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The Asia-Pacific Education Researcher
https://doi.org/10.1007/s40299-023-00806-1
REGULAR ARTICLE
On theRelationship Between Learners’ Emotions andCognition
intheTechnology‑Enhanced Learning Environment: The
Mediating Role ofLearners’ Learning Styles andSituational
Motivation
YilunYang1 · TianqiJiang2 · LipingChen1,3
Accepted: 11 December 2023
© De La Salle University 2024
learning environments, and most educational environments
do not have the necessary compatibility with the psychologi-
cal characteristics of students. Implications are presented.
Keywords Learners’ emotions· Learners’ cognition·
Technology-enhanced learning environment· Learners’
learning styles· Situational motivation
Introduction
The amount of learning and mutual understanding depends
on environmental factors such as the technology-enhanced
learning environments and their factors. Environmental
factors might have significant effects on a language learner
(Osterlie etal., 2019; Wang etal., 2023a, 2023b). However,
the learning environment is not limited to physical space and
facilities. For example, the emotional environment is very
important. The relationship between teacher and student, the
relationship between students (Wolsko & Lindberg, 2013),
the relationship between parents and children, as well as the
harmony of the attitudes of parents and teachers in the field
of education (Altintas etal., 2020), can be effective in the
level of students’ learning (Dirk & Nett, 2022).
Students’ learning is the product of their environments.
This learning is based on receiving, analyzing, and interpret-
ing environmental factors by their internal factors (cognitive
abilities) (Cohen etal., 2023). But the technology-enhanced
learning environments play a very decisive role in learning.
Language teachers cannot ignore the role of technologies
in young language learners’ lives to engage and motivate
them (Wang, 2023; Wang etal., 2023a). The interaction
between students and their social environment and their
motivation might play a significant role in learning (Wang
& Hemchua, 2022). Paying attention to various types of
Abstract Language teachers cannot ignore the role of tech-
nology in young language learners’ lives to engage and moti-
vate them. Therefore, the current research investigated the
mediating effect of students’ learning and cognitive styles
on the relationship between their emotions and situational
motivation. 1089 respondents were selected from different
colleges and universities, with different ages, genders, and
levels. Four questionnaires (Cognitive Scale of the Human–
Nature Relationship, Learners’ Emotion Questionnaire, The
Situational Motivation Scale, and Learning Style Question-
naire) were employed to collect the data. The researcher
used Tencent QQ, a widely used communication platform
in China, in conjunction with Wenjuanxing, an online sur-
vey tool, to distribute the questionnaire to all participants.
The results showed that 54% of changes in students’ situ-
ational motivation can be explained by interaction among
their emotions, learning, and cognitive styles. In addition,
it was revealed that less attention is paid to the mental and
psychological aspects of students in technology-enhanced
* Liping Chen
chenliping@njnu.edu.cn
Yilun Yang
yangyilun30@gmail.com
Tianqi Jiang
jiangtianqi0310@163.com
1 School ofForeign Languages andCultures, Nanjing
Normal University, No.1 Wenyuan Road, Qixia District,
Nanjing210046, People’sRepublicofChina
2 School ofTeacher Education, Nanjing Normal University,
No.1 Wenyuan Road, Qixia District, Nanjing210046,
People’sRepublicofChina
3 School ofForeign Studies, Nanjing Forestry
University, No.159 Longpan Road, Nanjing210037,
People’sRepublicofChina
Y.Yang et al.
1 3
learning environments in educational design is important in
the teaching–learning process. The reality is that it is almost
impossible to survive the working world without technology.
Therefore, it is better if language learners learn how to use
technologies in their classrooms sooner rather than later.
Language learners often struggle to stay on task or inter-
ested, particularly if it is not interactive. One of the main
benefits of technology-enhanced learning environments is
that it can make even the most mundane school tasks more
engaging, which will help language learners to stay focused.
By providing language learners with tools and platforms,
they are familiar with they are more likely to be engaged and
get more out of the learning experience. When they are more
engaged, they are all the more likely to enjoy the experi-
ence and retain the information that is being imparted.Even
when learners are recording their mental data, this think-
ing, and its results will be highly dependent on these new
mode of instruction (Barbaro & Pickett, 2016; Derakhshan
& Shakki, 2018). In other words, learning is the result of stu-
dents’ overall understanding of their environment. In terms
of psychology, cognition is a creative and active interac-
tion that goes on without interruption between students or
their internal factors and environment. This interaction is
normally conscious and the environment is processed by it
(Cohen etal., 2018).
