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A Guide to Analyzing PrOF Instructional Data Packets CRC Research Office 2009.

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Presentation on theme: "A Guide to Analyzing PrOF Instructional Data Packets CRC Research Office 2009."— Presentation transcript:

1 A Guide to Analyzing PrOF Instructional Data Packets CRC Research Office 2009

2 Background Information

3 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) Overview

4 Departmental dataCollege-wide data Differences, Changes and/or Commonalities “Looking back” at what happened A Guide to Data Analysis for Instructional Programs 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.

5 Student Access and DemographicsStudent Success A Guide to Data Analysis for Instructional Programs 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. 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 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

6 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.

7 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.

8 Analyzing the Data

9 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. The Big Picture

10 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

11 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

12 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

13 This graph shows a cycle of greater fall enrollments compared with spring and indicates an overall pattern of increasing unduplicated enrollments. A Guide to Data Analysis for Instructional Programs

14 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

15 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

16 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 A Guide to Data Analysis for Instructional Programs

17 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). DepartmentCollege wide

18 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. A Guide to Data Analysis for Instructional Programs DepartmentCollege wide

19 Making Meaning from the Trends and Differences

20 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 college- wide 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 college- wide data – Differences within the departmental data – Factors that might be limiting the growth and/or the success of students in the department.

21 Generating Planning Ideas 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. After analyzing your Department’s Program Review Data Packets, you may be able generate planning ideas by:


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