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\begin{document}

\title{The Relationship between Web-based Learning Tools, Self-Efficacy,
Depression-Anxiety-Stress on Family Life Quality under the Circumstance
of COVID-19}



\author[1]{shan chen}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Based on the pandemic of COVID-19 in 2020, to avoid crowds and mass
infection, web-based learning takes the place of traditional class
teaching to become the main learning method for adults, teachers, and
students, the study aims to check the relationship between web-based
learning tools, self-efficacy, depression-anxiety-stress, family life
quality, and general health in Chinese sample. Through a questionnaire
and measurement scale to collect data in June to July 2020, adopted SPSS
25.0 and Mplus 8.3 to test the reliability and validity, structural
equation modeling (SEM), multiple regression model (MRM), and other
methods are used for statistical analysis and conclusions.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Novel Coronavirus Disease 2019 (COVID-19 or 2019-nCoV) has caused over
one thousand million infections across the world and more than 500
thousand life deaths, which damaging the global economic system and
troubled burdensome health care system. It also affects the education
system. According to World Health Organization (WHO) recognition, three
pathema pandemic outbreak in 2019, which are SARS-CoV, MERS-CoV, and
COVID-19 (ICTV-CSG, 2020), but COVID-19 was a little different from the
first and two, it has highly infectious and readily transmitted through
the air and face to face communication in the same space (Jiang, Hillyer
\&Du, 2020). SARS-CoV was first discovered in Guangdong Province (2002)
with probably 10\% (774/8098) mortality rate (Du, 2009). Similarly,
MERS-CoV was becoming popular with Saudi Arabia (2012) with 34.4\%
(858/2494) mortality rated through 27 countries (WHO), while COVID-19
with mortality rated 4.4\% (570248/12957684) (WHO). Even COVID-19 with
extremely low mortality rate, but with a tremendous amount of infection.

On December 8, 2019, the novel coronavirus pneumonia pandemic (COVID-19,
hereinafter referred to as the pandemic) broke out with few confirmed
cases of a small scale in Wuhan, Hubei Province, and the situation was
increasing. Until January 20, 2020, this phenomenon exploded, it also
attracted widespread attention to governments and society. As the origin
and initial place of the pandemic, Wuhan has been banned from January 23
to April 8. All public transportation and gathering places are strictly
controlled on the movement towards people and risks in different
regions. With differentiated management, more than 10 million permanent
residents in Wuhan began a 76-day ban and stay at the home of banned
outdoor activities, and the defensive battle against the novel
coronavirus pneumonia pandemic was started on January 23, 2020
(www.huaxia.com). According to the People's daily newspaper (2020),
Wuhan was underdone the CDIV-19 from 27th December to 19th January with
a rapid expansion and rapid response to take actions. Various measures
have been taken to respond and overcome this catastrophe. Meanwhile, it
attacks all aspects of life, work, every normal order, and operating.
Not only does the center of disease control and prevention admit the
avoidance of crowd and throng (Chich, Clifton \& Peace et al., 2020),
but also world health organization suggested little large gatherings.
For education, the government announced that all school has to be shut
down to protect our student's safety and health, and proposed to attend
online classes.

The government (2020) reported that all kinds of schools and public
places will be close down through this spring semester (2020.3-2020.7),
high school and middle school can coordinate the situation under the
prevention and control of COVID-19 pandemic. Due to the COVID-19
pandemic, students cannot have traditional instruction, students and
teachers must have to structure the online course to prevent the
interpersonal infection of COVID-19. Under the COVID-19 pandemic
circumstances, it will be a very unusual living experience in need of a
special learning approach------web-based learning. A great disaster
often leads to economic turmoil and social chaos, the pandemic
transformed the way that we used to organize our working and learning,
it also changed the attitude when facing the disaster. As a consequence,
the widespread use of web-based learning tools. To examine the
contributory factor of the effectiveness of web-based learning, this
research was designed to inspect the relationship between web-based
learning tools, self-efficacy, depression-anxiety-stress, general health
condition, and family life quality to figure out whether the pandemic
may affect the quality of learning and life or not.

\textbf{Literature review}

\textbf{Development of Web-based learning}

Tracing back to the history of web-learning, it turns out to e-learning
and computer-aided education. E-learning was emerged from the 1960s to
learn knowledge on the internet or through the computer in higher
education, computer-based learning and training were used during
1975-1990, then web-based training or e-learning was begun to develop in
the 1990s with the tremendous development of internet technology and
computer (Kidd, 2010, In IRMA eds.). Web-based learning was relying on
website and computer when it connects to the internet to enhance
learning efficiency and bring learning convenience (Chumley-Jones,
Dobbie, \& Alford, 2002). Thus, an individual can use a mobile device to
learn anything anywhere without a computer or laptop. Although the
convenience it brings, few study results witnessed the learning
efficiency of Web-based learning to surpass traditional instruction
(Chumley-Jones, Dobbie, \& Alford, 2002). Based on constructivism
learning theory, web-based learning embrace a social environment to
bridge the interaction between teacher and student (Woo \& Reeves,
2007). This design has been alleviating the problem of being absent from
school and lack of face to face interaction and communication. For
example, The absence of being in the same learning environment for
teachers and students, the efficiency of Web-based learning is inferior
to traditional classes as it lacked direct effects on teachers and
students (Sher, 2008). However, in other cases, the benefits of
Web-based learning has been found to support its usefulness and
importance. A positive relationship between learning technology and
student engagement and learning outcomes have been found (Chen, Lambert,
\& Guidry, 2010). Although the benefits have been found, Web-based
learning cannot cover its shortage to take the place of traditional
instruction. Despite its weakness, it was still a compromising teaching
technique when students under the special circumstance of COVID-19.
During the period of COVID-19, students and teachers could not go to
school for instruction, they have to use Web-based learning tools to
study at the home of learning new tasks and accomplish the teaching
tasks.

