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Attack of the Killer courses

Attack of the Killer courses. How course taking patterns affect retention. Jaclyn Cameron. Research Analyst DePaul University Chicago, IL Presented at National Symposium on Student Retention CSRDE 4 th Annual Conference September 29 th -October 1 st 2008. The Inspiration.

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Attack of the Killer courses

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  1. Attack of the Killer courses How course taking patterns affect retention

  2. Jaclyn Cameron Research Analyst DePaul University Chicago, IL Presented at National Symposium on Student Retention CSRDE 4th Annual Conference September 29th-October 1st 2008

  3. The Inspiration • What we know • Academic performance and progress highly related to retention • Under-preparedness in Math, science, English related to student success • What we don’t necessarily know • Details concerning first year academic performance

  4. Defining the Role Higher attrition risk • “Killer” course: A course in which high proportions of students earn non-passing grades or withdraw after the drop date. • Previous appearances: • Supplemental instruction • Learning communities • Parallel courses • Educational centers (labs) • Implications

  5. Characters • First Year Freshmen (n = 7,226) • Community College Transfers (n = 3,059) • Killer courses • Minimum total enrollment • 150 – Freshmen (50 avg/yr) • 100 – Transfers (33 avg/yr) • Percent of D, F, W Grades >= 10%

  6. Character Development Student Roster Keyed on student ID, Unique All 1st Yr course enrollments Keyed on student ID, Duplicated All 1st Yr course enrollments of selected students Keyed on student ID, Duplicated (one record for each course enrollment) Once killer courses are identified, they are flagged as such in this table All unique 1st Yr courses w/ count of grades earned Keyed on individual course. Yields list of Killer courses All unique students w/counts of courses and grades Keyed on student ID. Includes all demographic and academic information

  7. The Set Credit hours of killer courses Race (URM) Credit hours of non-killer courses 1st Yr Retention (Y/N) Total credit hours taken in the 1st Yr GPA of killer courses GPA of non-killer courses CC Data: Total credit hours transferred in CC Data: Location of CC (City/Suburban)

  8. Act 1: The Killer Courses

  9. Act 1, Part 1:Freshmen Killer Course List

  10. Act 1, Part 2: Community College Transfer Killer Course List

  11. Act 1, Part 3: Comparing the Lists • Math, Math, and more Math • Transfers have more Math and higher DFW rates in general • Gen Ed’s vs. Major Requirements • Freshmen have more Liberal Arts, general education courses • Sequenced vs. Disordered • For Freshmen, the DFW proportion is higher in the later courses of the sequence • For Transfers, full sequences are rare, and early courses of a sequence have higher DFW rates.

  12. Act 2: The Investigation

  13. Act 2, Part 1: Two Questions • Do the amount of killer courses taken predict retention? • Percent of killer courses taken • Total number of course hours taken • Total credit hours of non-killer courses taken • Race • Does performance in killer courses predict retention? • Only students who took at least one killer course • Included the all the above plus: • Killer course GPA • Non-killer course GPA • Total killer courses taken x killer course GPA • CC Controls • # of hours transferred in • Location of CC (city/suburban)

  14. Act 2, Part 2: Regression 1: Freshmen Course Taking Activity • Two Blocks • Race • Ttl credit hrs, ttl Killer credit hrs, ttl non-killer credit hrs • Overall fit was significant (Χ2 = 1399.6, df = 4, n = 6753, p< .05) • 32% variance explained (Nagelkerke pseudo R2, -2 Log likelihood = 4625.77) • Correctly predicted 88.4% (Correct: 98.8% retained, 35.4% non-retained) • Significant Predictors • Total Credit Hrs (+) • Non-Killer Credit Hrs (+) • Killer Credit Hrs (+)

  15. Act 2, Part 3: Regression 2: Transfer Course Taking Activity • Two Blocks • Race, Transfer Hours, CC Location • Ttl credit hrs, ttl Killer credit hrs, ttl non-killer credit hrs • Overall fit was significant (Χ2 = 399.11, df = 6, n = 1755, p< .05) • 35% variance explained (Nagelkerke pseudo R2, -2 Log likelihood = 1131.47) • Correctly predicted 86.2% (Correct: 96.1% retained, 33.2% non-retained) • Significant Predictors • Mediated relationships: race (<>), transfer hrs (+), CC locale (City -) • Non-killer credit hrs (+) • Killer credit hrs (+) • Total hrs (-) ?

  16. Act 2, Part 4: Regression 3: Freshmen - Courses & Performance • Three Blocks • Race • Ttl credit hrs, ttl killer credit hrs, ttl non-killer credit hrs • Killer cum GPA, non-killer cum GPA, killer cum GPA x ttl killer hrs • Overall fit was significant (Χ2 = 851.41, df = 7, n = 4804, p< .05) • 30% variance explained (Nagelkerke pseudo R2, -2 Log likelihood = 2967.80) • Correctly predicted 89.6% (Correct: 99% retained, 29.7% non-retained) • Significant Predictors • Mediated relationships: race (+) • Total credit hrs (+) • Killer credit hrs (-) • Non-killer credit hrs (-) • Non-killer GPA

  17. Act 2, Part 5: Regression 4: Transfer - Courses & Performance • Three Blocks • Race, transfer hrs, CC locale • Ttl credit hrs, ttl killer credit hrs, ttl non-killer credit hrs • Killer cum GPA, non-killer cum GPA, killer cum GPA x ttl killer hrs • Overall fit was significant (Χ2 = 276.98, df = 9, n = 1282, p< .05) • 40% variance explained (Nagelkerke pseudo R2, -2 Log likelihood = 573.11) • Correctly predicted 91.4% (Correct: 98.2% retained, 32.6% non-retained) • Significant Predictors • Non-killer credit hrs (+) • Non-killer GPA (+) • Transfer hrs (+) • Killer GPA x Killer credit hrs (+)

  18. Act 3: The Verdict

  19. Act 3, Part 1: General Conclusion & Observations • Different killer courses = Different potential pitfalls • 1st Analysis: More courses = More likely to persist • Freshmen order: Total, Non-killer, Killer • Transfer order: Non-killer, Killer, Total • 2nd Analysis: Non-Killer GPA trumps all • Freshmen: Killer and Non-killer hours now negative • Transfers: Only Non-killer, but with Interaction term • Overall, may be a proxy for previous research • Uncovered possibility of mediated relationships

  20. Act 3, Part 2: Limitations • Lack of other known retention predictors • ACT, high school GPA, student satisfactions • Did not adequately predict attrition • Use of individual courses as a group • Aggregate killer courses by departments

  21. Act 3, Part 3: The Wrap-Up • Implications for advising, especially for different populations • Identify areas of improvement for student learning • GPA is a direct reflection of coursework; courses may not be relevant, but GPA is. • Faculty and staff respond to the name “killer” course

  22. the end…

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