Assessment of an online learning community from Technology Acceptance Model in Education

I-Fan Liua,*

, Meng Chang Chen b, Yeali Sun

a, David Wible

c, Chin-Hwa Kuo

d

aDepartment of Information Management, National Taiwan University, No.1, Sec. 4, Roosevelt

Rd., Taipei City 106, Taiwan b

Institute of Information Science, Academic Sinica, Taiwan c

Graduate Institute of Learning and Instruction, National Central University, Taiwan d Department of Computer Science and Information Engineering, Tamkang University, Taiwan

E-mail: D93725006@ntu.edu.tw

Abstract

Online learning communities are gradually altering the traditional learning style of people because of the pervasiveness of the Internet. The environment of the online learning community has been formed gradually as more and more people join websites and learn from each other. A total of 436 senior high school students in Taiwan participated in this research. To test the hypotheses of this research, we use structural equation modeling (SEM) method for validation. All hypotheses we proposed were supported. Finally, we list several implications of this research results as guidelines for developing an online learning community for future study.

1. Introduction

Online learning communities are gradually altering the traditional learning style of people because of the pervasiveness of the Internet. Members of these communities come from various places, and have different educational backgrounds and different proficiency levels; however, they meet for the mutual intention of learning a common subject, such as English learning. As a result, it is possible to create knowledge and share it with a large number of people via the Internet [1].

English has become an important tool of international communication in the era of globalization because of more frequent international exchanges for business and educational purposes. As a non-English speaking country, it is important for Taiwan, to

improve citizens’ English proficiency so that they can connect with the international community.

2. TAM Model

Davis [2] proposed the Technology Acceptance Model (TAM) to research the impact of technology on users’ behavior. The focus is on the process of using technology, where “perceived usefulness” and “perceived ease of use” are the two key factors affecting the intention of users to adopt a technology. Based on TAM as well as the extension and modification of the model in accordance with related literature, we propose a new conceptual model that can predict learners’ intention to use online learning communities.

2.1. External Variables

McGiven [3] observed that online course design is a key factor in determining the success or failure of online learning. Subsequently, Berge [4] suggested that online course design should be considered from the viewpoint of interaction between instructor and learner and peer-to-peer. Rovai [5] also pointed out that the requirements of learners should be considered when designing an online curriculum, so that the online learning environment meets learners’ needs is very important. This leads to the following hypotheses: H1. Online course design will positively affect the Perceived Usefulness of an online learning program. H2. Online course design will positively affect Perceived Ease of Use of an online learning program.

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H3. Online course design will positively affect Perceived Interaction with an online learning community.

In this study, we would like to investigate whether users are willing to adopt an online learning community. Therefore, we incorporate Intention to Use an Online Learning Community as the outcome variable in our conceptual model.

A well-designed user interface can help users manipulate a system more easily and reduce their cognitive load. Liu et al. [6] also pointed out that an interactive interface design could quickly guide users towards the correct way of learning. Thus, we have the following hypotheses:

3. Methodology 3.1. Subjects

H4. User interface design will positively affect the Perceived Ease of Use of an online learning community.

IWiLL (Intelligent Web-based Interactive Language Learning) is an online learning community in Taiwan for people who wish to learn a foreign language. A total of 592 senior high school students completed the questionnaire, and 436 of the responses were valid. Thus, the valid response rate was 73.6%.

H5. User interface design will positively affect Perceived Interaction with an online learning community.

Users may feel uncomfortable with computer assisted learning if they lack experience in using a computer [7]. Research has shown that previous online learning experience can affect learners’ perception of a new online curriculum [8]. Thus, we propose the following hypotheses:

4. Model Testing Results

The results of SEM are summarized in Table 1. Following previous researchers, we made some modifications to fit the entire model, such that the actual values of the ten indices listed are above the threshold of the recommended value. The entire model presents a good model fit, which means the actual data matches the conceptual model.

H6. Previous online learning experience will positively affect the Perceived Usefulness of an online learning program.H7. Previous online learning experience will positively affect Perceived Ease of Use of an online learning program. Table 1. Statistics of model fit measures H8. Previous online learning experience will positively affect the Intention to Use an Online learning community.

Model fit measure Recommended value

Model value

1. ../2 fdχ < 3.0 2.422. GFI > 0.9 0.903. AGFI > 0.8 0.874. NFI > 0.9 0.985. NNFI > 0.9 0.996. RFI > 0.9 0.987. IFI > 0.9 0.998. RMR < 0.05 0.039. RMSEA < 0.08 0.0510. Critical N > 200 231.84

2.2. Perceived Variables

Harrington and Levy [9] pointed out that while learning a foreign language, the relationship between face-to-face and computer-mediated interaction, as well as the impact of using technology on learning of a language should all be considered. Thus, we put forward the following hypotheses: H9. Perceived Ease of Use will positively affect the Perceived Usefulness of an online learning program. H10. Perceived Ease of Use will positively affect the Perceived Interaction with an online learning program.

