The Influence of Cognitive Presence, Social Presence and Teaching
Presence on Online Foreign Language Speaking Anxiety, L2
Motivational Self and Intended Effort-A Structural Equation
Modeling Approach
Qihui Chen
, Wenting Sun
and Xiaoling Wang
Institute of Modern Distance Education, Tongji University, Siping Road 1239, Shanghai, China
Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
Department of College English, Zhejiang Normal University, Jinhua, China
Keywords: Community of Inquiry, Cognitive Presence, Teaching Presence, Social Presence, L2 (Second Language)
Motivational Self System, Ideal L2 Self, Ought-to L2 Self, General Self-Efficacy, Intended Effort.
Abstract: Although a large number of online foreign language oral learners has emerged, little is known about the
interactive mechanism between online learning environmental factors and learners’ self-relevant factors.
Using the quantitative method, 466 questionnaires of Chinese young adult learners were collected to test the
hypothesized relationships between cognitive presence (CP), teaching presence (TP), social presence (SP),
L2(second language) motivational self (including Ideal L2 self (IL2) and Ought-to L2 self (OL2)), general
self-efficacy (GSE), online foreign language speaking anxiety (AN) and intended efforts (IE) in a structural
equation model. The findings illustrate: (1) Online foreign language speaking learners are in middle anxiety,
high GSE and high IE level; (2) Online learning environment is an overall ecology in which CP, TP, SP, IL2,
OL2, and GSE are highly correlated with each other; (3) In terms of causality, TP and OL2 enhance AN, GSE
weakens AN, GSE and OL2 strengthen IE while AN reduces IE. To facilitate online oral foreign language
learning, designers of online learning platforms should consider providing the choice of learning partners and
more meta-cognition support to guide learners to deal with negative evaluations and manage learning
Along with online international conferences have
increased sharply, opportunities for online
communication in foreign languages soar. In this
case, the demand for online oral foreign language
learning has continued to rise. It is reported that the
annual growth rate of China’s adult English training
industry is as high as 25% (Lu et al., 2015). A large
number of online foreign language learning platforms
emerged, such as Duolingo, LingoDeer, Liulishuo,
Shanbei vocabulary English. However, the negative
experience impacted by anxiety reduces learners’
interest and enthusiasm for learning (Dirkx, 2008).
Compared with other subjects, foreign language
learners suffer more anxiety. The mismatch between
the real self and the expressive self induces the
anxiety of communicators (Horwitz, 1986). Learners
who have high anxiety levels tend to escape from the
foreign language communication scenario; on the
other hand, less engagement in speaking activities
reduces feedback about the progress of language
performance, which may trigger learners’ speaking
anxiety in return. This means that self-relevant
cognition and feedback from others influence the
anxiety of foreign language learners.
Regarding self-relevant cognition in foreign
language learning, Dörnyei's (2009) second language
(L2) motivational self system integrates L2 learning
motivation and identity, including Ideal L2 self (IL2)
and Ought-to L2 self (IL2). Based on this system,
Papi (2010) found that IL2 weakens English anxiety,
Chen, Q., Sun, W. and Wang, X.
The Influence of Cognitive Presence, Social Presence and Teaching Presence on Online Foreign Language Speaking Anxiety, L2 Motivational Self and Intended Effort-A Structural Equation
Modeling Approach.
DOI: 10.5220/0010979000003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 173-180
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
and OL2 enhances English anxiety. Besides, self-
efficacy is another important self- relevant factor in
the L2 motivational self system (Ueki & Takeuchi,
Regarding the analysis of feedback from others in
online foreign language speaking learning,
Community of Inquiry (CoI) theory can be considered
because this theory promotes researchers to explore
the dynamic process of online learning experience
through a collaborative constructivist perspective
(Arbaugh et al., 2008), which includes three closely
related online presences and one overlap section-
cognitive presence (CP), teaching presence (TP),
social presence (SP) and meaningful learning
experience (Garrison et al., 1999). Using the CoI
model to specify the learning experience involved in
online foreign language oral learning environment
can further reveal the interaction mechanism between
online learning environment and self-relevant factors,
and help improve the construction of online learning
environments which require highly human-computer
interaction. In this regard, three research questions
were proposed:
1. How do cognitive presence (CP), teaching
presence (TP) and social presence (SP) predict
online foreign language speaking anxiety (AN)
and intended efforts (IE)?
2. Whether CP, TP and SP influence L2 motivation
(including IL2, OL2) and self-efficacy
(especially general self-efficacy (GSE))?
