THE RELATIONSHIP OF E-LEARNER’S SELF-

REGULATORY EFFICACY AND PERCEPTION OF E-LEARNING

ENVIRONMENTAL QUALITY

Presented by: Alemayehu E., Chinun B. and Kflezgi O.Central China Normal University

Researchers:Jong-Ki Lee, Woong-Kyu Lee

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COMPUTERS IN HUMAN BEHAVIOR Journal Published on 2008 Vol.24, 32 - 47SSCI Impact factor: 1.344 (2008), 2.88 (2015)

RESEARCHERS

Jong-Ki Lee

School of Business Administration,Kyungpook National University,Republic of Korea

Woong-Kyu Lee

School of Business Administration,Daegu University,Republic of Korea

INTRODUCTION Backgrounds & theories

IT BACKGROUNDS: E-Learning

Internet Technology Web-based Applications

Why is E-Learning User increasing in 2008?

WHAT HAPPENED IN 2008:“BACK TO THE FUTURE”

Beginning time of the digital world

BACKGROUNDS:

E-learning System Provider

E-LearningEnd Useras Self-Directed Learning

●The System’s User SatisfactionThe Quality of the Information

●The Learning EnvironmentThe Study Ability The Learner’s Regulatory

●Academic Achievement (degree or diploma)

INFORMATION SYSTEM SUCCESS MODEL: DeLone & McLean’ s ISS model (1992)

INFORMATION SYSTEM SUCCESS MODEL: DeLone & McLean ’ s an intergrated ISS model (2003)

RESEARCH MODEL Questions and Hypothesis

QUESTIONS: Based on DeLone & McLean ISS Model (2003)

Are there some relations between

Self-Regulatory Efficacy (SRE) And ISS Model variables ?

RESEARCH MODEL The Model is divided into 3 main parts to generate hypothesis.

Satisfaction and Academic Performance

Self-regulatory Efficacy (Moderating Variable)

Quality of e-Learning Environment

RESEARCH MODEL FRAMEWORK

HYPOTHESIS: H1: Satisfaction of e-Learning Environment (+ ➤) Academic Performance H2: Ease of Use LMS (+ ➤) Satisfaction of e-Learning Environment H3: Usefulness of Use LMS (+ ➤) Satisfaction of e-Learning Environment H4: Ease of Use LMS (+ ➤) Usefulness of Use LMS H5: Service of Quality (+ ➤) Satisfaction of e-Learning Environment H6: Information Representational Quality (+ ➤) Satisfaction of e-Learning Environment H7: Information Contextual Quality (+ ➤) Satisfaction of e-Learning Environment H8: Information Representational Quality (+ ➤) Information Contextual Quality

MODERATING VARIABLE HYPOTHESIS:

Researchers Studied Self-regulatory Efficacy in

higher learner VS lower Learner To define their relations

Based on ISS Model

MODERATING VARIABLE HYPOTHESIS:

H1a: SRE in > on SA → PA H2a: SRE in > on PEOU → SA H3a: SRE in > on PU → SA H5a: SRE in > on SQ → SA H6a: SRE in > on IRQ → SA H7a: SRE in > on ICQ → SA

Note: higher Lower group group

METHODOLOGY Population and Method

◉ Cross Sectional Study (2004) ◉ 37-item Likert’s 5 Points Scale Questionnaire

◉ Studied Population:

Students Students enrolled 225 Daegu University e-Learning course Participants

RESEARCH METHODOLOGY

▶ ▶

DEMOGRAPHY

W: 102 M:123

1st: 3 2nd: 39

3rd: 62 4th: 121

Culture Social Sci Science Engineer Physic74 95 16 32 8

e-Ever: 102 e-Never:123

RESULTS Key findings

RELIABILITYReliabilityStability or consistency of the measurementCronbach’s coefficient alpha (Muijs, 2004:73)

RELIABILITY:

RELIABILITY:

VALIDITY Validity It means to what extent an instrument measures what it intends to measure (Cohen, et al., 2000:105).

The main purpose of validity is to enhance the accuracy of the findings by avoiding or controlling the confounding variables

DEMOGRAPHIC ATTRIBUTES: Used AVE with Cronbach ⍺

AVE Advertising Value Equivalency is a measure that has been used in the public relations industry to 'measure' the benefit to a client from media coverage of a PR campaign. AVE's would commonly measure the size of the coverage gained, its placement and calculate what the equivalent amount of space, if paid for as advertising, would cost.

Used Correlation Coefficient of construct and AVE

The suggested measure model is estimated as a good discriminate validity because the AVE value is higher than the correlation coefficient of other constructs as shown in the Table.

DATA ANALYSIS

PLS (Partial Least Square)

It fits multiple response variables in a single model. Because PLS regression models the response variables in a multivariate way, the results can differ significantly from those calculated for the response variables individually.

You should model multiple responses in a single PLS regression model only when they are correlated.

PLS ANALYSIS OF FULL MODEL

PLS ANALYSIS OF SRE’S LOWER GROUP

PLS ANALYSIS OF SRE’S HIGHER GROUP

PLS ANALYSIS OF SRE’S LOWER GROUP AND HIGHER GROUP

Groups AP SA

SRE’s LG of e-learners

12.7% 60.9%

SRE’s HG of e-learners

16.7% 48.9%

GROUP ANALYSIS OF SRE

STATISTICAL HYPOTHESIS REJECTIONS:

DISCUSSION Consideration by augment

CONCLUSION & SUGGESTION

◉ A New model of ISS + SRE = One of Human Services to e-learner

◉ Quality of System = LMS + Quality of Assessment of Interaction Information and System

◉ Confirmation that SRE’s higher learner has high self-study and perceived learning strategy.

CRITICAL REVIEW Strength and weakness

E-LEARNING PROVIDERS:FROM 2008-2016

STRENGTHS-The researcher continually works on e-Learning and many articles related e-learning are published.

-Studied Samples are in the normal distribution; not differences between genders.

-e-Learning allows flexible learner-centered education provides inter-disciplinary approach

WEAKNESS- In the Article the number of students, who took an analysis questionnaire, is not clearly defined.

-To make the reader understand, the source of number indicated in Tables should be provided•

The 4th hypotheses correlation has error. Which is LMS to LMS. So, we suggested to be correlated with PU.

• In our group discussion we agreed the Topic had to be written e-Learners’ instead of e-Learner’s

EDUCATION = FUTURE So enjoy it!

REFERENCES:Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and higher education, 15(1), 3-8.

Gómez-Aguilar, D. A., Hernández-García, Á., García-Peñalvo, F. J., & Therón, R. (2015). Tap into visual analysis of customization of grouping of activities in eLearning. Computers in Human Behavior, 47, 60-67.

Kuo, Y.-C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and higher education, 20, 35-50.

Lee, J.-K., & Lee, W.-K. (2008). The relationship of e-Learner’s self-regulatory efficacy and perception of e-Learning environmental quality. Computers in Human Behavior, 24(1), 32-47.

REFERENCES:Onete, B., Vasile, V., & Teodorescu, I. (2016). APPLYING CRM TECHNIQUES IN ENHANCING eLEARNING. Paper presented at the The International Scientific Conference eLearning and Software for Education.

Sklar, E. S. (1976). The Possessive apostrophe: the development and decline of a crooked mark. College English, 38(2), 175-183.