Comparing Dropouts and Persistence in E-Learning Courses

1. Comparing Dropouts and Persistence in E-Learning Courses COMPUTER & EDUCATION 48 (2007) 185–204 Nova Southeastern University, 3301 College Avenue, Fort Lauderdale, FL 33314, USA Received 15 September 2004; accepted 4 December 2004 Author : Yair Levy Advisor : Prof. S.H. Huang Student : Chia-Shiang Lu

2. Outline • Introduction • Theoretical background • Hypotheses and methodology • Data collection and result • Conclusions

3. Introduction • Motivation • Dropout rates were around 25%–40% as compared to 10%–20% in on-campus courses • Confirm such findings • Purposes • Investigate the differences of “academic locus of control” and “students satisfaction” with e-learning among dropout and completer (or persistent) students in e-learning courses

4. Theoretical background • Dropout from e-learning courses • Locus of control • Students’ satisfaction

5. Hypotheses • H1. The ALOC score of dropout students will be more external than that of completer students in e-learning courses. • H2. The level of satisfaction of dropout students will be lower than that of completer students in e-learning courses. • H3a. The gender distribution of dropout students will be different than that of completer students in e-learning courses. • H3b. The college status of dropout students will be different than that of completer students in e-learning courses. • H3c. The age distribution of dropout students will be different than that of completer students in e-learning courses.

6. Hypotheses • H3d. The residency status of dropout students will be different than that of completer students in e-learning courses. • H3e. The academic major distribution of dropout students will be different than that of completer students in e-learning courses. • H3f. The graduating term of dropout students will be different than that of completer students in e-learning courses. • H3g. The GPA score of dropout students will be different than that of completer students in e-learning courses. • H3h. The weekly working hours of dropout students will be different than that of completer students in e-learning courses.

7. Major constructs

8. Methodology • Sample for survey • A. ALOC : 12-item • B. Students’ satisfaction : 7-item • C. Demographics • One-Way ANOVA and reliability check • Pearson correlations

9. Data collection • Include 18 undergraduate and graduate e-learning courses at a major state university in the southeastern US. • All courses were developed by the corresponding professor. • The e-learning platform used for all 18 courses was WebCT.

10. Result • 372 completers and 81 dropout students,18% dropout rate. • Twenty-five dropout(31% response rate) and 108 completer students(29% response rate) completed the survey • 30% overall response rate.

11. Reliability check

12. Result • (H1) is not supported • (H2) is supported

13. Analysis • Students satisfaction was found significantly different (at p < .01) between the two groups. • The level of students satisfaction with e-learning for dropout students is significantly lower than that of completer students in e-learning courses.

14. Analysis

15. Analysis

16. Result • H3b is supported • H3f is supported

17. Analysis • H3b (college status) • The college status of dropout students was found to be significantly lower (at p < 0.05) than that of completer students in e-learning courses. • H3f (graduating term) • The graduating term of dropout students from e-learning courses was found to be significantly higher (at p < 0.01) than completer students.

18. College status distribution

19. Analysis • Students attending e-learning courses that are in higher college status are less likely to drop as they may need to graduate in that term or next one.

20. Graduating term distribution

21. Analysis • Dropped students appear to graduate in a later term than completer students in e-learning courses.

22. Pearson correlation

23. Analysis • Students satisfaction and graduating term are correlated significantly (at p < 0.01 level) with the group indicator. • College status was found to be correlated significantly (at p < 0.05 level) with the group indicator. • Significant correlation (at p < 0.01 level) between college status and graduating term.

24. Conclusions • Discussion and findings • Students are likely to drop online e-learning courses if they have a lower college status and are in an earlier term of their academic studies. • Less experience students tend to drop more frequently than experience students. • Want to have higher grade.

25. Conclusions • Contributions • Inspire additional studies for this complex phenomenon and may spark future studies on factors behind the higher dropout rate in e-learning courses. • To reduce students frustrations and build mechanisms to help reduce dropout rates from e-learning courses.

26. Conclusions • Limitations • Low sample size • Wide range of students’ majors • Wide variety and diverse subjects of courses

27. Conclusions • Suggestions • Concentrate on measuring the factors within one or two closely related subjects to add reliability. • Uncover all of the factors that impact dropout from online e-learning courses. • Should use a less diverse population of courses and students majors.