Transcript A Guide to the Analysis of Instructional Data Packets for the 2009 PrOF

A Guide to Analyzing
PrOF Instructional
Data Packets
CRC Research Office 2009
Background Information
Overview
The PrOF data packets have been developed using information contained in
the PeopleSoft Student Information System. The data packets show the
student enrollment, demographic, academic success, semester-to-semester
persistence as well as departmental WSCH/Instructional FTE/Productivity
information for the past four academic years.
The data, which is presented both graphically and numerically, provides
information that will assist departments identify trends and differences
and to compare departmental data with college-wide data. These trends
and comparisons should inform the identification of strengths,
opportunities and planning ideas that will enhance program effectiveness.
If you have any questions about the information contained in these
packets, please contact the College Research Office at (916) 691-7385.
A Guide to Data Analysis for Instructional Programs
“Looking back” at what happened
Departmental data
College-wide data
Differences, Changes and/or
Commonalities
The PrOF data packets are arranged so you can look at trends within your departmental
data and compare it with the College as a whole. In many cases, you might find that your
departmental trends closely mirror overall College-wide trends, but you may see that your
departmental trends differ greatly from the College-wide data. This may have implications
for departmental planning.
A Guide to Data Analysis for Instructional Programs
Student Access and Demographics
Departmental Student Enrollment by:
Age group
Age group (collapsed)
Gender
Ethnic group
Educational goal
Educational level
Instructional mode
Course level
Freshman status
English primary language
Student Success
Departmental Average Course Success Rates by:
Age group
Age group (collapsed)
Gender
Ethnic group
Educational goal
Educational level
Instructional mode
Course level
Freshman status
English primary language
Semester-to-semester persistence rates
Departmental WSCH/Instructional FTE/Productivity
Degree and/or Certificates Awarded
The PrOF data packets graphically and numerically represent each of the
demographic and outcome measures listed above. The past four academic years
are analyzed and displayed in the charts to allow you to track trends over time.
GLOSSARY OF TERMS
Program Review Overview and Forecasting (PrOF)
Knowing the following terms will help you with your data analysis:
Department - the grouping of courses that are related in content.
Course Success Rate - the average percent of students who successfully
complete a class with a grade of "A", "B", "C" or "CR" compared to the overall
number of students enrolled in the class. (Students who dropped out before
the fourth week of classes are automatically excluded from the calculation.)
Numerator = Number of students (duplicated) with A, B, C, CR
Denominator = Number of students (duplicated) with A, B, C, D, F, CR,
NC, W, I
Persistence - the percentage of students who enroll in a particular department
(regardless of course outcome) for a given semester that enroll at the college
in the subsequent semester.
GLOSSARY OF TERMS (cont.)
Program Review Overview and Forecasting (PrOF)
Duplicated Enrollment - the number of total enrollments in a particular
department. A student is counted for every individual enrollment in a
particular department during a given term; in other words, if a student
enrolls in three courses in a given department for a given term, they are
counted three times.
WSCH – acronym for Weekly Student Contact Hours. This is the total student
contact hours for the semester.
FTE – acronym for Full-Time Equivalent. A professor teaching a full load would
be considered to be 1.00 FTE. Professors teaching overload or having a
reduced teaching load for a given semester are adjusted accordingly.
Productivity – the result of dividing the total FTE into the total WSCH.
Analyzing the Data
The Big Picture
The Big Picture
• As you review your data
– Look for trends, patterns or interesting differences in your
program/department data
– Look for trends, patterns or interesting differences when your
data is compared to college-wide data
– Think about factors that might contribute to these trends or
differences (scheduling, new interventions, new course design,
etc.)
– Think about challenges that might be contributing to these trends
or differences (facilities, decreased FTE, changes in curriculum,
scheduling or instructional mode, etc.)
• These trends, patterns, differences, factors and challenges
should inform the identification of program strengths,
opportunities and planning ideas in PrOF.
Identifying Trends
• Within your data
– Increases over the past four years (upward tendency in the
graph)
– Decreases over the past four years (downward tendency in
the graph)
– Cycles in the data (an up and down pattern in the graph)
– Noticeable changes over a shorter time period may
warrant further investigation, particularly if present on
multiple slides
• Examples
A Guide to Data Analysis for Instructional Programs
This graph shows that the department is experiencing an increase in the percentage of
African American and Hispanic students and a corresponding decrease in the percentage of
Asian/Pacific Islander and White students.
A Guide to Data Analysis for Instructional Programs
This graph shows that course success have improved for both modes over the past two
years. Course success rates in online courses were slightly higher than other types of
classes in 08-09, something that was not true in previous years. It should be noted,
however, that a small number of online classes in the department may exaggerate
observed trends.
A Guide to Data Analysis for Instructional Programs
This graph shows a cycle of greater fall enrollments compared with spring and
indicates an overall pattern of increasing unduplicated enrollments.
Identifying Differences
• Within your data
– Look for group(s) for which the data exceeds or is below
the data for other groups
– Look for years where the data differs from the other years
– Look for data points that don’t follow an observed trend
• When comparing your data with College-wide data
– Look for trends that differ from College-wide trends
– Look for situations where program data exceeds or is less
than College-wide data
• Examples
A Guide to Data Analysis for Instructional Programs
The fluctuation between the Fall 07 and Spring 08 headcount is much smaller
than the other fluctuations, a pattern that did not continue during the next
academic year.
A Guide to Data Analysis for Instructional Programs
This graph shows the department’s course success rates by the student’s enrollment
status (whether or not the student was a “first-time” freshmen). Course success rates
have varied over the four years. However, first-time freshmen course success rates
were slightly lower compared with other students for all years prior to 08-09.
A Guide to Data Analysis for Instructional Programs
Department
College wide
Comparing the department data with college-wide data shows that the department
is serving a younger student clientele compared to the rest of the college (note that
the scales on the two graphs are not the same).
A Guide to Data Analysis for Instructional Programs
Department
College wide
The department’s course success rates for African American student are greater, and have
increased more, than college-wide course success rates for the same group. In addition,
departmental course success rates for White students have increased, whereas college-wide
course success rates have decreased. The variation in the departmental data for American
Indian students may reflect the low number of students from this group taking classes in the
department, which may exaggerate observed trends.
Making Meaning from the
Trends and Differences
Implications of the Data
• Program strengths can be identified from
– Increases/upward trends within the departmental data
(overall or in one group)
– Areas in which the departmental data exceeds collegewide data
– Differences within the departmental data
• Opportunities can be identified from
– Decreases/downward trends in the departmental data
– Areas in which the departmental data is below collegewide data
– Differences within the departmental data
– Factors that might be limiting the growth and/or the
success of students in the department.
Generating Planning Ideas
After analyzing your Department’s Program Review Data
Packets, you may be able generate planning ideas by:
• Extending or expanding programs and/or changes that
may have contributed to program strengths or
improvements
• Identifying and addressing the factors that might be
negatively affecting growth or success in the
department
• Identifying and planning to implement best practices
within the department or from other institutions that
are similar to CRC.