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The Effects of Study Skills Training
and Peer Coaching of At-Risk-
Students on Retention and Passing Rates
in a Remedial Mathematics Course
Leonid Khazanov Michael George
Annette Gourgey Chris McCarthy
1
Developmental Algebra ProjectBorough Of Manhattan Community College
2
What is Developmental Algebra?
1. First course in Algebra2. Zero credit course3. Placement4. Student challenges poor math and study
skills math anxiety lack of motivation
3
Developmental Algebra ProjectGoals and Treatment
Main Goals1. Reduce attrition rate2. Improve course pass rate
4
Developmental Algebra ProjectGoals and Treatment
Main Goals1. Reduce attrition rate2. Improve course pass rate
Treatment1. Incorporate Study Skills
2. Identify at-risk-students and assign them peer coaches
5
Developmental Algebra Project Rationale
Q. Why teach study skills?
A. Research shows that a large proportion of developmental students have poor study skills and high levels of math anxiety.
6
Developmental Algebra Project Rationale
Q. Why target at-risk students?
A. At-risk students have the highest failure and drop out rates. Successful treatment of at-risk students could have the greatest impacts on overall pass and retention rates.
7
Developmental Algebra Project
Implementation
Q. How were at-risk students identified?A. We developed an instrument to identify at risk students that had three components: (1) Arithmetic Diagnostic Test, (2) Student Survey, (3) Classroom Observation.
8
Developmental Algebra Project
Implementation
Q. What are coaches?
A. Coaches are successful math students at BMCC who assume the roles of both tutor and mentor.
9
Developmental Algebra Project
Implementation
Coach Responsibilities:
Meet with each of their students once or twice a week to:
1. Discuss progress 2. Review homework3. Prepare for tests4. Explain the material from previous classes if
necessary5. Discuss counter productive behaviors such as
absences and lateness (and how to avoid them in the future)
6. Help student to improve math study habits and skills
10
Developmental Algebra Project
Implementation
Coaches Submitted Weekly Reports:
Reports contain information about what was accomplished at each meeting.
11
Developmental Algebra Project
Coach’s Reports
Protégé’s name: TDate: Oct. 12 and Oct 14 2010Time: 10 am to 11 am
Summary of Meeting: In this week we checked the homework problem on the first-degree inequalities, and also on the first-degree equations. He has the first test and he gets 85% so I think this is a good score for the first time. We also review everything from the beginning of the school, because he is going to have test this week.
12
Developmental Algebra Project
Coach’s Reports
Protégé’s name: Mrs. JDate: November 01, 2010(Monday)Time: 11 AM- 12 PM
Summary of Meeting: Mrs. J was a little down on hope because she didn’t exceed well on her midterm. Her score was a 20%. Throughout the whole time of our meeting I gave her friendly inspirational advice. My advice to her was “Don’t let one test ruin your day and bring you down”. “You still have more chances to achieve in your class and I will work with you every step of the way to prepare you for your final exam”.
13
Developmental Algebra Project
Coach’s Reports
Protégé’s name: CDate: November 01, 2010(Monday)Time: 5:30 PM- 6:30 PM
Summary of Meeting: C is such a sweet and kind heart woman. She is serious about passing this course. Knowing she is intimated of the numbers I always told her the numbers are more afraid of us than we are to them… [She] did astounding showing her my quick techniques. Furthermore, she always comes prepare with her questions to be answer. I am always able to help her and give her hints on her homework handouts….
14
Developmental Algebra Project
Coach’s Reports
Protégé’s name: RDate: Wednesday, Dec 1Time: 12 – 2 pm
Summary of Meeting: R has lot problems with the majority of exercises from the final review sheet. I been telling that he has to practice more and put more time and effort. He needs to memorize all this rules in order to do each problem and pass the final. Also, I let him do each problem twice so he doesn’t forget how to do them.
15
Developmental Algebra Project Implementation
Q. How were coaches recruited?
1. Directly from pre-calc and calculus classes (classroom visit).
2. Recommendations of colleagues.
3. Word of mouth.
16
Developmental Algebra Project Implementation
Q. How were coaches trained?
CITI Certification (IRB)
3 hour training session
Ongoing training
17
Developmental Algebra Project Implementation
Coach Training Components
1. Study skills training.
2. How to tutor.
3. How to mentor.
18
Developmental Algebra Project Implementation
Q. How were instructors trained?
Three hour training session.
Study skills expert.
Math Study SkillsBook (Alan Bass)
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Developmental Algebra Project
Experimental Design
Five instructors were randomly selected from a list of BMCC math faculty who were scheduled to teach two sections of Elementary Algebra (Mat 051).
One of section from each instructor was randomly designated as control and the other treatment.
20
What was the treatment?1) Study Skills Training
Instructors incorporated a series of short lessons developed by a study skills expert: effective note taking, managing time, overcoming math anxiety, etc.
21
What was the treatment?2) Peer Coaching
Peer coaches met with each of their students once or twice a week to:
Discuss progress Review homework Prepare for tests Explain the material from previous classes if
necessary Discuss counter productive behaviors such as
absences and lateness (and how to avoid them in the future)
Help student to improve math study habits and skills
22
Developmental Algebra Project Outcomes: Treatment vs Control
Table 1.
