English e-learning in the virtual classroom and the factors that influence ESL (English as a Second Language): Taiwanese citizens’ acceptance and use of the Modular Object-Oriented Dynamic Learning Environment

Abstract

French

Currently, the speed of knowledge innovation is accelerating with the prevalence of online learning, which penetrates the limits of both time and space. To date, studies examining English e-learning websites have rarely been conducted from the point of view of both the technology acceptance model (TAM) and information technology. In this research, we use the TAM as our basic theory to research the use of English e-learning websites. We find that (1) most English learners think that using the Internet to learn English is more convenient than using traditional methods; and (2) that most adult English learners think that using the Internet to learn English is more helpful than using traditional methods.

Keywords

adult English learners computer assisted language learning (CALL)English e-learning learning behavior learning management systems (LMSs)online learning Taiwanese citizens Technology Acceptance Model (TAM)

Introduction

English, a language that was first used in early medieval England, is now the most widely utilized language in the world. English is also one of the most common native languages in the world. English is widely learned as a second language in many countries and is the official language of the United Nations, as well as many other world organizations. Although English is not as widely utilized as Chinese and Spanish, it is due to the leadership role of 19th- and 20th-century American and British culture and politics in world affairs that English today has become an international language. Many higher-education ESL (English as a Second Language) programs have already adopted or are close to adopting open source LMSs (Learning Management Systems) as a computer-assisted language-learning (CALL) tool in their e-learning classes. As growing numbers of ESL students enroll in US universities and colleges, this community deserves more attention because it contributes to the academic practices of the USA (Liu, 2013). We need to know more about people’s adaptation to other countries, both in their academic study and in their daily lives, in order to help them succeed in their studies. Currently, the English language is used as a communication tool in many international meetings. It is also closely connected with most programming languages, which also ensures its wide use.

In Taiwan, it has become necessary to take English classes in junior high and elementary schools. University students are often required to achieve a degree of English proficiency. Universities in Taiwan often use original English-language textbooks for their subjects. In Taiwan, English is not only a necessary class, but also an important international language and a test subject in senior high schools and universities. As a result, there are many methods and many cram schools to cater for those wanting to learn English. Huang (2005: 48) says that ‘it is a leading factor in the educational success of students in Taiwan who are learning English from an increasingly younger age’.

Methods and tools for learning English

We have several methods and tools for learning English, and we can separate them into two types: traditional methods and e-learning. Students using the traditional methods learn English only in a classroom, from their teachers and books, within a limited time-frame. Learners can obtain the information they want only from their teachers, and students cannot access more data than they know how to ask for.

With e-learning, though students may learn in a classroom, they can use computers to immediately obtain more information in the class. They also can learn their English through e-learning websites. If they want, they can even learn English without a teacher, because e-learning websites can give them any information that they require.

With the advent of e-learning technologies in the past decade, the accessibility of training, teaching and learning has dramatically increased. At present, the challenge for the education business is how to attract learners to their e-learning services. In this study, a technology adoption model has been developed to predict the users’ intention of adoption and their continued use behavior, and Table 1 shows how we compare traditional methods and e-learning.

Table 1.

Basic characteristics of traditional methods and e-learning compared.

Characteristic	Classroom	e-Learning
Limits of time and place	Time and location dependent	Anytime, anywhere
	Physical – limited scale	Unlimited
Teaching content	Teacher centered	Student centered
Personalization	Push approach	Pull approach
	One learning path – lowest common denominator	Learning pace and path determined by user
Learning style	Rigid	Flexible

Source: Tan (2013a).

Table 1 shows that e-learning does not place as many restrictions on time and place as traditional learning, and because the learning tools of these two methods differ, their learning styles differ also. In traditional learning, students can learn only from their teachers; therefore, they cannot obtain the latest information immediately. However, in e-learning, students can learn English with the use of computers and by surfing the Internet. Therefore, they can obtain any learning information they need both quickly and effectively, and learn their English quickly as a result.

Table 2 compares the advantages and disadvantages of traditional learning methods and e-learning websites.

Table 2.

