Scholarly article on topic 'A Study of EFL College Students’ Acceptance of Mobile Learning'

A Study of EFL College Students’ Acceptance of Mobile Learning Academic research paper on "Economics and business"

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Abstract of research paper on Economics and business, author of scientific article — Hsin-Hui Chung, Shu-Chu Chen, Min-Hsiu Kuo

Abstract Mobile devices with Internet applications have dramatically increased the convenience of accessing information for EFL college students in language learning. This study used Technology Acceptance Model as a theoretical framework to examine the factors related to Taiwanese EFL college students’ behavioral intention to use mobile English vocabulary learning resources. Data collected from the questionnaires of eighty four EFL tertiary level college students were analyzed by using correlation analyses and regression. Results showed that the participants’ behavioral intentions had high positive correlations with mobile devices’ compatibility, self-efficacy, perceived ease of use respectively. It had a moderate positive relationship with usefulness. Regression analyses showed that perceived usefulness, perceived ease of use, self-efficacy, and compatibility account for 71% of the variance explained in behavioral intentions to use mobile English vocabulary learning resources. Compatibility is the best predictor of users’ behavioral intention of use.

Academic research paper on topic "A Study of EFL College Students’ Acceptance of Mobile Learning"

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Procedia - Social and Behavioral Sciences 176 (2015) 333 - 339

IETC 2014

A study of EFL college students' acceptance of mobile

learning

Hsin-Hui Chung a, Shu-Chu Chena*, Min-Hsiu Kuob,

a National Yunlin University of Science and Technology, University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C bNational Chia-Yi Senior High School, No. 738, Sec. 2, Daya Rd., East Dist., Chiayi City 600669, Taiwan, R.O.C

Abstract

Mobile devices with Internet applications have dramatically increased the convenience of accessing information for EFL college students in language learning. This study used Technology Acceptance Model as a theoretical framework to examine the factors related to Taiwanese EFL college students' behavioral intention to use mobile English vocabulary learning resources. Data collected from the questionnaires of eighty four EFL tertiary level college students were analyzed by using correlation analyses and regression. Results showed that the participants' behavioral intentions had high positive correlations with mobile devices' compatibility, self-efficacy, perceived ease of use respectively. It had a moderate positive relationship with usefulness. Regression analyses showed that perceived usefulness, perceived ease of use, self-efficacy, and compatibility account for 71% of the variance explained in behavioral intentions to use mobile English vocabulary learning resources. Compatibility is the best predictor of users' behavioral intention of use.

© 2015TheAuthors.PublishedbyElsevierLtd.This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the Sakarya University.

Key words: behavioral intention, perceived usefulness, perceived ease of use

1. Introduction

With the advance of modern technology, the society has been transformed to 'mobile society'. Mobile learning technologies have influenced many aspects of education, and provide new method for instructors to deliver knowledge and motivate students to engage in various learning activities (Derting & Cox, 2008;Mitra, 2007; Siozos et al., 2009).Through mobile learning, people can download different English learning apps to their smartphones,and other mobile devices via Apple App Store,Google Play,Windows Phone Store, and BlackBerry App World. Also, because of the rapid development of mobile technology in higher education, students using mobile devices with Internet accesses have expanded communication methods, opportunities for collaboration, access to traditional learning and information resources (Donaldson, 2010).Thus, mobile learning will become the milestone of the technology education.

* Corresponding author. E-mail address:chensc@yuntech.edu.tw

1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the Sakarya University.

doi:10.1016/j.sbspro.2015.01.479

2. The research models

The research direction of recent studies have focused on implementation of mobile learning in developed countries (James, 2008), environment settings for mobile learning (Brown, & Parsons, 2006; Chao & Chen, 2009; Liu& Jin, 2008; Virvou & Alepis, 2005), and users' acceptance in mobile learning (Liu & Li, 2009; Phuangthong & Malisawan, 2005).Many theories have been proposed to account for the user's acceptance of technology. One of the most widely used models to explain a potential user's behavioral intentions of using a technological innovation is Technology Acceptance Model (TAM) proposed by Davis (1989).

