Research shows that dropping out of school is not a singular event but rather the culmination of a long process of disengagement, and that mentoring can be effective in disrupting this progression. RISE helps students because it meets a basic need: there is someone who cares about them and is there to help them through their difficulties. At a critical time when obstacles may cause them to disengage from school, RISE mentors provide their mentees with someone to share their challenges, help them find solutions, and guide them in setting and achieving goals.

The latest RISE data using OLS regression shows that on average, comparing students who met at least 80% of the mentor relationship checklist to those who met less than 80%:

12%

HIGHER ATTENDENCE RECORD

6.4

HIGHER ATTENDENCE RECORD

8 points

HIGHER ATTENDENCE RECORD

5.7

HIGHER ATTENDENCE RECORD

EVALUATION RESULTS

STUDENT ATTENDANCE:

According to the OLS regression, on average,a student who met at least 80 percent on the relationship checklist would have 12 percent higher attendance record than a student who met less than 80 percent on the relationship checklist.

GRADES:

According to the OLS regression, on average, a student who met at least 80 percent on the relationship checklist would have a mean improvement in average math GPA of 6.4 points higher than a student who met less than 80 percent on the relationship checklist.

According to the OLS regression, on average, a student who met at least 80 percent on the relationship checklist would have a mean improvement in average ELA GPA of 8.0 points higher than a student who met less than 80 percent on the relationship checklist.

CREDIT ACCRUAL:

According to the OLS regression, on average, a student who met at least 80 percent on the relationship checklist would have a mean of 5.7 more credits earned than a student who met less than 80 percent on the relationship checklist.

So, on average, students who are active and successful participants in RISE are also gaining more credits, nearly 6, by the time they complete the program.

REGRESSION ANALYSIS

To better understand the relationship between success at program activities and academic performance

Conducted an ordinary-least-squares (OLS) regression analysis. Advanced analytical technique that allows us to understand whetherperformance on program indicators (relationship checklist, goal attainment, and positive feedback) predicts academic achievement during RISE.

Conducted a series of parallel analyses investigating the relation separately for school attendance, ELA grades, math grades, and credit accrual.

After controlling for attendance the prior marking period, success on program indicators was a significant predictor of student attendance, F(1,232) = 22.7, p < . 001.

Taken together, student performance on program indicators accounted for approximately 56% of the variation in student attendance, R2 = .56, p < .001.

After controlling for credit accrual the prior marking period, success on program indicators was a significant predictor of credit accrual during RISE, F(1, 237) = 57.3, p < . 001.

Similarly to attendance, student performance on program indicators accounted for approximately 25% of the variation in credit accrual, R2 = .25, p < .001, during RISE.

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