Advantages and disadvantages of traditional learning methods and e-learning websites.

	Traditional learning	e-Learning websites
Advantages	Immediate feedback	Learner-centered and self-paced
	Familiar to both teachers and learners	Flexibility of time and location
	Motivating learners	Cost-effective for learners
	Cultivating a social community	Potentially available to global learners
		Unlimited access to information
		Archival capability for knowledge reuse and sharing
Disadvantages	Instructor-centeredTime and location limitedMore expensive to provide	Lack of immediate feedback in synchronous e-learning
		Increased preparation time for the learners
		Not comfortable for some people
		Potentially more frustration, anxiety, and confusion
		Higher cost of buying the hardware facilities

Source: Zhang et al. (2004).

Literature review

English learners in Taiwan

Taiwanese citizens believe English is an important language because they think that learning it is one of the ways to connect to the world. English has become an element of the Taiwanese economy because it is crucial for working in the financial and technological sectors. The pervasiveness of technology in our lives also means learners must be exposed to these global trends at younger and younger ages. Moreover, applied linguistics research suggests that languages are acquired more effectively when they are learned early in life (Joiner, 1981; Krashen et al., 1979). Both of these factors contribute to the present-day reduction in the age of English learners in Taiwan.

Currently, Taiwanese English learners can learn English in many ways, from e-learning websites or language schools, by talking to English speakers on websites, etc. Although in Taiwan there remain some debates and questions about use of the English language, we cannot deny that learning English is an important and effective way to allow Taiwan to gain a foothold in the global village (Oxford-Carpenter, 1985).

English learners in Malaysia

Wong and Teo (2011) investigated 245 Malaysian student-teachers’ self-reported intentions to use (ITU) computers. Their study found the perceived usefulness (PU) of computer technology, its perceived ease of use (PEOU), and students’ attitude towards computer use (ATCU) to be significant determinants of ITU. The results suggest that the Technology Acceptance Model (TAM) is a reliable predictor of technology acceptance among student teachers in Malaysia.

e-Learning

The method by which education is currently being changed, e-learning, can be used in schools and is the most convenient and effective method for learning. More and more educational institutions are realizing that e-learning can help them extend their teaching superiority: ‘They increasingly realize that effective education’s strategic benefits can outweigh its costs’ (Hambrecht & Co, 2001).

Previous research on user acceptance of LMSs through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT) model has been carried out. For example, Yoo et al. (2012) found that employers in South Korea were influenced positively by factors such as effort expectancy and attitudes toward e-learning in the workplace. Chiu and Wang (2008) identified performance expectancy, effort expectancy, and positive subjective task value as drivers of success for college students when taking Web-based courses. Maldonado et al. (2010) revealed that e-learning motivation and social influence had an effect on learners’ acceptance of e-learning.

However, learners may change their attitude during their learning experience, and in certain instances the learning results did not develop as expected. e-Learning typically requires more self-motivation, and learners working alone can become more easily frustrated. It is important that researchers and developers of e-learning websites should search hard to identify any inconveniences or errors and fix them to provide better and better e-learning environments. ‘Most e-learning provides services for searching, downloading, and delivering learning content’ (Tan et al., 2012): they give their learners access to a variety of learning tools, such as books, audio, video, etc., to enhance their clients’ learning experience.

Background to the Technology Acceptance Model (TAM)

For decades, both academics and industry insiders have explored people’s interest in new technologies and attempted to predict or affect their likely use. Some evidence also shows that intention positively influences e-learning acceptance (e.g. Chen, 2011; Liao & Lu, 2008; Padilla-Meléndez et al., 2008; Toral et al., 2007). As Davis et al. (1989: 982–1000) note: ‘A wide body of research focuses on identifying factors affecting people’s intentions to use new technologies and how these intentions predict actual usage’.

The Technology Acceptance Model (TAM) is a theory developed by Davis in 1986 from the Theory of Reasoned Action (TRA: Fishbein & Ajzen, 1975). Composed of two main factors – perceived usefulness (PU) and perceived ease of use (PEOU) – the TAM models the hypothesis that perceived usefulness and perceived ease of use positively affect attitudes toward using (ATU), which subsequently positively affect behavioral intention to use (BIU) and actual system use (ASU), as modeled in Figure 1.

