Using the Kolbe A ™ Conative Index to Study Retention of Computer Science Students

1. Using the Kolbe A™ Conative Index to Study Retention of Computer Science Students Robert Lingard Brenda Timmerman Elizabeth Berry California State University, Northridge

2. Overview • The Retention Problem • What Does the Kolbe A™ Conative Index Measure? • Results From Previous Studies With Upper Division Students? • Current Studies With Freshmen? • Can We Improve Retention by Changing the Way We Teach? • Conclusions and Recommendations

3. The Retention Problem • At CSUN fewer than 20% of students who decide to major in Computer Science as freshmen complete the program. • Many Colleges and Universities report that the graduation rate in Computer Science is the lowest, or near the lowest, of all majors. • In order to improving retention we need to understand why students drop out.

4. The Kolbe Concept® • It identifies the conative instincts that drive the way one operates, e.g., the way one approaches problem solving. • It focuses on strengths and provides insight on how to help people be more productive and effective • It is universal, unbiased, and an individual’s Kolbe index tends to remain the same over time

5. The Kolbe Instinctive Talents • Fact Finder • Collects data, asks questions, probes • Follow Thru • Makes schedules, plans ahead • Quick Start • Innovates, takes risks, improvises • Implementor • Builds and constructs, creates models

6. Sample “Kolbe” Results

7. Kolbe Profile of Upper Division Computer Science Students

8. Kolbe Profile of Marketing Managers

9. Kolbe Profile Comparisons

10. Kolbe Profile Comparisons

11. Comparison of Conative Talents between Instructors and Students

12. Comparison of Implementors with Other Students in 1st CS Course

13. Weed out or Cultivate • Students have a wide diversity of preparation and Kolbe profiles • Frequently beginning Computer Science courses are directed at the well prepared and/or “fact-finder” students, weeding out the others

14. How Do We Cultivate? • Studies have shown that improved teaching techniques have increased both achievement and retention • This is important because many of the less prepared students are women and/or economically disadvantaged

15. What are Improved Teaching/Learning Techniques? • How to choose possible alternative techniques? • Trial and Error – finding out what works • Kolbe A™ Index – get to what might work quicker

16. US Military Academy, West Point • All students have to pass an introductory programming course, regardless of major • Robots are used to help students learn fundamental programming concepts • They claim that this approach increases retention and all students benefit from it

17. Stagecast Software • Stagecast Creator™ enables nonprogrammers to construct interactive visual simulations • Stagecast claims simulations are powerful teaching tools that make abstract concepts concrete.

18. Above Examples Are Consistent with Kolbe A™ Findings • They provide a learning environment that is not only more comfortable for the Kolbe implementors, but increases performance of all students • They make abstractions more tangible for those who need some concreteness while providing additonal stimulation to all students

19. Conclusions • The Kolbe A™ Index is a useful tool for suggesting teaching techniques that may improve the retention of beginning programming students. • We will undertake to develop, apply, and assess specific approaches to teaching and learning that make programming concepts less abstract. • To provide concrete, hands on learning experiences for students, tools like robot kits and visual simulation software may be useful. • The wide differences in levels of preparedness among beginning students must be addressed.