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Advances in Social Sciences Research Journal – Vol. 8, No. 10
Publication Date: October 25, 2021
DOI:10.14738/assrj.810.11076. Yongmei, H., & Yingxin, C. (2021). Learning Engagement and Its Influencing Factors Among High School Students in Economically
Developed Areas in China. Advances in Social Sciences Research Journal, 8(10). 332-344.
Services for Science and Education – United Kingdom
Learning Engagement and Its Influencing Factors Among High
School Students in Economically Developed Areas in China
Hou Yongmei
Department of Psychology
School of Humanities and Management
Guangdong Medical University, Dongguan, Guangdong Province, China
Cen Yingxin
Department of Psychology
School of Humanities and Management
Guangdong Medical University, Dongguan, Guangdong Province, China
ABSTRACT
Objective: To explore the characteristics and its relevant factors of learning
engagement among high school students in economically developed areas in China.
Methods: 808 high school students in Guangdong Province were selected by
stratified cluster sampling. They were investigated with Utrecht Work Engagement
Scale-Student-Chinese Version (UWES-S-CV), Academic Self-Efficacy Scale (ASES),
Interpersonal Comprehensive Diagnostic Scale (ICDS), Short-Form Egna Minnen AV
Barndoms Uppprostran (s-EMBU), Emotional Support Questionnaire for Middle
School Students to Understand Teachers (ESQUT) and a self-compiled general
personal information questionnaire. Results: (1) The total score of UWES-S-CV, ASES,
ICDS and the scores of three dimensions in UWES-S-CV, two subscales of ASES and
four dimensions of ICDS, as well as the mother overprotection of s-EMBU were at
the medium level. The total score of ESQUT and its four dimensions were at the
medium high level. The scores of father’s and mother’s warmth of s-EMBU were at
the high level, while the score of father’ negation, mother's negation and father's
overprotection were at the low level. (2) Multivariate stepwise linear regression
analysis showed that 12 factors such as student origin, school category, class
category, learning interest, the teaching method used most commonly, mother's
education level, learning behavior self-efficacy, learning ability self-efficacy,
father's warmth, mother's warmth, care and encouragement were positively
correlated with the total score of UWES-S-CV (β=.120~.433, all P<.01), while grade,
family economic status, self-rated learning burden, communication and making
friends, father’s negation and mother’s negation were negatively correlated with
the total score of UWES-S-CV (β=-. 073~ -. 407, all P <. 05). Conclusion The learning
engagement among high school students needs to be improved. Social environment,
family rearing style, school education and teaching style, as well as individual
factors may be the main influencing factors of high school students' learning
engagement.
Key Words: Learning Engagement, Relevant Factors, High School Students, Multivariable
Linear Stepwise Regression Analysis
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Yongmei, H., & Yingxin, C. (2021). Learning Engagement and Its Influencing Factors Among High School Students in Economically Developed Areas
in China. Advances in Social Sciences Research Journal, 8(10). 332-344.
URL: http://dx.doi.org/10.14738/assrj.810.11076
Learning engagement is the embodiment of work engagement in the field of learning, which
refers to a positive and fulfilling mental state related to learning, including three dimensions:
vitality, dedication and focus. Among them, vitality refers to having outstanding energy and
toughness in learning, being willing to work hard at learning without getting tired, and being
persistent in the face of difficulties; Dedication means that individuals have a strong sense of
meaning, pride and enthusiasm for learning, can devote themselves to learning and have the
courage to accept challenges; Concentration is a pleasant state of whole-heartedly engaging in
learning [1-2].
Learning engagement plays an important role in students' academic and social development.
Learning engagement can improve high school students' learning interest [3], make them have
stronger self-confidence, pride and satisfaction [4], more inclined to use deep cognitive
strategies [5], and get higher academic performance [6-7]. So they have lower possibility of
dropping out of school [8-9], higher mental health level [7] and academic subjective well-being
[10].
