Sustainability of Change Initiatives in Schools.

Stakeholder Information​

The individuals involved in this change include not just the tutors, but the students and parents as well. The shifting needs of the student are driving the shifting requirements of the tutors. Additionally, tutor management must be included, as their management must incorporate two separate types of tutoring programs. The advisor role must also be considered as a stakeholder, as they are an additional liaison between RP and the family.

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Data Collection

Data is imperative to gauge not just the need for change but its effectiveness as well, and a mixed-methods approach that is suggested in previous research on the impact of the number of tutors on student achievement will be used here (Jerjes, 2023b). Achievement data will be collected by the individual tutors inputting students’ academic grades into the Learning Management System (LMS), and will be provisioned by the student or parent. Demographic data is collected loosely in the LMS in reference to location and school attendance (socio economic status), but more detailed demographic information such as gender identification, native language, religion, or race are not collected. Parents and students must choose to reveal any learning challenges, which is highly encouraged for program success.

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Program data is collected through tutor program notes entered into the LMS or shared via document to other tutors and management, and incorporates as much information about the students’ in-school programs as possible. Length of tutoring program will also be collected, as well as goals, velocity of classes, and qualitative improvement captured through parent communication. Perception data will be collected via one-on-one meetings and completed surveys and include information about lesson planning, confidence levels, perceived successes and challenges, and areas of need. While there are multiple studies on creating automated checks for session quality, the parameters are untenable for an organization of this size (Nye et al., 2015 and Cukurova et al., 2022). Therefore, spot-checks for class quality based on a four-metric rubric will be conducted manually.

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Data Visualization

Visualization of data will depend on the results and form they are received in, but will include variations of histograms and point graphs for quantitative data, as well as pie charts, tables, and histograms for coded qualitative data. Some data will be received via LMS outputs into Tableau and excel or csv files. Survey data will be received in excel output form, and much of the qualitative and program data will need to be manually coded.

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The style of visualization will be geared towards clearly representing need and impacts on individual stakeholders. For example, data informing on the increases in “multiple tutor” programs might be visualized on a line graph, whereas coded outputs from tutor surveys on a pie chart. Utilizing visuals prior to training, during the professional development, and as part of continuing feedback mechanisms will help engage all stakeholders in the value of implementing the change itself. Some examples of current data are below.

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