Figure 1.

Technology Acceptance Model (TAM).

Previous studies

In general, many theoretical models of information system (IS) acceptance adopt this concept to identify behavioral determinants useful in guiding system design and implementation. For example, the TAM posits that IS acceptance is primarily determined by two systemic beliefs: perceived usefulness and perceived ease of use (Davis et al., 1989). Table 3 lists a range of research findings from 1975 to 2013 in which the TAM has been seen as a factor in user acceptance of information technology.

Table 3.

Previous studies of factors affecting user acceptance of technology.

Author	Theory	Finding
Fishbein & Ajzen (1975)	TAM, TRA	‘TAM was assumed on the strength of the theory of reasoned action (TRA); this is a psychological theory that seeks to explain how an individual’s action is decided by his/ her BI to perform it.’
Davis et al. (1989)	TAM	‘TAM has been used in several studies testing user acceptance of information technology.’
Taylor & Todd (1995)	TAM, TPB	‘Combining social influences and behavioral control into TAM, the authors explored the role of prior experience.’
Agarwal & Prasad (1997)	IDT, TAM	Karahanna et al. used IDT and TAM to examine pre-adoption and post–adoption attitudes.
Venkatesh & Davis (2000)	TAM	‘The degree to which a person believes that using a particular system would enhance his or her job performance.’
Venkatesh et al. (2003)	TAM, TRA	‘Researchers have simplified TAM by changing the attitude structure found in TRA from the current specification.’
Wixom & Todd (2005)	TAM	‘Attempts to lengthen TAM have generally taken one of three approaches: introducing elements from related models, additional or alternative belief elements, and examining antecedents and moderators of perceived usefulness and perceived ease of use.’
Shu-Sheng et al. (2007)	TAM	‘Researched instructors’ and learners’ attitudes toward e-learning methods. The study proffers guidelines for expanding e-learning surroundings.’
King & He (2006)	TAM	‘The study confirmed the value of using students as surrogates for professionals in some TAM studies, and perhaps more generally. It also revealed the power of meta-analysis as a rigorous alternative to qualitative and narrative literature reviews methods.’
Lee (2006)	TAM	‘Computer self-efficacy demonstrated significant influence on perceived ease of use. Effects of course attributes on perceived usefulness were negatively significant. Effects of content quality on perceived usefulness were significant.’
Polančič et al. (2010)	TAM	‘The successful use of frameworks is mainly dependent on two factors: continuous framework usage intention and the perceived usefulness of the framework.’
Tan et al. (2012)	TAM	‘The study was based on TAM, and empirically examines the factors influencing the online banking adoption behavior of clients in the Pescadores.’
Tan (2013a)	TAM, UTAUT	‘When English e-learning websites help learners increase their performance and are easy to use, there is an increase in their intention to use them. This suggests that web designers should improve knowledge management functions and improve user interfaces to be easier to operate.’
Tan (2013b)	TAM, UTAUT	‘The results confirm that three of the four core constructs (factors) have a significantly positive influence on behavioral intention, which in turn have a positive correlation with actual use. Thus, e-placement tests are more appreciated by the students and more likely to be utilized by them.’
Lakhal et al. (2013)	TAM, UTAUT	‘The result indicates that the main drivers of the behavioral intentions to use desktop video conferencing are, in order of importance: performance expectancy, facilitating conditions, general social influence and autonomy mediated by performance expectancy.’

Source: Tan (2013a, 2013b).

Purposes of this study

English, as one of the most used languages in the world, has many learners. In the past, Taiwanese citizens did not use English widely; but nowadays, because of globalization and the global importance of English, they have gradually increased their use of this language. Given this increased use of a globally significant language, we would like to understand why its learners have preferences for different learning techniques. Entrepreneurs should appreciate and make use of such systematic assessments of learners’ interest in e-learning, because understanding the factors that affect the attitudes and behavior of the users of their learning systems is crucial to promoting new learning technologies.