Emotion, as a very effective factor, can appear in features
such as lack of security, fear, anxiety, despair, doubt, and
make EFL students curious and try to learn and solve their
mental problems despite being in a technology-enhanced
learning environment. The set of factors affecting learning in
emotional and technology-enhanced learning environments
are the relationship between teacher and students, students’
relationship with each other, students’ relationship with the
environment, learning and educational content, suitability
of learning with students’ aptitudes, needs, and preparation,
an environment full of love and mutual respect (Zhao &
Wang, 2023).
Academic progress as a school variable has always been
the focus of researchers and education experts (Derakhshan
etal., 2020; Lee, 2020). In fact, due to the strong connec-
tions between emotions and academic progress, much atten-
tion has been paid to environmental motivation and the fac-
tors affecting it (Altintas etal., 2020; Derakhshan & Shakki,
2018; Derakhshan etal., 2023a, 2023b; Dirk & Nett, 2022;
Kiuru etal., 2020; Siddiquei & Khalid, 2021). When prob-
lems such as academic failure occur in technology-enhanced
learning environments, the emotions of the learners are men-
tioned as one of the important causes. Emotions are consid-
ered a basic concept in educational theories. Psychologists
and teachers also consider EFL students’ emotions as one of
the key concepts used to explain different levels of perfor-
mance (Guo etal., 2023; Labib etal., 2017; Lee, 2015; Lo
etal., 2021; Malmberg etal., 2015; Wu etal., 2023).
Because learning is an internal and permanent process
and students always search their life environment and dis-
cover relationships between phenomena, they expand their
cognitive structure (Richter & Hunecke, 2022). In general,
the theorists of cognitive theories believe that students can-
not learn a new and unfamiliar concept unless they can con-
nect it with the prior knowledge that they have in their minds
and obtained from their real experiences (Zylstra etal.,
2019). In their view, learning is an active process specific to
each student’s mind. This process consists of building men-
tal relationships between concepts and ideas on the one hand
and information and experiences from the real world outside
the mind on the other hand (Yeldham & Gao, 2021). One
of the factors influencing this process is students’ learning
styles. Chen (2010) believes that the most important respon-
sibility of teachers is to understand how the minds of learn-
ers’ work. A teacher should know that students learn from
each other in different ways, which indicates the difference
in the learning styles of students (Cohen etal., 2001). Know-
ing the mental processes in the teaching–learning process
is rooted in the cognitive theories of learning that try to
explain complex cognitive activities such as understanding,
remembering, and learning strategies. Therefore, knowledge
of human cognitive processes and finding ways to strengthen
and improve these abilities have always been of interest to
education specialists (Haerens etal., 2019). Until the last
decade, most of the research conducted in relation to educa-
tion by teachers was focused on the efficiency and effective-
ness of teaching methods. Most of them believed in making
changes in educational programs and little emphasis was
placed on learning processes as prerequisite factors related
to benefits of educational programs and teaching methods
(Lee, 2015). The quality of the learning process is dependent
on the frequency and extent of the learners’ use of different
learning strategies and different styles. Based on this, exam-
ining the capacity of EFL learners in technology-enhanced
learning environments has become one of the topics of
attention of educational and educational science specialists
in recent years. In this regard, the ability to use different
learning styles and metacognitive strategies in technology-
enhanced learning environments is considered the central
core in the processes of learning, problem solving, and deci-
sion making (Wolsko & Lindberg, 2013; Zhi etal., 2023).
