The improvement path of learning motivation for mobile social network learners by analyses of SEM and fsQCA

Introduction

From multimedia technology and network technology to mobile learning technology and

cloud computing, online technology and cloud computing technology, online learning based on

the network environment is becoming an essential way of learning in the future. CNNIC (China

Internet Network Information Center) released “The 42nd stattistical report on the development

on Intenet development in China” in July 2018, which revealed that by June 2018 there have

been 802 million Internet users, and the numer of mobile Internet users has been 788 million,

accounting for 98.25% of the total number of Internet users (CNNIC, 2018). The broad base

characterizes the mobile terminal; thus the function of the mobile social network paltform is

increasingly perfect. In order to satisfy the demands of learning society construction and lifelong

learning, the mode of “social + education” has been gradually developed and become mature.

Collecting fragment time reasonably and efficiently to do socialized learning is a beneficial

choice for learners to quickly capture real-time information, enrich and improve knowledge

structure and improve themselves effectively. (Köse, 2016) believes that social media affects

(Turkish) students generally in a positive manner. Specifically, it not only helps students for

improving their study time and problem understanding/solving abilities but also enables them to

focus on reaching to the desired information and make it easier to reach to new information and

improve their knowledge. It indicated that while mobile social network platforms provide

learners with the novel learning experience, they also bring advantages that traditional teaching

methods cannot reach. However, Lin, Hoffman, and Borengasser (2013) analyze students who do

not use Twitter compulsively and finds that very few students could stick to the end of the

semester. At the same time, in the process of using Twitter, many students want to become the

recipients of knowledge rather than become the senders or comment on the information. In a

word, it has been a general trend to use mobile social network platform for learning, but learners'

learning status and learning motivation are not the same. So, it is crucial to identify the factors

affecting learners' learning motivation on mobile social network platform and propose the

improvement paths. In terms of reliability and validity, both the confirmatory factor analysis

(CFA) and fuzzy set qualitative comparative analysis (fsQCA) have provided a better perspective

on the data when work together with complexity theory (Leischnig & Kasper-Brauer, 2015;

Mikalef, Pateli, Batenburg, & Wetering, 2015; Ordanini, Parasuraman & Rubera, 2013; Pappas,

Kourouthanassis, Giannakos, & Chrissikopoulos, 2015; Woodside, 2014; Wu, Yeh, &

Woodside, 2014).

This research introduces the TC model and establishes the model of factors influencing

learners’ learning motivation, which helps to analyze the important factors affecting learners on

mobile social network platform, so as to explore the improvement paths of learners' learning

motivation and provide reference valuable suggestions for mobile social network platform to

improve and optimize the functions.

Literature Review

There were researchers have recognized that motivation is important for academic learning

(Pintrich, 2003). According to the self-determination theory (SDT) (Ryan & Deci, 2002),

motivation refers to the reasons to underlie behavior, as well as students, engage in school

activities (Ryan & Deci, 2000). The motivational control and the process of self-regulation

learning have led to positive results on academic performance (Schunk, 2005). There has been a

significant relationship between motivation and self-regulated learning (Mahmoodi, Kalantarib

& Ghaslanic, 2014). The learning motivation refers to the internal psychological process in

which individuals initiate, maintain, and regulate learning behaviors under the action of internal

or external goals. In essence, learning motivation is also an ecological multi-influencing factor

model, directly determining learners’ stimulation and maintenance of learning process and state,

and affecting the depth, breadth, and validity of learners’ learning. Many variations in motivation

have evaluated across different school subjects (Elliot, 2005; Green et al., 2007; Pintrich, 2003).

In the traditional education environment, when measuring students’ motivation, most researchers

will make some assumptions (Deci & Ryan, 1985): firstly, the learning motivation, mental

development and knowledge level of the students are the same, Secondly, due to the small

number of the students, the assessment method is relatively fixed and straightforward.

