“The effects of LMS quality and e- learner’s characteristics regarding e- learner’s scholastic performance: A proposal for e-learning success model 2 ” ASBBS 15 th International Conference Feb. 21-24, 2008 Imperial Palace Hotel, Lasvegas, USA. Jong-Ki Lee (Research Professor, Kyungpook National University, South Korea) [email protected]http://LMS4U.kr
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“The effects of LMS quality and e-learner’s characteristics regarding e-learner’s scholastic performance: A proposal for e-learning success model 2 ”
ASBBS 15th International ConferenceFeb. 21-24, 2008
Imperial Palace Hotel, Lasvegas, USA.
Jong-Ki Lee (Research Professor, Kyungpook National University, South Korea)
e-Learner attend active learning process and promote self inner changes
The teacher in SDL remains available as an assistance and guide of learning; not as a unilateral knowledge source and messenger (Hwang, 1999).
Self-regulated learning strategy The learner must make constant efforts to sustain learning
motivation (Zimmerman, 1990). e-Learning strategy is needed for self-directed learning
and needed instructional design strategy based on self-directed learning.
Self-regulatory efficacy Empathy
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Research model
IQ
SVC
SQ
EMP
SA RR
SRS
H8
H7
H6
H4
H2
SRE
ESP
H1
H3
H5
SA: e-learner’s satisfaction on LMSSQ: system quality on LMSSVC: service quality on interactionIQ: information quality on LMSEMP: e-learner’s empathySRE: self-regulatory efficacy SRS: self-regulated learning strategyESP: e-learner’s expected performanceRR: real record performance
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Hypothesis
H1: A learner's satisfaction with LMS will be positively related to the e-learner’s expected performance.
H2: A learner's self-regulated learning strategy in e-Learning will be positively related to the e-learner’s expected performance.
H3: A learner's self-regulatory efficacy in e-Learning will be positively related to the e-learner’s expected performance.
H4: A learner's empathy will be positively related to the e-learner’s self-regulatory efficacy.
H5: A learner's expected performance will be positively related to the e-learner’s real record performance.
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Hypothesis 2
H6: The system quality of a learning management system will be positively related to the e-learner’s satisfaction.
H7: A learner's assessment of the service quality of interaction between a professor and learner will be positively related to the e-learner’s satisfaction.
H8: A learner's assessment of content quality will be positively related to the e-learner’s satisfaction.
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Methodology
Empirical Research Research contents
Objectives: Daegu University & Hansung Digital University cyber lectures, 4 subjects, 341 students’ questionnaire (41 items)
Scale: Likert-5 point Scale Analysis: PLS method
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PLS analysis
Indicator is fit to formative indicator
Be suited to Initial model stage It is not strict with sample size,
residual distribution. Research construct is fit to use PLS
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Scale
Variable item Researcher
SQ 4 DeLone & McLean (1992)
IQ 7 Lee et al.(2002)
SVC 4 Kettinger & Lee(1997)
SRE 4 Kim and Park (2001)
SRS 4 Zimmerman & Martines-Pons (1988)
EMP 6 Mehrabian & Epstein (1972 )
SA 3 Wang(2003)
RR 1 Real Record
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DemographicsItem Frequency Percent(%)
GenderMale 209 61.3
Female 132 38.7
Age
20-24 217 63.6
25-29 106 31.1
30-34 8 2.3
35-39 3 0.9
40-44 2 0.6
45-49 3 0.9
50 and above 2 0.6
Grade
Freshman 50 14.7
Sophomore 92 27.0
Junior 86 25.2
Senior 113 33.1
Major
Cultural science 137 40.2
Social science 117 34.3
Natural science 29 8.5
Engineering 39 11.4
Art and physical 17 5.0
Etc. 2 0.6
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Demographics (2)
Item Frequency Percent(%)
E-Learning career
First 153 44.9
2-3 times 146 42.8
4-5 times 36 10.6
6 times and above 6 1.8
Courses taken during e-Learning career
1 subject 179 52.5
2-3 subjects 122 35.8
4-5 subjects 13 3.8
6-7 subjects 22 6.5
8 subjects and above 5 1.5
Computer use time(1day)
Less than 1 hour 28 8.2
1-2 hours 179 52.5
3-6 hours 110 32.3
7-14 hours 20 5.9
15 hours and above 4 1.2
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Demographics (3)
Item Frequency Percent(%)
e-Learning time(1 week)
Less than 1 hour 10 2.9
1-2 hour 189 55.4
3-4 hour 100 29.3
5-7 hour 27 7.9
8 hours and above 15 4.4
Degree
Middle school graduation 4 1.2
High school graduation 233 68.3
University dropout 26 7.6
College graduation 25 7.3
University graduation 53 15.5
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Convergent validity
Construct Composite reliability AVE Cronbach α
SA 0.893 0.679 0.861
IQ 0.907 0.583 0.892
SVC 0.936 0.677 0.923
SQ 0.842 0.574 0.780
ESP 0.960 0.856 0.948
SRS 0.878 0.644 0.815
SRE 0.842 0.571 0.748
EMP 0.915 0.642 0.888
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Discussion
All hypothesis is accepted. Motivation problem
E-learner’s voluntary motivation is very important variable Many students are used to web based Information system.
Satisfaction on LMS is important to expected performance
E-learner’s empathy effect on self-regulatory efficacy for e-learning performance
Self-regulatory efficacy is important variable to e-learning performance
Self-regulated learning strategy is also important variable to e-learning performance
We must upgrade e-professor’s service quality on LMS
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Contribution
Theoretical contribution Suggestion of interdisciplinary model(IS, Education
engineering) Service quality is measured by interaction Confirm self-regulated learning strategy as important
variable in e-learning Confirm SRE as important variable in e-learning Confirm empathy as important variable in e-learning
performance
Practical contribution Divided e-Learning into learning contents and LMS Needed customizing to self-regulated learning strategy Needed customizing to self-regulatory efficacy Needed customizing to e-learner’s empathy in e-
learning
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Limitation of Research
Research of various sample Various subject Various organization and student.
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