Using the PDP Course-Level Analysis-Ready File (6 minutes)

Review the Postsecondary Data Partnership course-level analysis-ready file. Follow Prakesh as he uses the course-level analysis-ready file to understand which gateway English and Math courses students took and explores if retention rates differ based on which gateway course was taken.

Transcript In this tutorial, we discussed the PDP course level Analysis ready file. Elise is our institution's Provost. A few weeks ago, Elise shared information with faculty teaching math and English gateway courses that students who successfully completed those courses in their first year of college we're more likely to retain than those who do not complete those courses in their first year of college. Since then, she's heard from the chairs of both departments asking for additional information. Specifically, which math and English gateway courses did students take and how do first to second year retention rates vary by different gateway math and English courses. Elise forwards their request to Prakesh, the Director of Institutional Effectiveness. Prakesh knows that one of the PDP Analysis Ready files is the best source to answer these questions. He reviews the data dictionary for the Course Level Analysis Ready file and begins familiarizing himself with the data set. The first 5 fields are student identifying information. The next 6 fields include student ID, age, race, ethnicity, and gender. The next set of fields gives information about the student cohort and the term they entered the institution. It also gives information on the academic year and term in which the student took the associated course. The next 6 fields provide information on the students’ courses like course prefix, course number, section name, CIP code, where CIP stands for classification of instructional programs, and the course type like college level, undergraduate course or college developmental course. The next 2 columns code the course as a math or English gateway course or a corequisite developmental course. The next 2 columns give the course begin and end dates. The next 4 columns provide additional course information like the grade earned by the student, number of credits the student attempted, number of credits the student earned, and the delivery method of the course like face-to-face, online, hybrid. The next three columns indicate if the course is part of the institution score or general education, the core competency the course is aligned with, and whether the students successfully met that core competency. Column AG is provided by the National Student Clearinghouse and indicates if there is an enrollment record for that student at another institution. At the same time, because these are course level data, there are multiple rows per student, allowing for multiple points of comparison, including within a term or across terms. For example, if Prakash wanted to look at Olivia Martin's course activity across multiple terms, he would notice that she took several psychology classes in the fall, spring, and summer terms. Alternatively, if Prakash wanted to look at Liam Johnson's course activity for a single term, he would notice that he took classes like Principles of Accounting and Introduction to Geology in the spring term. Now, Prakash is ready to use the data set to answer the questions posed by the department chairs. The first question is which math and English gateway courses did students take? Prakesh filters to the most recent cohort. Then he creates a new variable which concatenates course prefix with course number. Using that new variable in combination with the Math and English Gateway indicator, he finds that first year students in the current cohort took 7 gateway math courses like Basic Statistics and Precalculus, and 4 English gateway courses like English Composition and Business Writing. The second question is how do retention rates vary by different gateway math and English courses? Prakash will need to merge the Cohort Analysis Ready file with the Course Level Analysis Ready file using student ID to know whether students retained in their second year of college. Prakesh uses the same fields as before with the retention field from the Cohort Level Analysis Ready file to calculate the retention rates by gateway course type. He finds that students who took basic statistics had the highest retention rate at 79%, while students who took precalculus had the lowest retention rate at 60%. He also finds that students who took Business Writing retained at the highest rate of 71%, while students who took English Composition had the lowest retention rate at 60%. Prakash drafts a report summarizing this information and emails it to Elise who shares it with the department chairs. Based on these data, the math department is implementing faculty led inquiry in action with their section level data to identify barriers impeding success for students, while the English department is implementing a corequisite writing lab course. In summary, the course level analysis ready file can be used to analyze the course level experience of students by section, grade, or delivery modality alongside student demographics. Merge with other analysis ready files or institutional data to construct a powerful student success research data set. Understand the impact of courses on student success.