1. Does Student- Teacher Interaction Matter in Distance Education? WAYNE FREEMAN DOUGLAS GLASS ST. ANDREWS UNIVERSITY A BRANCH OF WEBBER INTERNATIONAL UNIVERSITY

2. Overview Problem Literature Review Research Question Methods Analysis Discussion Conclusions

3. A previous version of this research was presented at the SoTL Conference recently in Savannah, Ga. Previous Presentation

4. The Problem How can student outcomes best be improved in distance education (DE) and at what ‘costs’?

5. Literature Review “Distance education (DE) can be much better and also much worse than classroom instruction (CI) based on measured academic outcomes” Research methodologies in DE are “woefully inadequate and poorly reported” Research should focus on what makes DE effective or ineffective – not on comparing CI and DE. (Bernard R. M., et al., 2009)

6. Literature Review Student-Teacher interaction highly valued & course was more satisfying (Nichols, 2011) Interaction an integral component of DE (Holden & Westfall, 2006) Asynchronous DE courses more positive versus synchronous DE courses compared to Classroom Instruction (Bernard, R.M., et.al., 2004)

7. Interaction is Important Student Content (Anderson 2003) Student TeacherStudent MODES OF INTERACTION

8. Interaction Equivalency Theorem Any one of them? Thesis 1 - Quality Student- Content Student- Student Student- Teacher Student- Content Student- Content Student- Content Student- Teacher Student- Teacher Student- Student Thesis 2 - Quantity Increased interaction = Higher learning quality? But more costs and time (Anderson 2003) (Miyazoe & Anderson, 2011)

9. Research Question How does a low level of student-teacher Interaction impact student satisfaction and achievement when student-content interaction is high?

10. The Pilot Study

11. Methods Research Design The quality of the quantitative literature of distance education (DE) is poor! lack of experimental control lack of procedures for randomly selecting participants lack of random assignment to treatment group poorly designed dependent measures failure to account for a variety of variables related to the attitudes of students and instructors (Bernard R., et. al. 2010)

12. Methods Research Design of Present Study Quasi-Experimental Sample – Undergraduate and graduate students at a small liberal as college in the South Control Group – students enrolled in an asynchronous tutorial with no facilitator (n= 15) Treatment Group – students enrolled in an asynchronous tutorial with a facilitator (n=20)

13. Methods Instrumentation Pre-Tutorial Student Background Survey - Demographics and Self Efficacy for Online Learning (Artino) Test of APA Knowledge Post-Tutorial Test of APA Knowledge Student Satisfaction Survey

14. Methods Data Collection/Preparation Collection Invitation to Participate developed Outreach to potential participants (67 students agreed to participate) Preparation Data consolidation from SurveyMonkey and Moodle Missing values/Multiple Imputation

15. Analysis Descriptive Statistics VariableControlTreatment Gender62% Female 38% Male 49% Female 51% Male Race70% White 27% Black 3% Other 67% White 17% Black 16% Other GPA3.1-3.5 Age26 years old24 years old Online Self-Efficacy4.7 out of 75.4 out of 7 Online Experience1.4 courses

16. Analysis Correlation SATISFACTIONPOSTTEST GROUP ONLINEEXP Significant ONLINESE Significant GENDER Significant AGE Significant GPA

17. Analysis Regression Coefficients a Model (R Square =.346) Unstandardized Coefficients Standardized Coefficients tSig. BStd. ErrorBeta 6(Constant) 49.9937.611 6.568.000 GENDER -13.9032.399-.369-5.795.000 SATIS 2.721.569.2864.783.000 ONLINEEXP 2.446.548.2814.466.000 PREQUIZ.349.100.2143.492.001 GROUP 7.7892.271.2063.430.001 RACE -5.5021.709-.199-3.219.002 a. Dependent Variable: POSTQUIZ

18. Analysis Analysis of Covariance (ANCOVA) FSig. GROUP 1.453.230 No significant difference in post-test scores between the Control and Treatment Groups

19. Limitations Small sample size/Low statistical power Convenience sample Self-reported data Limited to tutorial, not full course Measurement of satisfaction

20. Discussion/Conclusions Statistical significance in regression Singular pedagogy tested Student Motivations/ Attitudes ₋Learning Styles - see-hear-do ₋‘in’ vs ‘at’ college ₋Task Value Validity/ reliability across disciplines

21. References Andreson, T. (2003). Modes of interaction in distance education: Recent developments & research questions (Vol. Handbook of Distance Education). (M. Moore, Ed.) Mahwah, NJ: Lawrence Eelbaum. Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E. (2009). A meta-analysis of three types of inrteraction treatments in distance education. Review of Educational Research, 1243-1289. Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., & Wozney, L. (2004). How does distance education compare with classroom instruction? A meta analysis of the empirical literature. Review of Educational Research 3(74), 260-277. Holden, J. T., & Westfall, P. J.-L. (2006). An instructional media selection guide for distance learning. Boston: United States Distance Learning Association. Mayer, R. (2001). Multi-Media Learning. Cambridge, UK: Cambridge University Press. Miyazoe, T., & Anderson, T. (2009). The Interactive Equivalency Theorem: Research Potential and Its Application to Teaching. (pp. 1-6). Madison: 27th Annual Conference on Distance Teaching & Learning. Miyazoe, T., & Anderson, T. (2010 9(2)). The interactive equivalency theorem. Journal of Interactive Online Learning, 94-104. Nichols, J. (2011). Comparing Educational Leadership Course and Professor Evaluations in on-line and traditional instructional formats: What are the Students saying? College Student Journal 45(4), 862-868. Russell, T. L. (1999). The No Significant Difference Phenomenon. Chapel Hill: Office of Instructional telecommunications, NC State University.

22. Contact Information Wayne Freeman - freemanew@sa.edu Douglas Glass – glassdw@sa.edu