to all critical resources. Students and tutors are among these resources, as well as curriculum, physical space, and sustainable funding. The capacity to scale without digital solutions must also be assessed.  These solutions include rostering, matching, scheduling, and maintaining all of the data required by funders for reporting.  Also, examine, as well as legal and safety restraints.

When assessing your tutor resources, It is imperative to clarify where the tutors will come from. You have to ask the difficult questions, for example:

  1. Will local universities participate, or will I need to recruit on campus?
  2. Is there a willingness to source tutors across other supply options, such as retired teachers, non-profits, community groups, volunteers, etc.?
  3. Will some tutoring be conducted online for logistical reasons?

Step 3: Actions

Once your needs and Inputs have been aligned, it’s time to create a clear path. In order to execute your strategy, you need to assemble a step-by-step plan that utilizes all of your newly acquired critical network and data.  

Repeatable and scalable actions are needed. Every program will come with its own set of challenges, so planning well in advance is crucial. Here are a few examples.

  1. In some cases, districts may provide very narrow insight into which students should be tutored. 
  2. School schedules are often constructed in downtime (Summer), making advocating for tutor slots a timely exercise
  3. If the tutoring program is left to administer assessments, timing and choosing the assessment tool can slow things down considerably. This is especially true if you have varied core curriculum approaches across multiple districts.
  4. Challenges in hiring tutors, including:
    1. If you are using university students as tutors who need clinical experience, how well are your training materials aligned with the University’s approach?
    2. What are the criminal background check requirements in your state? 
    3. Do criminal background check requirements vary from district to district?

Step 4: Outputs

Outputs are immediate goals that you can (most often) quantify.  These goals can include a host of different metrics to help you guide the timeline and overall impact that your program is making as you begin the initial stages of implementation.  

It is imperative that the output component of a logic model does not measure success. These are merely metrics needed to understand how well you are executing. There are many outputs in a logic model. As an example, based on your program’s design, you might track:

  1. The number of tutors that apply are trained and tutor their first session.
  2. How many tutors report that they are enjoying the tutoring?
  3. How often are tutor managers interacting with tutors over a specific period?
  4. How many students feel like they are learning what they need to learn in a certain amount of sessions?
  5. How many students increased their GPA across a semester or a single year?

Step 5: Impact

In the final section of a logic model, you define your short-term, mid-term, and long-term goals. In addition to quantitative goals, qualitative goals can also be set. The National Student Support Accelerator recommends focusing your impact measurement on three key areas: learning, skills, and conditions.

Measures of learning can vary but are usually related to assessments, improvement gaps covered, and general attitudes toward learning. Also, by regularly gathering data on social-emotional learning (SEL), you can get a sense of student sentiment.  

Ultimately, the ability to teach oneself is a key goal, usually measuring studying habits and approaches to learning that build curiosity. It’s important to measure performance in order to understand the sustainability of the program. You should stay focused on the question, “Are students (and tutors) more confident academically, and more proud of their achievements than they were before tutoring?”


It is difficult to create a comprehensive logic model for your tutoring program, especially one that involves the community. Nonetheless, without this crucial work, you will inevitably hit roadblocks, which will slow things down and possibly halt progress. Furthermore, the actual process of assembling a logic model is equally as important to the information gathered. During this journey, you will establish connections with the stakeholders necessary to realize your vision. When you gain community input and buy-in on your project, you greatly increase your chances of success.  

For the most comprehensive guide to building a logic model for your tutoring program, we highly recommend spending some time leveraging the resources on the National Student Support Accelerator (NSSA) website.  Once on the NSSA website, click on the Toolkits tab in the menu.  Here you will find a treasure trove of resources.

At Pearl, we work with our partners to create a logic model so that they implement tutoring programs that actually work. If you are interested in learning more about how we can help you create an evidence-based logic model, shoot us a note at

Pearl Convenes Inaugural Community Tutoring Partnerships Summit Attended by State, University and District Leaders Across 18 States

Pearl Convenes Inaugural Community Tutoring Partnerships Summit Attended by State, University and District Leaders Across 18 States

Participants curated best practices that underscore the importance of diversifying tutor supply, establishing training and professional development pathways, and using evidence to validate programs

RICHMOND, Va. — Pearl, the leading research-based tutor management platform, announced today insights from its inaugural Community Tutoring Partnerships Summit focused on the rise of community tutoring partnerships. Community tutoring partnerships bring together states, districts and higher education institutions to leverage existing community resources with the goal of developing sustainable tutoring programs designed to solve each community’s greatest needs. 