With a lot of strength, web-based education improved students' autonomy,
informal and official learning through telecommunication, hypermedia,
and the availability of information and internet resources, which
recognized as personalized learning style to individuals (Magoulas \&
Chen, 2006). It also satisfied the students' information requests
(Lawless \& Brown, 1997) and is a benefit of boost cognitive ability
(Triantafillou, Pomportsis, \& Demetriadis, 2003). Web-based teaching
design using web tools to offer web-based learning and provide various
courses to meet the needs of learners (Liu, Glowatz, Zappatore, et al.,
2018). All of this makes the wide application of web-learning tools. As
an important factor, technology can be critical to web-based learning
for different kinds of learners. And other significant factors also are
found in studies. For example, gender differences have been found in
web-based learning for their special preferences, demands, and
assistance (Large et al., 2002; Roy \& Chi, 2003; Fan \& Macredie,
2006). For learner's communication, based on the web environment, when
people gather together in the online platform to learn knowledge, the
virtual community was created and managed to achieve this purpose (Elia,
Secundo \& Taurino, 2010, In Karacapilidis eds.). As an approach to
acquire digital competencies for students and teachers, web-based
learning provided interconnected social-cultural settings to take online
courses and make communication, but the management of this learning
process and elaborate courses design were definitely still needed
(Cartelli, 2010, In Karacapilidis eds.). When it comes to academic
achievements, its function was not that significant. According to
Chilton`s and Gurung's mixed study (Chilton \& Gurung, 2010, In
Karacapilidis eds.), Web-based learning technology may have little
effects on learning achievements objectively. But there are some studies
got a similar conclusion of the effectiveness of web-based learning and
the traditional class was insignificant (Allen \& Seaman, 2004; Allen \&
Seaman, 2006; Knight, 2007; Fillion, Limayem, Laferriere \& Mantha,
2010, In Karacapilidis eds.), in other cases, it has been proved that
the traditional class was outperformed than using web-based learning
technologies (Carnevale, 2002; Fillion, Limayem, Laferriere \& Mantha,
2010, In Karacapilidis eds.). Ke, Jewett, \& Ch\selectlanguage{ngerman}ávez (2010) found that
Web-based learning was imbued with cultural ideology, individual
character, and instruction atmosphere also is paid great attention
(Chávez \& Ke, 2013), diversity culture also is an impact factor
(Bentley, Tinney, \& Chia, 2005). Learning in a Web-based instruction
environment, researchers cannot ignore multiculturalism (Parrish \&
Linder-VanBerschot, 2010). Furthermore, several researchers found that
age has an impact on the achievements of Web-based learning in positive
research (Justice \& Dorman, 2001; Chyung, 2007; Hoskins \& van Hooff,
2005; Shnikareva \& Benson, 2007). Adults could transform the study time
flexibility and satisfy their promotion needs at the same time (Cercone,
2008; Chyung, 2007), thus to develop their higher-order logistical
thinking (Hoskins \& van Hooff, 2005; Shnikareva \& Benson, 2007). An
individuals' biological and psychological features were determined by
the environment and personal disposition, they need customized and
elaborated design with web-based learning content (Cercone, 2008).
Therefore, teaching approaches, feature to design, course contents,
social interaction, communication, gender, learning style, and
disposition can be important for the quality of web-learning, especially
for the special taste and experience in learners. Web-based learning
quality is subjected to individual psychology, social-psychological
factors, and web-based learning technology. Chapnick (2000) supposed
that an individuals' psychological quality, sociological context,
environment and resources, facilities and instruments, knowledge, and
skills are constructed as a measurement model to assess the web-based
learning community. A similar model was to reference this theory and
built a model to measure web-based learning willingness (Kaur \& Abas,
2004). So (2005) also conducted this kind of research to investigate the
willingness of web-based learning, he found that insufficient
preparation in teachers and significant gender difference. Su (2005)
examined a significant association between learning styles and academic
achievement in web-based learning.

Thus, there are a lot of existing researches that focus on the
influencing factors of web-based learning. Due to the lack of studies
focused on the examination of the impact on the family life quality and
web-based learning tools, self-efficacy, depression-anxiety-stress under
the condition of the outbreak of a pandemic. As learners were all
blocked in residence with rarely going out and gathering, the situation
of their web-based learning, the relationship between family life
quality, general health questionnaire and web-based learning tools,
self-efficacy, depression-anxiety-stress should be examined.

\textbf{Application of Web-based learning tools}

In the era of information technology in the 21st century, the rapid
development of information technology promotes the renewal of the
education and learning approach. As a new learning technology, web-based
learning is supplementing or replacing the position of traditional class
teaching now. Correspondingly, web-based learning tools gain a rapid
speed of innovation and application. As people often called the word
``Web'' stems from the Internet and World Wide Web, contains substantial
information and data set, and web-based learning are often synonymous
with e-learning, distance-learning or web-based learning
(Davidson-Shivers, Rasmussen, \& Lowenthal, 2018). Not only with the
convenience of autonomy, interactivity, customize and a lot of
strengths, but also with the help of transforming concepts and methods
of learning, web-based learning tools were available for learners with
high efficiency and low cost. Associating web-based learning tools of
the pandemic, web-based learning tools had been widely popularized and
used during the period of the pandemic. In response to the special
requirements for evacuation under the pandemic circumstance, students in
various universities, elementary and middle schools are basically not
able to attend school, and they will inevitably participate in and
complete web-based learning according to requirements and arrangements.
Therefore, web-based learning tools had become necessary learning tools.
However, under the circumstances of the pandemic, web-based learning
tools, state of health, emotional stress, self-efficacy, quality of life
may interact with each other and affect student's learning achievements.
For this reason and examine the effects, the study will explore the
relationship between web-based learning tools, state of health,
emotional stress, self-efficacy, quality of life, and its impact on
students' web-based learning status during the pandemic.

Web-based learning tools (WBLTs) was defined as an artificial learning
instrument, which can be connected to computer applications, mobile
device, internet network, or website (Kay, 2011). Under the assistance
of Web-based learning tools, students achieved better academic
performance than traditional instruction in Medical education (Ochoa \&
Wludyka, 2008). There is a wide range of WBLTs in the Chinese education
system now, such as Alibaba Dingding (www.dingtalk.com), Tencent meeting
(cloud.tencent.com), etc. Rely on WBLTs, teachers can organize teaching
activities online at home, thus prevent the pandemic from spreading,
reduce touching, and avoid face to face contacting. For this measurement
tool, collecting the requirements of users and improve the technology to
create a better web-based learning experience. When teachers and
students take online courses, they are inevitable to employ web-based
learning tools, and unavoidable be subject to prior psychological state
and living status.

