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Projects & Impact

We engage in both Grant-Funded and Institution-Commissioned Projects that utilize our Action Research Cycle to create innovative solutions.
Our current areas of focus are:

(1) Preparation for College-Level Mathematics

(2) Accessing Accurate College Knowledge

Our Action Research Process

Featured Projects

The SLAM Project:

The South Los Angeles Math (SLAM) Project launched in 2013 as an intersegmental partnership with LAUSD and Cal State LA to create an innovative solution to the college math readiness dilemma. The initial three-year pilot was funded by a grant from the Michael and Susan Dell Foundation.

Student Outcomes

1,102

Students Served

77%

Earned CSU Math Credit

Our Process

68% of first-time freshmen at Cal State LA place in remedial math courses.

SLAM researched the use of a dual-enrollment college Statistics course with support as an intervention strategy for high school seniors deemed at-risk for placing in remedial math courses using multiple measures. The university professor provided the college content on Mondays, Wednesdays, and Fridays; the high school teacher provided additional support on Tuesdays and Thursdays. The fall semester used Cal State LA’s MATH109 course; in the spring semester students transitioned into AP Statistics.

The initial 3-year pilot added one new cohort and one new high school each year for a total of six cohorts over three years.

In the first year at each high school, the teacher received 150 hours of job-embedded PD observing the college professor and students and collecting data. An additional 50 hours of collaboration outside of school time was provided with the professor, SLAM teachers, and College Bridge math expert team-grading assessments and lesson planning.

In the second year at each high school the high school teacher stepped into the professor role and a new high school teacher stepped into the support role. Both high school teachers were required to possess the minimum qualifications to teach at the university. The professor oversaw the instruction at schools in their second and third years. All grading continued to be done as a team in collaboration with the professor.

Key Findings from Initial 3-Year Pilot (six cohorts totaling 169 students)

  • SLAM students’ average college math remediation rate was 27%. The high schools in the study had CSU math remediation rates ranging from 46% – 83%; Cal State LA’s rate was 68%. SLAM’s target was a remediation rate below 30%. 
  • 91% of SLAM students matriculated to college and 93% persisted from year one to year two.
  • SLAM teaches students how to take responsibility for their learning. The final grade was mostly determined by a mid-term and final exam. Most students failed the mid-term. With each cohort we observed the same transformation take place where, after the mid-term, the students began forming study groups, seeking additional support from teachers, and studying during lunch and before and after school. 
  • Students must apply to be in the program. The SLAM project was designed to learn best practices for preparing students for college-level mathematics. Therefore, the student population must be students who are not prepared based on multiple measures. Further, the program requires hard work and students must be up for the challenges. We learned that an unprepared student who is placed in the program based on meeting set criteria will likely fail. Students, parents, counselors, teachers, and administrators must fully understand the purpose of the program and follow the Student Selection Process that was developed during the pilot.
  • SLAM changes students’ perception of themselves. Students who entered the program stating they are “bad at math” learned that they could be successful with hard work; students who entered the program thinking they were “good at math” learned that they had found it easy in the past because they had not yet been challenged.

Data Collection and Analysis

Both quantitative and qualitative data were collected to determine how well the students performed and why.

Student Selection Data using Multiple Measures

The quantitative data collected for student selection included course grades, SBAC scores, and GPA; teacher recommendations were the only qualitative data for student selection purposes but was ultimately found to be the strongest predictor of students success in the course.

Student Success Data

Quantitative data collected and analyzed for student success mid-term, final exam, and course grades were collected for quantitative analysis; data collected and analyzed included qualitative data consisted of field notes, student and teacher surveys, and teacher and professor interviews.

The evaluation report for year three can be viewed or downloaded below in the Research Reports section. See the M-PReP evaluation report for more information about the SLAM Project.

In year one, 75% of students passed the college class with all students reporting positively on their experience. We learned that the college course and AP Statistics were divergent in their approach and the positive affects the students reported from MATH 109 dissolved over time in AP Statistics. We changed the second semester into using the statistics learned from semester one to conduct a group research project.

Year two saw a decrease in pass rates that coincided with a district scheduling malfunction that placed students in the program who had the minimum qualifications but had not applied nor attended the student and parent orientation sessions. We modeled a formal student selection process on best practices and made the student and parent orientation mandatory.

In year three we learned that the teacher-teacher version to the job-embedded PD used in year two was not working. The alternative allowed a trained teacher-new teacher combination in place of the professor-teacher model. The newly trained teachers were not ready to train new teachers so that model was eliminated. The programmatic components were stabilizing with 82% of students passing with similar outcome across sites.

Testimonials

“I grew a lot - probably more than in my last ten years of teaching.”
~ SLAM Math Teacher
It helped me experience what a real college course would be like. The responsibility of showing up on time, having assignments ready, and being “present” in the class, is on me and only me.
~SLAM Student

Projects & Impact

We engage in both Grant-Funded and Institution-Commissioned Projects that utilize our Action Research Cycle to create innovative solutions.
Our current areas of focus are:

(1) Preparation for College-Level Mathematics

(2) Accessing Accurate College Knowledge

Action Research Cycle

Featured Projects

The SLAM Project:

The South Los Angeles Math (SLAM) Project launched in 2013 as an intersegmental partnership with LAUSD and Cal State LA to create an innovative solution to the college math readiness dilemma. The initial three-year pilot was funded by a grant from the Michael and Susan Dell Foundation.

Impact:

0
SLAM Students Served
0%
7% Earned CSU
GE B4 Math Credit

Our Process

1. Identify Solution

68% of First-Time Freshmen at Cal State LA Place in Remedial Math Courses

2. Develop Solution

SLAM researched the use of a dual-enrollment college Statistics course with support as an intervention strategy for at-risk high school seniors. The fall semester used Cal State LA’s MATH1090 course; in the spring semester students transitioned into AP Statistics.

3. Pilot Solution

The 3-year pilot added one new high school each year. In year one, the teacher received 150 hours of job-embedded PD co-teaching with a college professor. An additional 50 hours of collaboration outside of school time was provided with the professor, SLAM teachers, and College Bridge
math expert.

4. Evaluate

Data collected and analyzed included field notes, student and teacher surveys, teacher and professor interviews, and student assessments.

5. Revise

In year one, 75% of students passed the college class with all students reporting positively on their experience. We learned that the college course and AP Statistics were divergent in their approach and the positive affects the students reported from MATH1090 dissolved over time in AP Statistics. We changed the second semester into using the statistics learned from semester one to conduct a group research project.

Year two saw a decrease in pass rates that coincided with a district scheduling malfunction that placed students in the program who had the minimum qualifications but had not applied
nor attended the student and parent orientation sessions. We modeled a formal student selection process on best practices and made the student and parent orientation mandatory.

In year three we learned that the alternative to the job-embedded PD was not working. The alternative allowed a trained teachernew teacher combination in place of the professor-teacher model. The newly trained teachers were not ready to train new teachers so that model was eliminated. The programmatic components were stabilizing with 82% of students passing with similar outcome across sites.