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A Comprehensive Analysis of a PrOF Instructional Data Packet

A Comprehensive Analysis of a PrOF Instructional Data Packet. To illustrate the data analysis process. CRC Research Office 2009. A Guide to Data Analysis for Instructional Programs.

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A Comprehensive Analysis of a PrOF Instructional Data Packet

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  1. A Comprehensive Analysis of a PrOF Instructional Data Packet To illustrate the data analysis process CRC Research Office 2009

  2. 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 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.

  3. 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.

  4. A Guide to Data Analysis for Instructional Programs Student Access andDemographics Student Success Departmental Student Enrollment by: Departmental Average Course Success Rates by: Age group Age group Age group (collapsed) Age group (collapsed) Gender Gender Ethnic group Ethnic group Educational goal Educational goal Educational level Educational level Instructional mode Instructional mode Course level Course level Freshman status Freshmanstatus 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.

  5. 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.

  6. 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.

  7. The Big Picture • As you review your data • Look for trends, patterns or interesting differences in your data 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 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. TheBig Picture

  8. Identifying Trends and/or Differences in the Data

  9. 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 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.

  10. A Guide to Data Analysis for Instructional Programs 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 of the lines). This may just reflect program characteristics that limit the number of courses students can take concurrently within the department.

  11. A Guide to Data Analysis for Instructional Programs 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 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.

  12. 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 productivity in the spring semesters, with the notable exception of Spring 2009, where it recorded its highest productivity figures during this time period.

  13. A Guide to Data Analysis for Instructional Programs This graph shows departmental student headcount for the past 4 academic years 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.

  14. 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 clientele compared to the rest of the college (note that the scales on the two graphs are not the same).

  15. A Guide to Data Analysis for Instructional Programs 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.

  16. 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.

  17. A Guide to Data Analysis for Instructional Programs This graph shows departmental student headcount by Educational Goal. It shows that the 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.

  18. 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.

  19. 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). 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 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.

  20. A Guide to Data Analysis for Instructional Programs This graph shows the course success rate in the department’s courses over the past 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.

  21. A Guide to Data Analysis for Instructional Programs 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.

  22. 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 yearscourse 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.

  23. A Guide to Data Analysis for Instructional Programs Department College wide 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.

  24. 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 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.

  25. 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 department 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.

  26. 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.

  27. 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.

  28. 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 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.

  29. A Guide to Data Analysis for Instructional Programs 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.

  30. Making Meaning from the Trends and Differences

  31. 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.

  32. 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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