Report of Achieving the Dream Data Team October 10, 2007.

24
Report of Achieving the Dream Data Team October 10, 2007

Transcript of Report of Achieving the Dream Data Team October 10, 2007.

Page 1: Report of Achieving the Dream Data Team October 10, 2007.

Report of Achieving the DreamData Team

October 10, 2007

Page 2: Report of Achieving the Dream Data Team October 10, 2007.

2

Contents

Methodology

Developmental Courses with Observations

High Enrollment, High Failure Courses with Observations

High Failure 2000-level Courses with Observations

Advisement

Persistence and Retention with Observations

Next Steps

Page 3: Report of Achieving the Dream Data Team October 10, 2007.

3

Methodology

Achieving the Dream (AtD) defines student success in a course as a grade of A, B, C, or S; students earning a grade of D, F, U or W are defined as unsuccessful.

Initially, five Zero-level courses and five 1000-level course were identified as having a high rate of unsuccessful students and were analyzed using demographic data.

With guidance from our AtD Data Coach, the analysis shifted from demographics to other course attributes where change might have a broader impact.

Time of day Length of course Delivery method

Page 4: Report of Achieving the Dream Data Team October 10, 2007.

4

All Zero-level MATH and LS courses were selected since unsuccessful completion of these courses is a barrier to enrollment in college level courses.

1000-level courses were selected by high enrollment (300 or greater) and high failure rates (30% or greater).

2000-level courses were selected by high enrollment (100 or greater) and high failure rates (30% or greater)

All persistence and retention data is based on the ATD cohort, which includes all students who enter OCCC for the first-time in the fall semester.

AtD Data Team surfaced summary observations during meetings.

Methodology

Page 5: Report of Achieving the Dream Data Team October 10, 2007.

5

Developmental or Zero–Level Courses

All Zero-level MATH and LS courses were selected.

Larger percentage of students receive a D, F, or U than withdraw.

Online sections are less successful than traditional sections. 8-week sections are more successful than 16-week. The failure rate for College Reading I has increased over the

last three years. Night sections in Math courses are more successful than

other times of the day. Study Skills has a consistently higher failure rate in spring

semesters. Intermediate Algebra has a consistently higher failure rate in

fall semester.

Page 6: Report of Achieving the Dream Data Team October 10, 2007.

6

Developmental Failure Rates By Year

40.4%

53.2%

31.1%

47.3%

52.5%

32.7%

46.8%

53.0%

32.1%

AY 2005 AY 2006 AY 2007

Remdial LS Remedial Math Institution

Page 7: Report of Achieving the Dream Data Team October 10, 2007.

7

High-Enrollment, High-Failure Courses

1000-level courses were selected by high enrollment (300 or greater) and high failure rates (30% or greater).

Math and Science courses had a greater percentage withdrawing than failing. English and History courses were the reverse.

When offered, 2-, 5-, and 8-week sections had a lower failure rate than 16-week sections. (Exceptions: CS 1103, Math 1513)

When offered, online sections had a higher failure rate than traditional sections. (Exceptions: BIO 1023, BIO 1114, SOC 1113).

Telecourse sections had a higher failure rate than any other delivery method.

Page 8: Report of Achieving the Dream Data Team October 10, 2007.

8

Failure rates in night sections are equal to or lower than morning sections. (Exceptions: MATH 1513, CHEM 1115).

The following courses show a continued increase in failure rate over time: APPM 1313, BIO 1314,CHEM 1115, ENGL 1113, HIST 1483, and HIST 1493.

Courses that have consistently lower failure rates in fall semesters: BIO 1114, BIO 1314, ENGL 1113.

ENGL 1213 has a consistently lower failure rate in spring semesters.

Courses that have increased in number of students per semester: BIO 1023, BIO 1314, POLSC 1113.

CS 1103 is the only course that has continued to decrease in number of students per semester.

High-Enrollment, High-Failure Courses

Page 9: Report of Achieving the Dream Data Team October 10, 2007.

9

High-Enrollment, High-Failure By Year

34.1%

31.1%

36.1%

32.7%

36.4%

32.1%

AY 2005 AY 2006 AY 2007

1000 Level Institution

Page 10: Report of Achieving the Dream Data Team October 10, 2007.

10

2000 Level Courses

2000-level courses were selected by high enrollment (100 or greater) and high failure rates (30% or greater)

According to AtD Data Coach, high failure rates in 2000 level courses are unusual for AtD schools.

Most 2000-level courses had a higher withdrawal rate than failure rate (Exceptions: ECON 2113, MGMT 2053).

Night sections had a lower failure rate than other times of the day.

When offered, 5- and 8-week sections had lower failure rates than 16-week sections.

When offered, online sections had noticeably higher failure rates than traditional sections. (Exception: GEOG 2603)

Page 11: Report of Achieving the Dream Data Team October 10, 2007.

11

Five out of nine courses show an increase in the failure rate over time (Exceptions: ACCT-2123; ACCT 2113; COM-2213; MGMT-2053).

BUS 2023 has consistently lower failure rates in spring semesters.

GEOG 2603 has consistently lower failure rates in fall semesters.

One out of every two students who enroll in ACCT 2113 and BIO-2215 fail.

2000 Level Courses

Page 12: Report of Achieving the Dream Data Team October 10, 2007.