According to positive psychology, a positive psychological state of an
individual is often associated with good results and a high quality of
life (Linley \& Joseph, 2004). Mood disorders, like anxiety, depression,
and stress, to the detriment of students' physical and mental health,
may cause learning disorders (Bibi, Lin, Zhang, \& Margraf, 2020).
People with low self-efficacy always tend to avoid failure, to give up
easily with suspicion of their capabilities induce poor performance,
which as a part of positive psychology have had a significant effect to
buffer against emotional disorders (Aspinwall \& Staudinger, 2002).
Thus, general health as a barometer to reflect the mental health,
depression-anxiety-stress as a negative mood, self-efficacy, and family
life quality act as healthy energy to promote a better way of learning.
Under these complex circumstances of the pandemic, and without the
supervision and inspector in the classroom, individuals' learning
behavior and effects were hard to ascertain. As an auxiliary means to
assist traditional teaching mode, online teaching has been a dominant
learning approach to the novel coronavirus pandemic. The evaluation of
web-based learning tool may be effected by self-efficacy, family quality
of life, and depression-anxiety-stress. To make this relationship clear,
the research was conducted to validate the relationship between them
through a structural equation model.

\textbf{Method}

\textbf{Participants}

Data were collected between June 25 and July 21 in 2020. All of the
investigators are 83 males and 194 females (N=277) with a different
diploma and different professions. Among them, 253 are majority and 24
are ethnic (aging from 7 to 55), including 78 primary and secondary
school students, 150 people hold a bachelor degree, 38 people master
degree and 11 people doctoral degree from the eastern, central and
western region of China, the geographic distribution features are also
represented with random assigned.

Table 1 Demographic features of participants\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Demographic variables & Frequency & Percent & Total\tabularnewline
Gender & & & 277\tabularnewline
Male & 83 & 30\% &\tabularnewline
Female & 194 & 70\% &\tabularnewline
Race & & &\tabularnewline
Majority & 253 & 91.3\% &\tabularnewline
Ethnic & 24 & 8.7\% &\tabularnewline
Ages & & &\tabularnewline
7-12 & 7 & 2.5\% &\tabularnewline
13-17 & 58 & 20.9\% &\tabularnewline
18-23 & 127 & 45.8\% &\tabularnewline
24-33 & 53 & 19.1\% &\tabularnewline
34-55 & 32 & 11.6\% &\tabularnewline
Diploma & & &\tabularnewline
High school and below & 78 & 28.1\% &\tabularnewline
Bachelor degree & 150 & 54.2\% &\tabularnewline
Master degree & 38 & 13.7\% &\tabularnewline
Doctoral degree & 11 & 4.0\% &\tabularnewline
Identity & & &\tabularnewline
Teacher & 42 & 15.2\% &\tabularnewline
Primary school students & 7 & 2.5\% &\tabularnewline
Secondary school students & 60 & 21.7\% &\tabularnewline
College students & 73 & 26.4\% &\tabularnewline
Postgraduates & 22 & 7.9\% &\tabularnewline
PhD students & 8 & 2.9\% &\tabularnewline
Graduates & 65 & 23.5\% &\tabularnewline
\bottomrule
\end{longtable}

\textbf{Procedure}

Participants were voluntary with random sampling, all of them were
proficient in the Chinese language, all the questionnaires were
translated into Chinese version to suit the features of respondents, it
was organized by questionnaire website (www.wjx.cn) and send through
WeChat communication and social platform. Participants were required to
answer a set of questions, including demographic information, such as
ages, gender, identity, the general health questionnaire, web-based
learning tool scale, general self-efficacy scale, depression, anxiety
and stress, family quality of life scale. It also included an open-ended
question about collecting comments and suggestions. At the beginning and
the end of the questionnaire, it was completed with an introduction,
briefing, and acknowledgment.

\textbf{Measures}

\textbf{Web-based learning tool scale}

Web-based learning tool scale (WBLTs, Web-based learning tool scale) was
developed and compiled by Kay \& Knaack (2009) and revised by Kay
(2009). Its purpose is to evaluate the structure and efficacy of
web-based learning tools to promote ubiquitous learning. The WBLTs
designed by Kay \& Knaack (2009) can be used to identify the
effectiveness and practicality of web-based learning tools, which aims
to assess the structure and utility of technology to promote web-based
learning and online teaching quality, it contains three parts of
promoting learning, tools design and engagement. This tool has been
extensively tested for more than 800 students in the United States.
After verification and testing, the reliability of the questionnaire is
0.87. \textasciitilde{}0.93, with high reliability, content validity,
and structure validity. During the pandemic, learners' evaluation of
web-based learning tools will affect learners' learning efficiency,
learning results, learning motivation, learning attitude, etc. Given the
special circumstances of the pandemic, the study uses WBLTs to explore
learners' evaluation of web-based learning tools and their relationship
between self-efficacy, general health, and family life quality. The
internal consistency of WBLTs in this study is 0.9 (Cronbach's Alpha
coefficient), which means that the scale tool has high reliability to be
used.

\textbf{General self-efficacy scale}

Bandura (1977) put forward the theory of self-efficacy based on the
theory of social cognition to describe the ability of an individual to
complete a task or accomplish something under a specific situation. This
theory has received extensive attention in the area of psychology and
education, and its positive effects and influence have been tested.
There are a lot of studies have paid attention to the application and
effects of self-efficacy theory (Bandura et al., 2001; Schutte \&
Malouff, 2016). Self-efficacy directly affects learners' learning
cognition, learning motivation, and learning effect. It can be used to
investigate and analyze learners' meta-cognition, feelings, and
experience of web-based learning, and it also affected learners'
learning motivation, preferences, and perception of web-based learning
tools.

As the importance of self-efficacy and the particularity of the COVID-19
pandemic, learners' self-efficacy of web-based learning during the
pandemic is unknown. Analyzing learners' web-based learning
self-efficacy could be a benefit to understand learners' learning
behavior, condition, and problems. Refer to the General self-efficacy
scale (GSES, General self-efficacy scale) of Schwarzer \& Jerusalem
(1995). Zhang \& Schwarzer (1995) modified and designed the Chinese
version of the General Self-Efficacy Scale (GSES-C), which has been
widely used and has shown good reliability and validity in the research
field. Several studies have shown that the reliability of GSES-C is 0.76
(Cronbach's alpha coefficient) (Sun, Zhong, Xin \& Kang, 2018), the
first reliability is 0.83, and the second reliability is 0.91 (Wang et
al., 2001). This study uses GSES-C to investigate learners'
self-efficacy during the pandemic to figure out the effect of the
external environment. The internal consistency of the questionnaire is
0.939 (Cronbach's alpha), which indicates that the scale tool has high
reliability.