At present, the learning engagement of high school students is generally at the medium level
and needs to be improved [11-17], which affecting factors can be divided into environmental
factors and individual factors.
Environmental factors include social, family and school environment factors. Social
environmental factors are mainly various stress events [18], especially serious and persistent
social events like COVID-19 [19]; Family environment includes parental rearing style [9,17],
family atmosphere [11], parent-child relationship [20], family economic status [21], family
support [21], parents' expectations [22], etc. School factors mainly include the sense of
teachers' independent support [4], teachers' emotional support [12], independent supporting
teaching [4], schoolmate relationship [20], student-teacher relationship [20], class atmosphere
[23], learning pressure [24], teachers' transformative leadership [25], etc.
Individual factors include demographics, such as gender [11, 18, 26, 27], grade [26, 27] and
place of origin [26, 27], as well as psychological factors such as academic self-efficacy [20, 22,
12], academic self-concept [21], locus of control [17], resilience [17], psychological capital [11],
learning motivation [25], achievement goal orientation [27], psychological distance to goal [28],
self-control [28], basic psychological needs [4], academic emotion regulation ability [29],
academic self handicapping [27], perfectionist personality [30], social support [26].
To sum up, there are many domestic related studies on the influencing factors of high school
students' learning engagement, involving a wide range of contents. However, most studies
adopt the convenient sampling method and the sample can not represent the whole group of
college students; At the same time, the above studies focus on two or three limited factors, and
the factors involved and the conclusions are different [2-3]. So they fail to systematically reveal
the influencing factors and mechanism of high school students' learning engagement.
Based on the above analysis, this study intends to adopt a large sample and multi center
empirical research to systematically explain the current situation and influencing factors of
high school students' learning engagement from three aspects: demographic factors, teaching
related factors and personal factors.
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Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 10, October-2021
Services for Science and Education – United Kingdom
OBJECTS AND METHODS
Objects
A total of 1000 senior high school students were selected by stratified cluster sampling from 36
classes in 6 high middle schools in Guangzhou, Shunde and Dongguan in Guangdong Province.
808 valid questionnaires were collected, with an effective rate of 80.8%. The age ranged from
15 to 20 years, with an average of (17.22 ± 1.18) years old. There are 427 boys and 381 girls;
673 only children and 135 non only children; 224 from urban areas, 258 from towns and 326
from rural areas; 284 in senior one, 269 in senior two and 255 in senior three; 103 from rich
families, 251 from well-off families, 337 from ordinary families, 85 from food-and-clothing
families and 32 from poor families.
Tools
Utrecht Work Engagement Scale-Student -Chinese Version, UWES-S-CV
It is compiled by Schaufeli et al. (2002) [2], and revised into Chinese version by Fang Laitan et
al (2008) [31]. UWES-S-CV has 17 items, divided into three dimensions: vitality, dedication and
focus. Likert 7-point scoring method is used to score from 1 to 7 points corresponding to
“never” to “always”. The higher the score, the higher the degree of learning engagement. In this
study, Cronbach's a coefficient of the total table is 0.78, and Cronbach's a coefficients of the
three dimensions are 0.73, 0.74 and 0.75 respectively.
Academic Self-Efficacy Scale, ASES
It is compiled by Liang Yusong (2000) [32]. There are 22 questions, which are divided into two
subscales: self-efficacy of learning ability and self-efficacy of learning behavior. Likert 5-point
scoring method is used to score from 1 to 5 points corresponding to “complete non-compliance”
to “complete compliance”. The higher the score, the higher the academic self-efficacy. In this
study, Cronbach' a coefficient of the total scale is 0.80, and Cronbach'a coefficients of the two
subscales are 0.73 and 0.75 respectively.