Learning style is one of the sources of difference in
EFL students’ performance. If the teaching strategies are
not compatible with the EFL student’s learning style, it
becomes difficult to transfer information effectively from
the teacher to the students (Lo etal., 2021). Some are inter-
ested in visual presentation of information (such as photos,
diagrams, flowcharts, etc.). Others welcome verbal expres-
sion. Some students like to learn by doing and observing
and analyzing what happens, and other students like to think
more about the things they have planned to do. Therefore,
On theRelationship Between Learners’ Emotions andCognition intheTechnology‑Enhanced…
1 3
it is very important to know how students learn and to use
appropriate teaching methods in order to improve the qual-
ity of the learning experience technology-enhanced learning
environments (Labib etal., 2017). No specific study did not
investigate the relationship between EFL learning styles and
their situational motivation in technology-enhanced learning
environments. For this purpose, the present study examined
the relationship between students’ emotions and their situ-
ational motivations in technology-based environments with
the mediating role of learning styles and cognitive factors.
Review oftheLiterature
This study tries to use Bandura’s cognitive model to relate
the emotional, cognitive, and learning styles of students with
their situational motivation (Cheng & Monroe, 2012; Guay
etal., 2000).
Learning Styles
Most people learn through three types of learning styles:
visual, kinesthetic, and auditory. The thing to remember is
that, unlike intelligence and talent which are abilities, learn-
ing style is not an ability (Jie & Xiaoqing, 2006). In other
words, the learning style refers to how the learner learns,
not to the degree of success of the person in learning, and
naturally, the more your personal learning style is in har-
mony with what you learn, the more effective and enjoyable
learning will be (Lee, 2015).
The primary sort of learning style that was talked about
was the visual learning style. Visual learners process data
best when it is given pictures drawn on a chalkboard, dia-
grams, shapes, maps, or other realistic plans. Students who
are visual like to have directions printed as opposed to given
orally, and may frequently be careless while conceptualizing
or attempting to get a handle on another subject. Since visual
learners are great at perceiving how ideas and thoughts are
associated, they will more often than not succeed in jobs
that expect them to utilize visual abilities, go with choices in
light of information, or make things utilizing workmanship
and plan (Cohen etal., 2001). Auditory style is the second
kind of learning style. Auditory learners process data bet-
ter when something is said without holding back, like in
a talk. This sort of understudy can undoubtedly recollect
the expressions of others and really likes to talk through
points that are confounded or challenging to comprehend.
These people favor verbal directions and may rehash things
out loud at least a couple of times to commit them to their
drawn-out memory. They might pose inquiries to all the
more likely figure out the point (Jie & Xiaoqing, 2006). The
third sort of learning style that the study zeroed in on was
the kinesthetic learning style. Kinetic learners, additionally
called “tactile learners,” process data through experience as
opposed to being shown or told. These kinds of learners like
to do things that are more “involved.” They like to contact
and feel things and can without much of a stretch recollect
what they have done versus what they have heard or perused.
Individuals who utilize a kinesthetic learning style like to
make things with their hands and recollect data better when
they are truly involved (Jie & Xiaoqing, 2006; Lee, 2015).
Various researches have been done in the field of learn-
ing style. Tadayonifar and Entezari’s (2020) research results
showed that students prefer auditory and movement learn-
ing styles. Zylstra etal. (2019) showed that a large number
of students prefer the sensory learning style, while active,
visual, and sequential learning styles were less preferred.
Based on Yeldham and Gao’s (2021) study, absorbing,
active, reflective, introverted, intuitive, and emotional learn-
ing styles were the preferred learning styles for language
students. Richter and Hunecke’s (2022) study revealed that
most students prefer a movement learning style, and the stu-
dents’ learning style based on gender was the same in all
cases except for movement, and in this case, the proportion
of men was higher. Soga and Gaston (2016) demonstrated
that there was a significant difference between the learning
styles of students based on gender. The findings of Malm-
berg etal. (2015) presented that students prefer convergent,
adaptive, divergent, and absorbing learning styles in order.
Lee (2015) indicated that students preferred convergent,
absorbing, adaptive, and divergent learning styles in order.
According to the results of Chester etal. (2020), most under-
graduate students preferred movement and multi-method
learning styles over reading/writing, listening, and visual
learning styles.