\textbf{General health questionnaire}

At the same time, to understand the mental health of learners during the
pandemic, the study uses general health questionnaires to diagnose the
mental health of online learners, and explore whether the new crown
pandemic has an impact on the mental health of learners during web-based
learning Or influence. Goldberg (1972) developed the General health
questionnaire (GHQ, General health questionnaire) to help individuals
discover their own psychological problems or mental illnesses in
different situations. The scale includes 12 items, 20 items, 28 items,
30 Various versions of six projects or 60 projects, all of which show
good internal consistency and reliability. At the same time, they have
also been translated into 36 translations and have been used in many
countries (Goldberg \& Williams, 1988; Duncan-Jones \& Hernderson, 1978;
Ballinger, 1977; De Paulo et al., 1980; Sriram, Chandrashekar, Isaac \&
Shanmugham, 1989). Among them, the Cronbach's alpha of GHQ-20 is 0.894
(Penninkilampi-Kerola, Miettunen \& Ebeling, 2006), and the Cronbach's
alpha of GHQ-12 is 0.87 (Montazeri, Harirchi, Shariati, Garmaroudi,
Ebadi \& Fateh, 2003). This study explores the mental health status of
learners during the pandemic and their relationship between web-based
learning tool evaluation, self-efficacy, family life quality, etc. The
internal consistency of GHQ is 0.702, which means that the reliability
of the scale tool is average within the acceptable range.

\textbf{Depression, Anxiety and Stress (DASS-21)}

In addition to investigating the general health of the learner, it is
also necessary to understand the emotional state of the learner and to
investigate the emotional state and stress of the learner. Borrowing the
Depression Anxiety Stress Scale (DASS) measurement tool, developed and
designed by Lovibond \& Lovibond (1995), to investigate the impact of
the pandemic on the emotional state and stress of learners. DASS-21
involves 3 dimensions and contains 21 items. It has a wide range of
applications in the field of mental illness measurement. It is mainly
used to diagnose depression, anxiety, and stress. At present, DASS-21
has been translated into 42 versions of different countries. The
reliability and validity of the questionnaire are relatively high and
the reliability is high. The internal consistency is about 0.89~0.93
(Lovibond \& Lovibond, 1995; Henry \& Crawford, 2005 ), the internal
consistency in the study of Bibi, Lin, Zhang \& Margraf (2020) is 0.70
to 0.90. In other language versions of DASS-21, such as the Latin
American version of DASS-21, the internal consistency is between 0.88
and 0.90 (Gonz\selectlanguage{ngerman}ález-Rivera, Pagán-Torres \& Pérez-Torres, 2020), and in
the Hispanic version of DASS The internal consistency of -21 is 0.96 and
high construct validity (Daza, Novy, Stanley \& Averill, 2002). It can
be seen that the DASS-21 scale has high reliability and validity, and
has been widely used and verified in a multicultural context. As a
result, the study used the DASS-21 scale to investigate the emotional
state of online learners during the pandemic and its interaction with
web-based learning tool evaluation, self-efficacy, mental health, and
family life quality, its reliability is 0.959.

\textbf{Family quality of life scale}

Besides, it is necessary to investigate and grasp the quality of
learners' family life during the pandemic. The quality of family life
has an impact on learners' learning, psychological, emotional, and
living conditions, and is related to learners' subjective well-being. In
recent decades, the issue of family life quality has attracted
widespread attention and great attention from the academic community.
They have continuously tried to evaluate the family life quality, and
have evaluated it from multiple dimensions in the past decades (Hoffman
et al., 2006). Park et al., (2003) developed the Beach Center Family
Life Quality Scale based on the preliminary research of Poston et al.
(2003). The family quality life scale (FQLS, Family quality life scale)
has an internal consistency of 0.80\selectlanguage{english}~0.85 (Hoffman et al., 2006), which
has good reliability and validity, as well as in the circumstance of
other researches. Application and promotion. FQLS expands on five
aspects of family interaction, parenting, emotional well-being, material
well-being, and disability-related support to evaluate whether the
overall happiness and needs are met, and the evaluation of the quality
of life (Summers et al., 2005; Hsiao, 2013).

\textbf{Data analysis}

Descriptive statistics and related analysis were conducted with the
variables of web-based learning for the total sample. Bivariate
correlations between key variables were acted to test the impact on the
significant relationship between different variables. All statistical
analyses were conducted in the software of SPSS 25.0 and Mplus 8.3. SEM,
CFA, EFA, multiple linear regression model, and Hierarchical regression
model was performed to test the hypothesis in this study. Hu \& Bentler
(1999) suggested that the criteria of indexes which was designed to
examine the goodness of fit in a model are the value close to 0.95 for
CFI (comparative fit index) and GFI, 0.06-0.08 for SRMR and 0.06 for
RMSEA. This criterion was widely accepted in the field of psychology
(Zadworna-Cie\selectlanguage{polish}ś\selectlanguage{english}lak, 2020; Bacikova-sleskova \& Bavolar, 2020).

\textbf{Results}

\textbf{Descriptive statistics and related analysis}

First of all, the data type data level variable uses the spearman
correlation analysis method of correlation analysis, as shown in the
Table 2. It can be seen that gender has a significant negative impact on
self-efficacy (p\textless{}0.05), and age and education have a
significant positive impact on general health (p\textless{}0.05).

Table 2 Descriptive Statistics and Correlation Matrix\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
& Mean & SD & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9\tabularnewline
1.Gender & 1.70 & 0.459 & - & & & & & & & &\tabularnewline
2.Age & 3.16 & 0.970 & .141* & - & & & & & & &\tabularnewline
3.identity & 3.84 & 0.947 & .225** & .730** & - & & & & &
&\tabularnewline
4.Identity & 3.48 & 1.823 & .155** & -.161** & .078 & - & & & &
&\tabularnewline
5.WBLTs & 11.0664 & 2.25492 & -0.77 & -0.010 & 0.006 & 0.025 &
0.954\selectlanguage{greek}\textsuperscript{α} & & & &\tabularnewline
6.DASS & 6.1661 & 2.53514 & -0.095 & -0.077 & -0.035 & -0.102 & -0.319**
& 0.959\selectlanguage{greek}\textsuperscript{α} & & &\tabularnewline
7.GSES-C & 6.9356 & 1.52425 & -0.149* & 0.029 & 0.002 & -0.018 & 0.419**
& -0.257** & 0.939\selectlanguage{greek}\textsuperscript{α} & &\tabularnewline
8.GHQ & 5.7371 & 1.05760 & -0.153 & 0.127* & 0.140* & -0.106 & 0.195** &
0.289** & 0.312** & 0.702\selectlanguage{greek}\textsuperscript{α} &\tabularnewline
9.FQLS & 18.7977 & 3.55386 & -0.065 & 0.038 & 0.002 & 0.040 & 0.478** &
-0.373** & 0.537** & 0.221** & 0.961\selectlanguage{greek}\textsuperscript{α}\selectlanguage{english}\tabularnewline
\bottomrule
\end{longtable}