Interpersonal Comprehensive Diagnostic scale, ICDS
It is compiled by Zheng Richang et al. (1999) [33], mainly used to measure the degree of
interpersonal relationship and behavior distress. ICDS has 28 items, which are divided into four
dimensions: conversation, communication and making friends, one’s way to people and
heterosexual communication. The "No or Yes" scoring method is used to score 0 or 1 point
corresponding to “No” or “Yes”. The higher the total score, the more serious the interpersonal
relationship distress. The total score can be divided into three levels: little or no communication
trouble (0 ~ 8 points), a certain degree of communication trouble (9 ~14 points) and serious
communication trouble (15~28 points). In this study, Cronbach'a coefficient of the total table
is 0.84, and Cronbach'a coefficients of the four dimensions are 0.74 ~ 0.79, respectively.
Short-Form Egna Minnen av Barndoms Uppfostran, s-EMBU
Compiled by Marcus (2003) and revised into Chinese version by Jiang Jiang et al. (2010) [34],
which is divided into father's subscale and mother's subscale, with 21 questions each and the
same content, including three dimensions: negation, warmth and overprotection. Likert 4-point
scoring method is used to score from 1 to 4 points corresponding to “never” to “always”. The
average score of each dimension is used to express the subjects' perceived parental rearing
style. In this study, Cronbach’a coefficient of the total scale is 0.85, and Cronbach'a coefficients
of two subscales and 6 dimensions 0.81~0.78.
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Yongmei, H., & Yingxin, C. (2021). Learning Engagement and Its Influencing Factors Among High School Students in Economically Developed Areas
in China. Advances in Social Sciences Research Journal, 8(10). 332-344.
URL: http://dx.doi.org/10.14738/assrj.810.11076
Emotional Support Questionnaire for Middle School Students to Understand Teachers,
ESQUT
It is compiled by Gao Dongdong et al. (2017) [35]. ESQUT has 18 items, which are divided into
four dimensions: understanding, care, respect and encouragement. Likert 5-point scoring
Method is used to score from 1 to 5 points corresponding to “completely non-conforming” to
“completely conforming”. The higher the score, the higher the level of teachers' emotional
support. In this study, Cronbach'a coefficient of the total scale is 0.90, and Cronbach'a
coefficients of the four dimensions are 0.776 ~ 0.849.
Self-compiled General Personal Information Questionnaire
The CNKI, Wanfang database, VIP database, Baidu, google, Pubmed and other search engines
are used to search the literatures about learning engagement among high scool students (1381
in Chinese and 21092 in foreign languages). Based on that, the basic content of the
questionnaire are constructed, with a total of 22 items. Combined with the results of 3 collective
discussions with 10 representatives of high school students and 5 experts in the field of middle
school education, 6 items were deleted and 2 item was added. The final questionnaire for
general personal information involves 18 items, which includes age, grade, gender, only child
or not, family economic status, place of origin, class cadre or not, school category, class category,
discipline category, academic burden, learning interest, academic achievement, teaching
methods used most commonly, learning methods used most commonly, teaching aids used
most commonly, father's education level and mother's education level.
Data Processing
SPSS 20.0 is used for statistical analysis . Descriptive statistics is used to calculate the average
score and standard deviation of each scale; Pearson product moment correlation, independent
sample t-test and one-way ANOVA are used to explore the correlation between various
variables;
The main related factors of UWES-S-CV total score were analyzed by multivariable stepwise
linear regression.
RESULTS
Descriptive Statistics
It can be seen from table 1 that the total score and the scores of three dimensions of UWES-S- CV, the total score of ASES and the scores of two subscales, the total score of ICDS and the scores
of four dimensions, as well as mother overprotection of s-EMBU are at the medium level; The
total score of ESQUT and the scores of four dimensions are at the medium high level; Father 's
warmth And mother’s warmth of s-EMBU are at the high level, While father’s negation, mother’s
negation and father’s overprotection are at the low level.
