A G id t D t A
l i f
A
Guide
to
Data
Analysis
for
Instructional
Programs
g
User guide to interpreting your Department’s
PrOF data packet
A
Guide
to
Data
Analysis
for
Instructional
Programs
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
y
g
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 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 Student Success
Departmental Student Enrollment by:
Age group
Departmental Average Course Success Rates by:
Age group Age group
Age group (collapsed) Gender
Ethnic group Educational goal
Age group
Age group (collapsed) Gender Ethnic group Educational goal Educational level Instructional mode Course level Freshman status
E li h i l
Educational level Instructional mode Course level
Freshman status
English primary language 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
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
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
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
(regardless
of
course
outcome)
for
a
given
semester
that
enroll
at
the
college
GLOSSARY OF TERMS (cont.)
Program Review Overview and Forecasting (PrOF)
Duplicated
Enrollment
‐
the
number
of
total
enrollments
in
a
particular
department. A
p
student
is
counted
for
every
y
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
y
for
Full
‐
Time
Equivalent.
q
A
professor
p
teaching
g
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.
The Big Picture
The
Big
Picture
The Big
Picture
•
As
you
review
your
data
–
Look
for
trends,
patterns
or
interesting
differences
in
your
data
within the Department
g
within
the
Department
–
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
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
should
inform
the
identification
of
program
strengths,
opportunities
and
planning
ideas
in
PrOF.
Identifying Trends
Identifying
Trends
and/or
Differences
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows duplicated departmental enrollment for the past four academic years. Duplicated enrollment means that students who take more than one course within the Duplicated enrollment means that students who take more than one course within the department in a given semester are counted for each enrollment. This graph shows higher fall duplicated enrollments compared with spring and an overall pattern of increasing duplicated enrollments.
A
Guide
to
Data
Analysis
for
Instructional
Programs
D t t C ll id
Department College wide
Comparing duplicated departmental enrollment with the overall College‐wide figures shows that duplicated enrollment growth in the department is lower compared to College‐wide enrollment growth (as indicated by the different angles
f h li ) hi j fl h i i h li i h b f
of the lines). This may just reflect program characteristics that limit the number of courses students can take concurrently within the department.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows unduplicated departmental enrollment for the past four academic years. This graph shows unduplicated departmental enrollment for the past four academic years. Unduplicated enrollment means that if a student takes more than one course within the department in a given semester, they are counted only one time. This graph shows higher fall enrollments compared with spring enrollments and an overall pattern of increasing
d li d ll C i hi h i h h d li d ll h
unduplicated enrollments. Comparing this graph with the duplicated enrollment graph confirms that there are not many students who take more than one course in the department per semester.
Term‐to‐term Productivity By Department
A
Guide
to
Data
Analysis
for
Instructional
Programs
Term to term Productivity By Department
DEPT.
This table shows the department’s productivity over the past four academic years. Productivity is calculated by taking the total amount of Weekly Student Contact Hours (WSCH) and dividing that by the total amount of Instructional FTE used during the semester In this case the department has experienced a small drop in during the semester. In this case, the department has experienced a small drop in productivity in the spring semesters, with the notable exception of Spring 2009, where it recorded its highest productivity figures during this time period.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows departmental student headcount for the past 4 academic yearsg p p p y by “collapsed” age group. It shows that the department is experiencing slight growth in the 25 or over age group, with a slight drop in the under 25 age group.
A
Guide
to
Data
Analysis
for
Instructional
Programs
Department College wide
Sometimes comparing the department data with college‐wide data may yield new information In this case it shows that the department is serving a younger student information. In this case, it 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
This graph shows departmental headcount by gender It shows an overall trend of This graph shows departmental headcount by gender. It shows an overall trend of increases in the percentage of male students and a corresponding drop in the percentage of female students.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows departmental student headcount by ethnicity for the past 4 academic years, using the traditional ethnic group classifications. It 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 departmental student headcount by Educational Goal. It shows that theg p p y department is experiencing growth in the percentage of students who are seeking “Transfer” and “Degree/Certificate attainment”, with a corresponding drop in the percentage of students who are undecided about their goals or are seeking job skills development.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows departmental student headcount by previous educational level (as collected on the application for admission). It shows that the department is experiencing a slight increase in the percentage of students with a HS diploma.
Term‐to‐term Persistence
A
Guide
to
Data
Analysis
for
Instructional
Programs
Term to term Persistence
Your Department
This table shows the percentage of students enrolled in a class in the department who persisted to take another class at the college the subsequent semester (regardless of whether or not they enrolled in another class in your department). (regardless of whether or not they enrolled in another class in your department). It is interesting to note that for the most part, Spring‐to‐Fall persistence is slightly higher compared to Fall‐to‐Spring persistence. In addition, college‐wide persistence is higher than the persistence of students who had enrolled in classes
i h d Thi i h fl h b f d ll d
in the department. This might reflect that a greater number of students enrolled in departmental classes are closer to completing their educational goals compared with the general student population.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows the course success rate in the department’s courses over the past
f d i h i i ll f h
four academic years. It shows an increase in overall course success rates for the past academic year, but very little change compared with four years ago.
A
Guide
to
Data
Analysis
for
Instructional
Programs
D t t C ll id
Department College wide
Comparing the department’s average course success rates to the overall college rates shows that the department is on par with the overall college‐wide course success rates.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows course success by age group for the past 4 academic years. It shows that over the past two years course success rates have improved for all groups; over the past four years they have fluctuated, but have improved slightly, except in the 40 or over age group.
A
Guide
to
Data
Analysis
for
Instructional
Programs
Department College wide
Comparing the department’s course success rates to the College‐wide rates shows Comparing the department s course success rates to the College wide rates shows that the department’s course success rates by age group generally mirror or exceed the overall college’s course success rates by age group.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows the department’s course success rates by major ethnic group. It shows that success rates over the past two years have improved within each group In addition success success rates over the past two years have improved within each group. In addition, success rates over the past four years have improved for all groups except American Indian students. The most significant improvements have occurred within the African American student population.
A
Guide
to
Data
Analysis
for
Instructional
Programs
Department College wide
The department’s course success rates generally mirror the college‐wide trends, with the exception of course success rates for white students, which have increased in the departmentp , p but decreased overall at the college (note the scale of the graphs differ!). 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.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows department’s course success rates by the instructional mode. It 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 the department’s course success rates by the student’s enrollment
( h h h d “fi i ” f h ) C
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
This graph shows the department’s course success rates by course type (i.e. “Transfer”, 300‐
level or above; “College level” 100 through 299 level; or “Basic Skills” below 100 level level or above; “College‐level”, 100 through 299‐level; or “Basic Skills”, below 100‐level courses.) It shows that success rates have improved for each course level over the past two years and that average course success rates for “Basic‐Skills” have improved over the past four years.
A
Guide
to
Data
Analysis
for
Instructional
Programs
This graph shows the number of students who earned a departmental Degree and/or This graph shows the number of students who earned a departmental Degree and/or Certificate during a particular academic year. It shows that the number of certificates awarded per year has generally increased over the past four years and that there has been relatively no change in the number of degrees awarded per year.
