Peter Dwight Business Analyst Office Ins1tu1onal Effec1veness Pasadena City College

Transcription

1 Peter Dwight Business Analyst Office Ins1tu1onal Effec1veness Pasadena City College Dus-n Tamashiro Sr. Research and Planning Analyst Office Ins1tu1onal Effec1veness Pasadena City College

2 Agenda Ø Framing the Problem Ø Our Method/Process Ø The Data Ø The Tool (live Tableau demo) Ø The Student Focus Ø Future Possibili1es Ø Q & A Framing the Problem Our Process The Data The Tool (Tableau) Student Focus The Future Q & A

3 Hits A.empts Interest Demand??? Reasons and Requests Ø Waitlists Ø Impacted Courses Ø BoLleneck Courses Ø Declining Enrollment Ø Accessibility Ø Outcomes/Milestones Ø Time to Degree Ø Why are they dropping? Ø AB- 705 & the Possible New Funding Model

4 Waitlists ( huh! yeah! what is it good for? ) Ø Only 7 waitlist slots per sec1on, on average Ø Being on a waitlist provides false assurances of enrollment Ø Students must remove themselves from a waitlist in order to add another sec1on of same course Ø A generally subop1mal method for gauging interest and demand for a course PCC s model has tradi1onally been Waitlists + Historical Enrollment Trends + Anecdotal Input

5 Our process showcases the power of registra1on audit tables - - an untapped resource rou1nely collected behind the scenes - - as a tool for understanding student interest and course demand beyond what eventually becomes part of the college's and the student s official record.

6 Student Course Table vs RegistraFon Audit Table ( SFRSTCR <> SFRSTCA ) SFRSTCR: contains only current student registra1on status records; only records valid registra1on statuses from the first day of class onward. Basic data. SFRSTCA: contains all student registra1on transac1on records; transac1on records are sequenced, 1mestamped, and recorded with enough detail to fully reconstruct a student s enrolls, drops, and unsuccessful registra1on alempts due to pre/co- requisite requirements, retake limits, waitlist statuses, and closed/full/cancelled courses at any point in 1me. Lots and lots and lots of data.

7 Fall 2016 Total ALempts/Hits: 192,190 ALempts/Hits Resul1ng in Enrollment: 99,316 ALempts/Hits With Prior Issue(s) Resul1ng in Enrollment: 5,485 ALempts/Hits Rela1ng to Unmet Demand (eligible students): 15,787 ALempts/Hits Resul1ng from Legit Block (ineligible students): 71,602

8 SFRSTCA_RMSG_CDE (RegistraFon Message Code) Student Issues (ineligible students) Unmet Demand PREQ/CORQ: Prerequisite/Corequisite/Test Score error Ø Ini1al focus (Unmet Demand). TIME: Time conflict with CRN VADT: Census date prohibits registra1on REPH/REPT: Repeat hours exceeded MAXI: Maximum hours exceeded DUPL/MEXC: Duplicate Course / Mutual Exclusion Ø Crea1ng a Hierarchy for grouping each alempt. Ø Student Issue or College Structural Barrier? Ø Gauging the deficit CHRT/SAPR/ATTS/LEVL/PROG/STAT: Course Restric1on Ø Who are these students? College Issues (eligible students unmet demand) WLRS: Open - Reserved for Wait List WLNE: Waitlist No1fica1on Expired WAIT: Closed Waitlisted WAIF: Closed - Waitlist Full CLOS: Closed Sec1on Framing the Problem Our Process The Data The Tool (Tableau) Student Focus The Future Q & A

9 Collegewide - Fall 2016 Enrolled w/o Issue Registra-on ANempts Enrolled w/ Issue Unmet Demand Total 99,316 5,485 15, , % 4.5% 13.1% Students Unmet Demand All PCC 6,911 32, % (students who encountered at least one college- related block)

10 Division Level - Fall 2016 Registra-on ANempts Enrolled w/o Issue Enrolled w/ Issue Unmet Demand Social Sciences 18, % 1, % 4, % 24,053 Mathema1cs and Computer Science 12, % 1, % 3, % 16,909 Natural Sciences 8, % % 2, % 11,316 English 15, % 1, % 1, % 18,100 Visual Arts & Media Studies 5, % % % 6,695 Kinesiology, Health and Athle1cs 4, % % % 5,564 Business Computer Technology 7, % % % 7,880 Performing and Communica1on Arts 7, % % % 8,706 Languages 5, % % % 6,444 Counseling 5, % % % 6,413 Engineering & Technology 1, % % % 1,230 Noncredit 4, % % % 4,915 Health Sciences 2, % % % 2,177 Library % 4 2.2% 5 2.7% 186 Total Students Unmet Demand 2,173 2,067 1,393 1,

11 Top Ten Subjects - Fall 2016 Enrolled w/o Issue Registra-on ANempts Enrolled w/ Issue Unmet Demand Total MATH 10, % 1, % 3, % 14,476 ENGL 15, % 1, % 1, % 18,100 PSYC 3, % % % 4,364 HIST 3, % % % 3,975 CHEM 1, % % % 2,264 ART 2, % % % 3,404 POLS 2, % % % 3,577 KINA 1, % % % 2,216 BIOL 2, % % % 2,569 ANAT % % % 1,244 Students Unmet Demand 1,786 1,