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Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 10, October-2021
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Table 1 Descriptive statistics of total score and dimension (subscale) scores of each scale (n
=808)
Dimension Mi
n
Ma
x
M SD Number
of items
Average
score of
items
Standard
deviation of each
item
Vitality 19 39 23.7
6
6.14 6 3.96 1.02
Dedication 7 26 22.2
6
5.78 5 4.45 1.16
Focus 9 31 24.1
6
6.87 6 4.03 1.13
Total Score of
UWES-S-CV
27 10
4
70.1
8
18.7
9
17 4.13 0.66
Self-efficacy of
learning ability
18 51 34.8
0
6.54 11 3.16 0.59
Self-efficacy of
learning behavior
22 47 29.4
4
7.29 11 2.68 0.66
Total Score of ASES 33 96 64.2
4
13.7
5
22 2.92 0.63
Conversation 0 8 3.33 0.92 7 0.48 0.13
Comminication and
making friends
1 7 3.87 0.74 7 0.70 0.11
One’s way to people 1 8 3.45 0. 82 7 0.64 0.12
Heterosexual
communication
0 6 2.73 0.66 7 0.39 0.09
Total Score of ICDS 7 22 13.3
8
3.14 28 .48 0.11
Father’s negation 5 19 12.8
9
2.44 6 2.15 0.41
Father’s warmth 8 33 20.3
4
4.64 7 2.91 0.66
Father’s
overprotection
7 21 13.0
6
2.08 8 1.63 0.26
Mather’s negation 5 16 10.9
3
1.82 6 1.82 0.30
Mather’s warmth 14 37 24.6
3
5.29 7 3.52 0.76
Mather’s
overprotection
9 31 21.4
8
4.86 8 2.56 0.61
Understanding 4 23 13.1
6
3.79 4 3.29 0.95
Care 7 28 17.6
8
3.94 5 3.54 0.79
Respect 6 26 16.2
5
4.03 4 4.06 1.02
Encouragement 6 27 16.1
2
3.73 5 3.22 0.75
Total score of
ESQUT
19 95 63.2
1
14.8
8
18 3.51 0.83
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Yongmei, H., & Yingxin, C. (2021). Learning Engagement and Its Influencing Factors Among High School Students in Economically Developed Areas
in China. Advances in Social Sciences Research Journal, 8(10). 332-344.
URL: http://dx.doi.org/10.14738/assrj.810.11076
Correlation of scores of each scale
It can be seen from table 2 that the total score of UWES-S-CV is significantly correlated with the
total scores and the scores of each dimension (subscale) of ASES, ICDS, s-EMBU and QUEST
(all P < 0.01).
Table 2 Correlation between UWES-S-CV and scores of ASES, ICDS, s-EMBU and ESQUT
Dimension vitality Didication Focus UWES-S-CV
Self-efficacy of learning ability 0.51** 0.55** 0.50** 0.52**
Self-efficacy of learning
behavior
0.57** 0.40** 0.37** 0.47**
Total score of ASES 0.53** 0.48** 0.44** 0.43**
Conversation -0.19** -0.27** -0.23** -0.25**
Make friends -0.23** -0.29** -0.20** -0.26**
One’s way to people -0.24** -0.20** -0.22** -0.26**
Heterosexual communication -0.15** -0.17** -0.18** -0.29**
Total score of ICDS -0.27** -0.21** -0.24** -0.32**
Father’s negation -0.17** -0.26** -0.32** -0.24**
Father’s warmth 0.51** 0.41** 0.38** 0.44**
Father’s overprotection -0.22** -0.15** -0.10* -0.13**
Mather’s negation -0.18** -0.16** -0.30** -0.26**
Mather’s warmth 0.53** 0.44** 0.36** 0.42**
Mather’s overprotection -0.28** -0.23** -0.12* -0.18**
Understanding 0.23** 0.26** 0.18** 0.24**
Care 0.20** 0.22** 0.17** 0.21**
Respect 0.22** 0.29** 0.21** 0.25**
Encouragement 0.26** 0.32** 0.24** 0.30**
Total score of ESQUT 0.27** 0.31** 0.25** 0.29**
Note: * P < 0.05, ** P < 0.01
Multivariate stepwise linear regression analysis of factors related to learning
engagement of senior high school students
Variable assignment
Firstly, the possible situations (alternative answers) of various demographic classification
variables that may affect the total score of UWES-S-CV are assigned, and the results are shown
in Table 3.