Through examining the study of English in Taiwan our research has analyzed English learners’ learning behavior and attitudes toward English e-learning websites, and the number of English learners using these sites.

The research quantifies people’s perception, attitudes, intentions, and use of e-learning technologies. It aims to verify the significance of ease of use and usefulness for people’s adoption of e-learning websites. By appreciating how such factors affect people’s use of these new technologies it should be possible to improve them. We therefore determined that the specific aims of this study should be to:

understand the value of English e-learning in Taiwan and the reasons why learners want to learn English;

understand the value of e-learning websites in Taiwan and the reasons why learners want to use e-learning websites;

comprehend the types of skills learners want to obtain from these websites;

assess the attitudes of learners towards the English e-learning websites;

survey English e-learning websites that can assist learners with better learning conditions;

judge whether English e-learning websites are likely to play a significant role for learners;

determine the elements that influence learners’ intentions and behaviors to receive and utilize e-learning websites as primary study resources.

Research method

Participants

A questionnaire was developed to measure the perceptions of Taiwanese citizens. Four hundred questionnaires were sent out, and 400 were returned. Of these, 370 were valid, leading to a return rate of 90.25%.

All the participants in this study were Taiwanese. The questionnaire used in the study was adapted from the question items used by Venkatesh et al. (2003). The main purpose was to collect data on the intentions of Taiwanese participants for English e-learning website usage. The questionnaire was put up on the Google website (Google), and sent to a random sample of Taiwanese citizens. Figures 2 –4 present demographic information on the respondents, from which it should be noted that more females than males participated in the study. Most of the female respondents were aged 19–30, and had been learning English for more than seven years. Though this distribution is not representative of the Taiwanese population at large, it is representative of our school, as well as of an increasing number of college campuses around Taiwan, and an excellent target demographic for e-learning websites.

Figure 2.

Gender of study participants.

Figure 3.

Age of study participants.

Figure 4.

Duration of participants’ learning of English (in years).

Figure 5.

Research model.

Basic information

We used the completed questionnaires to understand participants’ backgrounds, including their gender, age, and the length of time they have been learning English (see Figures 2 –4). Of our participants:

35.9% were male, 64.1% were female;

8.1% were under 18, 87% were 19–30, and 4.9% were 31–50;

4.1% had learned English for less than 1 year, 5.4% for 1–3 years, 16.2% for 4–6 years, 37% for 7–10 years, and 37.3% for over 10 years.

Instruments

The survey questions, adopted from Venkatesh and associates (2003), collected demographic information and data on the overt variables for the five constructs in the model using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).

We used TAM to analyze our questionnaire. TAM hypothesizes that perceived usefulness (PU) and perceived ease of use (PEOU) affect the learners’ attitude toward using (ATU). When such influences are successful, it further hypothesizes that ATU will successfully affect the behavioral intention to use (BIU) (see Table 4).

Table 4.

TAM hypotheses and literature support.

Hypothesis	References	Literature support
H1: PEOU→ PU	Davis (1986)	Yes
Perceived ease of use positively affects perceived usefulness.	Davis (1989) Cheng et al. (2006) Davis et al. (1989)
H2: PEOU→ ATU	Chen et al. (2002)	No
Perceived ease of use positively affects attitude towards use.	Wixom & Todd (2005)
H3: PU→ ATU	Taylor & Todd (1995)	Yes
Perceived usefulness positively affects attitude towards use.	Venkatesh & Davis (2000)
H4: PU→ BIU	Fishbein & Ajzen (1975)	Yes
Perceived usefulness positively affects behavioral intention to use.
H5: ATU→ BIU	Lai & Li (2005)	Yes
Attitude towards use positively affects behavioral intention to use.
H6: BIU→ ASU	Fishbein & Ajzen (1975)	Yes
Behavioral intention to use positively affects actual system use.

SPSS (Statistical Package for Social Science) software was used to examine the data and performance reliability, correlation, regression, and path analyses.

A total of 400 students were surveyed using a standard questionnaire. Thirty were incomplete, leaving only 370 valid. The survey questions are listed in full in Appendix 2.