Learning Styles inTechnology‑Enhanced Learning
Environments
Researchers believe that three factors can affect students’
learning styles. The first factor is cognitive factors that pay
attention to how to process the knowledge gained by stu-
dents. The second factor is emotional factor in which the
learner’s internal behavior such as motivation, attitudes,
preferred physical conditions, and management of success
and failure are examined. The third factor is psychomotor
factors, which is related to the type of content that students
like the most, the type of content that they prefer, and the
amount of activity that is required in the learning environ-
ment (Wong & Nunan, 2011). Apart from the fact that with
the official entry of virtual education into the classrooms,
a major part of the learning communication has been dedi-
cated to this environment, until now various research, and
studies have shown that combined education and benefiting
from technology-enhanced education along with regular
education can enrich the teaching–learning process (Osterlie
Y.Yang et al.
1 3
etal., 2019; Papanikolaou etal., 2006; Parish & Treasure,
2003; Tadayonifar & Entezari, 2020). In general, technol-
ogy-oriented education is based on dimensions that teachers
and students need to master to have a successful technology-
oriented environment, including the following:
– Teaching methods and pedagogy in a technology-ori-
ented learning environment (Richter & Hunecke, 2022)
– The technical aspect and how to use the required techno-
logical tools (Trémeau etal., 2013);
– The dimension of evaluation and how to properly benefit
from evaluation in technology-oriented education (Yeld-
ham & Gao, 2021);
– Ethical dimension and attention to considerations in this
field (Järvenoja etal., 2019).
Emotions andSituational Motivation
Based on positive psychology, a multitude of studies have
been conducted in recent years (Derakhshan, 2022; Wang
& Derakhshan, 2023; Wang etal., 2021). Aside from the
numerous studies pertaining to second language emotions
(Osterlie etal., 2019), it was only after recent years that posi-
tive psychology studies started to emerge (Derakhshan etal.,
2023a, 2023b; Pan etal., 2023; Solhi etal., 2023; Zare etal.,
2023). There have been several prototypical relevant studies.
For instance, inside the framework of positive psychology,
Wolsko and Lindberg (2013), recruiting Chinese university
students as members, quantitatively observed that perceived
control and perceived value were positively correlated with
positive emotions and foreign language performance, and
negatively correlated with negative emotions, and negative
central emotions played a mediating role between learners
and foreign language performance. Besides, Yeldham and
Gao (2021) qualitatively explored factors for Chinese EFL
teachers’ boredom in online courses during the period of
Corona-virus in which he interviewed 216 teachers, and
found that the online mode of teaching brings more bore-
dom than the face-to-face mode and analyzed the causes and
proposed solutions from the macro-categories of student-
related, task-related, IT-related, and teacher-related factors,
among which IT-related factors and teacher-related solutions
were the most frequently raised. Moreover, Lo etal. (2021),
with the employment of mixed methods, collected quantita-
tive information from 2002 university students, alongside
qualitative information from 12 university students and
12 university teachers, reasoning those different emotions
solely and interactively predicted boredom and more com-
plex affiliations existed between performance and boredom.
Various researches have been conducted in relation to
the relationship between various personal, social, educa-
tional, and family factors with environmental motivation.
In the research that Altintas etal. (2020) conducted on the
relationship between attachment and interest to teachers and
parents with academic motivation in students, they came
to the conclusion that attachment to teachers and parents,
either separately or in combination, with environmental and
academic motivation. Due to the fact that in most of the
past studies, many possible factors related to environmental
motivation have not been paid attention to, in the current
research, a set of individuals, family, social, and educa-
tional factors related to situational motivation has been tried
together and be examined in interaction with each other.
Research Questions andHypotheses
To follow the above mentioned objectives, the following
research questions were raised (Fig.1):
RQ1 Is there any statistically significant relationship
between Chinese EFL learners’ emotions, cogni-
tion, learners’ learning styles, and their situational
motivation?
RQ2 Do Chinese EFL learners’ cognition and learners’
learning styles mediate the relationship between
learners’ emotions and their situational motivation?
Method
Participants
In order to minimize the sample selection bias, this study
employed a random sampling technique to collect partici-
pants’ data (Chen & Sun, 2012). An overall number of 1116
EFL students took part in the study. After excluding the
problematic data, a finalized sample of 1089 respondents
were conducted. They were selected from different colleges
and universities, with different ages, genders, and levels.