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Table 3 Variable assignment
Items options and assignment
1. Grade 0 = senior one, 1 = senior two, 2 = senior three
2.Gender 0=male, 1=female
3.Only child or not 0=no, 1=yes
4.Family economic status 0 = poverty, 1=food and clothing, 2 = well-off, 3 = rich
5.Origin 0=urban area, 1=town, 2=Rural area
6.Class leader or not 0 = no, 1 = yes
7. School Category: 0 = general middle school, 1 = key middle school
8. Class category: 0 = parallel class, 1 = key class
9. Subject category: 0 = liberal arts, 1 = Science
10. Self-rated academic burden 0 = basically none, 1 = relatively light, 2 = average, 3=
relatively heavy, 4 = Unbearable
11. Learning interest 0 = competely dislike, 1 = dislike, 2 = don't care, 3 = a little
interested, 4 = very interested
12. Academic achievement 0 = very poor, 1 = lower middle, 2 = medium, 3 = relatively
good, 4 = excellent
13. Teaching method used most commonly 0 = traditional teaching method, 1 = role play, 2 =
PBL teaching method, 3 = evidence-based practice
teaching, 4 = simulated debate
14. Learning method used most commonly 0 = individual learning, 1 = cooperative learning
15. Teaching aids used most commonly 0 = traditional teaching means, 1 = conventional
multimedia and 2 = network teaching platform
16. Father's education level 0 = primary school and below, 1 = junior high school, 2 = senior
high school and technical secondary school, 3 = junior college, 4 =bachelor, 5 = master, 6 =
doctor
17. Mather's education level 0 = primary school and below, 1 = junior high school, 2 =
senior high school and technical secondary school, 3 = junior college, 4 = bachelor, 5 = master,
6 = doctor
Multivariate stepwise linear regression analysis of related factors of learning engagement
among high school students
Taking the total score of UWES-S-CV as the dependent variable and the factors that may be
related to the total score of UWES-S-CV (including demographic variables, scores of 6
dimensions of s-EMBU, 2 dimensions of ASES, 4 dimensions of ICDS and 4 dimensions of
ESQUT) as the independent variables, a multivariate stepwise linear regression is carried out
within the 95% confidence interval, and the results are shown in Table 4.
It can be seen from table 4 that 12 factors such as student origin, school category, class category,
learning interest, the teaching method used most commonly, mother's education level, self- efficacy of learning behavior, self-efficacy of learning ability, father's warmth, mother's warmth,
care and encouragement are positively correlated with the total score of UWES-S-CV (β=.120
~.433, all P<.01), grade, family economic status, self-rated learning burden, communication and
making friends, father’s negation and mother’s negation are negatively correlated with the total
score of UWES-S-CV(β=-.073 ~ -.407, all P <. 05).
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Yongmei, H., & Yingxin, C. (2021). Learning Engagement and Its Influencing Factors Among High School Students in Economically Developed Areas
in China. Advances in Social Sciences Research Journal, 8(10). 332-344.