12

2000 Level Failure Rates By Year

36.2%

31.1%

37.8%

32.7%

40.1%

32.1%

AY 2005 AY 2006 AY 2007

2000 Level Institution

Page 13: Report of Achieving the Dream Data Team October 10, 2007.

13

Advisement

Why Advisement

Identified as key piece of student success

Opportunity to talk one-on-one with a student

Advising is both formal and informal

Advising is multi-faceted

Page 14: Report of Achieving the Dream Data Team October 10, 2007.

14

Advisement

OCCC DUAL MODEL OF ADVISEMENT

OCCC Administrative Procedure No. 5049 dtd 1-02-1991 OCCC Administrative Procedure No. 5056 dtd 6-01-1996

Advantages Trained staff; central access; economy of scale

Disadvantages Unclear definition of advising Communication of responsibilities

Page 15: Report of Achieving the Dream Data Team October 10, 2007.

15

Advisement

FA/VACenter

TransferCenter

SuccessCourse

MineOnline

International

Graduation FacultyAdvisor

DistanceAdvising

Course Catalog

ClassSchedule

Career & Employment

AdmissionsAcademicAdvisor

Advising

Page 16: Report of Achieving the Dream Data Team October 10, 2007.

16

Advisement

Communication – internal and external

Roles and procedures not clearly defined

Training for Faculty Advisors

Transition from Academic Advisor to Faculty Advisor

Self-Advisement

Loophole

Areas of Concern

Page 17: Report of Achieving the Dream Data Team October 10, 2007.

17

Persistence and Retention

Fall 2004 and Fall 2005 AtD Cohorts, which includes all students who enter OCCC for the first-time in the fall semester.

Persistence is defined as a student from a fall cohort attending the following spring semester. (Fall to Spring)

Retention is defined as a student from a fall cohort attending OCCC the following fall semester. (Fall to Fall)

Looking at the demographic profile of the two AtD Cohorts in comparison to all students enrolled at OCCC’s during the same time frame, the following differences can be seen:

AtD Cohorts have a higher percentage of males. AtD Cohorts have a higher percentage of 18-24 year olds. AtD Cohorts have a higher percentage in all race/ethnic

groups except Asian.

Page 18: Report of Achieving the Dream Data Team October 10, 2007.

18

Females persisted and were retained at a higher percentage than males.

Asian and Hispanics persist and are retained at a higher rate than other minority groups or Caucasians.

Black/African Americans have a lower success rate than other minority groups or Caucasians.

Asians have a higher success rate than other minority groups or Caucasians.

Minority groups as a whole persist and are retained at a lower rate than Caucasians. This is more evident in the Fall 2005 Cohort.

Page 19: Report of Achieving the Dream Data Team October 10, 2007.

19

Both persistence and retention declined from Fall 2004 Cohort to Fall 2005 Cohort in basically all areas. (Exception: 30-34 age group)

Although the persistence percent of 30-34 year olds decreased from Fall 2004 to Fall 2005, the retention rate increased.

The opposite was true for 40-44 year olds. A student in the Fall 2004 Cohort had approximately:

three in five chance of persisting one in two chance of being successful in spring classes almost a two in five chance of being retained less than a one in three chance of being successful in fall

classes

Page 20: Report of Achieving the Dream Data Team October 10, 2007.

20

Fall to Fall Retention

0

10

20

30

40

50

60

Males Females

GENDER

Fall 2004 Cohort Fall 2005 Cohort OCCC Fall Semester

0

10

20

30

40

50

60

70

80

24&Below 25-29 30-39 40-49 50&Above

AGE GROUPS

Fall 2004 Cohort Fall 2005 Cohort OCCC Fall Semester

Page 21: Report of Achieving the Dream Data Team October 10, 2007.

21

Retention Demographics

0

10

20

30

40

50

60

70

Persistence Retention

Fall 2004 Fall 2005 IPEDS/OCCC

Page 22: Report of Achieving the Dream Data Team October 10, 2007.

22

Breakout Group Process

1. Divide into three breakout groups. (see sheets on tables. )2. Each group should appoint a presenter and recorder. The recorder will list

items in the laptop provided.3. The tasks for each group are as follows:

Table 1 should brainstorm potential causes of the problems shown by the data about developmental course completion from the Data Team as well as any other data that may be relevant on developmental course failures.

Table 2 should brainstorm potential causes of the problems shown by the data about course completion in 1000-level (gatekeeper courses) and 2000-level courses from the Data Team as well as any other data that may be relevant on course completion failures.

Table 3 should brainstorm potential causes of the problems shown by the data about persistence and retention from the Data Team as well as any other data that may be relevant on retention failures.

4. Once you have brainstormed the list of issues or potential causes, separate them into Symptoms or Underlying Causes by adding a “S” or “U” next to the item.

5. Each group should then list any additional data they would like to see from the Data Team that would assist in analyzing their assigned subjects.

6. Save the list on the flash drive provided and each presenter will have five minutes to present to the combined group.

Page 23: Report of Achieving the Dream Data Team October 10, 2007.

23

AtD Data Team Members

Alan Stringfellow Brandi Henson E.J. Warren Harold CaseJoyce Morgan-DeesStephen CrynesYutika Kim

Page 24: Report of Achieving the Dream Data Team October 10, 2007.

24

LINKS2004 Cohort2005 Cohort

Zero Level1000 Level2000 Level

AtD Data Team Members