The first-of-its-kind day-long virtual summit was attended by leaders spanning 18 states and 39 higher education institutions serving hundreds of districts nationwide.  The event highlighted evidence-based strategies and lessons from the nation’s leading community-tutoring experts including Dr. Christy Borders E.d.D, Director of the Illinois Tutoring Initiative at Illinois State University, Faith Freeman P.h.D, Co-Director of Institute for Partnerships in Education at University North Carolina GreensboroSusanna Loeb P.h.D, Director of the Annenberg Institute of School Reform at Brown University, and Patrick Steck, Senior Director of Policy for Deans for Impact. 

“Community-designed partnerships as a segment are showing significant promise in the fight against learning loss,” said John Failla, CEO and co-founder of Pearl. “These partnerships enable local school systems to build sustainable innovation right into their model. The result is a meaningful, measurable impact to lead to academic outcomes.”

During the summit, the National Student Support Accelerator also launched its inaugural Higher Education Toolkit, a playbook aimed to support Higher Education Institutions in partnering with school districts to offer high-impact tutoring services. The biggest takeaway from the convening was a unanimous consensus among key stakeholders that understanding how to retain, recruit, manage and support tutors is the most pressing issue to ensure tutoring is sustainable and effective in driving student outcomes.

With the Biden administration calling for an additional 250,000 tutors and mentors to help students make up for lost instruction time due to the pandemic, tutoring has emerged as an essential component of K-12 education. Researchers, across multiple studies, find that high-impact tutoring can dramatically accelerate student achievement. 

However, creating an evidence-based tutoring program can be daunting, especially during unprecedented education staffing shortages and uncertainty on resources post ARP-ESSER funding. Schools and districts have significant questions about recruiting, training and coaching tutors, choosing the most efficacious compensation model and how to ensure sustained funding in the future, and how to structure and measure the performance of a wide variety of critical interventions. 

Having partnered with multiple state, university and district-led community-tutoring programs since 2020, Pearl is developing the nation’s most diverse dataset in the tutoring industry. The platform is foundational for managing and scaling hybrid tutoring through evidence-based best practices in collaboration with the Annenberg Institute at Brown University and its National Student Support Accelerator (NSSA) to safely gather data to continuously improve program design and measurably accelerate student outcomes. In March, Pearl announced that it had joined the innovative community-based tutoring program endorsed by Virginia Governor Glenn Youngkin in partnership with the Urban Leagues of Hampton Roads and Greater Richmond and four historically black colleges and universities (HBCUs).

Failla concludes, “Certain geographies and specialties are in deeper need today, and they’re seeking guidance and evidence to stand as clear examples for how to build high-impact tutoring programs based on efficacy. These best practices curated by our participants at state and district levels can serve as that north star.”

To read more about the ten key takeaways from the Community Tutoring Partner Summit, visit 

About Pearl

Based in Richmond, VA, Pearl is the leading research-based tutor management platform that streamlines operations, reports actionable data, and improves outcomes for districts, state agencies and organizations tutoring tomorrow’s workforce, today. The platform’s fully integrated classroom and administrative tools allow its partners to launch, scale and sustain evidence-based tutoring to support measurable outcomes for all communities. The Pearl ecosystem provides access to the nation’s largest and most respected industry partners including the Annenberg Institute at Brown University and the National Student Support Accelerator at Stanford University.  For more information visit

The Implications of Large Language Machine Learning Models in Tutoring

The Implications of Large Language Machine Learning Models in Tutoring

Since ChatGPT was released to the public via OpenAI the education community has been in constant chatter about its implications for learning. A large language model is a machine learning system that uses vast amounts of data to generate human-like language and perform natural language processing tasks such as text generation, translation, summarization, and sentiment analysis.