URL: http://dx.doi.org/10.14738/assrj.810.11076
Table 4 Multivariate stepwise linear regression analysis of main influencing factors of UWES-S- CV total score
Dependent Independent B SE β t P R2 Radj2
Variable Variable
UWES-S-CV Origin 2.347 .313 .216 5.438 <.001 .556 .552
School Category 4.534 .508 .412 8.144 <.001
Class Category 4.159 .609 .317 3.588 <.001
Learing interest
Teaching method
5.960 .813
3.001 .832
.376
.120
9.793
3.606
<.001
<.001
Mather’s education 4.208 .544 .299 5.725 <.001
self-efficacy of learning ability 5.581 .675 .433 5.474 <.001
self-efficacy of learning
behavior
3.716 .583 .323 4.360 <.001
Father’s warmth 3.467 .671 .251 5.264 <.001
Mather’s warmth 4.197 .544 .322 4.455 <.001
Care 3.385 .496 .275 5.266 <.001
Encouragement 4.283 .517 .362 7.370 <.001
Grade -2.943 .407 -.139 -4.413 <.001
Family economic status -1.228 .246 -.073 -2.249 .025
Self rated learning burden -2.413 .686 -.114 -3.518 <.001
Communication and making
friends
-4.764 .504 -.310 -4.295 <.001
Father’s negation -3.958 .413 -.323 -5.317 <.001
Mather’s negation -4.421 .480 -.407 -2.226 .033
DISCUSSION
The total score of UWES-S-CV and the scores of three dimensions, the total score of ASES
and the scores of two subscales, the total score of ICDS and the scores of four dimensions, as
well as mother overprotection of s-EMBU of this group are at the medium level, and the total
score of ESQUT and the scores of four dimensions are at the medium high level; Father's and
mother's warmth of s-EMBU are at the high level, and father's negation, mother's negation and
father's overprotection are at the low level; which is consistent with the results of previous
studies [12,17,32,36], suggesting that the current learning engagement and academic self- efficacy among high school students need to be improved; although they have high family
(especially parents’) care and teachers' support, they have obvious interpersonal problems.
The results of multivariate stepwise linear regression analysis shows that 12 factors such as
student origin, school category, class category, learning interest, the teaching methods used
most commonly, mother's education level, self-efficacy of learning behavior, self-efficacy of
learning ability, father's warmth, mother's warmth, care and encouragement are positively
correlated with the total score of UWES-S-CV, grade, family economic status, self-rated learning
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burden, communication and making friends, father’s negation and mother’s negation are
negatively correlated with the total score of UWES-S-CV.
Family economic status and student origin are independent predictors of UWES-S-CV total
score.
Family economic status negatively predicts UWES-S-CV total score. The learning engagement
of rural students is higher than that of town or city students, which is consistent with the results
of previous studies [37], suggesting that family social capital plays an important role in
children's learning behavior. Generally speaking, compared with families with poor economic
conditions or rural families, families with good economic conditions or families from cities and
towns have richer and better social capital, which can provide more development ways and
opportunities for their children, so that they do not need to take the college entrance
examination as the only way out; However, those with poor family economic conditions or rural
students have limited development ways and opportunities. They are more inclined to take the
college entrance examination as the ideal (or even the only) way out, so they put more time and
effort on their study.
Family rearing style is an independent predictor of learning engagement, mainly reflected in
the following factors such as higher education level of mother, and the warmth of father and
mother, can improve the learning engagement of senior high school students, while the rearing
styles of father’s and mother’s negation will reduce the learning engagement of senior high
school students, which is consistent with the results of previous studies [17, 38]. The
mechanism is that mothers with higher education level and the parents adopting positive
upbringing methods such as warmth and understanding are more conducive to cultivating
children' sense of internal control, making them believe that the success or failure of academic
depends mainly on their own ability and efforts, more likely to have stable emotions, higher
self-efficacy, stronger anti-frustration ability and resilience, and be able to deal with difficulties
positively and optimistically, showing higher learning engagement; On the other hand, negative
parenting styles like parents’ negation are easy to make students form the tendency of external
control, and make them believe that success or failure of the academic is determined by
uncontrollable external forces such as luck and opportunity and personal efforts will not
fundamentally change their academic situation. With weaker ranti-frustration and resilience,
they are prone to emotional fluctuations due to stress events (such as learning difficulties), and
face difficulties passively and pessimistically, showing lower self-efficacy and lower learning
engagement. Grade negatively predicts the total score of UWES-S-CV, consistent with the
research results of Sun Jing [39], and inconsistent with the research results of Zhang Jinchuan
[40] or Yuan Pingping [25], which may be due to different sampling areas. However, in this
study, age can not enter the regression equation, suggesting that physical natural maturity is
not the main influencing factor of learning engagement. Relatively speaking, mental maturity
(including the expansion of knowledge, improvement of understanding and judgment ability)
can more affect high school students' learning engagement. At the same time, with the increase
of grade, the demand for autonomy of high school students is also increasing, and the
approaching college entrance examination and job selection tasks further promote this
development. Students are more and more inclined to observe carefully, think deeply and
compare widely in order to make choices about their major or career, which occupies part of
time and reduce their learning engagement.