Fig. 1 The theoretical framework
On theRelationship Between Learners’ Emotions andCognition intheTechnology‑Enhanced…
1 3
The age range of the participants spanned from 15 to 24
years, providing a comprehensive view of the experiences
and perspectives of young learners in the EFL context. We
used QQ, a widely used communication platform in China,
technologically in conjunction with Wenjuanxing, an online
survey tool, to distribute the questionnaire to all partici-
pants. Before commencing the data collection process, we
prioritized ethical considerations of our study. To adhere to
the ethical standards in educational research, we obtained
explicit consent from each participant. A detailed overview
of demographic information is shown in Table1.
Instruments
To collect the data, the researcher used the following instru-
ments. These validated instruments were used in the reputa-
ble previous studies to determine similar purposes.
Cognitive Scale oftheHuman–Nature Relationship
The scale was derived from Cheng and Monroe (2012), Per-
rin and Benassi (2009), and Wolsko and Lindberg (2013).
This scale has 24 items divided into two dimensions: eco-
logical and environmental awareness, and ecological and
environmental affectiveness, and has a 5-point Likert-type
items ranging from 1 = SD, strongly disagree to 5 = SA,
strongly agree. The questionnaire was piloted by 50 partici-
pants of the same study. The reliability index of Cronbach
Alpha was 0.83 (r = 0.83).
Learners’ Emotion Questionnaire
This scale that has 35 items was adopted from Pekrun etal.
(2011) and used to measure learners’ emotions. The seven
subscales of this scale are joy (items 1–5), pride (items
6–11), love (items 12–17), anger (items 18–22), exhaustion
(items 23–29), and hopelessness (items 30–35). The ques-
tionnaire was piloted by 50 participants of the same study.
The reliability index of Cronbach Alpha was 0.89 (r = 0.89).
The Situational Motivation Scale (SIMS)
This questionnaire that was derived from Guay etal. (2000)
has 16 items and four subscales. The subscales are intrinsic
motivation (4 items), identified regulation (4 items), external
regulation (4 items), and amotivation (4 items). Situational
motivation alludes to the motivation people experience when
they are as of now captivating in an action. The question-
naire was piloted by 50 participants of the same study. The
reliability index of Cronbach Alpha was 0.78 (r = 0.78).
Learning Style Questionnaire
The Learning Style Survey was designed by Cohen etal.
(2001) to evaluate learners’ general way to deal with learn-
ing. It does not foresee your conduct in each example; how-
ever, it is an obvious sign of students’ general style incli-
nations. This study used three styles of this questionnaire;
visual, auditory, and kinesthetic learning styles. Thirty items
measure these three styles; 10 items for every learning styles.
It uses a Likert-scale that starts with 1 = Never applies to me,
to 3 = Often applies to me. The questionnaire was piloted by
50 participants of the same study. The reliability index of
Cronbach Alpha was 0.88 (r = 0.88).
Data Collection Procedure
The data collection process spanned from early July to late
July, 2023 with the help of Chinese EFL teachers from
different colleges and universities. Our data collection is
divided into three stages. In the first stage, we translated
all survey items into Chinese to ensure the accuracy and
clarity of the translated version. To further ensure the qual-
ity of the translated questionnaire, two experts in applied
linguistics reviewed and approved the translated materials.
In the second stage, we conducted a pilot study, serving as a
crucial preparatory step to refine the survey questionnaires
and identify any potential issues with the data collection pro-
cess. In the last stage, the Chinese e-version of the question-
naires was sent to 1200 participants through Tencent QQ by
means of Wenjuanxing. After being informed of the research
purpose and questionnaire description, the participants, who
were aware that the questionnaire was anonymous and the
data would be kept confidential, expressed their consent by
signing the consent form. It took each participant about 6–9
min to complete the questionnaire. Thus, a total of 1116
students participated in this survey, and after the problematic
data were excluded, 1089 valid cases were obtained.
Table 1 Demographic
information of the participants Demographic
information
category
N%
Age
< 18 284 26.08
18–22 790 72.54
> 22 15 1.38
Gender
Male 246 22.6
Female 843 77.4
Grade
Elementary 354 32.48
Intermediate 431 39.62
Advanced 304 27.9
Y.Yang et al.
