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Does Student-Teacher Interaction Matter in Distance Education?
WAYNE FREEMAN
DOUGLAS GLASSST. ANDREWS UNIVERSITY
A B RA N C H O F W E B B E R I N T E R N AT I O N A L U N I V E R S I T Y
A previous version of
this research was
presented at the SoTL
Conference recently in
Savannah, Ga.
Previous Presentation
The Problem
How can student outcomes best be improved in distance education (DE)
and at what ‘costs’?
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)
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)
Interaction is Important
Student <-> Content
(Anderson 2003)
Student <-> Teacher Student <-> Student
MODES OF INTERACTION
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)
Research Question
How does a low level of student-teacher Interaction impact student satisfaction and
achievement when student-content interaction is high?
MethodsResearch 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)
MethodsResearch 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)
MethodsInstrumentation
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
MethodsData 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
AnalysisDescriptive Statistics
Variable Control Treatment
Gender 62% Female38% Male
49% Female51% Male
Race 70% White27% Black3% Other
67% White17% Black16% Other
GPA 3.1-3.5 3.1-3.5
Age 26 years old 24 years old
Online Self-Efficacy 4.7 out of 7 5.4 out of 7
Online Experience 1.4 courses 1.4 courses
AnalysisCorrelation
SATISFACTION POSTTEST
GROUP
ONLINEEXP Significant
ONLINESE Significant
GENDER Significant
AGE Significant
GPA
AnalysisRegression
Coefficientsa
Model (R Square = .346)
Unstandardized Coefficients
Standardized Coefficients
t Sig.B Std. Error Beta6 (Constant) 49.993 7.611 6.568 .000
GENDER -13.903 2.399 -.369 -5.795 .000SATIS 2.721 .569 .286 4.783 .000ONLINEEXP 2.446 .548 .281 4.466 .000PREQUIZ .349 .100 .214 3.492 .001GROUP 7.789 2.271 .206 3.430 .001RACE -5.502 1.709 -.199 -3.219 .002
a. Dependent Variable: POSTQUIZ
AnalysisAnalysis of Covariance (ANCOVA)
F Sig.GROUP 1.453 .230
No significant difference in post-test scores between the Control and Treatment Groups
Limitations• Small sample size/Low statistical power• Convenience sample• Self-reported data• Limited to tutorial, not full course• Measurement of satisfaction
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
ReferencesAndreson, 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.