A Correlation Study: The A Correlation Study: The effect of online quality on effect of online quality on students’ learning outcome students’ learning outcome and attrition and attrition

By Dr. Joselina Cheng and Dr. Lisa MillerBy Dr. Joselina Cheng and Dr. Lisa Miller

Problem StatementProblem Statement

online courses is that they online courses is that they undermined course content, quality, undermined course content, quality, and design, affecting student and design, affecting student learning outcomes and course learning outcomes and course completion completion

Purpose StatementPurpose Statement

This quantitative research study This quantitative research study explores the relationship between explores the relationship between online course quality and online online course quality and online student learning outcomes and the student learning outcomes and the relationship between online course relationship between online course quality and online student attrition at quality and online student attrition at UCO for the fall semester of 2007.UCO for the fall semester of 2007.

Research Questions (RQ)Research Questions (RQ)

RQ1: What is the relationship between online RQ1: What is the relationship between online course quality and online student learning course quality and online student learning outcomes?outcomes?

RQ2: What is the relationship between online RQ2: What is the relationship between online course quality and online student attrition?course quality and online student attrition?

Research Question #1Research Question #1

Research Question #2Research Question #2

Research HypothesesResearch Hypotheses

HHaa1: There is a relationship between online 1: There is a relationship between online

course quality and online student learning course quality and online student learning outcomes.outcomes.

HHaa2: There is a relationship between online 2: There is a relationship between online

course quality and online student attrition.course quality and online student attrition. Methodology Methodology

Findings for Hypotheses Findings for Hypotheses

MethodologyMethodology

Research Design: Quantitative Research Design: Quantitative Method: CorrelationMethod: Correlation Time Dimension: Cross-Sectional (fall Time Dimension: Cross-Sectional (fall

2007 Semester)2007 Semester) Location: University of Central Location: University of Central

Oklahoma (UCO)Oklahoma (UCO) Population/Sample: Students of Population/Sample: Students of

certified online courses at UCOcertified online courses at UCO

Limitations/DelimitationLimitations/Delimitation

Small Sample SizeSmall Sample Size One Higher Education InstitutionOne Higher Education Institution One SemesterOne Semester Geographic Boundary Geographic Boundary

Research SignificanceResearch Significance Traditional universities are no longer protected Traditional universities are no longer protected

by geographic service due to intensive by geographic service due to intensive competition from virtual universities.competition from virtual universities.

Stakeholders of traditional universities can gain Stakeholders of traditional universities can gain insights from the study to provide quality online insights from the study to provide quality online education to sustain long-term education to sustain long-term competitiveness.competitiveness.

RecommendationsRecommendations Strategic leadershipStrategic leadership Institutional sponsorshipInstitutional sponsorship

24 * 7 help desk24 * 7 help desk Supportive policiesSupportive policies

Learning organizationLearning organization Multi-tier development for online students’ and faculties’ Multi-tier development for online students’ and faculties’

preparedness – ie: time management, webCT training, preparedness – ie: time management, webCT training, multimedia-based learning modules to address learning multimedia-based learning modules to address learning styles.styles.

Double-loop learning process – revise the feedback Double-loop learning process – revise the feedback mechanism and measures to detect students’ e-learning mechanism and measures to detect students’ e-learning obstacles obstacles

Future Studies Future Studies

Repeat the study with larger sample sizeRepeat the study with larger sample size Extend the study with a longer timeframe. Extend the study with a longer timeframe. Further analysis by breaking down “total Further analysis by breaking down “total

withdrawals” to the following:withdrawals” to the following: Student factors - Never attend class, job change, lack Student factors - Never attend class, job change, lack

of technical competency, lack of time managementof technical competency, lack of time management Faculty factors – Disorganized, lack of timely Faculty factors – Disorganized, lack of timely

feedbacks, etc.feedbacks, etc. Institutional factors – Lack of IT support, lack of Institutional factors – Lack of IT support, lack of

training, lack of technological infrastructuretraining, lack of technological infrastructure

Q & AQ & A

For demo of Virtual Tutor, please refer to For demo of Virtual Tutor, please refer to

Dr. Cheng’s website –Dr. Cheng’s website –

http://www.busn.ucok.edu/jcheng/index.htm

ReferencesReferences

Cao, J. (2005). Cao, J. (2005). Learning with Virtual Mentors: Learning with Virtual Mentors: How to make e-learning interactive and How to make e-learning interactive and effective?effective? The University of Arizona, AAT The University of Arizona, AAT 3176282.3176282.

Zhang, D. (2004). Virtual Mentor and the lab Zhang, D. (2004). Virtual Mentor and the lab system – toward building an interactive, system – toward building an interactive, personalized, and intelligent e-learning personalized, and intelligent e-learning environment. environment. The Journal of Computer The Journal of Computer Information Systems. Spring 2004Information Systems. Spring 2004, , 4444 (3) 35-43. (3) 35-43.