System 115 (2023) 103051
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Motivation in self-directed use of technology for English learning
among high, average, and low achievers
Zhujun An a, Chun Lai b, Zhengdong Gan a, *
a
b
Faculty of Education, University of Macau, Macao, China
Faculty of Education, The University of Hong Kong, Hong Kong, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
Motivation
Technology-assisted learning
English learning
EFL students
Adopting a multi-theoretical perspective, this study examined how various motivational factors
from three dominant motivational theories of volitional behaviors related to undergraduates’ selfdirected technology use for English learning, and how the relationships varied among high,
average, and low achievers. Confirmatory factor analysis results supported a holistic model of
nine motivational factors that drive these learners’ self-directed technology use for English
learning. The study found that motivational factors from different motivational perspectives
drove different aspects of self-regulation with technology. Specifically, constructs from the theory
of planned behaviors were essential to drive resource and metacognitive management; goal
orientation constructs were the salient motivators of learners’ use of technology for emotion
management to maintain vigor for learning; and constructs from all three theoretical perspectives
were critical to drive learners’ use of technology for goal setting and social regulation. Moreover,
the motivational drive for self-directed technology use for English learning varied for learners of
different proficiency levels. The findings suggest different motivational factors need to be
attended to when encouraging and supporting the use of technology for the regulation of different
aspects of English learning and among learners of different language proficiencies.
1. Introduction
Self-directed learning is “a process in which individuals take the initiative, with or without the help from others, in diagnosing their
learning needs, formulating goals, identifying human and material resources, choosing and implementing appropriate learning
strategies, and evaluating learning outcomes” (Knowles, 1975, p. 18). Self-directed language learning and use experiences with
technology have been an essential and common phenomenon of foreign language learning beyond the classroom (Benson, 2011;
Chappelle & Sauro, 2017; Lai, 2019). Language learning potentials of out-of-class technology use experiences have been well docu­
mented in the literature (Benson, 2011; Cole & Vanderplank, 2016; Zeng, 2020). Previous research has revealed that self-directed
technology use for language learning outside the classroom contributed significantly and uniquely to language development (Bre­
vik, 2019; Peters & Webb, 2018). However, the potential of such technological experiences will not be maximized if students do not
engage in the actual use of technology for language learning (Lai, 2013). Thus, what motivates students to use various technological
resources to maximize their language learning potentials is an important question for educators and researchers.
Previous studies have identified various factors that motivate learners’ use of technology for language learning, including
* Corresponding author.
E-mail address: Zhengdonggan@um.edu.mo (Z. Gan).
https://doi.org/10.1016/j.system.2023.103051
Received 26 August 2022; Received in revised form 24 February 2023; Accepted 18 April 2023
Available online 3 May 2023
0346-251X/© 2023 Elsevier Ltd. All rights reserved.
System 115 (2023) 103051
Z. An et al.
perceptions of technology use (e.g., perceived usefulness and effort expectancy), motivational beliefs (e.g., self-efficacy and task
values), external factors (e.g., subjective norms and facilitating conditions), and domain-specific learning motivation (e.g., language
learning motivation) (Chiu & Wang, 2008; Lai, 2013; Lai & Gu, 2011; Tarhini, Masa’deh, Al-Busaidi, Mohammed, & Maqableh, 2017;
Zhang, 2010). However, what is missing in the literature is a conceptualization of different motivational variables in the same model
and their potential contributions to technology-assisted language learning for students of different language proficiency levels. In the
field of English as a foreign language (EFL) learning, understanding the effects of different motivational dimensions on
technology-assisted language learning for students of different achievement levels is particularly important as it can provide guidelines
for instructors in designing specific learning activities for stratified teaching in college English classrooms. This study thus intends to
enhance our understanding of this issue by systematically investigating Chinese EFL students’ motivation in self-directed technology
use for English learning and exploring the relationship between the motivational variables and learning behaviors among high,
average, and low English achievers.
2. Theoretical background
Motivational variables are critical determinants of learning behaviors and learning achievement (Wigfiled & Cambria, 2010).
Motivation research usually targets the issue of why people decide to do something, how long they are willing to sustain the activity,
and how hard they are going to pursue it (Dörnyei & Skehan, 2003; Gan, 2020; Gan, 2004; Liem, Lau, & Nie, 2008). In the field of
technology-assisted teaching and learning, two dominant theories used to explain individuals’ intention to adopt technology for
learning purposes are theory of planned behavior (TPB) (e.g., Ajzen, 1991) and expectancy-value theory (EVT) (e.g., Wigfield & Eccles,
2000). The body of literature has documented an increasing number of research based on either or both of the two theories to explain
technology use in education (e.g., Chiu & Wang, 2008; Lai, 2013; Ranellucci, Rosenberg, & Poitras, 2020; Sun & Mei, 2022; Teo & Lee,
2010).
The TPB was an extension of Theory of Reasoned Action, a theoretical framework of psychological factors that drive individuals’
behavioral decision-making, to conceptualize internal and external factors that influence information technology adoption (Fishbein &
Ajzen, 1975; Venkatesh, Morris, Davis, & Davis, 2003). EVT was a theoretical framework developed by Atkinson in the 60s to un­
derstand the achievement motivation of individuals, and expanded by Wigfield and Eccles (2000) to conceptualize the motivational
factors that shape individuals’ achievement-related choices in completing a specific goal-oriented task. Although the TPB and EVT
each has distinct origins, they both focus on explaining human volitional behaviors, and their unique perspectives could provide
complementary explanations for motivation to use technology (Ranellucci et al., 2020).
We further added another major motivation theory on volitional behaviors, goal orientation theory, to explain students’
engagement in self-directed English learning using technology. Hence, this study situated these three dominant motivational theories
on human volitional behaviors in the language learning context to identify key motivational factors that influence students’ selfdirected technology use for English learning.
2.1. Theory of planned behavior
The TPB was proposed by Ajzen in 1991, which was an extension of the Theory of Reasoned Action (Ajzen, 2000). It has been
successfully applied in various contexts to explain individuals’ intentions and behaviors, including physical activity (Luszczynska,
Schwarzer, Lippke, & Mazurkiewicz, 2011), business (Koropp, Kellermanns, Grichnik, & Stanley, 2014), and technology use in edu­
cation (Teo & Lee, 2010; Zhou, 2016). The TPB postulates that an individual’s action is mainly determined by behavioral intentions
(Ajzen, 1991). According to TPB, a person’s intention to carry out a behavior is determined by three dimensions: (1) attitude toward
behavior, (2) perceived behavioral control, and (3) subjective norms. Moreover, behavior is a function of the salient belief antecedents
of these dimensions of the behavior. In general, individuals with favorable attitudes, high levels of perceived behavioral control, and
positive social influence tended to exhibit more motivation for performing a behavior.
According to the theory, attitude toward a behavior is “the degree to which a person has a favorable or unfavorable evaluation or
appraisal of the behavior in question” (Ajzen, 1991, p. 188). The belief antecedent of attitude is behavioral beliefs, i.e., individuals’
beliefs of the link between the behavior and a certain outcome. When individuals perceive a behavior as useful in enhancing task
performance, they may show greater intention for the behavior. Perceived usefulness is found to be critical predictors of both students’
and teachers’ active technology use (Lai, 2013; Sun & Mei, 2022; Teo & Lee, 2010). Investigating pre-service teachers’ technology use
intention through the lense of TPB, Teo and Lee (2010) found that attitude toward usage was a powerful and significant predictor of
student teachers’ behavioral intention to use technology. The finding supports Teo’s (2008; 2009) previous research which revealed a
close relationship between a positive attitude and technology use. Similarly, Sun and Mei’s (2022) study on pre-service teachers’
adoption of educational technology confirmed that attitudes toward technology use significantly predicted intention to use technol­
ogy. Building on the previous research findings, the current study included perceived usefulness as an important motivational variable in
EFL students’ technology use for English learning.
The salient belief of the perceived behavioral control dimension of behavior is control beliefs, which are the beliefs about “the
presence or absence of requisite resources and opportunities” (Ajzen, 1991, p. 196). Perceptions of resources and opportunities
originate in individual’s evaluation of his/her ability (e.g., self-efficacy) and perceptions of the availability of the support necessary (e.
g., facilitating conditions) to perform a certain behavior (Ajzen, 2002). Previous studies documented that perceived behavior control
significantly predicted individuals’ technology adoption in education (e.g., Lai, 2013; Sun & Mei, 2022; Tarhini et al., 2017). A number
of studies have also found self-efficacy to be a critical predictor that directly influences users’ technology acceptance in learning
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Z. An et al.
(Abbasi, Tarhini, Elyas, & Shah, 2015; Tarhini et al., 2017). Furthermore, some studies revealed that self-efficacy had an indirect effect
on technology use (Bai, Wang, & Chai, 2021; Lai, 2013; Sun & Mei, 2022). In addition, prior research has examined the critical role
facilitating conditions play in teachers’ and students’ technology acceptance (Lai, 2013; Sun & Mei, 2022; Teo, Huang, & Hoi, 2018).
Based on the literature, the current study therefore also included technology self-efficacy and facilitating conditions as the motivational
variables for EFL students’ self-directed technology use for English learning.
Subjective norms refer to an individual’s perception of how significant others think the individual should engage in or depart from a
behavior. Normative beliefs of “the likelihood that important referent individuals or groups approve or disapprove of performing a
given behavior” is the belief antecedent of subjective norms (Ajzen, 1991, p. 195). The importance of subjective norms in shaping
individuals’ technology adoption or e-learning has been discussed in numerous studies (Lai, 2013; Tarhini et al., 2017; Teo & Lee,
2010). Although Venkatesh et al. (2003) argued that social influence was important only in mandatory environment and its influence
was weak in voluntary environment, a number of studies conducted in voluntary settings revealed that subjective norms had a sig­
nificant effect on behavioral intention to use technology (Lai, 2013; Lai, Wang, Li, & Hu, 2016; Teo & Lee, 2010). Therefore, the
current study also considered subjective norms as a potentially important motivational factor affecting students’ self-directed tech­
nology use for English learning.
2.2. Expectancy-value theory
As one of the most influential motivational theories, the EVT is a comprehensive framework for explaining individuals’
achievement-related behaviors (Eccles & Wigfield, 2002). The theory postulates that motivation to perform a task is the product of two
main factors: expectancy of success in the task and the value a person attaches to success on that task. Expectancies refer to individuals’
beliefs about their abilities in an upcoming task (e.g., self-efficacy). Task values concern individuals’ personal significance or value
attached to a task (e.g., importance, utility, interest, and cost).
According to Eccles et al. (1983), there are four main categories of task values: intrinsic value, attainment value, utility value, and
cost value. Intrinsic value refers to the interest students have in the subject, or the enjoyment students get from performing a task.
Attainment value was defined as the importance of doing well on a given task (Eccles et al., 1983). Utility value refers to the perceived
usefulness of a task in relation to the individual’s short-term or long-term goals. Unlike the above task values (i.e., attainment, intrinsic,
and utility value) which represent adaptive or desirable values, cost value is conceptualized as the negative consequences of engaging
in a task, such as perceptions of time and effort demands, loss of valued alternatives, or negative psychological experiences (Eccles
et al., 1983). Research documented that the higher levels of expectancies a person has, the more interests he/she shows in the process,
the more important and useful he/she perceives the task to be, and the less negative aspects of doing the task will show up, all of which
will contribute to a higher acceptance of the task (Bai et al., 2021).
While EVT and TPB each have distinct origins, there is some overlap between the two theories (Ranellucci et al., 2020). For
instance, definitions and measurements of perceived usefulness in TPB and the utility value in EVT are highly similar. In addition, ex­
pectancy of success in EVT echoes one aspect of perceived behavioral control in the TPB: the extent to which the individual perceives his
ability to perform a certain behaviour, self-efficacy (Venkatesh et al., 2003). Considering the overlap of the constructs between TPB and
EVT, this study included the following value belief constructs from EVT to expand the set of behavioral beliefs in TPB: attainment value
(perceived importance of technology for English learning), intrinsic value (interest in using technology for English learning), and cost value
(cost of using technology for English learning).
2.3. Goal orientation theory
According to goal orientation theory, an individual’s goal orientation will affect his/her cognitive or emotional tendency towards
events and subsequent behavioral responses (Dweck & Leggett, 1988). Goal orientations refers to learners’ purposes of engaging in a
certain learning activity, and learners’ goal orientations determine how learners are going to approach a learning task and the efforts
they may invest (Ames & Archer, 1988).
There are two basic goal orientations, mastery goal orientation and performance goal orientation (Ames & Archer, 1988; Pintrich,
Conley, & Kempler, 2003). Mastery goal orientation focuses on mastery of learning and leads to the mentality of learning from errors.
Thus, learners with mastery goal orientation show high levels of effort and are more likely to take risks and try new things as long as
they enhance their knowledge and competencies (Dweck & Leggett, 1988; Greene, Miller, Crowson, Duke, & Akey, 2004). Perfor­
mance goal orientation focuses on outperforming others, and thus learners with this kind of goal orientation tends to be more con­
servative, have high anxiety, and lack persistence (Linnenbrink, 2005; Lou & Noels, 2017). Previous studies have shown that mastery
goal orientation is a significant predictor of learners’ adoption of technology (Cromley & Kunze, 2021; Huang et al., 2021; Lin, 2021;
McGloin, McGillicuddy, & Christensen, 2017; Mun & Hwang, 2003). Goal orientations provide broad purposes for learning, and hence
may complement EVT and TPB, which target specific behaviors, in predicting learners’ behaviors. Thus, in this study, we included two
goal orientation constructs in relation to English language learning – mastery goal orientation toward English learning and performance
goal orientation toward English learning.
3. The present study
The existing literature has a lot to offer in terms of the factors influencing technology adoption, and relations between motivational
variables and self-regulated learning. However, limited research has examined how the relationships manifest in self-directed informal
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Table 1
Summary of measurement scales in MSDTUQ (n = 285).
Constructs and items
Perceived usefulness
8.Technologies (e.g., language learning applications and online tests) are useful for monitoring
English learning progress.
10.Technological tools are useful for getting a good grade.
11.Technologies (e.g., Pigai web) are useful for finishing English assignments.
Self-efficacy
1.I believe I can use technologies well to learn English.
2.I can use technologies to create enjoyable English learning experience.
3.I am familiar with the use of technology-related skills in English learning such as retrieving
information online.
Facilitating conditions
19.I have friends/classmates who share strategies of using technologies for English language
learning.
20.When I have difficulties in using technologies for English learning, I have friends/classmates
from whom to seek help.
22.I often get ideas from teachers on potential technologies to use for English learning.
Subjective norms
23.People who influence my learning behaviors (e.g., teachers or peers) think I should use
technologies to support English learning.
24.People important to me think I should use technologies to support English learning.
25.My teacher is very supportive of the use of technologies for English learning.
Perceived importance of technology for English learning
5.Using technologies for English learning is an important skill for me
6.Using technologies to expand English use opportunities is very important for me.
7.Using technologies to seek help or support from others is very important.
Interest in using technology for English learning
12.I enjoy learning English with technologies.
13.In general, I found using technology for English learning is very interesting.
14. I like the diversified English learning resources provided by technologies.
Cost of using technology for English learning
16.Using technology to learn English requires too much time.
17.Because of other things that I do, I don’t have time to put into technology use for English
learning.
18.I have to give up too much to do well in technology-assisted English learning.
Mastery goal orientation toward English learning
26.In my English class, I prefer course material that really challenges me so I can learn new things.
27.In my English class, I prefer course materials that arouse my curiosity, even if it is difficult to
learn.
28.The most satisfying thing for me in English course is trying to understand the content as
thoroughly as possible.
29.When I have the opportunity in this English class, I choose course assignments that I can learn
from even if they don’t guarantee a good grade.
Performance goal orientation toward English learning
30.Getting a good grade in English class is the most satisfying thing for me right now.
31.The most important thing for me right now is improving my overall grade point average, so my
main concern in English class is getting a good grade.
Mean
(SD)
Factor
loadings
5.26
(1.36)
5.39
(1.24)
5.00
(1.47)
.82
4.78
(1.51)
5.06
(1.46)
5.17
(1.42)
.89
4.51
(1.54)
4.72
(1.49)
4.92
(1.49)
.62
4.97
(1.29)
4.91
(1.36)
5.34
(1.22)
.89
5.49
(1.27)
5.48
(1.31)
5.38
(1.32)
.87
5.29
(1.34)
5.39
(1.31)
5.61
(1.12)
.90
3.89
(1.54)
3.91
(1.53)
3.49
(1.55)
.70
4.96
(1.47)
5.11
(1.41)
5.48
(1.29)
4.92
(1.40)
.89
5.25
(1.49)
5.24
(1.47)
.58
Cronbach’s
alpha
CR
AVE
.83
.84
.64
.93
.93
.82
.81
.74
.50
.87
.88
.71
.89
.89
.74
.92
.92
.79
.82
.82
.61
.85
.86
.61
.82
.77
.47
.91
.65
.92
.91
.69
.79
.80
.83
.89
.81
.95
.82
.76
.85
.90
.63
.65
.48
(continued on next page)
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Table 1 (continued )
Constructs and items
Mean
(SD)
Factor
loadings
32.If I can, I want to get better grades in English class than most of the other students.
5.85
(1.24)
5.46
(1.40)
.92
33.I want to do well in English class because it is important to show my ability to my family,
friends, employer, or others.
Cronbach’s
alpha
CR
AVE
.69
learning contexts and how the relationships might vary across students with different language proficiency levels. Relying on a multitheoretical perspective, the present study aimed to systematically examine the motivational dimensions in EFL students’ self-directed
technology use for English learning and their relations with technology-based learning behaviors among high, average, and low
English achievers. By integrating multiple motivational variables in one measurement model, this study represents an important
endeavor to advance our understanding of the relations between the motivational variables, self-directed technology-assisted English
learning behaviors, and English learning achievement in the college English course context. Specifically, this study addressed the
following research questions.
1. What are the motivational factors that drive EFL students’ self-directed use of technology for English learning outside the
classroom?
2. How do these motivational factors relate to the self-directed technology-assisted English learning behaviors among high, average,
and low achievers?
4. Method
4.1. Context and participants
English is a compulsory subject across universities in mainland China (The higher education department of the Ministry of Edu­
cation, 2007). English classes in universities for non-English majors are usually dominated by their first language i.e., Mandarin. Thus,
technology plays an important role in getting access to authentic English-speaking communities and broadening students’ interna­
tional perspectives (Yang & Chen, 2007). The College English Curriculum Requirement (2007) in China specified that universities
should support technology-based English teaching and learning so that students can be provided with favorable English learning
environment. Under such circumstances, technological tools have been widely applied to English teaching and learning both inside
and outside the classroom in Chinese universities (An, Wang, Li, Gan, & Li, 2021; Zheng, Liang, Yang, & Tsai, 2016).
Data were collected from 285 second-year students (167 males, 118 females) from a university in southeast China. The mean age of
the participants was 19.30 and the standard deviation was 1.02. All the participants have received formal English education for at least
7 years at the time of our study. The participants were from diverse disciplinary backgrounds such as medicine, pharmaceutics,
anesthesiology, and medical statistics. Students reported high ownership of technological tools: all the students owned a smartphone
and 95% of the participants owned or had easy access to a laptop or an iPad. At the university, first- and second-year students are
required to take the college English course that covers English reading, speaking, listening, and writing skills. The participants meet
their English teachers in formal classes for around 3 h every week. All the English classrooms at the university are equipped with basic
technology infrastructure for teaching and learning such as multimedia. Although technology-based learning environment has been
encouraged and supported by the university, the use of technology was voluntary.
4.2. Measures
4.2.1. Motivation for self-directed technology use in English learning questionnaire (MSDTUQ)
To capture EFL students’ motivation in self-directed technology use for English learning, we developed the MSDTUQ based on the
TPB, EVT, and goal orientation theory. With reference to existing literature on motivation (e.g., Pintrich, Smith, García, & McKeachie,
1991; Wigfield & Eccles, 2000) and technology acceptance (e.g., Kosovich, Hulleman, Barron, & Getty, 2015; Lai, 2013; Lai et al.,
2016; Ranellucci et al., 2020; Teo & Huang, 2019), a total of 40 Likert-type items measured on a 7-point scale (1 = not suitable for me
at all and 7 = very suitable for me) were generated. The initial items of the MSDTUQ were reviewed by three researchers in the field of
motivated technology-assisted language learning. Then, modifications were made according to their comments in order to ensure all
the items represented the construct they were designed to measure. The revised item list was then distributed to 5 EFL students to
ensure all the items were easily comprehended by undergraduate EFL students. After several rounds of revisions, 33 items were
retained in the final instrument.
The finalized items assessed nine motivational variables for self-directed technology use in English learning: perceived usefulness,
self-efficacy, facilitating conditions, subjective norms, perceived importance of technology for English learning, interest in using technology for
English learning, cost of using technology for English learning, mastery goal orientation toward English learning, and performance goal
orientation toward English learning (see Table 1).
The subscale of perceived usefulness (4 items) was adapted from Lai (2013), and this subscale evaluated the degree of students’
perceptions of the usefulness of the task in relation to the students’ current and future goals and agenda.
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The subscale of self-efficacy (4 items) was modified from Lai et al. (2016) and Wigfield and Eccles (2000), which measured stu­
dents’ beliefs about their capabilities of using technology to support and facilitate their English learning.
Items in the facilitating conditions subscale (4 items) were modified from Lai (2013) and Teo and Huang (2019), which evaluated
the degree to which students perceive the availability of the support necessary to use technology for English learning.
The subscale of subjective norms (3 items) was adapted from Lai (2013) and Venkatesh et al. (2003). This subscale measured the
degree to which students perceive that important others believe they should use technology to learn English.
Items in perceived importance of technology for English learning (3 items) subscale was created based on Ranellucci et al.’s (2020)
research. It assessed students’ perceptions of the importance of doing well on using technology for English learning.
The interest in using technology for English learning subscale (4 items) was modified on the basis of Lai (2013) and Ranellucci
et al.’s (2020) study. It measured the degree to which students perceive using technology for English learning to be interesting.
The cost of using technology for English learning subscale (3 items) was adapted from Kosovich et al. (2015), which assessed
students’ unpleasant feelings related to technology use for English learning, e.g., perceptions of time and effort demands, loss of valued
alternatives, or negative psychological experiences when using technology to learn English.
The mastery goal orientation (4 items) and performance goal orientation (4 items) subscales were adapted from Pintrich et al.
(1991). The mastery goal orientation toward English learning assessed the degree to which the students perceive themselves to learn
English for reasons such as challenge, curiosity, and mastery. Performance goal orientation toward English learning evaluated the
degree to which the students perceive themselves to learn English for reasons such as grades, rewards, performance, evaluation by
others and competition.
4.2.2. Technology-assisted self-regulated English learning behaviour questionnaire
The Technology-Assisted Self-Regulated English Learning Questionnaire (TASRELQ) were adapted and modified from Lai & Gu’s
(2011) study. The questionnaire contains five subscales: resource management, goal setting, metacognitive regulation, affective
regulation, and social regulation. Resource management assessed how students make use of technology-based resources to learn
English. Goal setting measured the degree to which students focus on the end results of a certain task. Metacognitive regulation
referred to students’ control and self-regulated aspects of cognition. Affective regulation evaluated the degree to which students use
technological resources to reduce boredom or maintain interest in English learning. Social regulation referred to the actions that
students take to communicate or cooperate with others through technological tools. All the items were measured on a 7-point Likert
scale (1 = not true of me at all and 7 = very true of me).
4.2.3. College English course results
In this study, the participants’ English learning achievement was measured by their final exam scores on the College English course.
The final exam mainly included two parts: (1) listening and linguistic knowledge, (2) reading comprehension. The listening part
evaluated students’ English listening proficiency with twenty multiple choice questions, and its degree of difficulty is similar to that of
College English Test - Band 4 (CET-4). The linguistic knowledge part mainly tested contents that students have learnt in their English
class, such as vocabulary use and paraphrasing sentences. Reading comprehension part contains four passages which has similar
degree of difficulty to CET-4. The total score of the final English exam was 100, and each part accounted for 50%. All the participants
took part in the exam on the same day.
4.3. Data collection
An online survey was conducted on the platform of Wenjuanxing to collect data on EFL students’ motivated technology-assisted
English learning and their College English course results. Before the online survey, all the participants were informed that their re­
sponses to the survey were anonymous and would be used only for academic research. The participants completed the online survey
with no time limit. On average, it took the students 5 min and 20 seconds (SD = 142.44) to complete the survey.
Given that the participants’ first language was Mandarin, all the questionnaire items were translated into Chinese by the first author
and then verified by the co-author and two experienced College English course lecturers, all of whom are L1 Chinese speakers. Then
back translation was conducted following a standard back-translation process (Maneesriwongul & Dixon, 2004). Whenever there is an
apparent discrepancy, a second back-translation process was conducted.
4.4. Data analysis
We performed confirmatory factor analysis (CFA) to test the factor structure of the MSDTUQ without first conducting exploratory
factor analysis (EFA). The reason for this is that performing a CFA is more appropriate than performing an EFA if there is a strong
theoretical and empirical support underlying the factor structure of a newly proposed measurement (Brown, 2014). The MSDTUQ in
the current study was developed under a strong theoretical basis (i.e., expectancy-value theory, TPB, and goal orientation theory) with
robust empirical support. According toLee (2004), CFA is the best approach to analyze the dimensionality of a new scale when the
structure of the items selected from the existing literature is clearly supported by certain well-established theoretical frameworks.
Similarly, in this study, CFA was conducted to test the psychometric properties of the TASRELQ that was adapted from previous
studies. Multiple fit indices were employed to evaluate the model fit in CFA, including the chi-square statistic (χ2) and its degrees of
freedom (df), the associated p value, the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI),
Tucker-Lewis Index (TLI), and the Standardized Root Mean-square Residual (SRMR). According to Hu and Bentler (1999), an
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acceptable (good) model should have a CFI and a TLI value no less than 0.90 (greater than 0.95), a RMSEA value and a SRMR value less
than 0.08.
Descriptive statistics and correlation analyses among all the variables were performed to examine the criterion validity of the
MSDTUQ. In order to examine whether there were differences in the motivation in the use of technology for English learning and
learning behaviors among the high, average, and low achievers, the data were divided into three groups based on the participants’ final
exam scores of College English course. Following Ebel and Frisbie (1986), after confirming that the top 27%, middle 46%, and bottom
27% of the participants’ learning achievement were significantly different, we performed the multivariate analyses of variance
(MANOVA) to compare the high, average, and low achievers in terms of motivation for self-directed technology use in English learning
and technology-assisted learning behaviors. Finally, to analyze the effects of motivation on technology-assisted English learning be­
haviors, multiple regression analyses were performed.
5. Results
5.1. Motivational factors that drove self-directed technology use for English learning
The CFA results showed unsatisfactory model fit indices with X2 = 1390.59 (df = 459, p < .001); CFI = 0.88; TLI = 0.86; SRMR =
0.07; RMSEA = 0.08. Then, we tried to improve the model fit through addressing the item issues suggested in the modification indices.
Four items (i.e., item 4, 9, 15, and 21) were removed from the model due to their strong correlations with other constructs. The
modified model had a satisfactory model fit: X2 = 821.89 (df = 339, p < .001); CFI = 0.93; TLI = 0.91; SRMR = 0.06; RMSEA = 0.07.
Factor loadings and internal consistency estimates of reliability for the nine subscales in the MSDTUQ are shown in Table 1. Stan­
dardized factor loadings of most items are higher than 0.50 (the cutoff value with 0.50; Hair, Black, Babin, Anderson, & Tatham, 2010),
except one in performance goal orientation with the factor loading of 0.48. This indicates that the items were generally effective in
defining the latent variables. Internal consistency estimates of the reliability for the nine subscales in the MSDTUQ show that all the
subscales have good internal consistency (α = 0.81-0.92).
Moreover, the values of composite reliability (CR) and average variance extracted (AVE) were used to assess the convergent
validity of the measurement model, for which a value ≥ 0.60 and 0.50 respectively can be considered as acceptable (Hair et al., 2010).
The results show that the values of CR of the motivational factors range from 0.74 to 0.93, markedly exceeding the threshold value 0.60
(see Table 1). The AVE values of these factors are acceptable, with only one factor being slightly below 0.50 (i.e., AVE = 0.47 for
performance goal orientation). According to Fornell and Larcker (1981), even if AVE is less than 0.5, the convergent validity of the
construct is still adequate, provided that composite reliability is higher than 0.6.
Discriminant validity was then tested using the heterotrait–monotrait ratio of correlations (HTMT). If the HTMT value between two
factors is no greater than the threshold value of 0.9, discriminant validity can be assumed (Henseler, Ringle, & Sarstedt, 2015). As
shown in Table 2, most of the constructs exhibit discriminant validity according to this criterion, except for the value between perceived
usefulness and interest in using technology for English learning, which slightly exceeds 0.9. Such results demonstrate acceptable
discriminant validity of the MSDTUQ.
Psychometric properties of the TASRELQ were also checked through CFA. The fit indices showed that the model fit the data well in
the current study, X2 = 140.93 (df = 55, p < .001); CFI = 0.96; TLI = 0.97; SRMR = 0.03; RMSEA = 0.07. The standardized factor
loadings of the items ranged between 0.76 and 0.96. The Cronbach’s α coefficient of the whole TASRELQ was 0.95, and of the five
subscales were from 0.86 to 0.90 (see Table 3). The values of CR of the technology-assisted self-regulated learning factors ranged from
0.86 to 0.90, exceeding the threshold value 0.60. The AVE values of these factors ranged between 0.73 and 0.82, which are all above
the threshold value 0.50 (Hair et al., 2010). The HTMT ratio correlations between most constructs were lower than 0.90, except for the
correlation between goal setting and metacognitive regulation, which slightly exceeds the threshold value of 0.9 (HTMT ratio correlation
= 0.91).
Descriptive statistics and correlations among the motivational variables, technology-assisted learning behaviors, and English
learning achievement are presented in Table 4. Correlation analyses showed that most of the motivational variables had a medium to
strong association with the goal-setting, resource-management, affective-management and metacognitive-management dimensions of
self-directed technology use for English learning, with correlation coefficients primarily ranging from 0.60 to 0.73. However, their
associations with social regulation were small, with correlation coefficients primarily in the 0.20–0.35.
Table 2
HTMT ratio of correlations among the MSDTUQ factors.
1.Perceived usefulness
2.Self-efficacy
3.Facilitating conditions
4.Subjective norms
5.Importance
6. Interest
7. Cost
8.Mastery orientation
9.Performance orientation
1
2
3
4
5
6
7
8
9
–
0.79
0.75
0.73
0.90
0.93
0.05
0.78
0.41
–
0.62
0.62
0.85
0.76
0.22
0.75
0.35
–
0.86
0.62
0.65
0.14
0.70
0.35
–
0.72
0.70
0.15
0.78
0.51
–
0.86
0.08
0.82
0.48
–
0.09
0.81
0.48
–
0.02
0.12
–
0.53
–
7
System 115 (2023) 103051
Z. An et al.
Table 3
Summary of measurement scales in TASRELQ (n = 285).
Subscale and sample item
No. of items
Cronbach’s
alpha
CR
AVE
Resource management
I use technologies to seek more English learning resources.
Goal setting
I use technologies to help me achieve my English learning goals more quickly and efficiently.
Metacognitive regulation
I know how to use technologies to effectively monitor myself to achieve my English learning goals at each
stage.
Affective regulation
When I feel bored with learning English, I use technologies to curtail the boredom and increase the enjoyment.
Social regulation
I use technologies to connect with English native speakers.
3
.89
.89
.74
2
.89
.89
.82
3
.90
.90
.76
3
.89
.89
.73
2
.86
.86
.76
Among the motivational variables, all the variables in TPB exhibited strong associations with most facets of self-directed tech­
nology use. Among the value belief constructs in the EVT, only cost value belief had non-significant correlations with most dimensions
of self-directed technology use. Instead, cost value belief had a significant positive association with social regulation (r = 0.24, p <
.001). The more the learners felt that technology use cost time, the more likely they were going to use technology to socialize in
English. Among the two goal orientation constructs, mastery goal orientation towards English learning had strong associations with
self-directed technology use. In contrast, although performance goal orientation associated positively with self-directed technology
use, its correlations were much smaller. Performance goal orientation had a non-significant correlation with social regulation (r =
0.11, p > .05). Thus, the findings suggested that motivational factors, except cost value belief and performance goal orientation,
associated quite strongly with learners’ self-directed use of technology for English learning beyond the classroom. Moreover, learners’
use of technology for socialization in English behaved differently from the other dimensions of self-directed technology use.
Results of the regression analysis showed that the motivational variables as a whole could explain a significant proportion of the
variance in students’ technology-assisted English learning beyond the classroom (R2 = 74%; see Table 5). Four of the motivational
variables (i.e., perceived usefulness, facilitating conditions, cost of using technology for English learning, and mastery goal orienta­
tion) significantly predicted students’ overall self-directed technology use for English learning, among which facilitating conditions
and mastery goal orientation had the strongest effects. Separate regression analyses on each dimension of self-directed technology use
revealed that these dimensions were driven differently by motivational factors. Generally speaking, the motivational variables could
explain a large proportion of the variance in most of the self-directed technology use dimensions, ranging from 65% to 74%. However,
these motivational variables could only account for 29% of the variance in social regulation. The finding suggests that social regulation
might have been regarded less as a learning behavior, and thus these learning-oriented motivational variables could only explain a
small proportion of its variation.
Specifically, subjective norms and mastery goal orientation were significant predictors of resource management, and perceived
usefulness, self-efficacy, facilitating conditions, and mastery goal orientation significantly predicted metacognitive regulation. Thus,
learners’ use of technology to seek and organize English learning resources and their use of technology to plan and monitor their
learning process and strategies were determined primarily by TPB constructs and mastery goal orientation. Resource management was
predicted by the social component of TPB, but metacognitive regulation was determined more by attitude and perceived behavioral
control components. The finding suggests that resource management and metacognitive regulation relied more on motivational
sources that support the necessity (i.e., perceived usefulness and subjective norm) and feasibility (i.e., perceived behavioral control) of
the behavior and motivational sources that sustain effort investment (i.e., mastery performance goal).
Subjective norms, interest in using technology for English learning, mastery goal orientation and performance goal orientation
were significant predictors of affective regulation. Thus, affective regulation was determined primarily by the goal orientation aspects
of motivation, with a greater dependence on mastery goal orientation (β = 0.25, p < .001) than performance goal orientation (β = 0.09,
p < .05).
In contrast, perceived usefulness, self-efficacy, facilitating conditions, interest in using technology for English learning, and per­
formance goal orientation had significant effects on goal setting. The more learners perceived the learning potential and the support
available for technology use, the more internal interest they had for technology use and the more they wanted to perform better than
their peers, the more likely they would use technological resources to set and monitor English learning goals. Thus, learners’ use of
technology for goal setting relied on motivational sources from all three theoretical perspectives. Similarly, social regulation also relied
on motivational sources from different theoretical perspectives, such as perceived usefulness, facilitating conditions, interest in using
technology for English leaning, cost, and mastery goal orientation significantly predicted social regulation. In all, separate regression
analyses on motivational predictors of each dimension of self-directed technology use for learning revealed that different motivational
sources fueled different aspects of technology-enhanced self-regulated English learning.
5.2. Differences in motivational factors that drove self-directed technology use for English learning among high, average, and low achievers
MANOVAs were performed to compare the mean values of motivational variables and technology-assisted learning behaviors
8
Z. An et al.
Table 4
Descriptive statistics and correlations among the variables.
Variable
M
SD
1
2
3
4
TPB
1. Perceived usefulness
2. Self-efficacy
3. Facilitating conditions
4. Subjective norms
5.22
5.01
4.71
5.07
1.18
1.37
1.29
1.16
1
.69***
.62***
.62***
1
.54***
.55***
1
.73***
1
EVT
5. Importance
6. Interest
7. Cost
5.45
5.43
3.76
1.18
1.17
1.32
.77***
.81***
.04
.77***
.71***
− .19**
.53***
.56***
.12*
Goal orientation
8. Mastery orientation
9. Performance orientation
5.12
5.45
1.15
1.12
.65***
.32***
.67***
.29***
Self-regulation
10. Resource management
11. Goal setting
12.Metacognitive regulation
13.Affective regulation
14.Social regulation
15. Achievement
5.01
5.12
4.85
5.27
3.51
78.52
1.30
1.31
1.34
1.23
1.82
9.78
.68***
.71***
.71***
.61***
.35***
.27***
.64***
.65***
.67***
.66***
.28***
.38***
9
Theory
5
6
7
8
9
.63***
.62***
.12*
1
.79***
− .07
1
− .08
1
.58***
.28***
.67***
.42***
.71***
.40***
.71***
.40***
− .02
.10
1
.42***
1
.63***
.62***
.70***
.55***
.45***
.19**
.70***
.64***
.67***
.63***
.33***
.26***
.69***
.67***
.66***
.68***
.24***
.38***
.68***
.73***
.70***
.72***
.23***
.29***
.06
.01
.03
− .06
.24***
− .18**
.71***
.66***
.69***
.72***
.35***
.43***
.40***
.44***
.38***
.43***
.11
.22***
10
11
12
13
14
1
.80***
.79***
.75***
.45***
.36***
1
.82***
.71***
.40***
.30***
1
.73***
.53***
.30***
1
.29***
.41***
1
.10
Note. Importance = perceived importance of technology for English learning; interest = interest in using technology for English learning; cost = cost of using technology for English learning. ***p < .001,
**p < .01, *p < .05.
System 115 (2023) 103051
10
Technology-assisted learning
Resource management
.74
SE(B)
.06(.15)
.05(.13)
.04(.21)
.05(.11)
.06(.01)
.06(.09)
.03(.06)
.05(.24)
.04(.07)
.66
SE(B)
.08(.14)
.06(.10)
.06(.10)
.07(.24)
.08(.12)
.08(.05)
.04(.05)
.07(.24)
.05(.06)
β
.16
.15
.24
.11
.004
.09
.07
.24
.07
p
.011
.076
.000
.053
.949
.132
.048
.000
.055
.75
.11(.23)
.11(-.06)
.07(.16)
.09(.13)
.14(.10)
.12(.09)
.06(.11)
.11(.36)
.08(.08)
.24
− .07
.20
.14
.10
.09
.13
.30
.08
.050
.559
.029
.123
.484
.443
.055
.002
.280
.15(.18)
.15(-.11)
.10(-.02)
.12(.39)
.19(.25)
.16(.13)
.07(.06)
.15(.23)
.10(-.03)
.19
.18
.36
.01
− .04
.08
.06
.17
.12
.066
.021
.000
.935
.661
.450
.225
.034
.034
.13(.06)
.09(.12)
.10(.22)
.11(.15)
.12(.02)
.12(.07)
.06(.09)
.10(.22)
.07(.11)
.17
− .11
− .02
.38
.23
.12
.07
.17
− .02
.244
.450
.877
.001
.175
.409
.433
.134
.787
.610
.262
.092
.147
.476
.365
.865
.026
.568
.13(.17)
.10(.12)
.11(.15)
.14(.11)
.15(.20)
.17(-.02)
.09(.03)
.14(.32)
.08(.03)
β
.19
.14
.15
.09
− .01
.23
.003
.08
.13
p
.007
.031
.008
.137
.872
.001
.946
.167
.001
.18(.31)
.17(-.13)
.11(.18)
.13(.14)
.21(.15)
.18(.22)
.08(.13)
.17(.14)
.12(.18)
.06
.13
.23
.14
.02
.07
.10
.21
.10
.620
.176
.022
.184
.862
.576
.119
.032
.148
.12(.22)
.08(.18)
.09(.10)
.11(.07)
.11(-.12)
.11(.30)
.06(-.05)
.09(.09)
.07(.17)
.25
− .12
.19
.12
.13
.17
.13
.10
.13
.087
.434
.103
.294
.476
.236
.132
.421
.131
.185
.232
.175
.437
.170
.895
.716
.021
.731
.13(.16)
.11(.13)
.11(.17)
.14(.04)
.15(.09)
.17(.21)
.09(-.07)
.14(.30)
.09(.04)
Affective regulation
.69
SE(B)
.08(.20)
.06(.17)
.06(.30)
.07(.08)
.08(-.03)
.08(.13)
.04(.01)
.07(.18)
.05(.08)
.65
SE(B)
.08(-.13)
.06(.14)
.05(.05)
.07(.14)
.08(.08)
.08(.35)
.04(-.02)
.06(.26)
.05(.10)
β
.17
.17
.29
.07
− .03
.12
.01
.16
.07
p
.010
.004
.000
.242
.668
.093
.740
.006
.096
.16(.30)
.15(-.01)
.10(.29)
.12(.09)
.19(.08)
.16(-.05)
.08(.04)
.15(.38)
.10(.09)
.21
.19
.10
.07
− .10
.29
− .05
.08
.15
.068
.028
.272
.499
.288
.011
.415
.345
.013
.12(.30)
.08(.19)
.09(.41)
.10(-.08)
.11(-.14)
.11(.15)
.05(.01)
.09(.14)
.07(.09)
.26
− .01
.32
.08
.07
− .04
.04
.27
.07
.064
.961
.004
.442
.668
.769
.613
.017
.373
.229
.211
.148
.783
.542
.213
.426
.037
.650
.14(-.04)
.11(.16)
.12(.16)
.15(.24)
.16(.18)
.18(.32)
.09(-.02)
.15(.06)
.09(-.01)
p
.096
.109
.333
.034
.328
.000
.637
.000
.025
.11(-.05)
.10(-.01)
.07(.01)
.08(.11)
.13(.31)
.11(.46)
.05(-.02)
.10(.05)
.07(,10)
.27
.19
.40
− .07
− .12
.13
.01
.13
.08
.014
.019
.000
.456
.190
.195
.828
.118
.164
.11(-.08)
.08(.23)
.08(.21)
.10(-.01)
.10(.12)
.11(.10)
.05(-.02)
.09(.27)
.06(.17)
− .06
− .02
.02
.13
.34
.49
− .02
.05
.10
.613
.893
.827
.172
.017
.000
.754
.626
.164
.779
.165
.197
.115
.276
.082
.810
.695
.950
.17(-.16)
.13(.05)
.14(-.01)
.18(.32)
.19(-.19)
.21(.55)
.11(-.05)
.17(.26)
.11(.10)
β
.25
.14
.33
− .06
− .15
¡.25
.20
.25
− .04
p
.015
.133
.000
.495
.165
.016
.000
.004
.507
.29(.52)
.28(.02)
.18(.33)
.22(-.12)
.35(-.47)
.30(-.40)
.14(.43)
.29(1.06)
.19(.001)
.30
.02
.24
− .07
− .27
− .22
.31
.51
.001
.079
.922
.072
.592
.186
.192
.003
.000
.995
.32
.09
.45
− .19
− .07
− .15
.14
.01
.01
.059
.487
.001
.183
.603
.354
.117
.927
.893
.17
.18
.16
.20
.07
¡.60
.19
.41
¡.26
.383
.346
.314
.308
.766
.011
.118
.037
.046
.28
− .08
.26
.23
− .004
.11
.10
− .02
.27
.16
.480
.004
.012
.963
.269
.373
.732
.002
.011
.57
− .04
.17
.14
.21
.17
.27
− .02
.05
− .01
.29
SE(B)
.16(.38)
.12(.18)
.11(.46)
.14(-.09)
.16(-.22)
.16(-.39)
.08(.27)
.13(.39)
.10(-.06)
.45
.67
.70
.14
.15
.15
.03
.09
.18
− .06
.25
.04
β
− .12
.15
.06
.13
.07
.33
− .02
.25
.09
Social regulation
.75
.72
.74
.15
.14
.13
.09
.19
.02
.03
.27
.03
Metacognitive regulation
.65
.66
.76
.06
.13
.17
.17
.10
.13
.01
.27
− .05
.65
SE(B)
.08(.22)
.06(.13)
.06(.15)
.07(.10)
.08(-.01)
.08(.26)
.04(.003)
.07(.09)
.05(.16)
.59
.60
.74
.11(.06)
.09(.10)
.10(.17)
.12(.18)
.13(.09)
.15(.13)
.08(.01)
.12(.27)
.07(-.04)
p
.064
.078
.068
.000
.119
.519
.215
.000
.210
.63
73
.10(.18)
.07(.15)
.07(.31)
.09(.01)
.09(-.04)
.09(.07)
.04(.05)
.08(.16)
.06(.12)
β
.13
.11
.10
.22
.11
.05
.05
.21
.05
Goal setting
.26(.50)
.18(.13)
.19(.65)
.23(-.31)
.24(-.13)
.25(-.23)
.12(.19)
.20(.02)
.15(.02)
24
− .15
.06
− .01
.28
− .19
.49
− .05
.23
.10
.326
.686
.967
.072
.320
.011
.632
.132
.338
.27(.23)
.21(.20)
.23(.23)
.28(.29)
.30(.09)
.34(-.89)
.17(.28)
.28(.60)
.17(-.35)
Note. Importance = perceived importance of technology for English learning; interest = interest in using technology for English learning; cost = cost of using technology for English learning; Significant β
were highlighted in bold characters.
System 115 (2023) 103051
Total (n = 285)
R2
Predictors
Perceived usefulness
Self-efficacy
Facilitating conditions
Subjective norms
Importance
Interest
Cost
Mastery orientation
Performance orientation
High (n = 77)
R2
Predictors
Perceived usefulness
Self-efficacy
Facilitating conditions
Subjective norms
Importance
Interest
Cost
Mastery orientation
Performance orientation
Average (n = 131)
R2
Predictors
Perceived usefulness
Self-efficacy
Facilitating conditions
Subjective norms
Importance
Interest
Cost
Mastery orientation
Performance orientation
Low (n = 77)
R2
Predictors
Perceived usefulness
Self-efficacy
Facilitating conditions
Subjective norms
Importance
Interest
Cost
Mastery orientation
Performance orientation
Z. An et al.
Table 5
Multiple regression analysis predicting technology-assisted language learning behaviors (n = 285).
System 115 (2023) 103051
Z. An et al.
among high, average, and low English achievers. Regarding motivational variables, there was a significant main effect of achievement
level groups, Wilk’s λ = 0.81, p < .001, F (18, 548) = 3.31, partial η2 = 0.10. According to Cohen (1988), partial η 2 in the range of
0.01–0.06 is regarded as a small effect size, 0.06–0.14 moderate effect, and over 0.14 large effect. The association between students’
motivation in self-directed technology use for English learning and their English achievement levels was medium.
When the results for the dependent variables were considered separately, using a Bonferroni adjusted alpha level of 0.006, there
was a significant effect on the rating of four of the nine motivational dimensions, i.e., self-efficacy (F(2, 282) = 13.24, p < .001, partial
η2 = 0.09), perceived importance of technology for English leaning, (F(2, 282) = 12.44, p < .001, partial η2 = 0.08), interest in using
technology for English leaning, (F(2, 282) = 6.66, p = .001, partial η2 = 0.08), and mastery goal orientation (F(2, 282) = 20.06, p <
.001, partial η2 = 0.13). All the effect sizes were medium. An inspection of the mean scores indicated that high achievers generally
reported higher levels of motivation than the average achievers, who in turn outperformed the low achievers (see Fig. 1).
Post hoc comparisons using the Scheffé test showed that the differences in motivation between high and low achievers were
statistically significant on eight of the nine motivational variables, except facilitating conditions (see Table 6). However, the high and
average achievers were only significantly different in self-efficacy and mastery goal orientation. Similarly, the average and low
achievers were only significantly different in three motivational variables, i.e., self-efficacy, perceived importance of technology for
English leaning, and mastery goal orientation. The findings suggest that self-efficacy and mastery goal orientation were the motiva­
tional sources that need special attention.
Regarding self-directed technology use for English learning behaviours, there was an overall effect of achievement level groups,
Wilk’s λ = 0.84, p < .001, F (10, 556) = 5.12, partial η2 = 0.08. When the results for the dependent variables were considered
separately, using a Bonferroni adjusted alpha level of 0.01, there was a significant effect on the rating of four of the five technologyassisted learning behaviors (see Fig. 2), i.e., resource management (F(2, 282) = 13.05, p < .001, partial η2 = 0.09), goal setting (F(2,
282) = 6.78, p = .001, partial η2 = 0.05), metacognitive regulation (F(2, 282) = 6.61, p = .002, partial η2 = 0.05), and affective
regulation (F(2, 282) = 20.94, p < .001, partial η2 = 0.13). The high achievers generally reported a higher level of technology-assisted
language learning behaviors than the average achievers, who in turn outperformed the low achievers. Although there were no sig­
nificant group differences in social regulation, the higher achievers scored higher than average and low achievers.
Moreover, regression analyses revealed that prevailing motivational factors varied across the three groups (Table 5). Within the
high achiever group, two motivational factors (i.e., facilitating conditions and mastery goal orientation) had significant effects on
students’ technology-assisted learning behaviour. Within the average achievers, four motivational factors (i.e., self-efficacy, facili­
tating conditions, mastery goal orientation and performance goal orientation) were significant predictors of students’ technologyassisted learning behaviour. For the low achievers, only mastery goal orientation significantly predicted learning behaviour. Thus,
mastery goal orientation was consistently the motivational factor that significantly predicted learning behaviors across the three
groups. Nonetheless, mastery goal orientation determined different aspects of self-directed technology use for English learning: it
predicted metacognitive regulation and social regulation for high achievers, resource management and affective regulation for average
achievers, and resource management, goal setting and social regulation for low achievers. Thus, mastery goal orientation was
particularly essential for learners with lower English proficiency in using technology to seek and organize learning resources.
Moreover, facilitating conditions was a significant predictor for both high achievers and average achievers. However, for high
achievers, facilitating conditions was a determinant of their use of technology to plan and monitor their learning process and strategies
(i.e., metacognitive regulation); whereas for average achievers, facilitating conditions played a much more significant role, predicting
all aspects of self-regulation, with the only exception of goal setting.
In contrast, self-efficacy and performance goal orientation were significant determinants for average achievers, for goal setting,
metacognition regulation and affective regulation in particular. The differential findings across the three groups suggest that these
motivational factors played differential roles for learners of different proficiency levels.
Fig. 1. Mean Differences of Motivational Variables among High, Average, and Low Achievers. Note. Importance = perceived importance of technology
for English learning; interest = interest in using technology for English learning; cost = cost of using technology for English learning.
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Table 6
Pairwise comparisons for high, average, and low English achievers.
Perceived usefulness
Self-efficacy
Facilitating conditions
Subjective norms
Perceived importance of technology for English learning
Interest in using technology for English learning
Cost of using technology for English learning
Mastery goal orientation
Performance goal orientation
Achievement level
Achievement level
Mean differences
p
High
High
Middle
High
High
Middle
High
High
Middle
High
High
Middle
High
High
Middle
High
High
Middle
High
High
Middle
High
High
Middle
High
High
Middle
Low
Average
Low
Low
Average
Low
Low
Average
Low
Low
Average
Low
Low
Average
Low
Low
Average
Low
Low
Average
Low
Low
Average
Low
Low
Average
Low
0.57
0.26
0.31
1.09
0.61
0.48
0.37
0.14
0.23
0.57
0.26
0.31
0.90
0.37
0.53
0.67
0.37
0.30
− 0.59
− 0.26
− 0.33
1.10
0.61
0.49
0.44
0.23
0.21
.010
.300
.168
<.001
.006
.043
.193
.734
.456
.009
.293
.161
<.001
.077
.005
.002
.080
.191
.020
.388
.204
<.001
.001
.007
.048
.361
.405
Fig. 2. Mean differences of technology-assisted learning behaviors among high, average, and low achievers.
6. Discussion
6.1. A multi-theoretical perspective of motivation for self-directed technology use for English learning
This study constructed a nine-dimension (factor) model of motivation in self-directed technology use for English learning: perceived
usefulness, self-efficacy, facilitating conditions, subjective norms, perceived importance of technology for English learning, interest in using
technology for English leaning, cost of using technology for English leaning, mastery goal orientation toward English learning, and performance
goal orientation toward English learning. The measurement model displayed satisfactory psychometric qualities in relation to the reli­
ability and construct validity. Furthermore, high criterion-related validity was also obtained through the significant correlations of
these motivational variables with both learners’ English learning achievement and their self-directed use of technology for English
learning.
This integrative motivational model accounted for 74% of the variation in English language learners’ use of technology for self12
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Z. An et al.
regulated learning, which is significantly larger than previous research (30–40%) that were guided by one or two theories in the field of
technology adoption such as TPB or Technology Acceptance Model (Teo et al., 2018; Teo & Lee, 2010). The finding suggests that our
multi-theoretical perspective model is more powerful in explaining individuals’ technology adoption for self-directed learning than
single-theoretical models. It also provides evidence that the MSDTUQ was a reliable and valid measurement of EFL students’ moti­
vation for technology adoption in English learning, which confirmed the role of the constructs from TPB, EVT, and goal orientation
theory as significant predictors of individuals’ technology use in the literature (Huang et al., 2021; Lai, 2013; Sun & Mei, 2022; Tarhini
et al., 2017). This measurement model might add to our understanding of the role of motivational variables in technology-assisted
language learning, which can serve as a useful tool for teachers to understand and sustain the EFL students’ motivation for technol­
ogy use in EFL learning.
This study further revealed that perceived usefulness, facilitating conditions, cost of using technology for English learning, and
mastery goal orientation were significant predictors of these English language learners’ self-directed use of technology to regulate their
English learning. The four motivational factors were from different motivational theories, which suggests that these motivational
theories complement each other in predicting learner behaviors. The finding on the necessity of taking a multi-dimensional motiva­
tional perspective to understand students’ learning behavior concurred with findings from previous research (e.g., Gan, Fulton, & Li,
2022; Plante, O’Keefe & Theoret, 2013).
Moreover, this study found that different motivational perspective served as the primary source of drive for different aspects of selfregulation with technology. Specifically, TPB constructs were essential to drive the aspects of self-regulation that demand effort in­
vestment, namely resource and metacognitive management; goal orientation constructs were essential to drive learners’ use of
technology for emotion management to maintain vigor for learning; and constructs from all three theoretical perspectives were critical
to drive learners’ use of technology for goal setting and social regulation. The findings suggest that different motivational factors need
to be attended to when encouraging and supporting the use of technology for the regulation of different aspects of English learning.
Although this study confirmed previous research findings (Lai, 2013; Tarhini et al., 2017) on the various motivational dimensions
affecting students’ self-directed technology use for learning purposes, it has also extended our understanding on how various moti­
vational variables function differently for students of different achievement levels. The findings indicated that the motivational drive
for self-directed technology use for English learning varied for learners of different proficiency levels. Mastery learning orientation was
the most significant motivational driver for low achievers; whereas, additional motivational factors, such as facilitating conditions,
mattered for high and average achievers. Thus, findings from this study suggest a holistic yet differential approach in understanding
motivational forces for self-directed use of technology for language learning.
6.2. Significant motivational factors that predict self-directed technology use for learning
6.2.1. Goal orientation
Among the nine motivational variables, mastery goal orientation was found to be particularly vital, as it significantly predicted four
out of five dimensions of technology-assisted self-regulation of English language learning. Moreover, mastery goal orientation was a
significant predictor of self-directed technology use regardless of learners’ achievement level. Previous research studies have found
that mastery goal orientation was essential in supporting high academic achievement inside the classroom (Cerasoli & Ford, 2014; Gul
& Shehzad, 2012). This study suggests that mastery goal orientation also mattered significantly in the self-initiated learning contexts,
which echoes Lin’s (2021) research findings. Moreover, mastery goal orientation drove different dimensions of self-regulation for
language learners of different proficiency levels. For instance, it drove resource and affective regulation for average achievers, but
social and metacognitive dimensions for high achievers.
This study further found that performance goal orientation, although not a significant predictor of overall self-directed technology
use, did predict some sub-dimensions especially for average learners. Moreover, mastery goal orientation and performance goal
orientation contributed to different aspects of self-regulation: mastery goal orientation predicted the resource, metacognitive, social
and affective dimensions, whereas performance goal orientation determined the goal setting and affective dimensions. Performance
goal orientation significantly predicted some dimensions of self-regulated learning among average and low achievers, but not for high
achievers. The finding is understandable since average and low achievers might have cared more about their grades, and social
comparison might have played a greater role in shaping their learning behavior (Dijkstra, Kuyper, Werf, Buunk, & Zee, 2008; Garcia,
Tor, & Schiff, 2013). The finding suggests that the motivational function of performance goal orientation is dependent of learner
characteristics. In all, supporting Plante et al.’s (2013) argument that the impact of mastery and performance goal orientation to
academic achievement might vary across learning climate, this study further found that these two goal orientations might impact
different aspects of self-regulation. Findings from this study suggest that the influence of goal orientation on self-regulated language
learning technology was rather complex, depending on the dimensions of self-regulation and the types of learners.
6.2.2. Perceived usefulness
Perceived usefulness significantly predicted three out of five dimensions of technology-assisted self-regulation of English language
learning. The importance of perceived usefulness in predicting self-directed technology use for learning concurred with previous
findings (Chiu & Wang, 2008; Lai, 2013). This study found that perceived learning potential of technology use for English learning
beyond the classroom kept learners actively using technology to set and monitor short-term or long-term learning goals, use meta­
cognitive strategies to regulate learning, and socialize with English speakers. This can be accounted for by the TPB and EVT framework
which posit that favorable attitudes and a high level of utility value will lead an individual to be more likely to perform a certain
behavior (Ajzen, 1991, 2000; Wigfield & Eccles, 2000). It was found that perceived usefulness was essential especially for average
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learners. Thus, helping learners, learners with average language proficiency in particular, see the values of technological resources for
language learning is essential to boosting learners’ volitional use of technology beyond the classroom.
6.2.3. Facilitating conditions
Facilitating conditions was another significant motivational factor that drove self-directed technology use for English learning,
since it significantly predicted three out of five sub-dimensions of self-regulation. This result was consistent with previous research
showing that adequate facilities as well as technical, pedagogical and emotional support from teachers or peers help students realize
the educational potentials of technology, which ultimately increased the frequency of their technology adoption for language learning
(Hartshorne & Ajjan, 2009; Lai, 2013). However, the influence of facilitating conditions was not significant for low achievers’ learning
behaviors in the current study. As the low achievers reported relatively lower levels of mastery and performance goal orientation than
their high- and average-achieving peers, they might be less sensitive to technological facilities and pedagogical or emotional support
from others. This study found that facilitating conditions played a more significant role for average achievers than for high achievers,
as it determined different dimensions of self-regulation for this group of learners. For high achievers, facilitating conditions was found
to associate significantly with their use of technology to plan and monitor learning process and strategies. The finding hence suggests
that, despite being a consistent strong predictor of technology adoption, facilitating conditions may support different aspects of
learning for different learner groups. Thus, a more refined perspective needs to be taken to understand the contribution of facilitating
conditions for different types of learners.
6.2.4. Perceived cost of using technology for English learning
The most surprising finding in this study was the influence of cost of using technology for English learning. It was found to be a
positive predictor of technology-based self-regulated learning behaviors primarily on social regulation among high achievers. How­
ever, previous studies have frequently reported cost of technology use or technology use anxiety as a negative predictor of technology
use (Bai et al., 2021; Chiu & Wang, 2008; Gan et al., 2022). A possible reason is that the students’ perception of the time demands from
self-directed technology use might have prepared them mentally for the use of technology for socialization, which was often not
associated with learning (Kolhar, Kazi, & Alameen, 2021). For high achievers, the more aware and prepared for the time and energy
needed for self-directed technology use they were, the more likely they were to embark on the naturalistic use of English for social­
ization in technological spaces. Whereas, for lower achievers, this was not a significant predictor of social regulation, because the use
of language for socialising demands a certain level of language proficiency. Thus, in the particular context of social regulation among
high achievers, cost of using technology for English learning might serve as a facilitating anxiety force, which occurs when the difficult
level of the tasks triggers the proper amount of negative appraisal (Scovel, 1978; Williams, 2008). As researchers argued that although
too much negative appraisal of a task can lead to work avoidance or inefficient learning performance, a certain level of anxiety may be
beneficial (Chan & Wu, 2004; Zheng, 2008). The finding thus suggests a critical view towards the motivational function of cost value
belief on learners’ technology use.
7. Implications
This study has important implications for theory and practice. From a theoretical perspective, the measurement model constructed
in this research allows a more comprehensive understanding of the motivational factors that affect Chinese university students’
technology use for English learning. Our study has identified nine motivational dimensions that underlie students’ learning behav­
iours: perceived usefulness, self-efficacy, facilitating conditions, subjective norms, perceived importance of technology for English learning,
interest in using technology for English learning, cost of using technology for English learning, mastery goal orientation toward English learning,
and performance goal orientation toward English learning. While motivation has previously been confirmed as vital (Dörnyei & Skehan,
2003; Gan, Leung, He, & Nang, 2019; Wang & Gan, 2021), the extent to which different types of motivation impact on self-directed use
of technology for English learning has not been investigated systematically in the academic literature. The results of our study showed
that motivational variables impacted on self-directed technology-assisted learning behaviors in a differential way across high, average,
and low achievers.
In recent years, stratified teaching has become increasingly popular in College English teaching in mainland China (Huang & Wu,
2020), which involves grouping students based on different English proficiency levels and tailoring the teaching to the specific
problems of students. Our research findings support the importance of providing differential motivational support for learners of
different proficiency levels and for different dimensions of technology-enhanced self-regulation. In our study, mastery goal orientation
toward English language learning was found to be the dominant motivational variable underlying technology-assisted English learning
behaviors. This result suggested that nurturing learners’ interest in English learning should be at the core of any motivational in­
terventions. In order to increase students’ interest in English learning, English courses should be designed with interesting features
such as offering interesting tasks and cooperative learning activities. Recognizing students’ progress in English learning, giving praise,
and providing positive feedback might also be helpful in promoting mastery goal orientation (Bai et al., 2021; Partrick, Kaplan, &
Ryan, 2011).
8. Conclusions
This study attempted to fill the research gap by identifying a motivational framework for understanding Chinese undergraduate
students’ motivation in self-directed technology use for English learning and examining its relations with technology-assisted learning
14
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Z. An et al.
behaviors among high, average, and low achievers. The findings revealed nine motivational dimensions for students’ technology-based
English learning, providing a comprehensive profile of how Chinese EFL students were motivated to engage in technology adoption in
English learning. Significant differences in these motivational dimensions between the high, average, and low achievers suggest that
motivational variables in self-directed technology use are essential for English learning achievement.
The present study has some potential limitations that should be acknowledged. First, data in our study were mainly collected
through a self-report questionnaire, which might induce bias in social desirability and limit the ability to have an in-depth view of the
EFL learners’ motivation for technology-based English learning. Future studies may employ a variety of methodologies (e.g., in­
terviews) to understand EFL students’ motivation in technology use for language learning. Second, the current study might be limited
by the relatively small sample size and the representativeness of the sample. Since the participants were second-year university stu­
dents in mainland China. Future research should seek a larger sample size and employ participants from different cultural contexts to
increase the credibility of the results and psychometric properties of the newly developed instrument. Third, the cross-sectional design
of the study cannot ensure power of causality between EFL learners’ motivation, technology-assisted language learning behaviors, and
English learning achievement. Research in the future needs to adopt a longitudinal design to establish the causal relationships among
these variables.
Author statement
Zhujun An and Zhengdong Gan: Conceptualization, Methodology, Software . Zhujun An and Zhengdong Gan: Data curation,
Writing- Original draft preparation. Zhujun An, Chun Lai, Zhengdong Gan: Writing- Reviewing and Editing.
Acknowledgement
This study was supported by the Macao SAR Government Higher Education Fund under Grant HSS-UMAC-2020-12. We have no
conflicts of interest to declare.
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