1 3
Data Analysis
To answer the research questions, the researcher used SPSS
software (version 27) and AMOS (version 24). Through
employing Structural Equation Modeling (SEM) and func-
tions such as reliability, correlation, and Multiple Linear
Regression, the researcher analyzed the obtained data.
Results
Confirmatory Factor Analysis (CFA) was conducted to
check the reliability of the instruments, and to meet the
model fit. The instruments were the Cognitive Scale (26
items), Learners’ Emotion Questionnaire (35 items), The
Situational Motivation Scale (16 items), and Learning Style
Questionnaire (30 items).
The results of Table2 disclose that the model has met
goodness of fit and the model’s all variables are acceptable.
Consequently, CMIN/DF is 3.541, IFI = 0.900, CFI = 0.899,
PNFI = 0.722, TLI = 0.901, and RMSEA = 0.075.
Table3 presents the composite reliability and construct
reliability values for the questionnaires. Moreover, all AVE
values higher than 0.50 that confirm the convergent and
discriminant validity of the model. Furthermore, there was
a significant, strong, positive correlation between learn-
ers’ emotions and their cognitive styles, r (1086) = 0.62,
p < 0.001. In addition, learners’ emotions and their situ-
ational motivation were found to be strongly positively
correlated,r (1086) = 0.80, p < 0.001. Language learners’
learning styles are positively correlated with learners’
emotions and their situational motivation respectively, r
(1086) = 0.47,p < 0.001, r (1086) = 0.42,p < 0.001 (Table4).
The results of testing the direct relationships in the con-
ditional mediation model rejected H01 and revealed that
learners’ emotions have a significant positive impact on
students’ learning styles; on kinesthetic students (β = 0.364,
p < 0.001); on auditory students (β = 0.451, p < 0.001), and
on visual students (β = 0.297, p < 0.001). The results also
indicated that learners’ emotions have a significant posi-
tive impact on students’ situational motivation (β = 0.622,
p < 0.001). To test H2, the researcher conducted regression
analysis in SEM (Fig.2).
The results of the analysis indicate that the indirect
impact of learners’ emotions on their situational motivation
through learners’ learning styles and their cognitive styles as
mediators was significant at 95% confidence level (β = 0.54,
p < 0.001). It means that 54% of changes in students’ situ-
ational motivation can be explained by interaction among
their emotions, learning, and cognitive styles. In addition,
the results of Table5 show the specific interactions between
the variables. Six detailed paths are presented in the table.
The results reveal that while cognitive styles had a partial
mediation impact on the relationship between learners’ emo-
tions and their situational motivation, the mediations of eco-
logical and environmental effectiveness were negative and
the mediations of ecological and environmental awareness
were positive. The results also demonstrated that the direc-
tion of relations and mediation impact of learning styles are
different. It means that while learners’ learning styles have
a significant impact on their situational motivation their
mediation interactions are different.
Discussion
The results of examining the first hypothesis showed that
there is a significant relationship between learning styles and
Table 2 Assessment of goodness of fit through CFA
Criteria Threshold
Terrible Acceptable Excellent Evaluation
CMIN 6402.128
DF 1808
CMIN/DF 3.541 > 5 > 3 > 1 Acceptable
RMSEA 0.075 > 0.08 < 0.08 < 0.06 Acceptable
IFI 0.900 < 0.9 > 0.9 > 0.95 Acceptable
CFI 0.899 < 0.9 > 0.9 > 0.95 Acceptable
PNFI 0.722 < 0.5 > 0.5 Acceptable
TLI 0.901 > 0.9 > 0.9 > 0.95 Acceptable
Table 3 Reliability and validity of the variables
Significance value are shown in bold and asterisk
CR AVE MSV MaxR(H) Cognitive Scale Learners’ Emo-
tion Question-
naire
Situational
Motivation
Scale
Learning Style
Questionnaire
Cognitive Scale 0.83 0.85 0.869 0.901 0.924
Learners’ Emotion Questionnaire 0.89 0.91 0.921 0.896 0.625*** 0.932
Situational Motivation Scale 0.78 0.76 0.824 0.875 0.613*** 0.801*** 0.897
Learning Style Questionnaire 0.88 0.84 0.813 0.897 0.542*** 0.476*** 0.422*** 0.915
On theRelationship Between Learners’ Emotions andCognition intheTechnology‑Enhanced…
1 3
students’ metacognitive skills. This finding is not consistent
with the results of Dirk and Nett (2022) who found that there
is no positive and meaningful relationship between learning
styles and metacognitive skills. In explaining this research
finding, it can be said that despite the fact that learning styles
and metacognitive skills are important and key factors in stu-
dents’ academic success, in the teaching–learning process,
other components such as the quality of the teaching method,
educational environment, cognitive, emotional, and the psy-
chomotor skills of learners are also effective. The difference
in the relationship between learning styles and metacogni-
tive skills in this research points out that professors should
know that students learn from each other in different ways,
which indicates the difference in learning styles of learners.