The reliability of our research was assessed at Cronbach’s α = .918 ⩾ .7. This score, which is in excess of 0.7, indicates that our research is reliable.

Pre-test analysis

The surveys were pre-tested on four students, evaluated by one independent professor, and then modified according to their comments. The final survey demonstrated high reliability (see Table 5). We used the TAM to make the hypotheses listed in Table 4.

Table 5.

Reliability statistics.

	Cronbach’s alpha	Number of items
Total	.918	19

Results

The convergent and discriminant validity of each first-order construct were assessed in the measurement model. Each first-order construct was modeled as a reflective latent construct, accounting for its indicators. Three criteria were used for assessing convergent validity (Fornell & Larcker, 1981):

each item loading should be statistically significant and larger than .70;

the reliability composite (rc) for each latent construct should be larger than .70 and should be interpreted similarly to a Cronbach’s coefficient;

the average variance extracted (AVE) for each latent construct should exceed .50.

Discriminant validity between constructs was estimated using the criterion that the square root of every AVE should exceed the correlations among any pair of latent constructs (Chin, 1998; Fornell & Larcker, 1981).

Analyzing the questionnaire

Table 5 confirms the reliability statistics at .918 for Cronbach’s alpha with the number of items at 19. Table 6 presents a breakdown of the hypotheses on usefulness, attitude towards use, behavioral intention to use, and actual usage, and Figure 6 models the result. They show that perceived ease of use explains 30.1% of the variation for perceived usefulness. Perceived ease of use and perceived usefulness explain 19.1% of the variation of the attitude toward using. Perceived usefulness and attitude toward using account for 49.2% of the variation in the behavioral intention to use. Moreover, behavioral intention to use explains 16.1% of the variation for actual system usage.

Table 6.

Analysis of hypothesis.

Relation paths		Standardized coefficients (β)	Coefficient of determination (R²)	P-value	Sig.
H1: Perceived ease of use	Perceived usefulness	.549	.301	.000	***
H2: Perceived ease of use	Attitude toward using	.048	.191	.394
H3: Perceived usefulness		.409		.000	***
H4: Perceived usefulness	Behavioral intention to use	.502	.492	.000	***
H5: Attitude toward using		.317		.000	***
H6: Behavioral intention to use	Actual system usage	.401	.161	.000	***

– = p > 0.05; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.

Figure 6.

Scale validation: Analysis of the measurement model.

Reliability of the collected data in this study was assessed by the Statistical Package for Social Science. The composite reliabilities ranged from 0.76 to 0.95, which exceeds the recommended threshold value of 0.70 (see Table 7).

Table 7.

Reliability of research variables.

Research variable reliability	Cronbach’s alpha
PEOU (perceived ease of use)	.858
PU (perceived usefulness)	.854
ATU (attitude towards using)	.511
BIU (behavior intention)	.853
ASU (actual system usage)	.891
Total	.918

All factor results of items in this research model are higher than 0.50; most of them are above 0.70. Every item is loaded significantly (p < 0.01 in all cases) on its constructs. Therefore, all constructs in the model have adequate reliability and convergent validity. Correlation results are shown in Table 8.

Table 8.

Correlation of adoption factors.

	PEOU	PU	ATU	BIU	ASU
PEOU	1
PU	.549**	1
ATU	.272**	.435**	1
BIU	.414**	.640**	.536**	1
ASU	.460**	.435**	.268**	.401**	1

Correlation significant at p < 0.01.

A summary of the findings is presented in Table 9.

Table 9.

Confirmation of hypotheses.

Hypotheses	Confirmed
H1: Perceived ease of use positively affects perceived usefulness	Yes
H2: Perceived ease of use positively affects attitude towards use	No
H3: Perceived usefulness positively affects attitude towards use	Yes
H4: Perceived usefulness positively affects behavioral intention to use	Yes
H5: Attitude towards use positively affects behavioral intention to use	Yes
H6: Behavioral intention to use positively affects actual system use	Yes

Discussion

This study has produced the following findings:

The analysis shows that perceived ease of use has a positive effect on perceived usefulness.