This model, with high reliability and validity as reported in Adams (1992), included the constructs of perceived ease of use, perceived usefulness, and attitudes towards using and behavioral intention of use (1989). Based on the theory, users' perceived ease of use influenced the intention of users thereby affecting perceived usefulness (Davis, 1989).TAM has been applied in a wide variety of contexts and many technology acceptance studies identified the factors influencing the users' behavioral intentions of use and the actual use of mobile learning technology devices. These studies also showed the weight of these factors may differ as a function of different user types and e-learning technology types. For example, Ronnie, Christopher and Eugenia (2011) adopting the Technology Acceptance Model (TAM) examined students' behavioral intention to use an electronic portfolio system. The result showed that perceived ease of use (PEOU) had a significant influence on learners 'attitudes towards usage.

Seliaman and Turki (2012), based on Acceptance Model (TAM), explored Saudi university students' use of mobile devices and smart phones for accessing their course materials, searching for information related to their disciplines, sharing knowledge, conducting assignments. The results showed that students' perceived usefulness of mobile learning was closely related to the factors including their course materials accessing, searching for information related to their disciplines, sharing knowledge, finishing their homework.

Also, many researchers investigated students' attitudes and perceptions toward a new technology such as mobile learning, podcasting, and other technology-based applications (Al-Fahad, 2009; Alghazo, 2006; Andone et al., 2007; Boon et al., 2007; Croop, 2008; Fozdar & Kumar, 2007; Stockwell, 2008; Yousuf, 2007).For instance, Ahmad and Steve(2013) investigated the acceptance rate of university students' intention to adopt mobile learning. The result showed that 55% of the students accepted mobile learning in a higher education. The applicability of the TAM has been well supported by a considerable body of previous research across a wide range of educational settings (Pituch & Lee, 2006).

Venkatesh and Davis (1996) revised the TAM model with the inclusion of additional external variables, which influenced a person's acceptance of information systems. One of the important external variable in the TAM research included users' computer self-efficacy. It is found that individuals with high efficacy expectations were more likely to succeed in a given task. High self-efficacy individuals usually work harder and longer than low self-efficacy individuals (Wood & Bandura, 1989).

In addition to self efficacy as one of the external factors, the other factor (i.e. compatibility) was included in this study. Compatibility is related to the reasons why someone adopted new innovations. One of the most popular models is Rogers' Diffusion of Innovations (Sherry & Gibson, 2002). Rogers (2003) identified five constructs in his Diffusion of Innovations model, and it shaped the rate and likelihood of adoption (1995). . This model included the constructs of relative advantage, compatibility, complexity, trialability and observability. In this study, the researcher only chose compatibility as the other external variable because the compatibility of the innovation is closed related to the user's life and practices.

Base on the previous study, Technology Acceptance Model (Davis, 1989) was employed by many different researchers in different study fields. Importantly, studies on the TAM have incorporated domain-specific external variables into the standard model, providing an elaboration of its theoretical structure. Also, some studies also pointed out that the weight of these factors may differ as a function of different user types and e-learning technology types. The present study extended the TAM by including two additional constructs, Self-Efficacy and Compatibility, to provide further insight into the user acceptance in a specific learning context. Therefore, the researches aimed to investigate tertiary level EFL college students' Acceptance toward Mobile vocabulary learning app. The research questions were as follows:

1. What are the relationships among the constructs of the model?

2. Which factor can best predict users' behavioral intention of using mobile vocabulary learning resources?

3. Methodology

3.1 Participants and instruments

The participants were 84 tertiary level EFL college students including 68 male and 16 female Engineering students. They were asked to fill in a 20-item questionnaire, adopted from Davis' Technology acceptance model (1989) and Roger's Innovation Diffusion Theory (1995).The instrument consists of five constructs including perceived ease of use (4 items), perceived usefulness (4 items), self-efficacy (4 items), compatibility (4 items) and using intention (4 items). Using a 5-point Likert scale ranging from 1= strongly disagree to 5= strongly agree, each respondent was asked to indicate the extent to which she/he agreed or disagreed with the given statement. In terms of the reliability for each construct, Cronbach's a value for perceived usefulness was .87; perceived ease of use .86;self efficacy, 0.802; compatibility .901; intention to use .82. The definition of each construct, and the items included in each construct were illustrated below.