Cazan (2015) found that in the academic life, learning motivation was positively associated

with engagement and negatively associated with burnout dimensions (Schaufeli, 2002; Yang,

2004) through questionnaire survey and the statistical method of Pearson's correlation

coefficient. By using problem-solving or sequencing tasks can produce a different result (Willis,

1996). Wang (2019) indicated that the learner engagement in creative tasks is related to different

task constraints, which can enhance learners’ task outcomes (Haught-Tromp, 2017; Tin, 2012;

Tin, 2013). By carrying out a questionnaire and a semi-structured interview among Mainland

Chinese students in a Hong Kong University, Du and Jackson (2018) revealed that the

participants' sustained enhancement in motivation influenced by continuous interactions between

their perceptions of the changing learning and social contexts, their shifting ideal L2 selves, and

L2 self-concepts. On the learning environment of inquiry-based instruction, (Wang, Wu, Yu, &

Lin, 2015) revealed that after participating in the implementation of it, science learning

motivation and interest were both increased and significant variation observed regarding self-

efficacy and performance goals about learning motivation. With the advent of the web2.0 era,

MOOC (mass online open courses), and other online learning platforms have emerged one after

another. The functions of these online learning platforms are becoming increasingly perfect. To

begin with, open access, learning at a distance (online) and scale characterize MOOCs, which

makes it consisted with a large and diverse learner body who not only have a different

background but also wide-ranging motivations for enrolling in a course (Breslow et al., 2013).

On such learning platform, (Hood, Littlejohn, & Milligan, 2015) found that the statistical

differences in the scores of particular sub-processes for different groups of learners suggest that

both the other learning activities that participants engaged with as well as the connection

between their current roles may influence their learning behavior. What is more, in a digital

learning environment, there are five constructs of motivation, including escape, desire to learn,

self-development and academic progress. Simultaneously the motivation is significantly related

to satisfaction and it is a strong predictor of satisfaction (Shih, Chen, Chen, & Wey, 2013).

(Harandi, 2015) confirmed that e-learning is an element which affects students’ motivation.

(Yilmaz, 2017) found when undertaking academic tasks in the flipped classroom (FC) model of

instruction, the learner’s e-learning readiness was a significant predictor of the learning

motivation and learning satisfaction. Practically, in the study of (Kuo, Tseng, & Yang, 2019), a

STEM Interdisciplinary Project-based Learning (IPBL) approach was applied to teach a total

number of 45 college students registered in the departments of engineering and design. Empirical

analysis supported by data collected from 6-point Likert ‘Motivated Strategies for Learning

Questionnaire (MSLQ)’ indicates that such course improves the participants’ overall learning

motivation and self-efficacy of learning. Liu and Chu (2010) conducted a study which aimed to

investigate how ubiquitous games influence English learning achievement and motivation

through a context-aware ubiquitous learning environment. Various educational strategies,

including ubiquitous game-based learning, collaborative learning, and context-aware learning

help students to engage in learning activities based on the ARCS motivation theory (Keller,

1987; Keller & Suzuki, 2004). The motivation is an important precondition for students to

participate in the learning environment (Malone & Lepper, 1987), and plays a significant role for

students to use the mobile device in the learning activities (Vogel, Kennedy, and Kwok, 2009).

Ciampa (2014) found that tablet-based mobile learning can stimulate students' intrinsic and

extrinsic learning motivations in many aspects (Deci & Ryan, 1985). Any design for learning

with fun will be of great significance in intrinsic motivation (Lepper & Cordova, 1992).

Gamification in education has become popular for learners’ motivation since 2010. Similarly, Su

(2015) confirmed that the mobile game learning system based on smartphone enhances the

motivation of students to learn scientific knowledge.

With the emergence of social media, the mobile social network platform has become the

carrier of education, injecting "social" elements into online learning platform. The convenience

of operation, the high efficiency of interpersonal communication, the richness of content and the

accuracy of messages have become important factors for the promotion of WeChat and other

mobile social network learning platforms. Some scholars studied the development and

application of mobile social network online learning platform. Some researchers regard wikis

and forums as tools for online collaborative learning or instructional support for learners’

collaborative project-based learning (Biasutti, 2017; Chu et al., 2017; Koh, Lim, Koh, & Lim,

2011). Furthermore, Twitter is also incorporated into the college classroom and used as a site of

connection (Jacquemin, Smelser, & Bernot, 2014). Some relevant study shows that ease of social

media user was positively related to student motivations to use Twitter while willingness to self-

disclose online was negatively linked with classroom Twitter use (Denker, Manning, Heuett, &

Summers, 2018).