3. How do L2 motivation and self-efficacy predict
online foreign language speaking anxiety (AN)
and intended efforts (IE)?
The next section proposes hypotheses based on a
theoretical foundation; methodology is shown in
section 3 and results in section 4. Section 5 gives
more details in discussion, implications and
limitations while section 6 gives conclusions and
future directions.
The following section provides a quick introduction
about the used variables and describes hypotheses
among variables.
2.1 Variables Used
The most widely used definition of foreign language
anxiety is related to classroom language learning and
originated from the unique complex self-awareness,
concepts, emotions, and behaviors in the language
learning process, which includes communication
apprehension (CAN), fear of negative evaluation
(FAN) and test anxiety (TAN) (Horwitz et al., 1986).
Given the online language learning environment, this
study refers to this definition and subcategories but in
the online oral learning environment.
The L2 motivational self system proposed by
Dörnyei (2014) contains three main components: IL2,
OL2, and L2 learning experience, which has been
applied to different languages and cultural
environments (Ryan, 2009; Kong et al., 2018).
According to Dörnyei (2014), IL2 represents the ideal
image of L2 users that individuals hope to become in
the L2 field while OL2 self is affected by duties,
obligations or responsibilities, an attribute that others
believe that an individual should possess. The L2
learning experience is a specific contextual
motivation related to the immediate learning
environment and experience, for which CP, TP and
SP represent this factor in this study. Moreover,
adding other affective variables would improve the
prediction of the L2 motivational self framework,
such as L2 anxiety and self-efficacy (Kormos et al.,
2011; Dörnyei, 2014). By adding L2 anxiety and
intended effort, Papi (2010) extended this system,
which is one of the basic models of this research.
Referring to the definition of IE in the study of
Papi (2010), this variable was considered in this
study, which is defined as the time and energy that
learners plan to spend on the online foreign language
oral learning platform in the future. It is used as a
variable to measure learners' willingness to learn oral
foreign languages online.
GSE is defined as a personal belief, which refers
to an individual's comprehensive view of their ability
to perform in various situations (Judge et al., 1998).
In this regard, GSE focuses on the individual's
perception of the ability to meet task requirements in
different situations. Because online foreign language
oral learning involves multiple senses of self-
efficacy, such as computer self-efficacy, foreign
language oral self-efficacy, this study uses a wider
range of GSE to represent personal positive
inclinations and belief in the ability to complete tasks.
The experience process of CP is similar to the
learning process of foreign languages. Tobias (2013)
divided the language information processing process
into three stages: input, processing, and output.
Cleveland and Campbell (2012) pointed out that CP
describes the extent to which learners can construct
and confirm meaning through continuous reflection
and discourse. Ideally, CP includes triggering events,
exploring, integration and resolution. In this regard,
CP of this research refers to the degree of learners’
CSEDU 2022 - 14th International Conference on Computer Supported Education
knowledge understanding and construction in online
foreign language oral learning, including these four
Cleveland and Campbell (2012) emphasized that
teaching labor is the central organizational element of
online presence, which has the function of designing
and promoting online learning. Garrison et al. (1999)
determined three subcategories of TP from a large
amount of literature and inquiries: instructional
management, building understanding, and direct
instruction. The three subcategories of teaching
presence are highly similar to the comprehensible
input hypothesis of Krashen (1982) in the traditional
foreign language learning environment. Regarding
the definition and subcategories, TP of this study
refers to the guidance of the construction of foreign
language knowledge and social relations that learners
perceived during the online oral foreign language
learning process, including these three subcategories.
Cleveland and Campbell (2012) described SP
refers to the degree of social and emotional
connection between learners and others in an online
learning environment. Through a large number of
documents, Garrison et al., (1999) summarized that
SP includes three subcategories: emotional
expression, open communication and group cohesion.
These three factors are highly similar to the opposites
of willingness to communicate, group cohesion, and
fear of negative evaluation in the study of traditional
foreign language anxiety influencing factors.
