20080223 Lasvegas Conference Presentation

“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)

[email protected]

http://LMS4U.kr

ASBBS 15th International Conference, Las Vegas, USA,

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Contents

IIS Model transition e-Learner Research model PLS Analysis Discussion Contribution


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Theoretical background

Research purpose Suggest model of e-Learning effectiveness

Theoretical background Information system theory

TAM ISS Model

Education engineering theory Constructivism education

others Empathy & self-regulatory efficacy


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ISS Model transition

D&M IS Success Model, 1992

Updated D&M IS Success Model, 2003

Seddon & Kiew Model, 1997

Pitt et al. Model, 1995

System quality

Informationquality

use

Usersatisfaction

Individualimpact

Organizationalimpact

Servicequality

Systemquality

Informationquality

use

Usersatisfaction

Individualbenefit

Servicequality

PEOU

Informationquality

Perceivedusefulness

Usersatisfaction

System quality

Informationquality

use

Usersatisfaction

Individualimpact

Organizationalimpact


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Application of ISSLearning content

quality

Interaction servicequality

LMS quality

Learning environmentSatisfaction

IT AdaptedSERVQUAL

Contextual quality

Representational quality

IT AdaptedSERVQUAL

IT AdaptedSERVQUAL

satisfaction


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e-Learner

Learning achievement ability Self-directed learning

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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Correlation coefficient of construct and AVE

Variable

SA IQ SVC SQ ESP SRS SRE EMPRR

SA 0.824

IQ 0.624 0.763

SVC 0.667 0.648 0.822

SQ 0.542 0.623 0.612 0.757

ESP 0.541 0.427 0.445 0.367 0.952

SRS 0.442 0.425 0.421 0.361 0.489 0.802

SRE 0.448 0.443 0.427 0.402 0.489 0.598 0.755

EMP 0.465 0.448 0.525 0.383 0.395 0.456 0.494 0.801

RR 0.255 0.140 0.197 0.139 0.356 0.260 0.179 0.053 1.000

Symmetrical value is square root of AVE


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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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Thank You.

Have a good time...

20080223 Lasvegas Conference Presentation

Education

Transcript of 20080223 Lasvegas Conference Presentation