However, there are few studies on learners' learning motivation in the context of mobile

social network platforms. Moreover, with the continuous promotion of such platforms, the lack

of experiential learning and excessive fragmentation of time may affect learners' learning

motivation. Fragmented learning is the main learning mode of learners on the mobile social

network learning platform. This "fragmented" interactive communication form and learning

mode, on the one hand, promotes the communication and integration between subjects while

optimizing the internal and external network of learners. On the other hand, it emphasizes the

exertion of learners' subjectivity, which is conducive to the development of innovative thinking

and self-value.

To sum up, under the background of traditional education and online learning platform,

scholars' researches on learning motivation mainly focuses on exploring the factors affecting it.

In the context of the mobile social network, the researches on learning motivation are mostly at

the design and application of learning platforms, while there are few types of research on the

factors influencing users' learning motivation. Besides, most researchers adopt statistical

methods such as structural equation model to explore the influencing factors. Such methods base

entirely on the results of quantitative data to make choices, which is too data-driven, ignoring the

theoretical interpretation space of antecedent variables. As a result, this research strictly focuses

on the theme named ‘learners learning motivation based on the mobile social networking

platform.’ By introducing the TC model, the influencing factor model of learners' learning

motivation on mobile social network platform is set up. Combining SEM and fsQCA to explore

the influence factors of the impact of various combinations of learners' motivation, thus

effectively exerting the potential value of mobile social network learning platform in the field of

education.

TC model connotation and theoretical analysis

Wlodkowski (1999) proposed the TC model (The Times Continum Model of Motivation) after

comprehensive consideration of various motivation theories (Fig.1). Due to the continuity and

dynamics of learners’ learning behavior, the learning process is divided into an initial stage,

middle stage and late stage. In each stage, the learners’ learning stage and learning process is

different from each other, which leads to the inconsistency of the inflencing factors of their

learning motivation.

Fig.1. TC motivation model diagram

(1) In the initial stage of learning, the learning motivation is in a state to stimulate, that is,

the learner is just getting started with the learning platform and has not started formal learning.

At this time, learners’ learning attitude and their attitude towards the platform have strong

plasticity. Meanwhile, learners' learning needs are vague and wait clarified with appropriate

guidance. Therefore, in this stage, the following two elements are included.

Attitude: Attitude is a tendency and belief formed by the combination of learners'

understanding of the environment, their expectation of learning and existing experience in

learning similar materials. It includes four aspects: the learning content, the teachers, the learning

environment and themselves. In the initial running-in period with the platform, learners have an

unstable attitude towards the content, users, teachers of the platform and themselves. Through

preliminary guidance, learners can form a positive attitude towards using and learning, which is

helpful to enhance learners' learning motivation.

Need: Need refers to the internal motivation of learners to strive for a particular learning

goal. Clear learning demands are the premise of efficient learning. This research divides learners'

needs into learning need and social need. Before learning, the platform should perceive learners'

learning need and develop personalized learning strategies. As to learners who have not formed

specific learning need, the platform should guide and supervise them. Besides, to meet the

learning need, considering that learners are in the mobile social network environment, the

platform should pay attention to the social need of learners, such as the influence on learners

exerted by friends, family and knowledge imparters. Fully understanding and meeting learners'

social need is helpful to enhance learners' confidence and motivation.

(2) In the middle stage of learning, to maintain the learning motivation. Learners have an

in-depth understanding and experience of the platform, and the freshness of the initial stage

gradually lost. Therefore, in this stage, the following two elements are included.

Stimulation: The stimulation will use a variety of means to change students' feelings and

experience of the environment and learning. This research believes that with an in-depth

understanding of the platform, learners will naturally develop inertia and burnout. Timely

changes and innovation can help stimulate learners from the perspectives of perception and

cognition, improve their participation experience, arouse their curiosity about the platform and

indirectly affect their learning motivation.