Therefore, this study introduces SP into the online
foreign language oral learning anxiety influencing
factor model. It is defined as the strength of
expressing themselves and the strength of connection
with others that learners feel in the process of online
foreign language oral learning, including these three
2.2 Hypotheses
The CoI model regards online communities as places
that support online learning through the interaction
among CP, TP and SP (Thompson & MacDonald,
2005; Shea, 2006). The three factors were proved to
be interrelated when the theory was created (Garrison
et al., 1999), but additional empirical materials are
needed to test it (Arbaugh et al., 2008). Therefore, in
online foreign language oral learning, hypotheses
were established in this study:
H1: CP and SP positively influence each other;
H2: CP and SP positively influence each other;
H3: SP and TP positively influence each other;
In the process of foreign language oral learning,
learners are exposed to factors related to the possible
successful experience of language learning, including
courses, teachers, and peers. The path from foreign
language learning experience to foreign language
anxiety has been confirmed by many studies (Aida,
1994; Young, 1991). Here, hypotheses were
H4: CP reduces AN; H5: SP reduces AN;
H6: TP reduces AN; H7: CP increases IE;
H8: TP increases IE; H9: SP increases IE;
Tobias (2013) pointed out that increased effort
can compensate for the negative effects of learner
anxiety in the three language stages of input,
processing, and output. However, in general, the
speed of the L2 information exchange process is too
fast to allow this kind of compensation. Therefore, a
hypothesis was established:
H10: AN reduces IE;
Research by Ryan (2009) showed that IL2 is
significantly related to immersive motivation and
explains expected effort. Although IL2 and OL2
express learners’ learning motivation from different
sources internally and externally, both are
motivations in essence and can promote the effort.
Therefore, hypotheses were established:
H11: IL2 and OL2 influence each other;
H12: IL2 increases IE; H13: OL2 increase IE;
Research by Papi (2010), Ueki and Takeuchi
(2012) showed similar results that IL2 has a
weakening effect on L2 anxiety, and OL2
significantly increases L2 anxiety. Here, similar
hypotheses were established:
H14: IL2 reduce AN; H15: OL2 increase AN;
MacIntyre et al. (1997) pointed out that students
with low self-confidence in the L2 learning
environment are more inclined to have negative
expectations of their behavior and become more
anxious when facing language learning tasks. A
similar hypothesis was established:
H16: GSE reduces AN;
Bong and Skaalvik (2003) demonstrated that self-
efficacy has an important motivational force. The
empirical study by Kormos et al. (2011) verified that
self-efficacy in L2 learning has a strong influence on
L2 motivation. Nielsen’s (2009) research has shown
The Influence of Cognitive Presence, Social Presence and Teaching Presence on Online Foreign Language Speaking Anxiety, L2
Motivational Self and Intended Effort-A Structural Equation Modeling Approach
that the stronger the self-efficacy of L2 learners, the
greater the expected effort. Hence, hypothesized:
H17: GSE is positively correlated with IL2;
H18: GSE is positively correlated with OL2;
H19: GSE increases IE;
Many studies have pointed out that the
environment has an important influence on the
formation and maintenance of students' L2 learning
motivation (Csizer & Dörnyei, 2005; Ryan, 2009).
Kormos et al. (2011) illustrated that the surrounding
environment of students: family, friends, peers,
teachers, and guidance materials, all contribute to the
learner’s goal setting, attitude information, self-
efficacy, and persistence in learning activities.
Therefore, hypotheses were established:
H20a: CP is positively correlated with IL2;
H20b: TP is positively correlated with IL2;
H20c: SP is positively correlated with IL2;
H21a: CP is positively correlated with OL2;
H21b: TP is positively correlated with OL2;
H21c: SP is positively correlated with OL2;
Bandura (1977), Markus and Nurius (1986) found
that mastering history, social comparison, attribution,
and evaluation of important others are all important
to learners’ sense of self-efficacy. In this study, CP,
TP and SP represent the knowledge exploration,
knowledge-guided help, and emotion exchange with
other participants experienced by learners on an
online foreign language oral learning platform.
Therefore, established hypotheses:
H22a: CP is positively correlated with GSE;
H22b: TP is positively correlated with GSE;
H22c: SP is positively correlated with GSE.
In this section, pedagogical design features are
represented through Liulishuo (a popular foreign
language oral learning APP) as an example. Then, the
research procedure, participants and research
instrument are displayed.
3.1 Pedagogical Design Features
For the description of the pedagogical design of
foreign language oral learning platform, Liulishuo in
China was chosen because this app has 120 million
downloads, 4.9 points (5-point rating system), and
employs intelligent speech recognition technology.
Based on the authors’ learning experience in
foreign language oral learning platforms, here are
some common learning activities shared by platforms
and represent in Liulishuo App. When the first
loading on this app, learners are recommended to
locate their English preference within several levels.