If you have not experienced ChatGPT you should. It’s definitely an exciting next generation in how we think about searching on the internet. For almost a quarter of a century Google has been the cornerstone of where we find information online. Google presents relevant links based on search. With ChatGPT information is presented in natural language and feels much more like a chat with someone very knowledgeable about the question you ask. 

Although the conversation feels fairly natural and the answers are cogent, they are not always correct. That being said, if you have a decent understanding of a subject you will probably find that the chat feels authentic and most of the time pretty accurate.  

When the AI was released everyone at Pearl was interested in jumping quickly and trying to understand the implications for tutoring. We spend a lot of time thinking about equity and imagine a world where anyone that needs a tutor could have a tutor. None of us is convinced that AI could replace a human, but I do think that AI has the capacity to make someone better at tutoring or give more opportunities for people to teach.

Here are some interesting implications for the tutoring industry:


Without compromising any person’s identity a tutor admin can give a list of characteristics of a learner and then ask to see corresponding characteristics of a tutor. Below is an example of a question and ChatGPT’s answer:


“Based on the research, what are 5 strong characteristics to look for in a tutor that would match well with a 9 year old Asian dyslexic child coming from a poor socioeconomic background learning in English whose first language was Chinese?”


“Great question! Here are five strong characteristics to look for in a tutor that could match with an Asian 9-year-old dyslexic child coming from a poor socioeconomic background learning in English whose first language was Chinese:

  1. Patience: A tutor who is patient can help the child feel comfortable and confident as they learn. Dyslexia can be frustrating and challenging for a child, especially when they are also trying to learn a new language. A patient tutor can help the child to feel supported and encouraged.
  1. Multisensory approach: Dyslexic children can benefit from a tutor who uses a multisensory approach to learning. This means that the tutor can use different senses, such as sight, sound, and touch, to help the child learn. For example, the tutor could use visual aids, like pictures or diagrams, or use physical activities to help the child understand the material.
  1. Experience working with dyslexic children: It’s important to find a tutor who has experience working with dyslexic children. This experience can help the tutor understand the unique challenges that dyslexic children face and how to tailor their teaching methods to meet the child’s needs.
  1. Cultural sensitivity: A tutor who is culturally sensitive can help the child feel understood and respected. It’s important to find a tutor who is familiar with the child’s cultural background and can be sensitive to their unique needs and experiences.
  1. Flexibility: Finally, it’s important to find a tutor who is flexible and can adapt to the child’s needs. Dyslexic children can learn at different paces and in different ways, so the tutor should be willing to adjust their teaching methods to meet the child’s individual needs. Additionally, the tutor should be flexible in terms of scheduling and availability to ensure that the child can receive consistent support.”


Once ChatGPT or some version of this type of AI is available, an admin could ask AI to construct a calendar based on the data for two tutors.  Here is an interesting example of ChatGPT’s response to a basic scheduling prompt.


I am a tutor that needs to meet with my student three times a week for 30 minutes each time.  I would like to know the dates I can meet with that student in school based on a typical school calendar.


The specific school calendar will depend on the school district and location. However, in general, most schools in the United States operate on a traditional academic calendar, with two semesters per year (fall and spring) and breaks for holidays and summer vacation.

To determine the possible meeting dates, you can start by looking up the school district’s academic calendar online or contacting the school directly to get a copy of the calendar.

Assuming a typical school calendar, here is a general example of possible meeting dates for a tutor meeting three times a week for 30 minutes each time:

  • Fall Semester: September to December (approximately 16 weeks)
    • Possible meeting dates:
      • Monday, Wednesday, Friday: September 6th to December 16th (excluding holidays)
      • Tuesday, Thursday, Friday: September 7th to December 16th (excluding holidays)
  • Spring Semester: January to May (approximately 16 weeks)
    • Possible meeting dates:
      • Monday, Wednesday, Friday: January 10th to May 20th (excluding holidays)
      • Tuesday, Thursday, Friday: January 11th to May 20th (excluding holidays)

Keep in mind that these dates are approximate and may vary depending on the specific school calendar. It’s important to confirm the exact school calendar with the school or district to ensure that the meeting dates are accurate.”