Emotion: Emotion is an essential factor that makes learners feel that learning is closely

related to them. In the context of the mobile social network, learners' learning behavior is not

only affected by the platform, but also the role of interpersonal relationship. Positive mobile

network community, united collective atmosphere, and harmonious interpersonal relationship

have a positive impact on learners' learning motivation.

(3) In the late stage of learning, it is the end of a learning cycle for learners and the

beginning of whether to proceed to the next learning stage. Learners need to have an intuitive

understanding of learning results. Timely feedback mechanism and flexible incentive mechanism

can promote learners' willingness to continue using the platform. Therefore, in this stage, the

following two elements are included.

Ability: Ability is the learner's inherent desire for a sense of control and effectiveness in the

environment. This research believes that after completing a stage of learning, learners will have a

clear and systematic understanding of the whole process of learning through reviewing the

learning records, daily tests and final assessment results provided by the platform. The positive

effect of knowledge on learners reflected in the improvement of learners' ability, the gap between

learners' status and expected benefits thus narrowed. Also, learners' sense of control and efficacy

is satisfied to a certain extent.

Reinforcement: Reinforcement is an event that can maintain or improve the likelihood that

learners will continue to succeed. A complete stage of learning not only brings visible ability

improvement to learners but also increases the value of explicit knowledge acquired by learners.

Through introspection, learners will analyze the shortcomings, identify the factors that positively

affect learning outcomes, and make attributions timely. The cultivation of good learning habits,

high self-discipline awareness moreover, perfect professional literacy enables learners to enter

the next learning cycle with better learning and mental state, contributing to success more easily.

Hypotheses

From a full understanding of the TC model, this research believes that in three different stages

where learners learn by using mobile social network platforms, the influence of the platform on

learners will have a similar perception on learners and ultimately affect their learning motivation.

Therefore, based on the learning cycle of learners, this research introduces the TC model, then

analyzes the learning motivation of learners on mobile social network platform from the

perspectives of attitude, need, stimulation, emotion, ability, and reinforcement. The influencing

factor model of learners’ learning motivation on mobile social network platform is established

(see fig.2), and the following hypotheses proposed:

Fig. 2. Model diagram of factors influencing learners’ learning motivation social network platform

H1: Attitude will have a positive impact on learners' learning motivation.

Learners’ attitude mainly refers to their attitude toward platform content, platform users,

teachers and themselves. A positive attitude is a premise for learners’ motivation to be

stimulated.

H2: The need will have a positive impact on learners' learning motivation.

Learners' learning needs mainly refer to basic learning needs and social needs especially in

the context of mobile social network platforms. The satisfaction of learners' dual needs can

effectively stimulate learners' learning motivation.

H3: Stimulation has a positive impact on learners' learning motivation.

Learners’ learning stimulation refers to the stimulation of learners by mobile social

platforms, which mainly divided into perceptual stimulation and cognitive stimulation. When

learners' senses and cognition stimulated, their learning motivation maintained.

H4: Emotion will have a positive impact on learners' learning motivation.

The emotional connection between learners and their friends, teachers and family members

will promote their learning enthusiasm and maintain their learning motivation.

H5: The ability has a positive impact on learners' learning motivation.

Learners' ability mainly embodied in two aspects: control and efficacy. The skillful use of

the platform and the benefits brought to learners by the knowledge will make learners feel self-

satisfied. Their learning motivation will be further improved.

H6: Reinforcement has a positive impact on learners' learning motivation.

Learner reinforcement refers to the cultivation of learners' attribution ability, introspection

ability, and practical ability. They reflect the subtle effect of knowledge on learners and

promotes their learning motivation. According to the above six hypotheses, this paper proposes

corresponding measurement items (see table 1).

Table 1 variables and measurement items affecting learners' learning motivation on mobile

social network platforms.First-grade

indexesSecond-grade

indexesProblem

Attitude

Platform contentIf the platform can clearly display the detailed information of the courses, I will be more willing to use the platform to learn.

Platform usersIf the platform carries out strict review on learners' registration information, I prefer to use it for learning.

TeachersIf the platform regularly updates teachers' personal information, I will be more willing to use the platform to learn.

Learners themselves

If I use this platform voluntarily, my motivation to learn will last longer.