Then, according to users’ choice on preferred content
(e.g., grammar, vocabulary), interests (e.g., English
interview, daily conversation) and study schedule, a
learning plan is created. And after daily tasks, a
promotion can be clicked to share learning results on
social media. Besides basic sentences and vocabulary,
interesting topics can be added to a personalized
learning plan. Then, following and combined with the
voice recording of native English speakers, users
practice the speaking skills of relevant vocabulary
and sentences and obtain instrumental feedback on
inaccurate pronunciation. After one task, learners
gain a personalized radar chart of accuracy, rhythm,
fluency and pronunciation and weakness analysis in
pronunciation problems and sentence problems
combining recommended learning materials.
3.2 Procedure
First, an online foreign language speaking anxiety
questionnaire was organized based on the existing
ones. Then the survey was hosted on Wenjuanxing
(similar to SurveyMonkey) and was distributed in the
foreign language learning groups and universities
groups in the WeChat platform and QQ platform
(similar to WhatsApp and Facebook). After data was
collected, the reliability and validity of the
questionnaire were evaluated and confirmatory factor
analysis (CFA) of the hypotheses was verified.
3.3 Participants
The research mainly focused on Chinese young adult
learners. There were 466 valid questionnaires. The
average age is around 25 and the proportion of 18-30
years old is 89%. Males account for 38%, females
account for 62%. Among them, 74% are
undergraduate and postgraduate.
3.4 Research Instruments
Referring to existing questionnaires, mainly
Community of Inquiry instrument (Arbaugh et al.,
2008; Cleveland & Campbell, 2012), foreign
language anxiety scale (Horwitz et al., 1986), general
self-efficacy scale (Chen et al., 2001), L2
motivational self scale (Papi, 2010), online foreign
language speaking anxiety scale was developed. All
CSEDU 2022 - 14th International Conference on Computer Supported Education
questionnaire items were designed using a five-point
Likert scale, ranging from 5=strongly agree, to
1=strongly disagree. Educational technology experts
and English professionals were organized to translate
English version’s items into Chinese ones, and then
back to English scale to check the ambiguity of the
Chinese questionnaires. The Chinese questionnaires
can be found at the link 线及影响
因素 (
In this article, reliability and validity were evaluated
in SPSS 22. The Cronbach's Alpha coefficient of the
overall questionnaire is 0.971, meaning that the
reliability of the questionnaire is relatively high. The
KMO (Kaiser-Meyer-Olkin) value of the overall
questionnaire is 0.967, and the approximate variance
of Bartlett's spherical test is 16397.127 (p<0.001),
indicating that the sample data is suitable for factor
A confirmatory factor analysis (CFA) was
performed in AMOS 26 to measure the fitness
between the hypothesized model and observed data.
Root mean square error of approximation (RMSEA)=
0.056, Goodness-of-fit index (GFI)= 0.904, Adjusted
goodness-of-fit index (AGFI)= 0.874, Normed fit
index (NFI)= 0.943, TLI=0.958, Comparative fit
index (CFI)= 0.965, Parsimony normed fit index
(PNFI)= 0.776, Those indexes is consistent with the
goodness-of-fit indexes recommended by Hu and
Bentler (1999) and Wu (2009), meaning a relatively
good fit between the hypothesized model and the
observed data. In other words, the final version of the
online foreign language speaking anxiety model is a
reasonable representation of the data collected. More
detailed information about standardized factor
loading of variables can be found in table 1 and the
standard coefficient of paths in table 2.
4.1 Descriptive Statistics and
Measurement Model
The online foreign language speaking anxiety model
was tested, and measurement modeling results were
summarized in table 1. For short, results of
descriptive statistics were also combined in table 1.
The descriptive statistics results (details in Table
1) present that learners are in a state of moderate AN
(M=3.48). Comparatively, learners are at a relatively
high level of IE (M=3.91) and GSE (M=3.94). By
contrast, IL2 (M=3.68) and OL2 (M=3.64) are at the
mediate level. This displays that in the online
environment, the motivation to learn English is not
high though learners believe they can succeed in most
tasks. CP(M=3.72), TP (M=3.72) and SP (M=3.67)
also have similar levels, meaning that foreign
language learners may not receive large information
to promote content exploration, learning activities
organization and group cohesion online.
Table 1: Summary of the measurement model.