Therefore, different opportunities for learning experiences
should be created in the classroom and the teaching–learning
process should not be limited to the study of books (Altintas
etal., 2020; Lo etal., 2021).
Examining these hypotheses indicated that while there is
a significant relationship between kinesthetic learning style
and cognitive styles and its subscales, the direction of the
relationship is different from the relationship between vis-
ual learning style and cognitive subscales. While Chen and
Sun (2012) confirmed the positive and significant relation-
ship between kinesthetic learning style and cognitive skills,
the findings of this study found a significant and negative
relationship between the variables. It is consistent with the
results of Altintas etal. (2020) who found that there is a sig-
nificant negative relationship between kinesthetic learning
styles and cognitive subscales.
Table 4 Standardized regression weights of the variables
Estimate S.E C.R p
Kinesthetic ← Learners’ Emotion 0.364 0.014 13.978 0.001
Auditory ← Learners’ Emotion 0.451 0.014 7.132 0.001
Visual ← Lear ners’ Emotion 0.297 0.011 25.636 0.001
Affectiveness ← Visual 0.942 0.071 14.975 0.001
Awareness ← Visual 0.963 0.080 14.130 0.001
Affectiveness ← Auditory −0.645 0.067 −9.500 0.001
Awareness ← Auditory −0.690 0.075 −9.147 0.001
Affectiveness ← Kinesthetic 0.511 0.073 7.053 0.001
Awareness ← Kinesthetic 0.571 0.082 6.125 0.001
Situational motivation ← Kinesthetic 0.443 0.085 1.657 0.098
Situational motivation ← Auditory −0.384 0.080 −2.210 0.027
Situational motivation ← Visual 0.970 0.097 10.227 0.001
Situational motivation ← Awareness 0.532 0.051 5.567 0.001
Situational motivation ← Affectiveness −0.485 0.061 −2.887 0.004
Situational motivation ← Lear ners’ Emotion 0.622 0.039 10.594 0.001
Fig. 2 The final measurement model
Y.Yang et al.
1 3
The findings demonstrated that ecological and environ-
mental awareness in learning is one of the categories that
deal with the role of the individual in the learning pro-
cess. Learning in this approach is a constructive process
and potential learners are able to control and regulate the
learning process. They can monitor and regulate different
aspects of cognition, motivation, and behavior as well as
the environment around them. Learners with more skill in
ecological and environmental awareness direct their learning
experiences actively and in a variety of ways, and when-
ever necessary, they change the learning strategies used in
response to their needs, the characteristics of the task, and
the environmental conditions, and successfully stabilizing
and increasing their motivation level during the time they
are busy doing homework. Since in the present study, visual
learning style is the first most used learning style among stu-
dents, and people with visual learning style have the great-
est ability in practical application of ideas and theories in
solving problems. These students can do their homework
successfully and take responsibility for their own learning by
making practical use of their learning and ideas. In explain-
ing this research finding, it can be said that learners with
visual learning style have strong imaginations and emotions
and are looking for different answers.
In contrast to Tadayonifar and Entezari (2020) who claim
that there is a significant and positive relationship between
the visual learning style and the metacognitive skills, the
results of this study indicated that the relationship between
visual learning style and students’ cognitive skills is signifi-
cant and negative. This finding is compatible with the results
of Wolsko and Lindberg (2013); Yeldham and Gao (2021)
that show a significant and negative relationship between the
visual learning style and cognitive skills. This means that in
the course of teaching–learning in the classroom, skills such
as imagination, creativity and students’ emotions are not
emphasized as it should be. On the other hand, the students
themselves do not have much knowledge about how they
learn, and they cannot make a positive connection between
their learning styles and metacognitive skills. When they
use the visual style, their metacognitive skills decrease and
this indicates that the development of students’ imagination
is not valued and the low levels of cognitive domains such
as knowledge are still emphasized.