3.2 Data analysis

The statistical software SPSS 17 was used to calculate these collected data. For the research questions, descriptive statistics, correlation analyses and multiple regressions were used to answer the research questions.

4. Results

4.1Descriptive statistics of the constructs

Descriptive statistics showed that among the five constructs, ease of use had the highest rating (mean=16.04), followed by self -efficacy (mean=15.95), behavioral intention (mean=15.18) while compatibility (mean=15.02) had the lowest rating.

In terms of each item in the construct of usefulness , referring to the fact that learners believed that mobile vocabulary learning app can promote their efficiency in learning vocabulary, item 5 had the highest rating whereas item 6 had the lowest one(see Table 1).

Table 1. Items in the Construct of Usefulness

Item M SD

5 Using learning vocabulary through mobile phones is not restricted by time and place 4.17 .691

6 Using learning vocabulary through mobile phones can help me access the information I needed. 3.76 .887

7 Using learning vocabulary through mobile phones enhance my effectiveness on my learning. 4.10 .830

8 Learning vocabulary through mobile phones provides helpful guidance in performing tasks. 3.93 .929

In terms of learners' performance in the construct of perceived ease of use, they believed learning vocabulary through mobile phones can save time (see item 2) while "learning vocabulary through mobile phones is convenient" had the lowest rating in the construct (see Table 2).

Table 2. Items in the Construct of Ease of use

Item M SD

1 Learning vocabulary through mobile phones is easy for me. 4.11 .822

2 Learning vocabulary through mobile phones saves time. 4.12 .827

3 Learning vocabulary through mobile phones is convenient. 3.88 .884

4 Learning vocabulary through mobile phones is easy to use. 3.93 .875

According to Innovation Diffusion Model theory, the definition of Self-Efficacy was defined as users' ability of using mobile application through mobile interface. Item 9 had the highest rating while item 12 had the lowest one (see Table 3).

Table 3. Items in the Construct of Self-Efficacy

Item M SD

9 I could complete learning vocabulary tasks through mobile phones if there is no one around to tell me what to do 4.15 .814

10 I could complete learning vocabulary tasks through mobile phones if someone had helped me get started 3.86 .880

11 I could overcome the difficulties encountered when I used mobile phones to learn vocabulary. 3.62 .943

12 I could complete learning vocabulary tasks through mobile phones whatever mobile phones how difficult is. 3.39 1.042

According to Innovation Diffusion Model theory, compatibility refers to the fact that users believed that learning vocabulary through mobile phones was related users' life experiences. Item 16 had the highest rating while item 15 had the lowest one (see Table 4).

Table 4. Items in the Construct of Compatibility

Item M SD

13 To use learning vocabulary through mobile phones, I don't have to change anything I currently do. 3.79 .958

14 Using learning vocabulary through mobile phones does not require significant changes in my existing work routine. 3.80 .861

15 Using learning vocabulary through mobile phones is same as using other software I have used in the past. 3.75 .930

16 Using learning vocabulary through mobile phones can reinforce from computer. 3.85 .898

In terms of Using Intention, it refers to the intention of individuals of using mobile in the future (Taylor & Todd, 1995). Thus, the researcher defined it as a person's intention of using mobile phones in learning vocabulary. As shown in Table 5, item 17 had the highest rating while item 20 had the lowest one.

Table 5. Items in the Construct of Using Intention

Item M SD

17 I am willing to use mobile phones to learn vocabulary 3.99 .843

18 I will continue using mobile phones to learn vocabulary in the future. 3.76 .939

19 Overall, I will learn vocabulary through mobile phones. 3.71 1.001

20 I will recommend others learning vocabulary through mobile phones 3.58 .947

4.2Results of research question 1

Correlation analyses showed that perceived ease of use and perceived usefulness, self-efficacy, compatibility and using intention had high correlation among one another (/<.01). As shown in Table 6, the participants' behavioral intentions had high positive correlations with mobile devices' compatibility (r=.829, /><.001), self-efficacy (r=.762, /><.001), perceived ease of use (r= .709, /><.001) respectively. It had a moderate positive relationship with usefulness (r=.679, p<.001).