Latent variables Items SFL
CP1 0.842***
CP2 0.868***
CP3 0.883***
CP4 0.879***
TP1 0.902***
TP2 0.918***
TP3 0.830***
Social Presence
SP1 0.907***
SP2 0.904***
SP3 0.887***
Ideal L2 self
(IL2) (M=3.68)
IL21 0.840***
IL22 0.829***
IL23 0.875***
Ought-to L2 self
(OL2) (M=3.64)
OL21 0.757***
OL22 0.829***
OL23 0.851***
General self-
GSE1 0.824***
GSE2 0.872***
GSE3 0.819***
Online foreign
speaking anxiety
CAN 0.858***
NAN 0.916***
TAN 0.781***
Intended Effort
IE1 0.863***
IE2 0.822***
IE3 0.825***
Note: M=Mean, SFL=Standardized factor loading,
***P<0.001, CP1=Triggering Event, CP2=Exploration,
CP3=Integration, CP4=Resolution, TP1=Instructional
Management, TP2= Building Understanding, TP3= Direct
Instruction, SP1=Emotional Expression, SP2=Open
Communication, SP3=Group Cohesion, CAN=
Communication Apprehension (CAN), NAN=Fear of
Negative Evaluation, TAN=Test Anxiety.
4.2 Structural Equation Model
The conducted CFA’s results were summarized in
table 2. The results present that all the mutual
influence relations have been proved (see table 2, H1-
H3, H11, H17, H18, H20, H21 and H22). Only a
small part of the causal relations has been proved (see
table 2, H10, H13, H15, H16 and H19). More detailed
analysis can be found in section 5.
The Influence of Cognitive Presence, Social Presence and Teaching Presence on Online Foreign Language Speaking Anxiety, L2
Motivational Self and Intended Effort-A Structural Equation Modeling Approach
Table 2: Summary of structural equation modeling.
Path SC H
-0.379 Not
0.88*** Not
SP ->AN (H5) -0.145 Not
IL2->AN (H14) -0.014 Not
OL2->AN(H15) 0.527*** Supporte
-0.145* Su
-0.129* Su
0.284*** Su
CP->IE (H7) 0.279 Not
IL2->IE (H12) 0.116 Not
0.226** Su
0.344 Not
-0.192 Not
TP<->CP (H2) 0.954*** Supporte
SP<->TP (H3) 0.928*** Supporte
SP<->CP(H1) 0.936*** Supporte
0.647*** Su
0.662*** Su
0.678*** Su
IL2<->CP (H20a) 0.757*** Supporte
IL2<->TP (H20b) 0.727*** Supporte
0.774*** Su
0.657*** Su
0.612*** Su
OL2<->SP (H21c) 0.692*** Supporte
GSE<->SP (H22c) 0.673*** Supporte
GSE<->TP (H22b) 0.650*** Supporte
0.672*** Su
Note: ***P<0.001, **P<0.01, *P<0.05, SC=Standard
coefficient, “-” means negative relation, “->” means lead to,
“<->” means mutual influence.
Among the influencing factors of IE, the path
coefficient of AN is -0.129 (see table 2). The research
results show that the anxiety experienced by learners
in the process of online foreign language speaking
learning belongs to obstructive anxiety. This result is
different from Shih (2019), in which AN has no
significant impact on IE. This may be related to the
sample. The sample of Shih (2019) are students from
senior high school in Taiwan and English is a
compulsory subject and an important subject of the
entrance college examination.
The strong correlation between CP, TP and SP
validates the close relationship described by Garrison
et al. (1999) in online foreign language oral learning.
IL2, OL2 and GSE are highly positively
correlated, which correlation coefficient is not less
than 0.65(see table 2). This echoes personality
psychology about the close connection between IL2
and OL2(Leary, 2007).
5.1 TP Enhances AN
The path coefficient of TP is 0.88 (see table 2), which
rejects H6. The research results show that the sense
of TP has the greatest impact on AN. It means that
although the sense of TP may enhance learners'
attention on online learning tasks, learners' sense of
tension and even anxiety are increased. This may be
related to the program setting of the online foreign
language learning platform. The teaching
management and direct guidance of the existing
foreign language oral learning platform are mainly
based on the procedure automatic response. Without
considering personal learning characteristics, the
repeated imitation process may increase the anxiety
of learners. At the same time, this result also verifies
the research results of Grant et al., (2013) about
negative effects generated in the traditional foreign
language learning environment may affect learners'
online learning experiences.