*p\textless{}0.05, **p\textless{}0.01

WBLTs=Web-based learning tool; DASS=Depression-anxiety-stress;
GSES-C=General Self-efficacy; GHQ=general health; FQLS=Family quality of
life

\selectlanguage{greek}\textsuperscript{α}\selectlanguage{english}=Cronbach's alpha coefficient

\textbf{Reliability and validity test}

The research conducted a reliability and validity analysis of the
questionnaire in the study, using the Cronbach's Alpha coefficient to
verify the reliability of the questionnaire and questions on SPSS25.0
software, and then using factor analysis to verify the content validity
of the questionnaire tools and questions. Researchers often use the
Cronbach's alpha coefficient to examine the internal consistency of
measurement tools (Kaplan, 2009), in consequence of the Cronbach's alpha
was above 0.7, means the tools are acceptable, 0.8 means good
reliability, and 0.9 means preferable (Kline, 2011; George \& Mallery,
2003). Thus, if the reliability of the questionnaire item is greater
than 0.8, it can be judged that the questionnaire tool has high
reliability and stability. If the reliability of the questionnaire item
is greater than 0.7, it can be judged that the reliability of the
questionnaire is average and within the acceptable range.

Through internal consistency analysis, the Cronbach's Alpha coefficient
of the web-based learning tool scale (13 items in WBLTs) is
0.954\textgreater{}0.9, which is highly reliable. The Chinese version of
the general self-efficacy scale (10 items in GSES-C) has Cronbach's The
Alpha coefficient is 0.939\textgreater{}0.9, which has a high
reliability. The Cronbach's Alpha coefficient of the
depression-anxiety-stress scale (21 items of DASS) is
0.959\textgreater{}0.9, which has high reliability. The Cronbach's Alpha
coefficient of the family quality of life scale (FQLS) (25 items) is
0.961\textgreater{}0.9, which has high reliability. the Cronbach's Alpha
coefficient of the general health questionnaire (12 items of GHQ) is
0.702\textgreater{}0.7, and its reliability was within an acceptable
range.

Also, factor analysis and Kaiser-Meyer-Olkin (KMO) test were always used
to confirm the construct validity, and the content validity was examined
by literature review and expert appraisal (Catalano, 2018). This study
conducted an exploratory factor analysis on five questionnaires to test
the structural validity of the questionnaire, selecting the maximum
variance orthogonal rotation method, KMO and Bartletts' test, and
principal component analysis (extract factor if the characteristic value
is greater than 1, the absolute value of the factor load is greater than
0.5, and the results are shown in the table). Utilizing factor analysis,
the KMO value of the web-based learning tool scale is
0.940\textgreater{}0.9 (p\textless{}0.000, satisfied with the Bartletts'
test), the factor loading is between 0.647~0.848, and the cumulative
variance contribution rate of three common factors is 79.046\%. Its
validity is relatively high. The KMO value of the Chinese version of the
self-efficacy scale (10 items of GSES-C) is 0.928\textgreater{}0.9
(p\textless{}0.000, satisfied with the Bartletts' test), the factor
loading is 0.487~0.795. The cumulative variance contribution rate of
the co-factors is 73.502\%, and its validity is very high. The KMO value
of the depression, anxiety and stress scale (21 items of DASS) is
0.947\textgreater{}0.9 (p\textless{}0.000, satisfied with the Bartletts'
test, the factor loading is 0.625~0.809, and the cumulative variance
contribution rate of 3 common factors is 73.244\%, and its validity is
high. The KMO value of the Family Quality of Life Scale (FQLS 25 items)
is 0.950\textgreater{}0.9 (p \textless{}0.000, satisfied with the
Bartletts' test), the factor loading is 0.506~0.934, the cumulative
variance contribution rate of 5 common factors is 79.334\%, and its
validity is high. The KMO value of general health scale (12 items on
GHQ) is 0.917\textgreater{}0.9 (p\textless{}0.000, satisfied with the
Bartletts' test), the factor loading is 0.660~0.832, and the cumulative
variance contribution rate of extracting 2 common factors is 75.688\%,
and its validity is very high.

Finally, confirmatory factor analysis (CFA) was used to verify the
reliability, convergence validity, and discriminate validity of each
questionnaire. As shown in Table 3 and Table 4, four major measurement
tools as follows: web-based learning tool scale,
depression-anxiety-stress scale, general self-efficacy scale, and family
life quality scale, are mainly used in confirmatory factor analysis.
Firstly, after the test of the proposed model, the fitting index of the
proposed model was very unsatisfactory. Therefore, the independent
variable of the general health questionnaire was directly removed.
Furthermore, during the process of confirmatory factor analysis and
structural equation model construction, the general health questionnaire
was excluded from the SEM. In addition, it can be seen from Table 3 and
Table 4 that the reliability and validity of other questionnaires
reached a good level excluding the general health questionnaire.

Table 3 Reliability and Convergence validity\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Dimension & Item & Parameters of significant test & Parameters of
significant test & Parameters of significant test & Item Reliability &
Item Reliability & Composite Reliability & Convergence
Validity\tabularnewline
& & Estimate & S.E. & Est./S.E. & P-Value & R-square & CR &
AVE\tabularnewline
W & WBLT1 & 0.858 & 0.021 & 41.045 & *** & 0.736 & 0.897 &
0.746\tabularnewline
& WBLT2 & 0.931 & 0.017 & 54.716 & *** & 0.867 & &\tabularnewline
& WBLT3 & 0.796 & 0.026 & 30.643 & *** & 0.634 & &\tabularnewline
D & DAS1 & 0.703 & 0.035 & 20.073 & *** & 0.494 & 0.865 &
0.683\tabularnewline
& DAS2 & 0.906 & 0.023 & 38.877 & *** & 0.821 & &\tabularnewline
& DAS3 & 0.857 & 0.025 & 34.177 & *** & 0.734 & &\tabularnewline
G & GSESC1 & 0.956 & 0.028 & 34.103 & *** & 0.914 & 0.916 &
0.845\tabularnewline
& GSESC2 & 0.881 & 0.029 & 30.698 & *** & 0.776 & &\tabularnewline
F & FQLC1 & 0.834 & 0.020 & 41.446 & *** & 0.696 & 0.901 &
0.661\tabularnewline
& FQLC2 & 0.952 & 0.009 & 100.43 & *** & 0.906 & &\tabularnewline
& FQLC3 & 0.911 & 0.013 & 71.168 & *** & 0.830 & &\tabularnewline
& FQLC4 & 0.854 & 0.018 & 46.169 & *** & 0.729 & &\tabularnewline
& FQLC5 & 0.377 & 0.053 & 7.098 & *** & 0.142 & &\tabularnewline
\bottomrule
\end{longtable}