Perceived ease of use does not have a positive effect on attitude.

Perceived usefulness has a positive effect on attitude.

Perceived usefulness has a positive effect on behavioral intention to use.

Behavioral intention to use has a positive effect on attitude.

Learners like to use e-learning websites to learn English, rather than traditional learning methods.

Most English learners think that using the Internet to learn English is more convenient than using traditional ways.

The SPSS analysis shows that English learners like to use the Internet to learn English in preference to more traditional methods, such as sitting in a classroom to listen to a teacher. When learners have a more positive attitude toward learning English, they will be more likely to use e-learning websites. In summary, the results support the claim that the TAM provides a good tool for understanding and applying technology in learning surroundings.

Also, with regard to learners’ use attitudes, because perceived usefulness did have a significant correlation with attitude toward use, it may appear that the informative nature of the websites has no effect on them. However, perceived ease of use had no direct effect on behavioral attitudes; therefore, ease may be more important for the perception of usefulness and may diminish the apparent impact of perceived usefulness. Moreover, perceived usefulness continued to have a significant impact on the intention to use, regardless of its failure to affect attitude. This finding is related to the quality of e-learning. From the SPSS results, we found that perceived ease of use does not mean that learners want to use e-learning websites. If website designers want more learners to use their websites and feel satisfied with them, they should improve their e-content. The importance of ease of use supports Tan’s (2013a) contention that ‘Web designers should improve knowledge management functions and improve user interfaces to be easier to operate’.

Recommendations for future research

Further research should broaden the scope of the current study, which was restricted to this particular field of applied linguistics. Future studies could: (1) collect data from different fields (such as Social Science, Information Management, Psychology, Language Education, Science, and so on) or other regions to increase external validity; and (2) examine efficiency in e-learning from different perspectives, such as the quality of Web-design.

Implications for practice

Web-designers should always pay attention to learners’ needs when devising their e-learning systems. If e-learning system functions can be provided for learners from various backgrounds (such as Language Learning, Teaching Methods, Management, ESL, and so on), and with a range of needs from theory to practice, such provisions would be helpful for maintaining and enhancing the learners’ loyalty to English e-learning websites.

Footnotes

Appendix

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author biography

Paul Juinn Bing Tan, attended Oklahoma City University, USA to study psychology in 1986. He then went on to earn his masters of education in teaching English as a Second Language and Counseling at Oklahoma in 1991. In 1994, he decided to further his education after Mikhail Gorbachev visited Taiwan, so he attended Moscow State Pedagogical University. In 1997, he received his doctorate degree in the faculty of Pedagogics, after which he joined NPU (National Penghu University) in August 2000. Prior to attending Moscow State Pedagogical University, he was an Associate Professor at the Institute of Education, Moscow State Pedagogical University (Russia) where he taught English, Chinese and Psychology. He currently serves as an Assistant Professor at the department of Applied Foreign Languages of National Penghu University in Taiwan.

References

Agarwal

Prasad

(1997) The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences 28(3): 557–582.

Chen

J-L

(2011) The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education 57: 1501–1511.

Chen

Gillenson

Sherrell

(2002) Enticing online consumers: An extended technology acceptance perspective. Information & Management 39(8): 705–719.

Cheng

TCE

Lam

DYC

Yeung

ACL

(2006) Adoption of internet banking: An empirical study 921 in Hong Kong. Decision Support Systems 42(3): 1558–1572.

Chin

(1998) Issues and opinion on structural equation modeling. MIS Quarterly 22(1): 7–16.

Chiu

Wang

ETG

(2008) Understanding web-based learning continuance intention: The role of subjective task value. Information & Management 45: 194–201.

Davis

(1986) A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and results. Doctoral dissertation, MIT Sloan School of Management, Cambridge, MA.

Davis

(1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3): 319–352.

Davis

Bagozzi

Warshaw

(1989) User acceptance of computer technology: Comparison of two theoretical models. Management Science 35(8): 982–1003.

10.

Fishbein

Ajzen

(1975) Belief, Attitude, Intention, and Behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

11.