CP and SP have no significant impact on AN and
IE. The real-time interaction between learners is still
low due to the technical reasons of the online foreign
language learning platform and different individuals’
learning schedules. According to the authors’
experience in Liulishuo and Duolingo (two foreign
language speaking learning Apps), there is no
recommendation of learning partners to help
individuals practice and reflect on what has been
learned online.
5.2 IL2, OL2, GSE, CP, TP, SP
Promote Each Other
The path coefficients data show that IL2, OL2, GSE
are highly positively correlated with CP, TP, SP
respectively, for which the correlation coefficient is
not less than 0.61 (see table 2). The results of this
study support the argument of Cohen and Norst
(1989) about the close connection between language
and self.
5.3 OL2 Increase AN, GSE Reduce
AN, Both of Them Increase IE
The path coefficients from OL2 and GSE to AN are
0.527 and -0.145 respectively (see table 2), which
implies that OL2 and GSE have a significant impact
on AN. This indicates that external environmental
pressure strongly affects learners’ AN.
CSEDU 2022 - 14th International Conference on Computer Supported Education
OL2 and GSE have a significant impact on IE, and
the path coefficients are 0.226 and 0.284 respectively
(see table 2). This result supports the view that
instrumental motivation can increase learning
behavior (Gardner & MacIntyre, 1991) and Bandura's
(1977) view that people with low self-efficacy are
more likely to give up.
5.4 Implications and Limitations
Based on the research results, here are some
suggestions for the design of foreign language
learning platforms.
Recommend learning partners to practice and
reflect on how to use what has been learned online in
real life. Although the platform collects a large
amount of corpus and provides communication
templates, flexibility is lacking when real practices
are faced by real people in their lives.
Enrich the display forms of TP. In terms of course
setting, the use of concept maps to show the internal
connections between learning content and individuals
preferences can enhance learners’ perception of the
structure of learning content, help learners clarify
their learning goals, and reduce additional cognitive
load (Hwang et al., 2011).
Strengthen learners' real-time recognition of their
anxiety, present different instructions according to the
degree of anxiety, guide learners to reduce their sense
of learning anxiety, and increase their attention to
learning content. If permits, wearable devices can be
used to identify the learner's heartbeat and other
physiological information, roughly estimate the
emotional intensity, and then propose different
reminders to calm the mood.
Guide learners to correctly deal with negative
evaluations. On the cognitive hand, learners need to
summarize the causes of errors and find ways to solve
them about specific tasks; on the meta-cognition hand,
learners need chances to compare their current and
previous learning results, and think about whether to
improve study plan further. This means that the online
platform should consider providing more learning
analytics data to support meta-cognition processing.
Some limitations affect the generalization of the
conclusions to a certain extent. First, there is no
comparative study between different learning
environments (e.g., online, face-to-face or blended)
and different technologies used (e.g., AR/VR, voice
chatbot). Then, this study also did not analyze the
specific functional applications of each learning
The descriptive statistics show that online foreign
language speaking learners are in middle anxiety,
high GSE and IE level. The structural equation
modeling shows that TP, OL2 increase AN while
GSE significantly reduces AN; GSE, OL2 increase IE
while AN reduces IE. The high correlation between
CP, TP, SP, IL2, OL2, and GSE proves that learners’
internal state and online learning environment is a
dynamic cyclic process. In this regard, personalized
or adaptive learning both from cognition and emotion
could improve the learning experience. Some
suggestions about human-computer interaction in
online learning platforms are recommended.
With the help of artificial intelligence and big
data, online education pays more attention to
individual learners. Smart tutors, smart teaching
assistants, etc. enrich the virtual learning
environment. Online learning at this stage and in the
future has higher requirements for human-computer
interaction and collaborative learning. But learning
still happens in people, and people have both reason
and emotional aspects. Therefore, online learning
with a strong interactive nature should pay more
attention to the emotional state of the learner, to
explore which emotions have a positive effect on the
learner of different features, and how to reduce the
emotions which hurt the learning experience.
Moreover, future studies are recommended to analyze
the design elements in an online learning environment
representing CP, TP, SP from a platform perspective.
Comparative studies of CP, TP, SP (representatives
and their relationships with learning performance and
evaluation) between different disciplines and
different difficulty levels are also good direction.
We would like to thank Pro. Raphael Zender
(Humboldt-Universität zu Berlin) for his feedback on
an earlier version of this paper and Zeyue Zhu (Tongji
University) for the collecting of the questionnaire.
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Motivational Self and Intended Effort-A Structural Equation Modeling Approach
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