NeedLearning needs

If the platform can master my knowledge needs before I start formal learning and develop personalized learning content for me, I will be more willing to use the platform for learning.

Social needsIf the platform encourages me to share my learning content with others, I will be more willing to use the platform for learning.

Stimulation Perceptual If the platform presents rich and diverse learning contents, I will feel more

stimulation motivated to learn.

Cognitive stimulation

If the platform constantly updates the learner interface, my learning interest will be enhanced and my learning motivation will be stronger.If the platform regularly launches some creative activities, such as knowledge competition and topic discussion to promote me to think and learn in depth, my learning motivation will be stronger.

Emotion

FriendsWith my recommendation, my friends also begin to use this platform for learning, and I will prefer to use this platform for learning.

TeachersWhen I share my learning progress with my teacher, his objective and fair evaluation and improvement suggestions will encourage me to continue my study on this platform.

FamiliesWhen I share my learning results with my family, their recognition and encouragement will encourage me to continue my study on this platform.

Ability

Environment control force

I am familiar with the various functions the platform provides me, so I prefer to use the platform for a long time.

Self-efficacy

If I am among the top students in the platform, I will be more willing to use the platform.The learning records and assessment records provided by the platform enable me to intuitively see my learning progress. This effectively maintains my learning motivation and makes me more willing to use them.

Reinforcement

Attribution abilityThrough the writing of learning summary, I will have a deep self-reflection on online learning. I will carry out better learning on the platform.

Self-reflection ability

Based on the comments of teachers on the platform, I will recognize my advantages and disadvantages and make attributions for my learning achievements, so as to carry out better learning on the platform.

Practical abilityI will apply the knowledge taught by the platform to daily study and life to solve daily problems.

Data Analysis

The implementation of research

Based on the six hypotheses above, this research applied a five-level Likert Scale to design a

questionnaire according to the measurement items. In order to more effectively and accurately

analyze the learning motivation of learners on mobile social network platforms, the choose of

respondents should adhere to two principles. First, the surveyed learners have no restrictions on

age and identity; secondly, all the surveyed learners have used mobile social network platforms

for learning and communication. In this research, an online survey is applied to attribute and

collect the questionnaires. Total 301 questionnaires finally collected, and 260 questionnaires

retained after removed 41 invalid ones. The effective rate of questionnaires is 84.6%.

In this questionnaire, from the perspective of gender distribution, there are 142 men,

accounting for 54.6%, and 118 were women, accounting for 45.4%. From the perspective of

identity, 161 students are learners, accounting for 61.9%; 99 non-students are learners,

accounting for 38.1%.

Structural equation modeling (SEM)

Reliability and validity test

In terms of the reliability test, Cronbach's value was used to measure the reliability of the whole

questionnaire. The overall reliability of the questionnaire was 0.929 (see table 2), indicating that

the reliability of the data was relatively high and the results of the questionnaire were relatively

real and practical. In terms of validity test, firstly, exploratory factor analysis shows that KMO

value is 0.937 (KMO value >0.9) and Bartlett spherical test approximate chi-square value is

2295.249 (p < 0.001), indicating that the results of the questionnaire were suitable for factor

analysis. Secondly, the confirmatory factor analysis is adopted to improve the convergent

validity analysis. The factor classification result is consistent with the index system, and the

cumulated variance explained about 71.695% volume of the latent variables. The load factors

were more significant than 0.5, and the combination of reliability is greater than 0.6 (see table 2).

These illustrate that the convergent validity of the questionnaire is good, and meet the

requirements of analysis.

Table 2 Test results of reliability and validity of the measurement modelLatent variables Title numbers Cronbach’s α Composite reliability

Attitude 4 0.854 0.8108Need 2 0.624 0.6709

Stimulation 3 0.750 0.7064Emotion 3 0.799 0.7297Ability 3 0.729 0.6859

Reinforcement 3 0.742 0.6719

The model fitting

AMOS was used to verify and analyze the model. Through the calculation of the model and

multiple correction analysis, the final model output results shown in Fig. 3. Table 3 showed some

significant adaption index, where the chi-square degree of freedom ratio of the model is 1.567<

3.000, RMSEA (root mean square error of approximation) value is 0.047<0.08, TLI (Tucker-

Lewis index) value is 0.961>0.90, NFI value is 0.926 greater than 0.90, IFI (incremental fit

index) value is 0.972>0.90, and CFI (comparative fit index) value is 0.971>0.90. All of these

indexes meet the acceptable standards of the model. Therefore, according to the statistical test

analysis, the theoretical causal model diagram can be matched with the actual data.