Table 6. The Inter- correlation among the constructs

Variables Ease of use Usefulness Efficacy Compatibility Intention

Ease of use -

Usefulness .770** -

Efficacy .692** .67** -

Compatibility .76** .77** .80** -

Intention 70** .67** .76** .82** -

** _p<0.01

4.3Results of research question 2

In order to clarify the relative contribution of these variables, the researchers conducted linear regression analyses. Results showed that perceived usefulness, perceived ease of use, self-efficacy, and compatibility account for 71% (R2=0.719) of the variance explained in behavioral intentions to use mobile English vocabulary learning resources (see Figure 1).

Perceived ease of use and Perceived usefulness explain 54% of the variance in behavioral intention of use (R2=546).Self-efficacy, perceived ease of use and compatibility combined to explain67% (R2 =679) of the variance in perceived usefulness while self-efficacy and compatibility explain 60% (R2=604) of the variance in perceived ease of use.

Note: *** p <.001

Figure 1. Results of Multivariate Regression Analyses

One indicator of the predictive power of path models is to examine the explained variance or R2 values. R2 values are interpreted in the same manner as those obtained from multiple regression analysis. They indicate the amount of variance in the construct that is explained by the path model (Barclay et al., 1995).The path coefficients and explained variances for the proposed model in this study were shown in Figure 1 and Figure 2.According to Figure2, Compatibility can best predict users' behavioral intention of use (p<.001).

Based on the findings in the study, we also found compatibility had significant effects on perceived ease of use and the behavioral intention, which were in agreement with previous studies (Chang & Tung, 2008; Chau & Hu, 2001; Hardgrave et al., 2003; Wu &Wang, 2005).

Self-efficacy

Compatibility

Behavioral Intention of use

T?2 = A 71 Q

R2 = 0.719

Perceived usefulnes s

Perceived Ease of use

Note: * p <.05; *** p <.001

Figure 2 Results of Regression Analysis

5. Conclusion

This study used Technology Acceptance Model as a theoretical framework to examine the factors related to Taiwanese EFL college students' behavioral intention to use mobile English vocabulary learning resources. According to the results, college students' behavioral intentions had high positive correlations with mobile devices.

Regression analyses also showed that Perceived usefulness, perceived ease of use, self-efficacy, and compatibility account for 71% (R2=0.719) of the variance explained in behavioral intentions to use mobile English vocabulary learning resources(see Figure 1).Learners' behavioral intention to use mobile English vocabulary learning resources was significantly determined by Compatibility.

As previous studies demonstrated, we found that the TAM appeared to provide researchers a theoretically sound model used to predict the users' behavioral intention to use the mobile learning systems. According to TAM, perceived usefulness and perceived ease of use had a significant positive effect on learners' behavioral intention to use the m-learning systems. Such was the case in this study; the m-learning systems users thought that the higher perceived usefulness resulted in a higher behavioral intention to use the m-learning systems. Furthermore, these findings supported existing research that both usefulness and ease of use were believed to be important factors in determining the acceptance of m-learning systems, as proposed by Davis et al. (1989). Finally, this study also indicated that perceived ease of use had a positive direct effect on perceived usefulness. The results were in agreement with what Venkatesh and Davis (2000) found in their study.

Based on findings, some suggestions for future studies were proposed. Firstly, researchers may include actual use behaviors or other external variables in their future studies that might influence students' behavioral intentions on mobile learning. Secondly, the study investigated tertiary level EFL university students from one university. It was suggested that inclusion of more university students or other mobile devices, systems were strongly suggested for future research. Thirdly, the impact of culture on mobile learning acceptance could be studied in experimental settings. Furthermore, many researchers explored students' behavioral intentions toward mobile learning, but few investigated teachers' behavioral intention on mobile learning. It might be interesting to compare teachers' and students' behavioral intention on mobile learning and teaching. Finally, it can include actual use behaviors or other external variables that affect the acceptance of mobile learning, in order to predict and explain users' acceptance of mobile learning in future studies.

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