We’re starting to get into the interesting part now. Obviously, it is imperative that the teacher’s knowledge confirms, in some way, whether the ChatGPT response is helpful. As an example, ChatGPT can help tutors contextualize or better consider a student’s learning style or interests in order to support their tutoring. Let’s see if ChatGPT builds some math problems based on two factors (age and affinity) based on my son’s interest in trucks and construction equipment.


My son is 12 years old and has a strong affinity for trucks and construction equipment.  Based on his age and interests please produce 3 great ways to teach him long division. 


If your son has a strong interest in trucks and construction equipment, incorporating these interests into his long division lessons may help him stay engaged and motivated. Here are three great ways to teach him long division:

  1. Use visual aids: Use pictures or diagrams of trucks and construction equipment to explain the steps of long division. For example, you could use a picture of a dump truck to represent the dividend and use smaller pictures of construction equipment, such as bulldozers or excavators, to represent the divisor and quotient. This can help your son visualize the process and make it more concrete and meaningful.
  1. Turn division into a game: Create a game that uses trucks and construction equipment to teach long division. For example, you could create a game where your son has to use a dump truck to carry a certain number of building materials and use excavators to divide the materials into equal groups. Each time he successfully divides the materials, he can move to the next level of the game. This can make the learning process more fun and interactive.
  1. Use real-life examples: Find examples of how trucks and construction equipment are used in real-life scenarios that involve division. For example, you could use examples of construction projects where trucks have to haul and divide large quantities of materials. This can help your son see the practical applications of division and make it more relevant to his interests.

Remember, every child learns differently, so it’s important to find an approach that works best for your son. Incorporating his interests and making learning fun can go a long way in helping him stay motivated and engaged in the learning process.


Assessment is the final interesting implication for tutoring.  At Pearl, we believe the key to tutoring is quick formative assessments that verify student learning and identify areas where knowledge or understanding might be lacking. Once again, this is a great way to leverage several variables in a question. The following are two examples that you may find interesting:


Please give me three formative assessment questions that will help me validate that my 9 year old student understands long division.


  1. Can you explain the steps involved in long division and how they relate to each other?
  1. Can you solve a long division problem independently and explain how you arrived at your answer?
  1. Can you apply long division to solve real-world problems and explain how you arrived at your answer?


Please provide me with 3 problems to solve that would assess my 9 year old’s understanding of the basics of long division.


  1. 84 ÷ 6 = ?
  1. 145 ÷ 5 = ?
  1. 232 ÷ 8 = ?

Note: These problems are designed to test the student’s understanding of the basic steps of long division. When assessing your student’s responses, make sure to check that they have correctly used the divisor, dividend, quotient, and remainder, and that their final answer is correct. It’s important to provide feedback on their work and address any areas where they may need additional support or practice.


When tutoring programs consider using ChatGPT or any of the other emerging large language model AI tools, they must validate the tools carefully.  It is possible to speed up certain processes, but there is a cost associated with it.  There is a possibility that AI is extrapolating biases that are not aligned with your ethics.  Additionally, tutors should cultivate value that reflects their approach to tutoring and not rely too heavily on computers.  Relationships are at the heart of tutoring, so it’s important to foster connections and care throughout the process. Nevertheless, keeping up with the changing world is essential.  There is no doubt that our students will benefit from this new tech; therefore, as educators to the next generation, we need to keep our eyes on the ball. 

Now, for kicks, let’s let ChatGPT have the final word ?


What are the implications of Large Language Machine Learning Models in Tutoring


Large language machine learning models have several potential implications in tutoring, including:

  1. Personalized Learning: These models can help provide more personalized learning experiences for students by tailoring the content and pace of the instruction to their individual needs, interests, and learning styles.
  1. Immediate Feedback: The models can provide immediate feedback to students on their work and progress, helping them identify areas where they need additional practice or support. This can help students stay engaged and motivated and can also help teachers more effectively monitor student progress.
  1. Automated Scoring and Grading: These models can help automate the grading and scoring of student work, freeing up time for teachers to focus on providing individualized instruction and support to students.
  1. Improved Accessibility: These models can help make tutoring more accessible to a wider range of students, including those with disabilities or who speak different languages.
  1. Improved Instructional Design: These models can help identify patterns in student learning and suggest improvements to instructional design and content, helping teachers to more effectively support student learning and improve outcomes.