Fig. 3. Modified model output diagram

Table 3. Structural model analysis of the overall model suitability test results.Statistical test A criterion or threshold for adaptation Results of model 1 test

Chi-squared degrees of freedom <3 1.567Value of RESEA <0.08（the smaller, the better） 0.047

Value of TLI >0.90以上 0.961

Value of NFI >0.90以上 0.926

Value of IFI >0.90以上 0.972

Value of CFI >0.90以上 0.971

Value of CAICTheoretical model value is less than independent

model value and saturated model value608.683<1246.530608.683<2804.095

The results show (see table 4) that in the six paths from H1 to H6, H3 is not significant

under the condition of P<0.05, while the other five hypotheses are all significant.

Table 4. The test results of path coefficient between variables were studied.Learning motivation Estimate S.E. C.R. P Significance

H1 Attitude 0.595 0.142 4.190 *** significantH2 Need 0.411 0.131 3.138 0.002 significant

H3 Stimulation -.207 0.188 -1.102 0.270 Non-significantH4 Emotion 0.497 0.168 2.949 0.003 significantH5 Ability 0.493 0.217 2.275 0.023 significantH6 Reinforcement -0.668 0.277 -2.409 0.016 significant

Model conclusion analysis

Test results of the research hypothesis can be obtained based on table 4 (see table 5). It indicated

that attitude, need, emotion and ability are the positive influencing factors of learners' learning

motivation on the mobile social network platform. Stimulation does not affect on learners'

learning motivation. Reinforcement has shown a negative effect on learners' learning motivation.

When learners hold a positive attitude towards the platform and its related objects, learning

needs of learners are identified and met, the emotional link between learners and teachers, family

and friends, the learner's abilities emerge after learning, learners' learning motivation inspired

and sublimated. At the same time, the stimulation of the platform on learners does not affect on

learners' learning motivation, that is, when learners' learning motivation has formed, the platform

cannot affect learners' learning motivation by changing the interface or launching creative

activities.

However, based on table 4, it can also be seen that reinforcement does not have a positive

impact on learners' learning motivation, but has a negative impact. Because in the mobile social

networking platform, learners voluntarily undertake fragmented learning. In this situation,

learning content is discrete and learning time and place is not continuous, not fixed. Therefore, if

the best strategies in the traditional teaching mode are fully replicated, such as supervise and

urge learners to write comments in a fixed learning stage or let teachers summarize and evaluate

learners, learners may lose their learning autonomy and independence due to the feeling of

supervision. Under such emotion, the reinforcement measures in a traditional way have negative

effects on learners' psychology and learning motivation. Based on this, this research believes that

the reinforcement factors have a negative impact on learners, so hypothesis H6 is invalid (see

table 5). However, in the following combinatorial study, reinforcement is indispensable as a

negative influence factor, so the factor is represented by reinforcement (-).

Table 5 Results of hypothesis test

Hypothesis Description Whether to accept

H1 Attitude has a positive impact on learners' learning motivation. AcceptH2 Need has a positive impact on learners' learning motivation. AcceptH3 Stimulation has a positive impact on learners' learning motivation. RejectH4 Emotion has a positive impact on learners' learning motivation. AcceptH5 Ability has a positive impact on learners' learning motivation. AcceptH6 Reinforcement has a positive impact on learners' learning

motivation.Reject

Fuzzy-set qualitative comparative analysis (fsQCA)

Variable selection and calibration

From the above research conclusions, it indicated that attitude, need, emotion, ability, and

reinforcement (-) are important factors affecting learners' learning motivation. Therefore, this

research selects these five variables as the antecedent conditions to analyze the factors improving

learners' learning motivation. In this research, a result of the questionnaire regarded as a case.

