Support Systems Needed to Expand Successful High-Dosage Tutoring Programs

Many states and school districts are eager to scale up high-dosage tutoring (HDT) programs—a proven way to accelerate student learning—particularly in the wake of disruptions caused by the COVID-19 pandemic. The intervention usually involves three or more tutoring sessions per week with groups of three or four students during the school day. But prior research as well as recent reports from the field have documented the challenges of getting these programs successfully off the ground, including recruiting tutors in a tight labor market, fitting sessions into already packed school days, and ensuring consistent student participation. To help practitioners address those challenges and ramp up their HDT programs, the University of Chicago’s Education Lab and MDRC launched the Personalized Learning Initiative (PLI) in 2022, in collaboration with a consortium of researchers and funders. PLI is studying and supporting the expansion of HDT programs in school districts across the country. As part of that project, members of the PLI research team have had the opportunity to speak with multiple school districts and tutoring vendors that have already been trying to implement such programs on a rapid timeline.

So far, the research team has observed localities that have encountered challenges that have delayed on-time rollout of full-scale HDT, in some cases by a semester or more. What has held back those efforts? In part, the answer is limited infrastructure to support the scheduling, technology, data entry, and staffing required to launch and manage a large new tutoring initiative. Putting these supports into place takes a significant amount of time and planning and requires a substantial effort at the district and school levels.

This post shares what the PLI team has learned so far about what it takes to build this infrastructure, along with a planning tool that districts can use as they set out to implement HDT programs. The post refers specifically to districts, but the information is applicable to other types of education agencies managing a tutoring initiative, such as states or charter networks. The National Student Support Accelerator also offers detailed planning resources that complement the tool offered here.

Building a Tutoring Support Infrastructure

Based on the research team’s observations and conversations with practitioners in the field, here are three broad sets of tasks that district administrators might consider as they work toward launching or scaling up their tutoring programs. As shown in Table 1, the tasks fall into three phases: Prepare, Launch, and Monitor.

Table 1. Key Steps for Creating and Maintaining an Infrastructure to Support Tutoring

Step When What Who


Starting in the fall of the prior school year before launch, and ongoing to support tutor recruitment

  • Establish the goals of the tutoring program and build buy-in among all stakeholders.
  • Identify and secure funding.
  • Define criteria for student eligibility.
  • Design program requirements that are aligned with evidence (dosage, student/tutor ratio, setting, curriculum).
  • Develop systems and procedures to hire, vet, and train tutors and key personnel. If using vendors, agree on a contract.
  • Secure and install data system to monitor student attendance and progress.
  • Define clear roles and communication routines for key program partners (for example, district staff, vendor staff, school staff).

District staff members, with key input from schools and communities


The spring or summer before program rollout, revisited each school year

  • Identify students for tutoring and plan to refresh that list with new school entries in the fall.
  • Hire tutors with appropriate qualifications and availability; check in on vendor progress in hiring if using a vendor.
  • Create a tutoring schedule alongside the master school schedule and include time for tutor prep and tutor-teacher collaboration.
  • Obtain necessary physical resources, including technology, materials, and facility space.
  • Identify school-based staff members to supervise tutors and the tutoring program. Ensure these staff members have time in their schedules to manage the program effectively.
  • Train the tutors. Topics may include instructional materials and effective strategies for delivery, developing strong student-tutor relationships, and use of data systems.
  • Provide teachers with an orientation about the program.
  • Communicate with families about the program.

School leaders (or their designees), with support from district and vendor staff members


During program operation

  • Ensure tutors’ daily entry of student attendance and progress into data system.
  • Observe tutoring to ensure adherence to the program model and instructional quality.
  • Regularly review student participation and progress data and share with appropriate district, vendor, and school staff members (including teachers) to support program improvement and collaboration.
  • Facilitate tutors’ participation in ongoing professional development.
  • Update families on the tutoring program.

School leaders (or their designees), with support from vendors and district staff members


Developing buy-In. While the tutoring model parameters may be refined over time, the initial vision needs to align with other district priorities if it is to be sustained. Building the needed consensus among district leaders and specific district offices that will interact with the tutoring program (for example, offices associated with teaching and learning, data collection, and the delivery of student support services) often takes months to solidify. At the same time, given the central role of school building personnel in the success of any tutoring program, support from school leadership early on is also important.

Hiring tutors. Developing a plan for hiring and training qualified tutors is an essential but challenging step. Many of the districts the research team has been talking to are using outside vendors to supply tutors, and doing that effectively takes time. Vendors must be vetted and may need to undergo a lengthy centralized procurement process. Next, the districts should consider which student data platforms the tutors will need to access to inform their work and to support program monitoring efforts. Getting clearance for tutors, especially those hired by external vendors, to access student data systems, and managing the integration of vendor data systems with a district’s data ecosystem takes time and requires careful consideration.


Scheduling. HDT programs are most successful when students have dedicated times in the school day set aside for tutoring. Districts and schools the research team has talked to have faced challenges in adhering to this best practice. For example, because of delays associated with tasks in the Prepare phase (such as tutor hiring and training), programs are often not ready to begin until a few weeks or even months into the school year. School schedules are already complicated to create to begin with, and it can be hard for schools to adjust after the start of the year to accommodate an intervention that requires a significant amount of time for participating students. Last-minute and unexpected scheduling changes to make room for tutoring sessions can mean that scheduling feels haphazard to teachers and students and thus can affect support for the intervention.


Entering data. To properly manage an HDT program, schools and district staff members need real-time data on student participation and progress to enable mid-course adjustments. But getting actionable and accurate data isn’t a given. Tutors and vendors often do not have access to a school’s internal learning management or student information system. And even when they do, tutors may not consistently record the necessary information. Indeed, as the team has observed, data entry is often one of the first tasks to get skipped by tutors when their time with students is already short. Even when a data system is in place, inconsistent data entry can cloud the picture of student attendance and progress. To address this challenge, tutors need access to user-friendly data systems, training on data entry, and time in their day to complete it. Further incentives and support may also be necessary to promote consistent data entry. Finally, school leaders charged with overseeing the initiative need to be encouraged and supported to monitor data regularly and to intervene when implementation is not at expected levels. Aligning management of the tutoring program with preexisting school management teams that regularly review data (for example, teams charged with delivering intervention services more generally) may help.

Looking Ahead

While HDT may appear to be a simple intervention—tutors offering personalized instruction to students—the PLI research team is seeing that significant time and resources to prepare, launch, and manage an infrastructure around the tutors is needed for a program to be implemented at scale. As part of its ongoing work on the PLI project, the research team will continue to support PLI partner localities in developing and sustaining this infrastructure. The team will also document and share key lessons learned from efforts to build and harness these support systems in schools and districts across the country.

Document Details

Publication Type
Issue Focus
July 2023
Condliffe, Barbara, Rebecca Davis, and Jean Grossman. 2023. “Support Systems Needed to Expand Successful High-Dosage Tutoring Programs.” New York: MDRC.