However, there are also some potential challenges and limitations to the use of large language machine learning models in tutoring, including issues of privacy and data security, the potential for bias in the algorithms used to analyze and evaluate student work, and the need for teachers to be trained in how to effectively use and interpret the results of these models.

The End…

Breaking Down the 3 Benefits of District Partnerships in Higher Education

Breaking Down the 3 Benefits of District Partnerships in Higher Education

Across the nation, Community Partnerships are forming, where HigherEd institutions partner with local K/12 school districts to provide evidence-based tutoring to younger students in their backyard. These partnerships offer states and districts an interesting alternative to traditional, for-profit tutoring partnerships. These community partnerships offer 3 unique advantages that could lead to a greater impact on student success than traditional tutoring programs and a more sustainable tutoring model. 

  1. If designed correctly, these partnerships can help improve the academic achievement of K/12 students. Providing access to high-impact tutoring, which meets the standards set forth by the National Student Support Accelerator, gives students the individualized support they need to succeed in school and mitigate learning loss. This can help close the achievement gap and ensure that all students have an equal opportunity to succeed.
  1. These partnerships can provide valuable professional development opportunities for university students interested in careers in education and create a pipeline of talent for local districts. By participating in tutoring programs, university students can gain hands-on experience working with K/12 students and can develop the skills and knowledge they need to become effective educators. They will also build relationships with local principals and superintendents, leading to career opportunities in the schools and inevitably addressing the teacher shortage. Deans For Impact is a non-profit that is focusing on these partnerships and has developed specific training content for future educators who are participating in community-tutoring partnerships. 
  1. Last but certainly not least, these local partnerships between universities and local school districts can foster a sense of pride and ownership within the community. By working together to support the academic success of K/12 students, universities and schools can demonstrate their commitment to the local community and can help build stronger, more supportive relationships with families and other community members. Because both the university and district are in the same town/region, these programs can provide more culturally relevant instruction and mentorship to their students. We see this in the state of Virginia, where Urban Leagues are partnering with HBCUs to provide academic mentorship to students in local districts. This approach can help create a sense of shared responsibility for student success.

Overall, these sustainable partnerships between universities and local school districts can provide a wide range of benefits for K/12 students, university students, and the community as a whole. By providing evidence-based tutoring and other support, such partnerships can help improve academic achievement, develop a talent pipeline for future teachers, and create a more collaborative, supportive learning environment for all students.

Tutoring needs a standard approach to assessments

Tutoring needs a standard approach to assessments

The many variations across hundreds of tutoring RFPs

Each year our public school districts issue hundreds of requests for proposals (RFPs) for high-impact tutoring support, but there is no standard approach to collecting feedback and assessments of the tutoring. Many districts require their tutoring providers to administer academic assessments, while others do not. Here at Pearl, our partners typically follow one of these 4 approaches to assessments:

  1. The tutoring provider uses a specific assessment tool (like iReady or NWEA MAP) to assess students at all stages of a student’s tutoring experience.
  2. The tutoring provider assesses each student’s progress (choosing their preferred benchmark, formative, and other assessment types) with no specific assessment tool requirement other than the assessment questions used must meet the state’s ESSA standards.
  3. The tutoring provider supports students but is not required to administer the assessments. In this case, the district runs the assessments and advises the tutoring provider accordingly.
  4. A state-based program serving multiple districts is required to use different assessment tools depending on the requirements of each district they serve. In these cases, the state agency is also left with the complicated task of comparing district progress with varied types of outputs and insights from multiple assessment tools.

The Pearl platform provides the technical scaffolding for state and district tutoring programs, nonprofits, and enterprise-level tutoring companies. For each of these different client types, we take an agnostic approach to how academic progress is assessed. Sometimes our partners have their own assessments, like district-funded programs, and other times they simply follow the requirements defined in the contracts they serve, such as outside tutoring companies responding to RFPs. 