Before using fsQCA software to analyze, first of all, five continuous variables including

emotion, attitude, need, ability, and reinforcement (-) should take the average. According to the

three qualitative breakpoints that structure a fuzzy set: the threshold for full membership, the

threshold for full non-membership and the cross-over point, the research calibrates the data from

five-level Likert Scale with the threshold of 5, 1 and 3. Fuzzy membership scores obtained. Then

the necessity analysis is carried out. If a specific condition is a necessary condition for forming

the result, that is, when the result appears, the condition must exist, this condition will be ignored

in the subsequent standard analysis. This condition has an influence on the result, so the

necessity analysis is of great significance for obtaining the correct result. Generally, when the

consistency score is above 0.9, the variable is considered a necessary condition for the result.

According to the analysis results, the consistency of all factors is lower than 0.9, so there is no

necessary condition, and the next analysis can be carried out without eliminating any condition.

Conditional combination analysis

Conditional combination analysis is to measure the influence of different combinations of

dependent variables on the results when a single conditional variable does not constitute a

necessary condition. In the analysis module, the consistency threshold value set as 0.8 (Ragin,

2006). At the same time, in order to ensure that the number of cases analyzed constitutes the

majority of the original cases, the frequency threshold is set as 1, where any combination of

dependent variables analyzed with at least one sample.

Results

As can be seen from table 6, the overall coverage rate of this analysis is 0.933, and the overall

consistency is 0.734. It indicates that these combination configurations of antecedent conditions

have a relatively high degree of interpretation to the object of the research. The combination with

the same antecedent conditional configurations are merged and finally summarized into four

learning motivation trigger paths (s1-s4) to improve the learning motivation of mobile social

network platform learners.

Table 6. Learning motivation and antecedent conditional configurations.

GroupLearning motivation

S1 S2（a） S2（b） S3（a） S3（b） S4

Attitude ⊕ ● ● ●Need ⊕ ● ● ⊕ ⊕ ●

Emotion ⊕ ⊕ ●Ability ● ⊕ ⊕ ⊕

Reinforcement (-) ● ● ⊕ ●Consistency 0.787 0.888 0.941 0.813 0.874 0.953

Raw coverage 0.737 0.673 0.673 0.691 0.588 0.547Unique coverage 0.057 0.008 0.084 0.019 0.004 0.000

Solution consistency 0.734Solution coverage 0.933

Notes: ● represents the presence of a causal condition. ⊕ represents the absence or negation of

casual condition. Blank cells represent irrelevant conditions.

(1) The improvement path of learning motivation aimed at learners with a definite learning

purpose

Path one: “ability=•~Attitude•~ Need.” This path is mainly suitable for learners with a

definite learning purpose. This path has the highest coverage (0.737), so it can be used as the

core path to improve learners' learning motivation. In this configuration, the platform can focus

on the elements that enable learners to feel the improvement of their abilities. Part of the benefits

brought by knowledge to learners is the apparent value of knowledge. When learners perceive

that knowledge brings real benefits, they will be more willing to choose this platform for

learning. Therefore, the platform can set up learners' learning files. In the learning cycle, the

platform can regularly conduct knowledge assessment for learners and continuously feedback the

assessment results to learners, so that learners can have an intuitive understanding of their

learning process and results. Under such conditions, the platform does not need to pay much

attention to whether the learners have a positive attitude or have a stable emotional connection

with the platform.

(2) The improvement path of learning motivation aimed at learners who focus on the

learning experience

Path two: “Attitude •Need• ~Ability” and “Attitude• Need• Reinforcement (-).” Path two is

mainly suitable for learners who pay attention to the learning experience, and the coverage of

two subpaths is the same. In this configuration, the platform needs to take measures to improve

learners' attitude towards the platform and fully identify learners' needs. A positive attitude and

satisfaction of needs can effectively improve learners' learning motivation. In subpath S2 (a), if

the platform can impress learners using improving and publicizing teacher information,

displaying course information in detail and meeting learners' learning needs, it is not necessary to

pay attention to whether learners' ability is improved. In subpaths S2 (b), if the platform will also

be able to focus on the influence of reinforcement on learners, the learners' learning motivation

will be enhanced, and learning experience will be optimized. To be specific, the platform has a

good command of learners’ need in advance through the questionnaire, assessment, and

interview, accordingly provide personalized services in a fragmentation learning environment. It

is also essential to put forward reinforcement strategy different from the traditional mode, such

as the daily clock in service, record the total time of learning and so on.