W=Web-based learning tool; D=Depression-anxiety-stress; G=Self-efficacy;
Q=general health; F=Family quality of life

***p\textless{}0.001

As shown in Table 3, reliability and convergence validity analysis,
dimensions, all items, and standardized estimation coefficients are all
greater than 0.6. The significance estimate is divided by the standard
error to obtain the Z value (Est./SE) of the significance estimate. Z
value\textgreater{}0.96 means significant, and all p
values\textless{}0.001, which means that all items are significant. The
item reliability is the square of the standardized estimated value. The
R-square of the item is above 0.36 means acceptable, and above 0.5 means
good. Item reliability (Square multiple correlations, SMC or R-square:
the square of the standardized factor loading) refers to the explanatory
ability of the dimension to the items, composite reliability is the
internal consistency of the dimension topic, and AVE refers to the
dimension to the items. The average explanatory ability of AVE is
greater than 0.5, indicating that the dimension has a good explanatory
ability, and the value of AVE was above 0.36 means acceptable. The
convergence validity of all dimensional items reached the recommended
level. Diagonal bold values are the root sign value of AVE, and the
lower triangle is the Pearson correlation between the dimension
according to the output table of Tech 4 in Mplus 8.3.

Table 4 Reliability and validity analysis\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
DIM & Item reliability & Composite reliability & Convergence validity &
Discriminate validity & Discriminate validity & Discriminate validity &
Discriminate validity\tabularnewline
& STD.LOADING & CR & AVE & W & D & G & F\tabularnewline
W & 0.796-0.931 & 0.897 & 0.746 & \textbf{0.864} & & &\tabularnewline
D & 0.703-0.906 & 0.865 & 0.683 & -0.200 & \textbf{0.826} &
&\tabularnewline
G & 0.881-0.956 & 0.916 & 0.845 & 0.383 & -0.135 & \textbf{0.919}
&\tabularnewline
F & 0.377-0.952 & 0.901 & 0.661 & 0.419 & -0.293 & 0.531 &
\textbf{0.813}\tabularnewline
\bottomrule
\end{longtable}

Mention: The bold font on the diagonal is the root value of AVE, and the
lower triangle is the pearson correlation of the dimension.

W=Web-based learning tool; D=Depression-anxiety-stress; G=Self-efficacy;
F=Family quality of life

According to Table 4, every dimension corresponds to its own
corresponding items. The standardized load of all factors is between
0.417 and 0.959, the combined reliability CR is greater than 0.7, and
the convergence validity AVE is greater than 0.5, all of which are
within the acceptable desirable range. Within the range, the
discriminate validity starts with the square root on the diagonal.
According to the recommendations of Fornell and Larcker (1981), but the
square root value of AVE on the diagonal, and the lower triangle is the
Pearson correlation between the dimension. The comparison results show
that the root value of the dimension is greater than the correlation of
other related dimensions, so the dimension has discriminate validity.

In brief, the measurement tools used in the study have already shown
good reliability, structural validity, convergence validity, and
discriminate validity, and overall reliability and validity are at a
high level. Moreover, the measurement tools have been verified and used
by researchers in different countries, regions, and different cultures,
which shows that the quality of measurement tools is good enough. Based
on the good quality of scale tools, the research uses a structural
equation model, multiple regression model, and hierarchical regression
model to further explore the influence relationship between the
independent variables and dependent variables.

\textbf{Structural equation model}

As shown in Fig 1, there is a significant positive correlation between
self-efficacy and family quality of life with a correlation coefficient
of 0.533 (p\textless{}0.001). There is a significant negative
correlation between self-efficacy and depression-anxiety-stress, the
correlation coefficient is -0.196 (p\textless{}0.01). There is a
significant negative correlation between family quality of life and
depression-anxiety-stress, the correlation coefficient is -0.297
(p\textless{}0.001).

Self-efficacy scale have a direct positive impact on web-based learning
tools, with a path coefficient of 0.234 (p\textless{}0.01). Family
quality of life have a direct positive impact on web-based learning
tools, with a path coefficient of 0.272 (p\textless{}0.001).
Furthermore, depression-anxiety-stress has no significant impact on
web-based learning tools. Among all the dependent variables, family
quality of life have the greatest impact on the web-based learning
tools, while the one that has the greatest impact on self-efficacy is
the web-based learning tools.

Fig 1. Standardized structural equation model\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

W=Web-based learning tool; D=Depression-anxiety-stress; G=Self-efficacy;
F=Family quality of life

***p\textless{}0.001;**p\textless{}0.01

Table 5 Fitness of Model\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Suggested standard & Model fit & Requirements\tabularnewline
ML x\textsuperscript{2} & smaller & 140.116 &\tabularnewline
Df & bigger & 59 &\tabularnewline
x\textsuperscript{2}/df(Normed Chi-sqr) &
1\textless{}x\textsuperscript{2}/df\textless{}3 & 2.375 &
meet\tabularnewline
CFI & \textgreater{}0.95 & 0.968 & meet\tabularnewline
TLI & \textgreater{}0.9 & 0.958 & meet\tabularnewline
RMSEA & \textless{}0.08 & 0.071 & meet\tabularnewline
SRMR & \textless{}0.06 & 0.053 & meet\tabularnewline
\bottomrule
\end{longtable}

Hu \& Bentler (1999) proposed the goodness of model fit are: CFI
(Comparative Fit Index) and GFI should be close to 0.95, SRMR close to
0.06 or 0.08, and RMSEA close to 0.06. As shown in Table 5, the model
test results show that the degree of freedom of the model is equal to
59, the chi-square value of the overall model fit is 140.116, the ratio
of chi-square degrees of freedom is 2.375, and the significance
probability value is p\textless{}0.000, RMSEA=0.071\textless{} 0.080,
SRMR=0.053\textless{}0.06, CFI=0.968\textgreater{}0.950,
TLI=0.958\textgreater{}0.950, all indicators are within the acceptable
range and reach the model adaptation standard, so the model can be
accepted.