In a “best case” scenario tutoring providers would have: 

  1. A diagnostic or benchmark assessment from the student’s school (or district) to inform gaps in learning and insights into how that student learns most effectively
  2. A viable feedback loop with a student’s school teacher(s)
  3. A formative assessment tool that:
    1. Meets the applicable state ESSA standards
    2. Aligns with the school’s curriculum 
    3. Is agile enough to be used on the fly
    4. Provides a way to regularly measure student academic progress
    5. Informs clear opportunities for tutors to employ an individualized learning pathway for their students
  4. A final “end of tutoring” assessment to measure progress against the benchmark test

If more districts had the resources to increase their level of system interoperability, the above “best case” assessment pathway would be more widely viable. It is common for even sophisticated districts to lack an elegant method for merging assessment data from providers with SIS student records. In addition, tutors may be unable to access useful academic-related data because of security concerns and requirements.

In the absence of a standard assessment methodology, a host of problems arise. To determine learning gaps, tutors often need to conduct several sessions without a baseline. If a tutor does not use a formative assessment approach aligned with the student’s starting point, he or she may be left guessing about the student’s understanding gaps. Also with so many assessment approach variations, it is difficult to compare programs district-to-district or state-to-state. 

To develop a successful tutoring program, four key data categories are needed (attendance, dosage, academic progress measurement, and SEL insights). In the absence of academic progress that relates to individual students, tutor providers are unable to measure the true picture of progress or show clear ROI to stakeholders. With the final ESSER cliff fast approaching it is critical that programs robustly measure efficacy. Only programs that demonstrate evidence of effectiveness will be sustainably funded in the future. 

Tutoring Program Designs Addressing The COVID Education Recovery

Tutoring Program Designs Addressing The COVID Education Recovery

Various tutoring approaches are being implemented across the US to address learning loss.  The most common are high-impact tutoring, on-demand tutoring (sometimes referred to as homework help), and a hybrid model in which certain students in a program receive scheduled high-impact tutoring and the general student population has access to opt-in on-demand academic support. 

High-Impact-Tutoring (also sometimes referred to as High-Dosage-Tutoring)

A high-impact-tutoring model optimally leverages the NSSA’s “Tutor Quality Improvement System (TQIS) and include “substantial time each week of required tutoring, sustained and strong relationships between students and their tutors, close monitoring of student knowledge and skills, alignment with school curriculum, and oversight of tutors to assure quality interactions.”  Although this model has strong examples that are grant based and self administered at the state or district level, it can also be outsourced to a tutoring vendor. 

On-Demand Tutoring (also sometimes referred to as Homework Help)

24/7 access to tutors via chat and whiteboard across all key subjects is the most popular style of on-demand tutoring.  On-demand homework help (or as-needed academic assistance) allows students to meet with tutors as needed, often in a short time frame (like 10-15 minutes) to discuss something they do not understand – usually related to preparing for a test or completing homework (often due the next day).  Curriculum or learning management systems are not usually required in this model, although tools aligned with specific approaches may be used. Most often, this model is outsourced to an outside tutoring company, but some examples of this model are found in community tutoring partnerships, such as Dakota Dreams in South Dakota. 

Combining High-Impact-Tutoring and On-demand Tutoring (Hybrid)

On-demand homework help and high-impact tutoring are combined in the final model. In the early stages of exploring this hybrid model, vendors are scheduling out high-impact tutoring as usual but also giving students the option to receive on-demand help in other subjects online. Furthermore, on-demand access is being explored across an entire district in the same environment where high-impact tutoring is taking place. 

Deciding on Your Approach

An effective tutoring program should not be developed based on a best guess or someone’s perception of what is possible.  The design of a successful tutoring program should be based on logic.  According to the National Student Support Accelerator’s (NSSA) toolkit for program design, any tutoring program should outline: needs, inputs, actions, outputs, and impact. 

Needs: what needs the program address?

Inputs: what goes into the program?

Actions: what actions does the program take?

Outputs: what happens as a result of those actions?

Impact: What are the benefits of participating in the program?

Based on the logic model and clear definition of each component of the model, stakeholders can design a program for the desired outcome.  

VIRTUAL SUMMIT: "The Future of High-Impact Tutoring with AI" | June 5 from 12-3 pm ET
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