Fornell

Larcker

(1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 48: 39–50.

12.

Hambrecht

& Co. (2001) eLearning: A key strategy for maximizing human capital in the knowledge economy. Primelearning.com White Paper. Available at: http://www.primelearning.com/approach/pdf/maximizinghumancapital.pdf.

13.

Huang

(2005) Taiwanese students talk about English in Taiwan and their lives. Languages, Communities, and Education: 45–54.

14.

Joiner

(1981) The Older Foreign Language Learner: A challenge for colleges and universities. Washington, DC: ERIC Clearinghouse on Languages and Linguistics; available from Englewood Cliffs, NJ: Prentice-Hall. (Language in Education Series no. 34)

15.

King

(2006) A meta-analysis of the technology acceptance model. Information & Management 43(6): 740–755.

16.

Krashen

Long

Scarcella

(1979) Age, rate and eventual attainment in second language acquisition. TESOL Quarterly 13: 573–582.

17.

Lai

(2005) Technology acceptance model for internet banking: An invariance analysis. Information & Management 42(2): 373–386.

18.

Lakhal

Khechine

Pascot

(2013) Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computing in Higher Education 25(2): 93–121.

19.

Lee

(2006) An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review 30(5): 517–541.

20.

Liao

(2008) The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education 51(4): 1405–1416.

21.

Liu

Jing

(2013) e-Learning in English Classrooms: Investigating factors impacting on ESL (English as Second Language) college students’ acceptance and use of the Modular Object-Oriented Dynamic Learning Environment (Moodle). Graduate Theses and Dissertations. (Paper 13256). Available at: http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=4263&context=etd.

22.

Maldonado

UPT

Khan

Moon

Jeung

(2010) e-Learning motivation and educational portal acceptance in developing countries. Online Information Review 35(1): 66–85.

23.

Oxford-Carpenter

(1985) A New Taxonomy of Second Language Learning Strategies. Washington, DC: ERIC Clearinghouse on Languages and Linguistics.

24.

Padilla-Meléndez

Garrido-Moreno

Aguila-Obra

ARD

(2008) Factors affecting e-collaboration technology use among management students. Computers & Education 51(2): 609–623.

25.

Polančič

Heričko

Rozman

(2010) An empirical examination of application frameworks success based on technology acceptance model. Journal of Systems and Software 83(4): 574–584.

26.

Shu-Sheng

Hsiu-Mei

Gwo-Dong

(2007) Surveying instructor and learner attitudes toward e-learning. Computers & Education 49: 1066–1810.

27.

Tan

PJB

(2013a) Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. SAGE Open 3(4): 2158244013503837.

28.

Tan

PJB

(2013b) Students’ adoptions and attitudes towards electronic placement tests: A UTAUT analysis. American Journal of Computer Technology and Application 14–24.

29.

Tan

PJB

Potamites

Lin Wens-Chi (2012) Applying the TAM to understand the factors affecting use of online banking in the Pescadores. ARPN Journal of Science and Technology 11(2): 1022–1028.

30.

Taylor

Todd

(1995) Assessing IT usage: The role of prior experience. MIS Quarterly 19(4): 561–570.

31.

Toral

Barrero

Martínez-Torres

(2007) Analysis of utility and use of a web-based tool for digital signal processing teaching by means of a technological acceptance model. Computers & Education 49(4): 957–975.

32.

Venkatesh

Davis

(2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 45(2): 186–204.

33.

Venkatesh

Morris

Davis

(2003) User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3): 425–478.

34.

Wixom

Todd

(2005) A theoretical integration of user satisfaction and technology acceptance. Information Systems Research 16(1): 85–102.

35.

Wong

Teo

(2011) Student teachers’ acceptance of computer technology. Technology Acceptance in Education: Research and issues 43–61. DOI: 10.1007/978–94–6091–487–4_3.

36.

Yoo

Han

Huang

(2012) The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Computers in Human Behavior 28: 942–950.

37.

Zhang

Zhao

Zhou

Nunamaker

(2004) Can e-learning replace classroom learning? Communications of The ACM 47(5): 75–79.