Figure 2 shows the causal relationship between constructs and the standardized path coefficients, R2.We apply a t-test to examine the statistical significance. We observe that Online Course Design had a significant positive effect on Perceived Usefulness (=0.56, p<0.001), Perceived Ease of Use ( =0.22,p<0.05), and Perceived Interaction ( =0.44, p<0.001).Hypotheses H1, H2, and H3 were supported.

H11. Perceived Usefulness will positively affect the Intention to Use an Online Learning Community. H12. Perceived Ease of Use will positively affect Intention to Use an Online Learning Community. H13. Perceived Interaction will positively affect Intention to Use an Online Learning Community.

2.3. Outcome Variables

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(1) The Intention to Use an Online Learning Community is strongly and directly affected by Perceived Usefulness and indirectly by Online Course Design. Thus, when adopting an online English learning community in the future, we recommend that a comprehensively designed online English course should become the first priority. By developing user-centered programs, we will be better able to satisfy the needs of users.

05.0* <p ; ;01.0** <p 001.0*** <p

(2) Encourage users to gain experience of online learning or learning English with information technology. For example, users could surf other English learning websites in advance so that it is easier to adapt to a possibly more complicated online learning environment in the future.

(3) Some efforts should be made in designing the user interface. For example, English vocabulary and phrases could be shown by multimedia like flash animation so that a simple way could be used by the system to get learners deeply interested in English online learning.

Figure 2. The proposed model’s test results

OCD=Online Course Design; UID=User Interface Design; POLE=Previous Online Learning Experience; PU=Perceived Usefulness; PEOU=Perceived Ease of Use; PI=Perceived Interaction; IUOLC=Intention to Use an Online Learning Community

6. References User Interface Design had a significant positive effect on Perceived Ease of Use ( =0.47, p<0.001)and Perceived Interaction ( =0.17, p<0.05).Hypotheses H4 and H5 were also supported. Previous Online Learning Experience had a significant positive effect on Perceived Usefulness ( =0.15, p<0.05),Perceived Ease of Use ( =0.15, p<0.05), and Intention to Use an Online Learning Community (=0.31, p<0.001). Hypotheses H6, H7, and H8 were also supported. Perceived Ease of Use had a significant positive effect on Perceived Usefulness ( =0.21,p<0.001) and Perceived Interaction ( =0.29,p<0.001). Hypotheses H9 and H10 were supported.

[1] Jin, Q., “Design of a virtual community based interactive learning environment”, Information Science, 40(1-2), 171-191, 2002. [2] Davis, F. D. (1986). Technology acceptance model for empirically testing new end-user information systems: theory and results. MA, USA: Massachussetts Institute of Technology, 1986. [3] McGiven, J., “Designing the learning environment to meet the needs of distant students”, Journal of Technology and Learning, 27(2), 52–57, 1994. [4] Berge, Z. L., “Interaction in post-secondary web-based learning”, Educational Technology, 39(1), 5–11, 1999. [5] Rovai, A. P., “A constructivist approach to online college learning”, The internet and higher education, 7(2), 79-93, 2004.Paths that affect the Intention to Use an Online

Learning Community have an explained variance of 0.76. Apart from Previous Online Learning Experience, such paths include Perceived Usefulness ( =0.44,p<0.001), Perceived Ease of Use ( =0.12, p<0.05),and Perceived Interaction ( =0.12, p<0.05). Finally, hypotheses H11, H12, and H13 were also supported.

[6] Liu, I. F., Chen, M. C., and Sun, Y., “The Design of a Web-Based Learning Platform: A Case Study in Taiwn”, The 14th International Conference on Computers in Education (ICCE2006), Beijing, China, 2006. [7] Reed, W. M., Oughton, J. M., Ayersman, D. J., Giessler, S. F., and Ervin, J. R Jr, “Computer experience, learning style, and hypermedia navigation”, Computers in Human Behavior, 16, 619-628, 1995.

5. Conclusion [8] Cereijo, M.V.P.,Young, J., and Wilhelm, R.W., “Factors facilitating learner participation in asynchronous Web-based courses”, Journal of Computing in Teacher Education, 18(1), 32–39, 1999.

The contribution made by the research is it adds external variables to the original TAM, and uses an extra perceived variable to predict the use of an online learning community. As this is an English learning community, we now list several implications of the research results as guidelines for developing an online English learning community.

[9] Harrington, M., Levy, M., “CALL begins with a “C”: interaction in computer-mediated language learning”, System,29(1), 15-26, 2001.

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