(3) The improvement path of learning motivation aimed at learners who learn actively

Path three: “~Need• ~Emotion• ~Ability• Reinforcement (-)” and “~Need• ~Emotion•

Attitude• ~Reinforcement (-).” This path is mainly suitable for active learners. The subpath

coverage of the former is 0.691, higher than that of the latter (0.589). In this configuration, as

learners are more active in learning and have a particular understanding of their own needs, they

will also actively establish an emotional connection with the platform, so the platform does not

need to focus strictly on the needs of learners and the emotional relationship between learners

and the platform. In subpath S3(b), the platform can leave an excellent impression on learners in

the early stage of learning, such as clearly showing the learning objectives, the syllabus, the

difficulty of the course, it is still attractive to learners, and learners' stickiness to the platform will

be maintained even with the existence of means of reinforcement under the traditional model. In

subpath S3(a), if the platform did not use the appropriate measures to allow learners to feel the

ascension of ability, then the platform at later learning stage have to use reasonable and proper

rules to enable the learners to explore the value brought by the knowledge subtly, such as the

rigor of thinking, the enhancement of attribution skills, formation of the self-reflection habit etc.

In this way, even if learners do not intuitively feel the improvement of their ability, they will still

use this platform for learning.

(4) The improvement path of learning motivation aimed at learners who learn passively

Path 4: “Need• Emotion• ~Ability• Reinforcement (-).” This path is mainly suitable for

passive learners who are greatly influenced by external learners. In this configuration, the

platform needs to focus on learners' learning needs and social needs and make personalized

recommendation services for the needs to let learners feel valued. In addition, in the process of

learners using the platform for learning, teachers should communicate and interact with learners,

actively respond to questions raised by learners and focus on learners' feelings towards the

platform. In the later stage of learning, even if the learner has completed the learning completely,

if inappropriate reinforcement measures used, the learner may have an aversion to this, thus

reducing the learner's learning motivation. Therefore, in every stage of learning, the platform

needs to pay attention to the learning state of learners. The absence of any link will lead to the

extinction of learning motivation.

Conclusion and propspect

This research applies the TC model and then use the structural equation model to explore

whether six factors: attitudes, needs, stimulating, emotion, ability and reinforcement can

influence learning motivation in mobile social networks or not. From the empirical analysis, the

method of qualitative comparative yields configurations that can improve learners' learning

motivation. The conclusion is in the following.

From a methodological perspective, this research demonstrates the usefulness of

combination SEM and fsQCA techniques. SEM enable investigations to observe the strength of

relationships between variables in the model, whereas fsQCA yields configurations between

variables that lead to a particular outcome. As a result, these two techniques should approach as

complementary.

From a theoretical perspective, the empirical analysis shows that when learners' attitude is

positive, different needs of learners are identified and satisfied, emotions between learners and

the platform are established and maintained, and learners' ability is improved, learners' learning

motivation will be stimulated, maintained and improved finally. Simultaneously external

stimulation has little effect on learners. However, if the reinforcement strategy in traditional

teaching mode replicated without any change, learners' motivation will be reduced. Qualitative

comparison study shows that four main paths can promote learners' learning motivation. The

paths respectively aim at four different learners: learners with strong learning purpose, learners

paying attention to the learning experience, learners learning actively, learners learning

passively. If the platform can respectively treat those four types of learners with a differentiation

strategy, learners' learning motivation can efficiently be improved promptly.

In general, the research still has some limitations. Due to the subjectivity and one-sidedness

of the data collected in the questionnaire, future studies should try to obtain relevant "primary

data" and combine objective data with subjective data to ensure the authenticity and reliability of

the data. Besides, we can also explore more factors that may affect learners' learning motivation

through in-depth interviews and other methods, and then expand the influencing factor model to

improve the explanatory power of the model.

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