Outline. Background and Informadon on scaling Disjuncdve Placement. Results of .... Trigonometry, or Sta?s?cs BY HS GPA;
TMI: Transforma-on through Messaging and Integra-on of Placement Changes
MATHEMATICS DEPARTMENT
Saburo Matsumoto, Dept. Chair Joe Gerda, Professor
INSTITUTIONAL RESEARCH, PLANNING & INSTITUTIONAL EFFECTIVENESS
Daylene Meuschke, Dean Preeta Saxena, Sr. Analyst
Outline Background and Informa/on on scaling Disjunc/ve Placement
Results of placement Speed Bumps-‐Adjus/ng and readjus/ng
Integra5ng Math faculty in Data explora5ons “Data Dives” Integra/on for broader efforts (Canyons Completes) Integra-ng Student Services in Data explora-ons “Why does Math placement maAer in my advisement?” Integra-ng Students: Messaging
Changes and Timeline 2012 -‐2015 Fall
2016 Fall
Accelerated Pathways
Improved Placement
• Created a sta-s-cs pathway • Pre-‐Sta-s-cs
• Mul-ple Measures • Disjunc-ve • Two Placements
(2 levels à 1LBT) • Removed Arithme-c
Current Math Sequence (2015-‐present) Elem. Algebra
Arithme5c
Int. Algebra
STEM Transfer Courses
Pre-‐ Algebra
Sta/s/cs
Moved to non-‐credit
Int. Algebra For Stats Accelerated Pathways
Improved Placement What we know about Accuplacer? Self-‐reported • H.S. GPA • Last Math Course • Grade in last Math course
Direct Placement in Sta/s/cs Floors (STEM) Accuplacer Score + Mul/ple Measure weight (13% to 20%)
Two Placements Students receive up to 2 placements
1. Direct Placement in Sta>s>cs* q HS informa>on
2. STEM Placement
q Floor (HS Math course & grade) and/or q Accuplacer score + MM weights
*Everyone goes through Accuplacer Assessment
Improved Placement
2011
2015
2016
Transfe r Interm. Alg. Elem. Alg. Alg. Prep.
Arithme-c
STEM Disjunc-ve
STATISTICS Disjunc-ve
(n=4363)
(n=4363)
Impact: Transfer Comple-on in 1 term 600
40.0%
497
500
29.4%
30.0%
400 300
16.6%
258
20.0%
200 10.0% 100 0
0.0%
incoming Fall 2015 (n=1557)
incoming Fall 2016 (n= 1691)
Transfer Comple-on 1 semester by Race/Ethnicity 60%
33% 22% 7%
9%
15%
La/nx/ Hispanic African Am./ Black
37%
37%
43%
17%
White
Asian
Filipinx
2015 (n) 2016 (n) La-nx 748 858 African Am./Black 74 75 White 550 516 Asian 78 110 Filipinx 81 91
Speed Bumps on the way to Accelera/on Collect data, monitor data and make adjustments
• Intermediate Algebra • Pathway Problem
Success in Below-‐Transfer courses
Placed vs. Others
100%
90%
80%
Average Success Rate
80%
70%
55%
60%
50%
48%
57%
62%
68%
49% 48%
47%
40%
32%
30%
20%
10%
0%
Algebra Prep.
Elem. Algebra
Geometry
Intermediate Alg. for Sta(s(cs
Intermediate Algebra
Integra(ng Math faculty in Data explora(ons “Data Dives”
Adjus5ng our speed LEVEL 1
• Moved ‘floor’ to lower level
MATH 058 Algebra Prepara-on (5 units)
LEVEL 3 MATH075 Intermediate Algebra for Sta-s-cs (5 units)
MATH 111 Finite Math (4 units) MATH 130 Elementary Teachers (3 units) MATH 140 Introductory Sta-s-cs (4 units) PSYCH 104/ SOCI 137 Sta-s-cs for the Social & Behav. Sciences (3 units) ECON 291 Sta-s-cal Method in Business & Econ (3 units)
• Re-‐assessed Data
MATH 060 Elementary Algebra (5 units)
MATH070 Intermediate Algebra (5 units)
MATH083 Geometry (5 units)
LEVEL 2
MATH 103 College Algebra (4 units)
MATH 102 Trig (3 units)
MATH 240 Math Analysis for Business & Social Science (5 units)
MATH 104 PreCalculus (5 units)
MATH 211 Calculus I (5 units)
‘Floor’ Adjustment Success in Intermediate Algebra 31% to 51% Placed
Others 46%
50%
46%
31%
Fall 2016
Fall 2017
Placers (N)
Other (N)
Fall 2016
414
593
Fall 2017
211
622
Course Success Rates in Intermediate Algebra by GPA 63%
18%
[2.0, 2.7) (n=121)
24%
[2.7,3.0) (n=132)
38%
[3.0, 3.5) (n=162)
[3.5, 4.0] (n=57)
Floor’ students based on Grade of C or beAer in HS Algebra 2, Trigonometry, or Sta-s-cs BY HS GPA; (Placed 2016 and enrolled in Math 070 in 2016-‐17 , Fall, Winter, Spring)
Re-‐Adjus5ng our Speed • Math faculty decided that 3.5 GPA students were allowed back in to Intermediate Algebra 63% 18% [2.0, 2.7) (n=121)
24%
[2.7,3.0) (n=132)
38%
[3.0, 3.5) (n=162)
[3.5, 4.0] (n=57)
“CANYONS COMPLETES” “(IE)2’s Canyons Completes ini(a(ve is designed to facilitate posi(ve movement towards comple(on of degrees, cer(ficates, and skills building courses for students through improved programs, processes and services.”