It can be seen from Table 5 and Table 6, that part from the hypothesis
of the research model has been verified (p\textless{}0.001). Among them,
the residual of the model has no negative value, and the correlation
between the independent variables is less than 0.7, which means that
there is no co-linearity. Therefore, according to the research
hypothesis, except for the independent variable of general health, there
is a significant influence relationship between web-based learning
tools, self-efficacy and the quality of family life.
Depression-anxiety-stress has no significant positive impact on
web-based learning tool.

Table 6 Hypothesis analysis of model fit\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
DV & IV & Estimate & S.E. & Est./S.E. & P-value & R\textsuperscript{2} &
Hypothesis\tabularnewline
W & F & 0.272 & 0.071 & 3.840 & 0.000*** & 0.225 &
Support\tabularnewline
& G & 0.234 & 0.070 & 3.348 & 0.001** & & Support\tabularnewline
& D & -0.087 & 0.063 & -1.373 & 0.170 & & Not support\tabularnewline
\bottomrule
\end{longtable}

W=Web-based learning tool; D=Depression-anxiety-stress; G=Self-efficacy;
F=Family quality of life

***p\textless{}0.001

Based on the structural equation model, the research explores the
mediating effect on different independent variables, using the Bootstrap
method to iterate 1000 times, and the 95\% confidence interval CI =
{[}0.130~0.383{]}. As shown in Table 7 and Fig 2, web-based learning
tool has a significant effect on self-efficacy (\selectlanguage{greek}β\selectlanguage{english}=0.243, SE=0.064,
p\textless{}0.000), web-based learning tool has a significant effect on
family quality life (\selectlanguage{greek}β\selectlanguage{english}=0.503, SE=0.110, p\textless{}0.000), web-based
learning tool has a significant effect on family quality life has a
significant effect on self-efficacy (\selectlanguage{greek}β\selectlanguage{english}=0.484, SE=0.061,
p\textless{}0.000), family quality life as a mediator. The quality of
life plays a mediating role of the two (\selectlanguage{greek}β \selectlanguage{english}= 0.243, t = 3.814, p =
0.000\textless{}0.001), depression-anxiety-stress as a predictor has a
significant indirect negative influence on self-efficacy.

Table 7 Mediating effects of specific indirect effects\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Specific indirect effects & \selectlanguage{greek}β & Product of Coefficients & Product of
Coefficients & Product of Coefficients & 95\% CI & 95\% CI & 95\% CI &
95\% CI\tabularnewline
& & & & & Bias corrected & Bias corrected & Percentile &
Percentile\tabularnewline
& & S.E. & Est./S.E. & P-Value & Lower & Upper & Lower &
Upper\tabularnewline
W-F-G & 0.243 & 0.064 & 3.814 & 0.000*** & 0.130 & 0.383 & 0.130 &
0.383\tabularnewline
\bottomrule
\end{longtable}

W=web-based learning tool; G=Self-efficacy; F=Family quality of life

***p\textless{}0.001

Fig 2. Indirect effects predicting self-efficacy, web-based learning
tool as a predictor, family quality life as a mediator.
*p\textless{}0.05,**p\textless{}0.01,***p\textless{}0.001. CI=Confidence
Interval\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Multiple regression model}

Based on data analysis, the study uses multiple linear regression
equations to determine the quantitative correlation between multiple
independent variables and dependent variables (see Table 8). All
independent variables have significant predictive power for dependent
variables. In the statistical data onto collinearity of all independent
variables, the VIF value is less than 10 means that there is no
collinearity between the respective variables, and the regression
equation of the model is stable and reliable. The residual values of
{[}-2, +2{]} can explain most of the predicted values and prove the
validity of the regression equation. Among the four predictors, four
variables have significant predictive power for family quality of life,
followed by self-efficacy, web-based learning tools,
depression-anxiety-stress and general health. The multiple correlation
coefficient between the four predictors and the dependent variable
family quality of life is 0.636, the coefficient of determination
(R\textsuperscript{2}) is 0.405, and the F value of the regression model
integrity test is 46.205 (p=0.000\textless{}0.05), so the four
predictions The variables can effectively explain 40.5\% of the
variation to family quality of life.

From the perspective of the predictive power of each variable, the most
predictive factor of family quality of life is the self-efficacy
independent variable, with an explanatory variation of 29\%; followed by
the web-based learning tool, which explains the variation 34.9\%; the
predictive power of the remaining two independent variables
depression-anxiety-stress and general health are 37.0\% and 40.5\%
respectively. From the perspective of standardized regression
coefficients, the \selectlanguage{greek}β \selectlanguage{english}values of the four predictors in the regression
model are 0.336, 0.209, -0.247, 0.222, where a positive number means
that its influence on family quality of life is positive, and a negative
number mean its It has a negative impact on family quality of life.

The non-standardized regression equation is as follows: family life
quality = 7.577 + 0.783 * self-efficacy + 0.329 * web-based learning
tools-0.346 * (depression-anxiety-stress) + 0.747 * general health

The standardized regression equation is as follows: family quality of
life=0.336*self-efficacy+0.209*web-based learning
tool-0.247*(depression-anxiety-stress) + 0.222* general health

Table 8 Results of multiple regression analysis\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
coefficient\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
R\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
^R\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
F value\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
^ Fvalue\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
B\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Beta\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Intercept\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7.577\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
G\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.538\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.290\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.287\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
112.085***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
112.085***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.783\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.336\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
W\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.591\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.349\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.344\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
73.362***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
24.898***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.329\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.209\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
D\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.608\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.370\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.363\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
53.444***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.211***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-.346\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-.247\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Q\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.636\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.405\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.396\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
46.205***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15.797***\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.747\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
.222\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

W=Web-based learning tool; D=Depression-anxiety-stress; G=Self-efficacy;
Q=general health; F=Family quality of life

***p\textless{}0.001;**p\textless{}0.01; *p\textless{}0.05

\textbf{Hierarchical regression model}

From the results of the hierarchical regression analysis (see Table 9),
it can be seen that the two independent variables of
''depression-anxiety-stress'' and ''general health'' in Model 1 can
explain 24.8\% of the variance of the dependent variable of family life
quality. The F value is 45.060 (p\textless{}0.000), reaching a
significant level of 0.05. It shows that the regression coefficients of
the two predictors of ''depression-anxiety-stress'' and ''general
health'' are both significant, and the standardized regression
coefficient \selectlanguage{greek}β \selectlanguage{english}values of these two independent variables are -0.426
(p\textless{}0.000) and 0.438 (p\textless{}0.000), respectively, both
reached a significant level. Among them, the influence of
depression-anxiety-stress on the family life quality is negative, and
the influence of general health on the family life quality is positive.
If the two independent variables of web-based learning tools and
self-efficacy are further invested, the overall explanatory variation
increases by 15.7\% (^R\textsuperscript{2}), and the F value of the
significant change is equal to 35.877 (p\textless{}0.000), which means
web-based learning tools and self-efficacy have significant effects on
the quality of family life. The F-value of the stratified binary linear
regression overall tests was 46.205 (p\textless{}0.000), reaching a
significant level of 0.05. It represents that the four predictors have
significant explanatory power for family life quality, and their common
explanatory variation is 40.5\%.