12 Top Ten Courses - Fall 2016 Enrolled w/o Issue Registra-on ANempts Enrolled w/ Issue Unmet Demand Total MATH131 2, % % 1, % 4,323 ENGL001A 3, % % % 4,840 MATH125 1, % % % 2,599 POLS001 2, % % % 3,361 ANAT % % % 1,228 PSYC001 1, % % % 2,423 SOC001 2, % % % 2,651 MATH % % % 813 HIST007A 1, % % % 1,663 ENGL001C % % % 1,284 Students Unmet Demand

13 Tabluea ewe?? uu hlps://public.tableau.com/views/math125_tableau_master- ALL/Dashboard1?:embed=y&:display_count=yes Or hlp://goo.gl/ej8b2f

14 6AM to 11PM

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16 2500 Students A.empFng to Register Fall 2016: MATH ,191 Students ALemp1ng to Add 1,141 1, Seats? Students Who Successfully Added 54% 0

17 Fall Registrants by Gender F M MATH125 Total Female Male by Ethnicity H A W X B T P N Registrants MATH125 2,191 Total 1,133 Hispanic 1,058 Asian White Unknown Black Mul1 (ALempted) (100%) (51.7%) (48.3%) Pacific Islander Na1ve American Registrants Registrants 1,173 (Successful) (ALempted) (100%) 2, (100%) (52.8%) 1, (68.9%) (47.2%) 298 (13.6%) 237 (10.8%) 39 (1.8%) 86 (3.9%) 42 (1.9%) 2 (0.1%) - - % Successful Registrants 53.5% 1, % 52.4% 822 (Successful) (100%) (70.1%) 145 (12.4%) 130 (11.1%) 18 (1.5%) 35 (3.0%) 22 (1.9%) 1 (0.1%) - - % Successful 53.5% 55.3% 48.7% 54.9% 46.2% 40.7% 52.4% 50.0% - -

18 Projected Impact of ShiU Count of ALL Students Interested in Enrolling (in Math125) Fall 2016 A B H P T W X MATH125 Asian Black Hispanic Pacific Is Mul1 White Unknown Total F ,133 M ,058 Total , ,191 Count of Students Interested (Unmet Demand) Fall 2016 A B H P T W X MATH125 Asian Black Hispanic Pacific Is Mul1 White Unknown Total F M Total

19 Projected Percent of Interested Students That Would ve Enrolled, if Unmet Demand Category Students Were Not Blocked NOTE: General Linear Model for Propor1ons Applied formula predic1ng average fill rate from course- by- issue headcounts, periods, and whether outcomes traced back to a College or Student Issue (e.g., CLOS, WAIT, WAIF, MAXI; coded 0/1). Fall 2016 A B H P T W X Total F 64% 58% 61% 0% 50% 62% 59% 59% M 60% 58% 60% 100% 59% 59% 65% 59% Total 61% 57% 59% 100% 55% 60% 56% 56% Percent of Interested Students Who Actually Enrolled Fall 2016 A B H P T W X Total F 46% 36% 57% 0% 45% 60% 55% 55% M 50% 48% 54% 50% 59% 49% 35% 52% Total 49% 41% 55% 50% 52% 55% 46% 54% Projected Percentage Point Difference (+/- ) Due to Change Fall 2016 A B H P T W X Total F 18% 23% 4% 0% 5% 2% 5% 5% M 9% 6% 4% 0% 0% 12% 12% 6% Total 13% 16% 3% 50% 2% 5% 10% 3% Projected Enrollment Increase 64 3%

20 Future PossibiliFes Ø Ed Plans - Counseling, Student Services Ø Linking Comprehensive Ed Plans, DegreeWorks, SSSP Ø Using priority registra1on or interven1ons Ø FTES - Enrollment Management, Scheduling Ø New Forecas1ng methods/strategies Ø Waitlists recognized as a holding space for a course rather than a tool for gauging demand Ø Real- Time monitoring of unmet demand before classes even start Ø True Transparency for making data- driven decisions Ø Student Readiness - Counseling, Student Services Ø Understanding Ineligible Student ALempts Ø Focus on for improving issues of Customer Service, Front- End User, or Actual Curriculum

21 RelaFon to Ongoing IniFaFves Course schedules are unpredictable and o3en set to accommodate college needs, not student needs. (CCRC What We Know About Guided Pathways: Cafeteria Model (Status Quo)) Does the way we schedule courses enable students to take courses when they need them, plan their lives around school from one term to the next, and complete their programs (IEPI: Guided Pathways: Keeping Students on Path: Reflec1ve Ques1ons) The board of governors may establish governing the use of these and other measures, instruments, and placement models to ensure that the measures, instruments, and placement models selected by a community college demonstrate that they guide English and mathema@cs placements to achieve the goal of maximizing the probability that a student will enter and complete transfer- level coursework in English and mathema@cs within a one- and credit ESL students will complete transfer- level coursework in English within of three years. (AB705)

22 Conclusion and Next Steps Ø A new approach to equity tracking focusing on course accessibility and availability Ø Development of a common code and common toolkit that reduces average student 1me to comple1on by improving course accessibility Ø New scheduling prac1ces driven by student needs and strategic plans priori1es rather than classroom/faculty availability/etc. (i.e., college needs) Ø Expanded considera1on of the ways in which course scheduling impacts student equity and student success Ul-mately, we our goal is to minimize the impact of these rou-ne structural barriers, so that personal academic and student barriers are their only hurdles to success.

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