(IE)2 reviews student success data and performance indicators in order to iden-fy opportuni-es to support student success. The three-‐year Canyons Completes workplan will iden-fy strategies to meet or exceed performance set standards, with ac-vi-es par-cularly targeted toward comple-on.
Implement Peer Check-‐ins
Re-‐Engineer Early Alert
Increase Career Explora/on
Curricular Mapping and Meta-‐Majors
Develop Equity-‐Minded Prac//oners
Enhance Noncredit Program
Improve
Communica/on
to Students
18
Which should students be choosing? Which are students choosing? Sta/s/cs vs. STEM pathway is primarily determined by: • Program of Study • Transfer Ins-tu-on • Math Placement
Which pathway are students pursuing? Fall 2017 Ac-ve Students (N=16530)
• 21 % are pursuing STEM
As of September 15, 2017
STEM 21% NON-‐ STEM 79%
STEM includes: Biology, Computer Science, Geography, Geology, Physics, Engineering, Mathema-cs. Note: Chemistry and Astronomy are considered STEM but are not program majors.
Which pathway are students comple5ng degrees in? Nursing STEM
• 11% of all Degrees awarded are in STEM
(n=103), 5.9%
(n=200), 11.3%
Non-‐STEM* (n=1456), 82.8%
Degree Completers 2016-‐17 (N=1759)
Where are they star/ng?
FIRST PROGRAM
DEGREE PROGRAM*
Transfer-‐Level STEM placements for 2016-‐17 degree completers DEGREE PROGRAM*
FIRST PROGRAM
NON-‐STEM 545 students
424 (78%) 52 (51%)
NON-‐STEM 476 students
95( 17%) STEM 99
45 (45%)
students
2 (2%)
STEM 140 students
26 (5%) 28 students
Below Transfer-‐Level STEM placements for 2016-‐17 degree completers FIRST PROGRAM DEGREE PROGRAM* NON-‐STEM 801 students
NON-‐STEM 784 students
745 (93%) 39 (74%)
39 (5%) STEM 53
students
STEM
11 (21%) 3 (6%)
50 students
17 (2%) Nursing 20 students
Mapping trajectory to Math placement: NON-‐STEM to STEM DEGREE PROGRAM* FIRST PROGRAM 1,169 NON-‐STEM NON-‐STEM 1,346 students
134 STEM
Of students who started in NON-‐STEM and ended in NON-‐STEM, 64% placed below transfer. Of students who started in NON-‐STEM and ended in STEM, 30% placed in below transfer.
Mapping trajectory to Math placement: STEM to NON-‐STEM DEGREE PROGRAM*
FIRST PROGRAM
91 NON-‐STEM 91 students STEM 152
students
56
STEM Total 56 students
Of students who started in STEM and ended in NON-‐STEM, 43% placed below transfer. Of students who started in STEM and ended in STEM, 20% placed in below transfer.
48 students went to Nursing
Takeaways Super Majority are NON-‐STEM (completers and ac/ve) Students who place below transfer… • 90% NON-‐STEM starters end up in NON-‐STEM • 75% STEM starters end up in NON-‐STEM If you start in STEM but place below transfer level, likelihood of comple/ng STEM degree is about 21%.
PATHWAY PROBLEM
What percent of the students who were given the op(on, chose: STEM Math-‐058
OR
Sta/s/cs Math-‐140
2016-‐17 Placements: All received Direct Placement
FIRST COURSE ENROLLED
ALL received direct placement into Transfer Sta/s/cs
N=1,365
Messaging • Counselors have this informa-on Not all students see counselors Very few see counselors at the beginning • How do we convey this informa-on? • Emails to students • Assessment Center’s role shin to advising • Who needs to know about, and relay this informa-on?
How are you, if you are, considering Math placement in your Guided Pathways efforts?
How do we guide students?
Joseph Gerda
Mathema-cs Professor
[email protected]
Saburo Matsumoto Mathema-cs Department Chair
[email protected]
Daylene Meuschke Resources:
Saxena, P., Meuschke, D. M. & Gribbons, B. C. (2017, February). Research Brief #120 Math Disjunc(ve-‐Mixed Placement. College of the Canyons; Santa Clarita, CA.
Saxena, P., Meuschke, D. M. & Gribbons, B. C. (2016, December). Research Brief #131 Math Disjunc(ve Placement Highlights. College of the Canyons; Santa Clarita, CA. Saxena, P., Meuschke, D. M. & Gribbons, B. C. (2018, forthcoming). Research Brief #134 Math Disjunc(ve Placement Success. College of the Canyons; Santa Clarita, CA.
Ins-tu-onal Research, Planning and Ins-tu-onal Effec-veness, Dean
[email protected] Preeta Saxena
Ins-tu-onal Research, Sr. Research Analyst
[email protected] 