Professor (group) Number of students
Number passed (%)
Number failed (%)
Number incomplete (%)
Number withdrawn (%)
1 (treatment) 26 3 (11%) 20 (77%) 3 (11%) 0 (0%)
1 (control) 24 8 (33%) 9 (28%) 4 (17%) 3 (13%)
2 (treatment) 26 9 (35%) 13 (50%) 3 (11%) 1 (4%)
2 (control) 25 7 (28%) 7 (28%) 5 (20%) 6 (24%)
3 (treatment) 26 4 (15%) 8 (31%) 6 (23%) 8 (31%)
3 (control) 27 10 (37%) 6 (22%) 2 (7%) 9 (33%)
4 (treatment) 27 16 (59%) 2 (7%) 4 (14%) 5 (19%)
4 (control) 27 14 (52%) 4 (15%) 3 (11%) 6 (22%)
5 (treatment) 24 15 (63%) 0 (0%) 6 (25%) 3 (13%)
5 (control) 25 3 (12%) 4 (16%) 10 (40%) 8 (32%)
Totals (treatment) 129 47 (36.4%) 43 (33.3%) 22 (17.1%) 17 (13.2%)
Totals (control) 128 42 (32.8%) 30 (23.4%) 24 (18.8%) 32 (25.0%)
Retention Results
23
Control Group
Treatment Group
Prof. 1 Prof. 2 Prof. 3 Prof. 4 Prof. 5
Pro
po
rtio
n o
f S
tud
en
ts R
eta
ine
d
0.0
0.2
0.4
0.6
0.8
1.0
Retention Proportions
Statistical Analysis: RESULTS ARE SIGNIFIGANT EVIDENCE THAT THE TREATMENT INCREASES RETENTION RATES
p-value = 0.01299 (2x2x5 Exact Test, Combinatorial)
Aggregated Retention Results
24
Control Group
Treatment Group
Statistical Analysis: RESULTS ARE SIGNIFIGANT EVIDENCE THAT THE TREATMENT INCREASES RETENTION RATES
p-value = 0.01212 (2-sample test for equality of proportions with continuity correction, Chi)
Control Treatment
Pro
po
rtio
n o
f Stu
de
nts
Re
tain
ed
0.0
0.2
0.4
0.6
0.8
1.0
Combined Data: Retention Proportions
75% of 128 students
87% of 129 students
Passing Rate Results
25
Control Group
Treatment Group
Statistical Analysis: RESULTS DO NOT PROVIDE SIGNIFIGANT EVIDENCE THAT THE TREATMENT INCREASES PASS RATES
p-value = 0.3013 (2x2x5 Exact Test, Combinatorial)
Prof. 1 Prof. 2 Prof. 3 Prof. 4 Prof. 5
Pa
ss R
ate
0.0
0.2
0.4
0.6
Pass Rate Proportions
Aggregated Passing Rate Results
26
Control Group
Treatment Group
Statistical Analysis: TREATMENT GROUP HAD A HIGHER PASSING RATE, BUT THE RESULTS DO NOT PROVIDE SIGNIFIGANT EVIDENCE THAT THE TREATMENT INCREASES PASS RATES.
p-value = 0.3160 (2-sample test for equality of proportions with continuity correction, Chi)
Control Treatment
Pro
po
rtio
n o
f P
assin
g S
tud
en
ts
0.0
0.1
0.2
0.3
0.4
0.5
Combined Data: Pass Rates
32.8% of 128 students36.4% of 129 students
27
Statistical Analysis: High Risk Students (4+ total risk points) have a lower passing rate (p=.0021). Method: 2-sample test for equality of proportions with continuity correction
0 to 3 4 to 6
Diagnostic Test: All Students
Total Risk Points
Pas
s R
ate
0.0
0.2
0.4
0.6
The Diagnostic Test we developed works
Passing Rates vs. Total Risk Points.
Aggregate data:
45% of students diagnosed as low-risk passed the class
24% diagnosed as high-risk passed class
28
Peer-coaching seems to have a positive effect on pass-rates after a threshold of
about 4 sessions.
Passing Rates vs. Number of Coaching Sessions Attended
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Number of meetings attended.
Proba
bility o
f Pass
ing0.0
0.20.4
0.60.8
1.0
1 / 2
0 / 1 0 / 4 0 / 1
2 / 5
1 / 1
4 / 6
2 / 2
1 / 3
1 / 2 1 / 2 1 / 2
0 / 1 0 / 0
1 / 2
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
P-value for Ha: Pass Rate w Coaching > Pass Rate Control.
Minimum number of meetings attended.
p-valu
e0.0
0.10.2
0.30.4
0.50.6
0.15270.1703
0.1396
0.0494 0.0352 0.02840.0471
0.1615
0.38160.3635
0.4454
0.5 0.5 0.5 0.5
29
Statistical Analysis: Peer Coaching Increase Pass Rates (p= 0.03356) (2x2x4 Cochran-Mantel-Haenszel Test for Count Data, Exact Test, Combinatorial)
Peer coaching (mentoring) works.
30
Peer coaching (mentoring) works - cost issues:
30/100 = 30% high-risk students passed without peer coaching
14/26 = 54% high-risk students passed with peer coaching
Roughly speaking, for every 5 high-risk students treated
we expect to get
1 extra student pass.
If it costs $100 per treatment, that is $500 per additional pass.
31
Borough of ManhattanCommunity College
What is next? CUNY
It appears that peer coaching is an effective aspect of intervention for at-risk-students.
Next step: finding cost effective ways to provide peer coaches to more students.
Contact Information: Leonid Khazanov:
[email protected] George:
[email protected] McCarthy: