IAFOR Journal of Education: Technology in Education

Volume 8 – Issue 2 – 2020

Understanding the Characteristics of English Language Learners’
Out-of-Class Language Learning through Digital Practices
Ali Dincer
Erzincan Binali Yildirim University
Turkey47

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Volume 8 – Issue 2 – 2020

Abstract
This study aims to understand the extent to which English as a foreign language learners use
technology for their autonomous language learning beyond the classroom. With a crosssectional survey design approach, the study focuses on learner characteristics. It firstinvestigates the existing language learner profiles of 512 English major university students
concerning autonomous language learning and out-of-class technology engagement. Then,details regarding the characteristics of existing learner profiles in terms of language
proficiency, daily technology use time, a variety of digital tool use and the most beneficial
tools are outlined. Within this frame, cluster analyses suggested two clusters: moreautonomously engaged with digital tools and less autonomously engaged with digital tools.
The findings showed that more autonomously engaged students tend to have greater languagelearning proficiency than the less autonomous group. The more autonomously engaged
students also spent more time daily and used various digital tools in comparison to lessautonomous technology users. While online websites and social media were the mostfrequently used digital tools for both groups, the use of podcasts, blogs and online language
courses differed. According to the provided tool lists of learners, students benefited
significantly from social media, online websites, dictionaries, and intelligent tutoring systemapplications (apps). Online games, YouTube, Instagram and other smartphone apps, whichallow students to practice vocabulary and speak with foreigners, also had considerable
influence on language development. The study findings provide insights for language teachersaiming to extend learners’ in-class language-learning experiences beyond the confines of theclassroom.
Keywords: autonomous language learning, learning through digital practices, out-of-classlanguage learning, technology engagement

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Following the rapid development of digital technology, the worldwide digital population now
encompasses nearly 60% of the global population, with over 4.5 billion active users (Kemp,
2020). According to a recent report and statistics, the average time spent on the Internet isalmost seven hours per day, mainly on social media, watching television, listening to streaming
music and gaming. Among these activities, the use of social media is the most favored Internet
activity, and 45% of the world population is on social media, spending a minimum of two hours
each day. Almost all social media users visit social media sites via their smartphones.
Millennials are the biggest users of social media among the generations (Kemp, 2020; Moshin,2020). These statistics show that people are engaged with technology much more than before,
for information seeking, content creation, playing games, broadcasting, communication,
education, and pleasure, among other things. As a result, technology is undeniably an
indispensable part of daily life. English language, with over two billion native and non-native
speakers, serves as a communication tool by delimiting boundaries between people fromdifferent countries and creating an authentic virtual atmosphere (Ethnologue, 2019).
Unifying the potential of widespread use of digital technology and the importance of English
in the globalized world, it is necessary to understand what goes on outside the classroom tomeet the constantly changing needs of today’s digital learners. Unlike the great importance ofbridging inside and outside learning, the majority of applied linguistic research focuses on what
transpires inside the classroom as well as classroom-based application, and there remains
limited research concentrating on learning language beyond the classroom, specifically indigital settings (Chick, 2018; Lai, 2015, 2017; Reinders & Benson, 2017; Richards, 2015).
Little attention has been given to how language learners bolster their language skills and what
language learner profiles exist in learner-led informal settings (Gonulal, 2019).
This article focuses on English as a foreign language (EFL) learners’ out-of-class language
learning experiences through digital practices and reports on a survey study conducted with
university students in Turkey. Specifically, it investigates the existing characteristics of English
major language learners with a closer focus on their technology-mediated autonomous
language learning experiences. The article firstly reviews relevant research for understanding
language learners’ out-of-class digital practices and then presents the study methodology andfindings. Lastly, it discusses the findings regarding the emerging learner profiles and draws
conclusions. Therefore, it expands our understanding of what language learners do beyond theconfines of school to learn English in the digital era.
Literature Review
High proficiency of language achievement and development depends on learners’ out-of-classengagement as well as in-class engagement (Benson, 2011; Chick & Ho, 2017; Dincer &Dariyemez, 2020; Lai, 2017; Lai et al., 2015; Richards, 2015). Although great strides havebeen made in understanding foreign language learners’ out-of-class language learning practices
and gains for language development in earlier studies (e.g., Benson, 2011; Hyland, 2004; Inozu
et al., 2010; Murray & Kojima, 2007), less is known about what today’s youth do to study
language beyond the classroom in the digital era that exists today. Internet and digitaltechnology have become ubiquitous and changed the traditional teaching routines of people allover the world (Larsen-Freeman & Anderson, 2013; Mazer et al., 2007). With a transition fromtraditional definitions of computer-assisted language learning (CALL) to more modern
methods including mobile learning, the importance of digital tools and integration oftechnology into daily life has exponentially gained popularity in the language education
domain (Reinders & White, 2016).

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Learners’ online informal language experience has great value for language development in
the age of digital abundance (Chick, 2018). Recent research has emphasized that engagementin the prevalent digital practices for language learning not only plays a supporting role inlanguage development for learners, but also a complementary role for in-class language
learning (Alice & Ho, 2017; Lai, 2017; Lai et al., 2018; Nunan & Richard, 2015). Digital tools
provide meaningful and authentic language-learning opportunities for learners and might serve
as a panacea for learners who have no or limited change in exposure to authentic daily language
in an out-of-school setting (Dincer & Dariyemez, 2020; Gonulal, 2019; Lai et al., 2016;
Richards, 2015; Xodabande, 2018). Today, learner autonomy and technology-enhanced
language learning have not been isolated from each other; they are together and interconnected(Reinders & White, 2016). To excel in language, learners should have an inner capacity to beaware of their needs and desire to track their progress (Alice & Ho, 2017). Despite its
importance for language development, there is limited focus on technology-enhanced out-ofclass language learning, and there is a recurrent call for more research into autonomous
language learning through digital practices and learner characteristics (Chick, 2018; Dincer &Dariyemez, 2020; Gonulal, 2019; Lai, 2017; Lai et al., 2018; Reinders & Benson, 2017;
Reinders & White, 2016).
There is a scarcity of literature about language learners’ online informal language learning, andthe connection between autonomous language learning and digital practices in the applied
linguistics domain is not clear. Via interviews, Lai et al. (2016) elicited the perception matches
between language teachers and students regarding teacher involvement in fostering
autonomous language learning with technology beyond the classroom. They found that there
are mismatches between perceptions and teachers, although teachers have minimal
responsibility for the autonomous language development of students and play a passive role inlearners’ out-of-class use of technology for language learning as they overestimate students’skills. In later research, Chick (2018) provided research agendas on autonomous language
learning beyond the classroom and digital practices by way of a qualitative ethnographic
approach. Based on the model of Benson (2011) for searching out-of-class language learning,she mapped her language-learning experience on Duolingo (an intelligent tutoring system).
Chick found that, in her learning experience, there exist environmental factors beyond her
control and it is hard to turn a leisure activity into recreational learning. Duolingo provides a
structured pedagogy for learning and goes beyond the structural lessons. Although the locus ofcontrol is voluntary participation in the starting point, such digital practices restrict decisions
on learning at some points.
Connecting various studies (e.g., Benson, 2011; Richards, 2015), Chick concluded that
autonomous language-learning experience is a useful indicator of successful language learning,and digital practices might be tools for understanding language learning as a whole.
Emphasizing the use of any digital tool for various purposes, Lai and colleagues (2018)
recently identified three types of technological experiences of language learners engaged
outside the classroom using multiple data collection techniques. These types are instructionoriented (i.e., conscious and intentional information-seeking process for expanding knowledge
and language development), entertainment and information-oriented (i.e., not fully
unintentional and a disorganized method of obtaining and sharing information for daily life
needs and personal needs), and social-oriented (i.e., intentional use of social media to interact
and communicate with English speakers). These distinct learner profiles are important for
educators and researchers to understand language learners’ out-of-class experiences.

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In another study, Xodabande (2018) researched 114 Iranian EFL learners’ preferences invarious digital technologies, in a context where people have some online restrictions andcensorship. Xodabande found that, despite the restrictions, EFL learners are highly engaged
with technology for foreign language, whereby electronic dictionaries, Internet sites and films
are the most favored digital tools. Gender was also found to be a significant factor. While malestudents engage in online games to facilitate learning, female students are more inclined tolisten to English music. In more recent research, Kuznetsova and Soomro (2019) surveyed 137
foreign language learners’ out-of-class Web 2.0 practices for learning various languages,
including English. They found that video sharing websites and social networking sites are themost widely used digital technologies and male students’ digital practices are significantly more
frequent than females, verifying some earlier research (Cai et al., 2017; Xodabande, 2018).
Despite significant progress in understanding the nature of language learners beyond theclassroom, the literature has been invaded by the umbrella term “out-of-class language
learning”, which comprises all activities and includes the technology. The extant research does
not provide a clear picture of language learners’ digital practices without teacher guidance anddirection. There is a need for research on autonomous language learning with digital practices
beyond the classroom. One certain aspect of out-of-class language learning with technology isthat “there is a great range of diversity in environments, intentionality, interest, structure, andduration” (Chick, 2018, p.76).
Improving understanding of characteristics of language learners regarding autonomy andtechnology engagement and investigating their engagement in the digital practices for language
learning might serve as a master key to unlock innovative techniques for classroom pedagogy
and bind formal to informal learning (Chick, 2018; Kuzetsova & Soomro, 2019; Lai & Gu,
2011; Lai et al., 2015). Considering the affordances of language engagement through digitalpractices beyond the classroom and the scarce research on autonomous language learning
through digital practices, there is much yet to be discovered on specific language learners’
distinct characteristics in this unexplored field of research. By way of a survey approach, thisstudy specifically focuses on learning English beyond the classroom with digital tools andstudents’ own initiatives, and aims to understand to what extent EFL learners use technologicaltools for their self-directed language learning beyond the classroom. With a particular focus on
language learners’ characteristics, the following research questions guided the study:
1. What different language learner profiles exist among English language learners in termsof autonomous language learning and out-of-class technology engagement?
2. What are the characteristics of language learner profiles in terms of language
proficiency, daily technology use, and digital tools used?
Theoretical Framework
This survey research was grounded on the main tenets of modern motivation theory, selfdetermination theory [SDT] (Deci & Ryan, 1985; Ryan & Deci, 2017). SDT provides anunderstanding of the psychological and social foundations of autonomous learning in life (Louet al., 2018). Briefly, it suggests that people have innate psychological needs (i.e., autonomy,competence and relatedness), and these universal needs are met through people’s interactions
in a social context (Deci & Ryan, 1985; Ryan & Deci, 2017). Within language education, thesatisfaction of the needs is crucial for motivated engagement, and results in more autonomous
language learning and engagement (Noels et al., 2019).

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Despite the increased research on what happens in the classroom, limited research focus has
been placed on understanding what happens beyond the classroom, specifically language
learning through digital practices within the SDT framework. Connecting the SDT and therecent perspective on the technological experiences of language learners (Lai et al., 2018), thisstudy hypothesizes that autonomous language learning is positively linked to technology
engagement in language learning. Accordingly, the students who feel more autonomy inlanguage learning are prone to be more engaged with language learning beyond the classroom
through digital tools. Then, investigating the learner profiles will shed light on future research
agendas on autonomous language learning through digital practices in the language-learning
domain.
MethodResearch Design
A cross-sectional survey design was adopted to investigate the nature of language learners’
out-of-class language learning experiences. According to Creswell (2012), this is the mostpopular form of survey design in educational research. The researcher collects data to makeinferences about a particular population of interest at a given point and thereby takes snapshotsof the population (Creswell, 2012; Hall, 2008). With a non-experimental approach, this study
focused on emerging profiles of English language learners’ out-of-class language learning
through digital practices. It investigated their profiles from various perspectives such aslearners’ perceived language proficiency, daily time spent for language learning, and variety
of digital tools in language learning.
Study Context
The study was conducted with English major students studying in the English Language
Teaching (ELT) and English Language Literature (ELL) departments of three state universities
in Turkey. The students who graduate from these departments with a BA diploma and certain
training certificates might work as English teachers in state schools after taking teacher
placement tests. It was expected that these students’ experiences with digital tools would be
different from the students in the non-English major departments where mother tongue is themedium language.
Participants
512 university-level language learners (n = 362; 70.7% female) whose ages ranged from 17 to
32 (M = 20.59; SD = 2.46) participated in the study. They were enrolled in the two English
majors related to learning and teaching English at a tertiary level in Turkey (ELT = 304; ELL= 208). Students were from various classes concerning their majors, and a majority of themwere in the first year of their major (n = 200; 39.1%). Their overall perceived language
proficiency from receptive (reading and listening) and productive skills (writing and speaking)changed from A2 to C2 in accordance with six levels (A1, A2, B1, B2, C1, C2) of the Common
European Framework Proficiency Matrix (n = 185; mostly B2, 36.1%).
Instruments
A survey form including demographic details (i.e., gender, major, and year in the university,
perceived level of proficiency), a language learning through digital practices questionnaire,
psychometric scales of learner autonomy and out-of-class technology engagement were usedas the data collection instruments.

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Language learning through digital practices. The questionnaire consisted of three questions
regarding students’ digital practices. The first question was about daily time spent using
technology, from the least (one hour) to high (four hours). The second question was about
digital tools students might use while learning on their own. The students chose what digital
tool/s they used to learn a foreign language on their own from the given 10 most cited digital
tools in the relevant literature (e.g., Benson, 2011; Lai, 2017; Richards, 2015). The third
question was in the open-ended format, about the most beneficial digital tool/s used withregularity and their reasons of preferences.
Learner autonomy. The scale was adapted from Nakata (2011) to measure the students’autonomous language-learning experiences. It was a five-point Likert scale from 1 (Strongly
Disagree) to 5 (Strongly Agree). It had eight items concerning out-of-class learning (e.g., I
decide what to learn outside the classroom; α = .77). Higher mean scores from the items
indicate higher agreement and autonomy levels.
Out-of-class technology engagement. The scale (Lai et al., 2018) assessed the degree to whichlearners engage in language learning by using digital tools with a five-point Likert scale from
1 (Strongly Disagree) to 5 (Strongly Agree). The scale started with the prompt “I use digital
tools outside the classroom, mainly …” and was followed by 11 items. The scale had three
dimensions: instruction-oriented technological experience had three items (e.g., to help mememorize the vocabulary and grammar; α = .70); the entertainment- and information-oriented
experience had five items (e.g., to use the language to go after personal interest; α = .74); andthe social-oriented technological experience dimension had three items (e.g., to connect withnative speakers or other learners of the foreign language; α = .80). In each dimension, a higher
mean indicated stronger agreement and engagement.
Data Collection and Analysis
The data were collected using a paper-based survey in spring 2019. After departmental
approval was obtained for data collection, the students were invited to participate in the study.
The students were informed about the purpose of the study and instructed about the meaningof learning English with digital tools with examples by the collaborators. They were also
informed about anonymity and encouraged to answer the open-ended question. It tookapproximately 15 minutes to complete the survey.
To answer the research questions, first, the surveys were screened, and those with a significant
amount of missing data (e.g., not completing one of the scales) and outliers (i.e., strongly
agreeing with all items or strongly disagreeing with all items) were excluded (25 out of 537).
After the reliability analysis, a cluster analysis followed, allowing researchers to extract
learners’ out-of-class technology profiles. The analysis enabled the researcher “to create a new
categorical variable that minimizes the amount of variation within categories” (Staples &Biber, 2015, p. 243). Following the steps of the Staples and Biber (2015) analysis, ahierarchical cluster analysis was run, with the Z-scores of two main variables (i.e., out-of-classtechnology experience and autonomous language learning). The analysis suggested a twocluster solution. Based on the determined language learner profiles, both quantitative andqualitative data from the survey were descriptively analyzed. For the quantitative data analysis,
the SPSS 22.0 packet program was used. In the qualitative data analysis, NVivo software
version 12 was used to review word frequencies and to produce a words cloud. In thepresentation of the quantitative findings, descriptive tables were used. For the qualitative data,
students’ excerpts from the open-ended questions were provided with the capital letter of thecluster and the student data identification number (i.e., M123).

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Findings
Learner Profiles in Out-of-Class Language Learning Through Digital Practices
The first research question concerned existing language learner profiles in terms of autonomous
language learning and out-of-class technology engagement. The hierarchical cluster analysis
suggested a two-cluster solution (Cluster 1, n = 330; Cluster 2, n = 182). The findings arepresented in Table 1.
Table 1: Comparison of learner profiles in terms of autonomous language learning and outof-class technology engagement
Main variables

Sub-variables

Autonomous
language learning

Learnerautonomy

Out-of-classtechnology
engagement

Instructionoriented
Entertainment
and informationoriented
Social-oriented

Cluster 1
M
4.12

SD
.44

Cluster 2
M
SD
3.40
.57

4.11

.50

3.38

.83

4.25

.44

3.44

.63

4.11

.71

2.99

.84

The results indicated that the students in Cluster 1 highly agreed with the scale items and were
thereby named ‘More autonomously engaged with digital tools’ (M ≥ 4.00 = Agree). The
students in Cluster 2 had a moderate level of agreement and were named ‘Less autonomously
engaged with digital tools’ (M < 4.00 = Moderately Agree).
After the determination of the clusters, the demographic characteristics of the groups wereanalyzed. According to the descriptive statistics, both clusters had similar demographic
characteristics, such as age (Cluster 1: Mage = 20.67, SD = 2.42; Cluster 2: Mage = 20.43; SD =2.53) and department distribution.
Two groups of descriptive findings are presented in Table 2. According to Table 2, most of theparticipants in both groups were females (Min. = 64.3%), from the ELT department (Min. =57.1%), and first year of study (Min. = 39.4%).
Table 2: Demographics of the clusters
Demographics
Gender
Major
Year inuniversity

Male
FemaleELT
ELL
Preparatory
First year
Second year
Third year
Fourth year

More autonomously
engaged (n = 330)
f
%
85
25.8
245
74.2
200
60.6
130
39.4
55
16.7
122
37.0
43
13.0
39
11.8
71
21.5

54

Less autonomously
engaged (n = 182)
f
%
65
35.7
117
64.3
104
57.1
78
42.9
40
22.0
78
42.9
16
8.8
21
11.5
27
14.8

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In sum, there were two types of learner profiles in terms of digital practices and autonomous
language learning beyond the classroom. The clusters had similar demographic details
regarding frequencies in the variables (i.e., gender, major and year at the university).
Characteristics of language learner profiles
The second research question focused on understanding the nature of existing language learnerprofiles in terms of language proficiency, daily time spent, variety of digital tools used andmost beneficial tools.
Language proficiency. First, the clusters’ perceived language proficiency levels werecompared in terms of receptive and productive language skills. Table 3 indicates thefrequencies and percentages in terms of perceived language proficiency levels in receptive
skills: reading and listening.
Table 3: Comparison of clusters in terms of receptive skills
Receptive skills
Reading

Listening

A1 (Beginner)
A2 (Elementary)
B1 (Intermediate)
B2 (Upper Intermediate)C1 (Advanced)
C2 (Proficiency)
A1 (Beginner)
A2 (Elementary)
B1 (Intermediate)
B2 (Upper Intermediate)C1 (Advanced)
C2 (Proficiency)

More autonomously
engaged (n = 319)
f
%
0
0.0
8
2.5
32
10.0
111
34.8
108
33.9
60
18.8
3
0.9
33
10.3
72
22.6
91
28.5
76
23.8
44
13.8

Less autonomously
engaged (n = 181)
f
%
0
0.0
10
5.5
44
24.3
78
43.1
39
21.5
10
5.5
13
7.2
33
18.2
42
23.2
48
26.5
37
20.4
8
4.4

According to Table 3, students who are more autonomously engaged with digital tools havehigher perceived receptive language proficiency levels than those less autonomously engaged
with digital tools (i.e., reading and speaking = B2 and C1). Over half of the students in bothgroups (i.e., Min. = 51%) perceived their levels to be over level B1.
Students’ comparisons in terms of perceived levels in productive skills are presented in Table
4, which indicates the frequencies and percentages of the productive skills: writing andspeaking.

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Table 4: Comparison of clusters in terms of productive skills
Productive skills
Writing

Speaking

A1 (Beginner)
A2 (Elementary)
B1 (Intermediate)
B2 (Upper Intermediate)C1 (Advanced)
C2 (Proficiency)
A1 (Beginner)
A2 (Elementary)
B1 (Intermediate)
B2 (Upper Intermediate)C1 (Advanced)
C2 (Proficiency)

More autonomously
engaged (n = 319)
f
%
0
0.0
28
8.8
73
23.0
98
30.8
88
27.7
31
9.7
12
3.8
34
10.7
102
32.0
74
23.2
71
22.3
26
8.2

Less autonomously
engaged (n = 181)
f
%
0
0.0
28
15.5
56
30.9
54
29.8
38
21.0
5
2.8
14
7.7
52
28.7
46
25.4
38
21.0
25
13.8
6
3.3

In parallel to the findings of the receptive skills, the students in the more autonomously engaged
with digital tools cluster showed higher perceived proficiency levels in productive skills than
the students in the second group (i.e., more autonomously engaged = B2 and C1). Thesestudents had lower proficiency in writing skills in comparison to speaking skills (moreautonomously engaged = Max.writing 30.8% in B2 level; more autonomously engaged =Max.speaking = 32.0% in B1 level). While the highest majority of the more autonomously
engaged students perceived their speaking skills at B1 (i.e., more autonomously engaged =speaking 32.0%), the students in the less autonomously engaged with digital tools group
perceived themselves at level A2 (i.e., less autonomously engaged = speaking 28.7%).
Daily time spent. Second, the clusters were compared in terms of their daily time spent using
digital tools in language learning, and findings are presented in Table 5.
Table 5: Comparison of clusters in terms of daily time spent
Daily timespentMin. 1 hour
Min. 2 hours
Min. 3 hours
Min. 4 hours

More autonomously engaged(n = 330)
f
%
94
28.5
140
42.4
62
18.4
34
10.3

Less autonomously engaged(n = 181)
f
%
82
45.3
70
38.7
25
13.8
4
2.2

The table shows that the students differed in terms of daily time spent on digital tools.
According to the table, the majority of students in the more autonomously engaged with digital
tools group spent a minimum of one to two hours (i.e., 70.9%). The great majority of students
in the less autonomously engaged group also spent a minimum of one to two hours learning
English digitally on a daily basis (i.e., 84.0%). Further, the table shows that students in the
more autonomously engaged group spent much more time on digital language learning with aminimum of two hours than the less autonomously engaged group.

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Digital tool use. Fourth, the clusters were compared in terms of the variety of digital tools thatlearners use, as shown in Table 6.
Table 6: Comparison of clusters in terms of tool variety
Tool variety

More autonomously engaged(n = 330)
f
%
3
0.9
26
7.9
59
17.9
64
19.4
63
19.1
48
14.5
40
12.1
16
4.8
8
2.4
3
0.9

1 tool
2 tools
3 tools
4 tools
5 tools
6 tools
7 tools
8 tools
9 tools
10 tools

Less autonomously engaged(n = 182)
f
%
6
3.3
47
25.8
37
20.3
41
22.5
27
14.8
14
7.7
8
4.4
2
1.1
0
0.0
0
0.0

Table 6 shows that the students in the more autonomously engaged group differ more in termsof digital tool variety than those less autonomously engaged. While the majority of students inthe more autonomously engaged group use a minimum of five to 10 different digital tools (i.e.,
73.2%), the majority of students in the less autonomously engaged group use one to four digital
tools for language learning (i.e., 71.9%).
In addition to the digital tool variety comparison, students’ preferences for most frequently
used digital tools were determined. The comparison of clusters with regards to the tool
frequency is presented in Table 7.
Table 7: Comparison of clusters in terms of tool use frequency
Tools
1. Social media
2. Dictionaries
3. Websites
4. Grammar &spelling checkers
5. Intelligent
tutoring systems
6. Blogs
7. Forums
8. Podcasts
9. Online courses
10. Automatic
speech recognition

Moreautonomously
engaged (n = 330)
f
%
297
90.0
285
86.4
223
67.6
169
51.2
159

48.2

118
100
93
88

35.8
30.3
28.2
26.7

69

20.9

Tools
1. Social media
2. Dictionaries
3. Websites
4. Grammar &spelling checkers
5. Intelligent
tutoring systems
6. Online courses
7. Forums
8. Blogs
9. Automatic speech
recognition
10. Podcasts

57

Less
autonomously
engaged (n = 182)
f
%
156
85.7
151
83.0
91
50.0
64
35.2
52

28.6

39
35
29
28

21.4
19.2
15.9
15.4

25

13.7

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While the first five tools listed in the table were the most commonly used digital tools by bothgroups, the remaining five tools differed between the groups. While social media, online
dictionaries and online websites for language learning were the most frequently used digital
tools for both groups, the use of podcasts and online language courses differed.
Most benefited digital tools. Fifth, students’ answers to the open-ended question about themost beneficial digital tools for learning English were descriptively analyzed (N = 307). Whileanswering the question, some of the students listed more than one digital tool as mostbeneficial, thereby making frequencies by tool higher than the number of participants. It should
also be noted that two extra tools emerged from the data: online games and smartphone apps,
allowing for speaking with native speakers for a fee. The findings are shown in Table 8.
Table 8: Comparison of clusters in terms of most beneficial digital tools
Tools
1.Social media
2.Websites
3.Dictionaries
4. Intelligent
tutoring systems
5. Online courses
6. Online games
7. Grammar &spelling checkers
8. Blogs
9. ForumsMoreautonomously
engaged (n = 215)
f
%
89
41.4
50
23.3
49
21.4
42
19.5
18
13
9

8.4
6.1
4.2

7
6

3.3
2.8

Tools
1. Social media
2. Websites
3. Dictionaries
4. Intelligent
tutoring systems
5. Online courses
6. Podcasts
7. Forums

8. Blogs
9. Grammar &spelling checkers
10. Podcasts
5
2.3
10. Online games
11. Apps-Speaking
5
2.3
11. Apps-Speaking
12. Automatic2
0.9
12. Automatic
speech recognition
speech recognition
Note. Values might not end in 100% due to rounding.

Lessautonomously
engaged (n = 92)
f
%
32
34.8
23
25.0
20
21.7
14
15.2
5
5
4

5.4
5.4
4.4

2
2

2.2
2.2

2
1
0

2.2
1.1
0.0

According to the most beneficial tool lists, students’ preferences provided similar findings to
the previous digital tool frequency list table. Social media, online websites, dictionaries andintelligent tutoring system apps were the most beneficial tools for all students. Different fromthe tool lists, some students indicated the benefits of online games and smartphone apps, whichallow them to speak with native English speakers and people who use English forcommunication.
In addition to frequencies, to assess specific tools among all students’ answers, a word cloud
was generated, with a restriction of words with a minimum length of five letters and the 30
most frequently used words with stemmed words. The results are shown in Figure 1.

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Figure 1: Word cloud
The word cloud formed from 307 students’ open-ended responses indicated that, in addition tosome broad tools stated, students also use specific tools. These tools are YouTube (online
video-sharing platform and social media tool), BBC English (online language service of BBC
World Service), Tureng (a bilingual online Turkish-English dictionary), Instagram (a social
networking service), Duolingo and Memrise (intelligent tutoring systems for language
learning).
The students emphasized the many benefits of learning English with digital tools. Digital tools
allow learners to access the content anytime they want using their cellphones. On this issue,
one student (M159) said: “I think YouTube is the most useful for me because I accesseverything about learning the language with easily [sic].” Another participant (L91) added: “Ihave benefited much with social media websites because I always can access it on my phone
all the time.”
Digital tools allow learners to practice numerous skills at once. L4 said: “YouTube, because I
can improve more skills, such as listening, reading, speaking, compared to other tools.” Anotherstudent said: “Podcasts, because they enhanced both my speaking and listening skills.” (L47)
In addition to language development gains, students emphasized that language learning withdigital tools is a win-win process. While being entertained, learning comes as a bonus, andlearners broaden their horizons. On this issue, M81 said: “YouTube and Instagram. Especiallymemes, I follow on Instagram because it entertains me while learning.” Focusing on the roleof BBC English regarding language development, L90 pointed out that: “In this way, I not only
improve my listening skills but also I know what happened all around the world.”
Digital tools allow learners to access high-quality content in accordance with their own tastes
and repeat the content anytime they want. Regarding this topic, L66 stated that: “BBC English

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learning is the best. It is very qualified, and it has many good contents.” M50 said: “Duolingo
is very beneficial because you can repeat until you learn.”Moreover, digital tools allow students to practice the language in an interactive, authentic
language learning setting and learn the daily language and colloquial expressions that are
difficult to gather in-class. Emphasizing the social interaction version of YouTube, student
M196 indicated that “Because on this platform, I watch dozens of videos, and we can discussin the comments with other people.” Focusing on the social role of online games, H45 said:“Games, because I hear original sentences which I could not hear before and interact withnatives [English] in Clanset.”
Some of the students also highlighted the complementary role of digital tools for in-classeducation. For instance, M75 indicated: “I benefit much from online websites. My lessons [inclass] are not enough to study learning English.” Emphasizing the limited in-class course hours,
L83 stated: “I have benefited from Blogs much because to improve my English, I have to use
listening texts.”
Discussion
This study has explored the learner profiles in self-directed language-learning initiatives
through digital practices and mapped the nature of university students’ out-of-class English
language engagement using a survey design. Grounded on the main tenets of SDT, it was
hypothesized that autonomous language learners are active learners outside the classroom, as
well as in the classroom. They successfully self-direct their language learning without teacher
direction. Then, the study connected learner autonomy and out-of-class technology
engagement for learning English and aimed to investigate distinct characteristics of language
learners. Two research questions guided the study, and the findings for each research question
have been outlined.
To answer the research questions, first, a hierarchical cluster analysis was conducted,
suggesting two distinct profiles in terms of autonomous language learning through digitalpractices: “More autonomously engaged” versus “Less autonomously engaged” students. Thisfinding verified the hypothesis of the study. As expected, the students who feel greater feelingsof autonomy in their learning are also the ones who are highly engaged in the learning process.
In concurrence with the relevant literature, the findings showed that these two variables areclosely associated and students with high levels of learner autonomy tend to have strong outof-class technology engagement (Chick, 2018; Lai & Gu, 2011; Lai et al. 2015, 2016; Reinders& White, 2016). Further, it should be noted that, unlike the earlier literature suggesting that
EFL learners have some problems taking responsibility in their learning process and needsignificant teacher guidance (Eksi & Aydin, 2013; Inozu et al., 2010), the means for bothgroups’ out-of-class digital language-learning experience and autonomous language learning
were relatively high. This outcome corroborates the recent studies about the findings of thetechnology-enhanced language practice, which suggest that students in the digital age takemore initiative in their language learning despite waiting from their teachers (Haidari et al.,2019; Lai et al., 2018). This might be related to the rapid development of digital tools,
smartphones and apps in the last decade (Jurkovič, 2019).
Based on the emerged learner profiles, a number of comparisons were conducted between thegroups to understand the different learner profiles’ engagement with a digital tool. Regarding
the contrast within the perceived language proficiency levels, students who are more

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autonomously engaged with digital tools tend to have a higher proficiency both in receptive
and productive language skills than the less autonomously engaged students. In parallel toliterature (e.g., Dincer & Yesilyurt, 2017), students have lower proficiency levels in productive
skills in EFL contexts. According to daily time spent and variety of used tools, moreautonomously engaged students spend greater time daily for learning English online and use awider variety of digital tools than the less autonomously engaged students. As Lai and Gu(2011) stated, perceived language proficiency is associated with learners’ use of technology toseek language-learning resources and opportunities beyond the classroom. Students who havestudied the language for a long time and have higher proficiency levels tend to spend moretime daily and assume more personal responsibility (Orhon, 2018).
With regard to the most frequently used digital tools, social media, online dictionaries, websites
for language learning, grammar and spelling checkers, and intelligent tutoring systems were
found to be the most popular for both groups, which is consistent with a number of studies (Caiet al., 2017; Kuznetsova & Soomro, 2019). Despite the affordances for language development,
tools such as blogs, forums and automatic speech recognition are the least popular. Althoughblogs and forums serve as social-oriented technological experience for learners, they are theleast popular activities in earlier studies (Lai & Gu, 2011; Lai et al., 2018). In addition, there
is limited research on the use of automatic speech recognition tools in language learning, as
they have only recently gained popularity due to the prevalent IOS and Android applications.
With the improvements in accurate speech recognition, automatic speech recognition
technology has become ubiquitous and important for fostering learner autonomy (McCrocklin,
2016). In conjunction with the frequency of use of the tools, students in both groups benefited
most from social media, websites for language learning and online dictionaries. Learners
named language-learning websites like BBC English learning, social media sites such as
YouTube and Instagram, and digital games as very helpful tools. It was shown that English
major students are more aware of the benefits of the tools and use these effectively for language
development. In contrast, Xodabande (2018) found such online websites and computer games
to be perceived as less effective tools in enhancing language development. However,
Xodabande’s results were country-specific, and most social media sites are filtered; these tools
do not require much effort to push learners to use them actively. The majority of tools provide
receptive activities and entertainment, and information-oriented activities are more common indigital language-learning practices (Jurkovič, 2019; Lai, 2015, 2017; Lai et al., 2015, 2018).
Furthermore, the findings of this study suggest that online digital tools allow learners topractice skills with ease at all times. In parallel to the entertainment- and information-oriented
technological experience (Lai et al., 2018), students share or access information useful in daily
life while being entertained. In addition, this unintentional process might turn into theincidental acquisition of the language, and naturalistic learning might occur (Sockett, 2014).
Online digital tools allow students to practice English in an interactive setting focused on daily
language. Most digital tools, especially social media, provide authentic language and increase
learners’ exposure to target language outside the classroom, especially in EFL settings (Hyland,
2014; Richards, 2015; Xodabande, 2018). Such tools might be used to compensate for the
deficiencies of formal education, such as limited course hours and oral practice (Lai et al.,2016; Richards, 2015; Xodabande, 2018). As Lai et al. (2016) emphasized, learning language
through digital practice beyond the classroom might serve a compensatory role for limited
course time or for practicing lesser known or unclear aspects that are not addressed in theformal education.

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The limitations of this study involve the study participants and data collection methodology.
First, although two learner profiles emerged, both groups had relatively high means in language
learning with technology outside the classroom. This might be related to the study group, as
the participants were students in the English major. English major students might have greater
needs as well as more self-determined motivation levels than learners who study the language
for more external reasons, such as passing an exam or a career promotion (Dincer & Yesilyurt,2017). In this vein, the other groups of students in different branches might be a strategic new
focus of research for further inquiry. Second, this study adopted a survey approach togeneralize and understand what transpires outside the classroom in terms of language learningwith digital tools. Although this methodology fit the research goals well, it lacked presentationof the causal relationships between the reasons for use and specific tools, and says little about
predominant language use in all digital activities. Then, one-on-one interviews are needed forin-depth analysis and understanding of how these digital tools helped learners to learn English
beyond the classroom. It might also be interesting to adopt a mixed-method or longitudinal
design to understand the relationship and activities, including language use beyond theclassroom (Jurkovič, 2019). As Reinders and Benson (2017) suggested, asking learners to keep
diaries or reflective journals to track their engagement and individual strategy use in-depth ona daily basis might illuminate the specific tools used in this lesser known terrain of applied
linguistics.
Conclusion
The study findings provide further understanding of the nature of language learners’ out-ofclass language learning experiences with digital tools. The research provided findings in which
tools are used by more autonomously engaged and less autonomously engaged learners. The
findings reveal that learners are highly engaged with digital tools, and that these digital tools
afford for learners to individualize and monitor their language development. Additionally, thestudy highlights the necessity for language teachers to homogenize students’ out-of-classexperiences and in-class language learning. With the divergence of digital tools and the use ofsmartphones in daily life, the boundaries between in-class and out-of-class have gradually
blurred (Ma, 2017). Therefore, it seems imperative for teachers to guide their students’ learning
outside the classroom and link in-class teaching to their students’ out-of-class activities by
understanding their students’ engagement with digital tools (Reinders & Benson, 2017).
Research on what transpires beyond the classroom with digital tools for language learning
remains a raw area, and there are many questions waiting to be answered. Any research attempt
to understand language learners’ practices outside the classroom in the digital era would assist
in developing thorough understanding of the language-learning process.
Acknowledgements
The author thanks Hakan Demiroz (PhD) and Tuba Isik (PhD Student) for their data collection
assistance.

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Corresponding author: Ali Dincer
Contact email: adincer@erzincan.edu.tr

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