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Yongmei, H., & Yingxin, C. (2021). Learning Engagement and Its Influencing Factors Among High School Students in Economically Developed Areas
in China. Advances in Social Sciences Research Journal, 8(10). 332-344.
URL: http://dx.doi.org/10.14738/assrj.810.11076
School category, class category, learning interest, teaching methods used most commonly and
self-rated learning burden are independent predictors of UWES-S-CV total score, which is
consistent with the results of previous studies [4, 24, 40, 41], suggesting that school factors play
important roles in high school students' learning engagement. Because school is the main place
for high school students to study, the quality of students, school management and teaching style
are very important to students' academic development and the development of personality and
sociality. Compared with general high schools and parallel classes, key high schools and key
classes have better quality of students, higher students' learning ability, more scientific
management mode, and are better at guiding students to arrange time reasonably and use
learning methods appropriately. Therefore, students are more likely to have the sense of
acquisition and happiness of learning; Compared with the traditional one-way teaching
method, interactive teaching methods such as role-playing, PBL teaching method, evidence- based practice teaching and simulated debate can better inspire students' independent
thinking, give full play to their creativity, make them realize the interest of learning and their
potential in autonomous learning, and encourage them to increase learning engagement.
Academic burden will reduce learning engagement. Because most high school students have a
relatively negative attitude towards academic burden and believe that the academic burden is
heavy [42]. This excessive sense of academic pressure will lead to individual physical and
mental fatigue, reduce the self-efficacy and subjective value of learning, and then reduce
learning engagement. Learning interest can reduce learning pressure and improve learning
engagement. Because people tend to act according to their own wishes, and most of the learning
contents that can arouse learning interest meet the basic psychological needs of individuals [4],
which can mobilize individual positive learning emotions and improve daily academic
resilience, so as to reduce learning pressure, enhance learning self-efficacy, learning acquisition
and happiness, improve learning engagement. Learning behavior and learning ability self- efficacy positively predict learning engagement, which is consistent with the results of previous
studies [20, 22, 12, 23, 37], suggesting that academic self-efficacy can improve learning
engagement. Academic self-efficacy [43] refers to people's expectation of whether they have
the ability to complete tasks in the learning situation, which reflects the subjective evaluation
of students on the extent to which they can control the environment and their learning
behavior. After mastering the corresponding knowledge and skills, academic self-efficacy has
become the decisive factor of individual’s learning effectiveness. It affects people's choice of
learning tasks, persistence of learning activities, and attitude towards learning difficulties. In
the face of learning difficulties, those with low academic self-efficacy think more about their
own shortcomings and see difficulties more difficult than reality, so they bear greater pressure,
avoid difficulties and even cancel learning actions. However, those with higher academic self- efficacy tend to choose challenging learning tasks, establish higher learning goals, stimulate
stronger learning motivation and will, persist in the face of difficulties, recover quickly in the
face of setbacks, deal with difficulties rationally, and create conditions to achieve learning goals.
Care and encouragement are positively correlated with the total score of UWES-S-CV, while
communication and making friends are negatively correlated with the total score of UWES-S- CV, which is consistent with the results of previous studies [12,14]. When senior high school
students get more care and support from peers (the less the score of communication and
making friends, the less the trouble of communication and making friends, the higher the peer
support), and feel more understanding, care, support, encouragement, tolerance and other
caring behaviors from teachers, they will feel their own value. In order to achieve superior
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results and prove their ability, they tend to pay attention to their mastery of knowledge and the
improvement of their ability. They are more enthusiastic and energetic in learning, conducive
to maintaining a focused learning state and a higher degree of learning engagement [44-45].
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