Conclusion
The current study investigated the mediating impact of stu-
dents’ learning and cognitive styles on their emotions and
situational motivations in technology-enhanced learning
environments. In general, people’s needs, their expecta-
tions from the educational environment, and how they com-
municate with the environment are also effective factors in
people’s sense of the environment, and these factors should
be taken into consideration in studies and investigations. To
formulate the research model, the physical characteristics of
the environment affect the feelings and valuations towards
the environment. The compatibility between environmental
capabilities and human needs makes it a criterion for inter-
preting the relationship between man and place. The most
important effective factors of the sense of the environment
can be investigated in two categories, meanings and activi-
ties. In the group of meanings, identity, and beauty, and in
the level of activities, there are social interactions, a sense of
community and satisfaction, which in the educational envi-
ronment is manifested due to social and emotional interac-
tions, a sense of the environment. In addition, the way of
communication and previous experiences are also effective
in the sense of the environment. Other factors such as how
to choose and relate to the environment and the personal and
social characteristics of the users are also factors influencing
the sense of the environment.
Implications andFuture Studies
In the design of many types of educational environments,
less attention is paid to the mental and psychological aspects
of students, and most educational spaces do not have the
necessary compatibility with the psychological character-
istics of students, in this sense, they can affect social and
behavioral performance as well as their attitude and insight
towards education should be influential. Behavioral and psy-
chological effects created in students in terms of placement
in different environments cause a change in the desire to
Table 5 Structural model assessment: indirect effects
Ind1 = learners’ emotion to visual to ecological and environmental
affectiveness to situational motivation
Ind2 = learners’ emotion to visual to ecological and environmental
awareness to situational motivation
Ind3 = learners’ emotion to auditory to ecological and environmental
affectiveness to situational motivation
Ind4 = learners’ emotion to auditory to ecological and environmental
awareness to situational motivation
Ind5 = learners’ emotion to kinesthetic to ecological and environmen-
tal affectiveness to situational motivation
Ind6 = learners’ emotion to kinesthetic to ecological and environmen-
tal awareness to situational motivation
Parameter Estimate Lower Upper p
Ind1 −0.054 −0.096 −0.013 0.008
Ind2 0.093 0.053 0.140 0.001
Ind3 −0.011 −0.003 −0.022 0.005
Ind4 0.019 0.033 0.011 0.000
Ind5 −0.018 −0.039 −0.005 0.005
Ind6 0.029 0.017 0.049 0.000
On theRelationship Between Learners’ Emotions andCognition intheTechnology‑Enhanced…
1 3
learn, sensitivity to environmental stimuli, concentration,
and educational motivation. The results of some research
show the obvious effect of color, light, and materials used
in design as the main interior components and elements on
the students’ spirit, mind, and attitude.
Future studies can focus on other learning styles and
contextual factors in determining the relationships between
learners’ emotions and their situational motivation. These
factors are culture dependent; therefore, these factors can be
investigated in different cultures and the results can be com-
pared with the current findings. The findings of this com-
parison can be leading point in developing more efficient
international teaching materials.
Acknowledgements This work was supported by Nanjing Normal
University, People’s Republic of China. The university has no role
in the design and implementation of this study. The authors are also
grateful to the insightful comments suggested by the editor and the
anonymous reviewers.
Author Contributions All authors listed in the study have materially
participated in the research and article preparation. Additionally, all
have approved the final article.
Funding This work was supported by Jiangsu Education Department
(No. 2022SJZD122).
Data Availability The datasets generated and analyzed during the
current study are available from the corresponding author on reason-
able request.
Declarations
Competing Interests The authors declare that they have no compet-
ing interests.
Consent to Participate Informed consent to participate was obtained
from all individual participants included in the study.
Consent for Publication Informed consent for publication was
obtained from all individual participants included in the study.
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