Table 9 Hierarchical linear regression results\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& Predictor variables & Model 1 & Model 1 & Model 2 & Model
2\tabularnewline
& & \selectlanguage{greek}β & t value & \selectlanguage{greek}β & t value\tabularnewline
Psychological states & D & -.426 & -7.658*** & -.247 &
-4.576***\tabularnewline
& Q & .438 & 7.862*** & .222 & 3.975***\tabularnewline
Learning experience & W & & & .209 & 4.037***\tabularnewline
& G & & & .336 & 6.096***\tabularnewline
& F value & 45.060*** & 45.060*** & 46.205*** & 46.205***\tabularnewline
& R\textsuperscript{2} & .248 & .248 & .405 & .405\tabularnewline
& ^F value & 45.060*** & 45.060*** & 35.877*** &
35.877***\tabularnewline
& ^R\textsuperscript{2} & -248 & -248 & .157 & .157\tabularnewline
\bottomrule
\end{longtable}

W=Web-based learning tool; D=Depression-anxiety-stress; G=Self-efficacy;
Q=General health; F=Family quality of life

***p\textless{}0.001;**p\textless{}0.01; *p\textless{}0.05

\textbf{Conclusions and reflections}

\textbf{Research conclusion}

Firstly, to promoting web-based learning, learning during the pandemic
is mainly affected by self-efficacy, web-based learning tools, and
family life quality. Depression-anxiety-stress may have a little adverse
effect. There are correlations effects of self-efficacy,
depression-anxiety-stress, and family life quality. Learners' negative
emotions in depression-anxiety-stress may affect their self-efficacy and
family life quality, or maybe the use of web-based learning tools, but
the effect was not significant. Thus depression-anxiety-stress emotions
will be harmful to learners' learning. Family life quality and
self-efficacy have a positive impact on web-based learning tools. This
shows that high family life quality and self-efficacy maybe benefit
learners to use web-learning tools to promote learning quality. Besides,
the negative emotions such as depression-anxiety-stress during the
pandemic are adverse to improve the quality of family life and
self-efficacy. Thus, negative emotions should be to take care of mental
health to prevent the low efficiency of learning. The use of web-based
learning tools could promote study at home, family life quality, and
self-efficacy, and family life quality can promote self-efficacy. A
higher level of self-efficacy and family life quality help learners to
better use web-based learning tools to promote learning effectively. At
the same time, there is a positive correlation between self-efficacy and
family life quality, as a privilege to promote the use of web-based
learning tools, which means that learners with high self-efficacy may
have a better family life quality.

Secondly, from the point of the factors affecting web-based learning
during the pandemic. Family life quality during the pandemic was not
only affected by the individual's psychological states of depression,
anxiety, and stress, but also the general health, use of web-based
learning tools, family environment, etc., it will also affect the
quality of learners' web-based learning directly and indirectly. There
are interactive influences among various factors. Based on the results
of the multiple regression model, it can be seen that self-efficacy,
web-based learning tools, general health, depression-anxiety-stress as
independent variables have a significant impact on the family quality of
life of the dependent variable. Among them, depression-anxiety-stress as
a kind of negative emotion has had a negative and significant impact on
the quality of family life. The results of the hierarchical regression
model further proved this conclusion. After incorporating the web-based
learning tools and self-efficacy into the model, the model's predictive
ability on the dependent variable was significantly enhanced.

\textbf{Countermeasures and suggestions}

Based on the research results and conclusions, the study believes that
learning during the pandemic period needs to fully consider learners'
individual factors, especially mental health and
depression-anxiety-stress will greatly affect learners' use of web-based
learning tools. Although web-based learning tool is a convenient
instrument for learners to get access to knowledge and skills, the
isolation between teachers and students was uncertain to make assurance
about this kind of benefits with high dropout rate (Davidson-Shivers,
Rasmussen, \& Lowenthal, 2018; Hew \& Cheung, 2014; Allen \& Seaman,
2013). Paying attention to these problems and guiding the learners to
overcome these mental health problems to quickly getting rid of the
negative emotions of depression-anxiety-stress, is very important for
individual control, which will help learners improve their concentration
and quality of learning in web-based learning. On the other hand, family
life quality is also very important to the self-efficacy of online
learners. During the pandemic, it is necessary to ensure a high quality
of family life, a relaxed family atmosphere with love, trust and faith,
communication, and nurturing could promote learners' web-based learning.
As a mediator between web-based learning tool and self-efficacy, family
life quality could reduce learners' negative emotions in
depression-anxiety-stress, and enhancing self-efficacy, and promoting
learners' use of web-based learning tool. Secondly, the study also
designed a multiple-choice question on the investigation of the reasons
for the use of web-based learning tools. Among them, 70.87\% and 20.71\%
of learners used web-based learning tools to study mainly due to
regulations and requirements, while 53.72\% and 49.51\% of learners use
web-based learning tools for autonomous learning and self-improvement
reasons. Therefore, although the reasons why learners use web-based
learning tools are diversified, regulations and requirements have been
predominant effect, and autonomy learning of an individual was left
behind. It can be seen that web-based learning tools have been widely
used during the pandemic. Finally, the use and improvement of web-based
learning tools are also very important for web-based learning during the
pandemic. Learners' evaluation and opinions on web-based learning tools
must be worth attention. The development and improvement of web-based
learning tools as a technical problem has been further improved.
However, existing research pays little attention to the evaluation of
learners' use of web-based learning tools. In addition, there are some
limitations in this study. For example, there are not enough
investigation samples.

\textbf{Compliance with Ethical Standards}

\textbf{Conflict of interest} The authors declared no conflicts of
interest with respect to the authorship or the publication of this
article.

\textbf{Ethical Approval} All processes performed in studies involving
individual participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
standards.

\textbf{Informed Consent} Informed consent was obtained from all
individual participants included in the study.

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