Tutoring Analytics Framework

Establishes baseline operational tracking to ensure reporting compliance, resource allocation, and basic program accountability. Key metrics include:

• 1.1 Student Visits Tracking: By visit type, location, subject, professor, college/school, tutor, etc.
• 1.2 Student Hours Logged: By type of service, modality, duration, etc.
• 1.3 Utilization Trends: Peak times, wait-times, no-show and cancelation rates.
• 1.4 Courses & Subjects Served: Active inventory aligned with gateway priorities.
• 1.5 D/F/W Rates: Tracked by course, subject, and academic discipline.
• 1.6 Unfilled Tutoring Requests: Logged by course/term to predict demand and hiring needs.
• 1.7 Longitudinal Participation Trends: Semester-to-semester and year-to-year volume growth.
• 1.8 Participant Course Grades & GPAs: Disaggregated by class section (e.g., BIOL 101) and discipline.
• 1.9 Student Demographic Utilization: Race/ethnicity, sex, first-generation status, and Pell grant eligibility.
• 1.10 Tutor Staff Diversity & Demographics: Staff profile evaluation for inclusive representation.
• 1.11 Survey Data & Testimonials: Quantitative feedback and qualitative themes comparing participants vs. non-participants.

Moves beyond raw numbers to compare participant vs. non-participant outcomes, determine intervention efficacy, and measure cognitive/affective growth. Key metrics include:

• 2.1 Participant vs. Non-Participant Grade Analysis: Aggregate comparisons by course, support type, sex, ethnicity, first-gen, and Pell status.
• 2.2 Student Population Sub-Group Comparisons: Usage and performance across entry cohorts (HS GPA bands, test scores, residency).
• 2.3 Support Modality Performance Analysis: In-person vs. online, individual vs. group, PAL/SI, drop-in vs. scheduled.
• 2.4 Retention & Graduation Rate Comparisons: Multi-term persistence and 4/6-year graduation differentials.
• 2.5 Transitional Coursework Success Rates: Developmental and co-requisite intervention pass rates disaggregated by population.
• 2.6 Tutor Staff Cognitive & Affective Growth: Personal, professional, and leadership development of student tutors.
• 2.7 Dosage Threshold Benchmarking: Minimum tutoring contact hours needed for statistically significant grade gains.
• 2.8 Cohort Penetration Rate & Impact: Proportion of high-need target populations actively served.
• 2.9 Academic Progression & Credit Velocity: Earned credit hours and degree milestone progression.
• 2.10 Early Alert Conversion Rate Analysis: Sequential logistic regression evaluating early alert student referrals.
• 2.11 Direct Assessment of Learning Outcomes: Cognitive and metacognitive skill gains, concept mastery, and problem-solving confidence.

Applies econometric modeling, quasi-experimental controls, predictive analytics, and financial ROI evaluation. Key metrics include:

• 3.1 Systemic Equity Analysis: Historically marginalized population performance compared to white student baselines.
• 3.2 Marginalized Participant Cohort Performance: Low-income/Pell, underserved, and first-gen participant outcomes vs. non-participants.
• 3.3 Course Failure Pattern Clustering: Identifying systemic course failure sequences and co-enrollment bottleneck pairs.
• 3.4 English as an Additional Language (EAL) Performance: Pass rates, language acquisition, and persistence for EAL participants.
• 3.5 Cost-Effectiveness & Retained Tuition Revenue: Cost per participant and net tuition dollars preserved through retention.
• 3.6 Predictive Outreach & Decision-Tree Modeling: Identifying student sub-profiles most likely to respond to targeted outreach.
• 3.7 Embedded Tutoring Impact Assessment: Gateway active-learning courses compared to traditional lecture sections.
• 3.8 Propensity Score Matching (PSM): Quasi-experimental matching to control for self-selection bias and isolate true treatment effect.
• 3.9 Repeat-Attempt Control & ANCOVA Modeling: Evaluating course repeaters utilizing tutoring vs. matched non-participant repeaters.
• 3.10 Time-Series ARIMA Supply/Demand Forecasting: Modeling hourly and term demand trends to optimize staffing and budgets.
• 3.11 Relative Course Grade Modeling: Controlling for covariates (GPA, test scores, demographics) to measure impact in rigorous STEM courses.