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TYPE Brief Research Report
PUBLISHED 18 September 2024
DOI 10.3389/fpsyg.2024.1268734
OPEN ACCESS
EDITED BY
Mohamed Rafik Noor Mohamed Qureshi,
King Khalid University, Saudi Arabia
REVIEWED BY
Vera Gelashvili,
Rey Juan Carlos University, Spain
Andreia de Bem Machado,
Federal University of Santa Catarina, Brazil
Zhe Li,
Osaka University, Japan
Cristina Tripon,
Polytechnic University of Bucharest, Romania
*CORRESPONDENCE
Jian Lian
lianjianlianjian@163.com
RECEIVED 28 July 2023
ACCEPTED 29 August 2024
PUBLISHED 18 September 2024
CITATION
Wan Y, Lian J and Zhou Y (2024) Injecting
competition into online programming and
Chinese- English translation classrooms.
Front. Psychol. 15:1268734.
doi: 10.3389/fpsyg.2024.1268734
COPYRIGHT
©2024 Wan, Lian and Zhou. This is an
open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic practice.
No use, distribution or reproduction is
permitted which does not comply with these
terms.
Injecting competition into online
programming and Chinese-
English translation classrooms
Yinjia Wan1, Jian Lian2*and Yanan Zhou3
1School of Foreign Language Studies, Shandong Jiaotong University, Jinan, China, 2School of
Intelligence Engineering, Shandong Management University, Jinan, China, 3School of Arts, Beijing
Foreign Studies University, Beijing, China
The introduction of competition has the potential to enhance the ecacy of
students’ learning performance. Nevertheless, there have been contradictory
findings about the impact of intergroup competition on students’ learning
performance and engagement. Therefore, further comprehensive investigations
for this problem are necessary. In order to bridge this gap, the present
study seeks to ascertain the ecacy of intergroup competition in relation to
students’ academic performance and motivation. Consequently, we present
the concept of intergroup competition and implement it within the context
of an online programming course and an online Chinese-English translation
course. The participants of this study consist of sophomore students majoring
in Computer Science and English. Initially, a total of 108 sophomore students
majoring in Computer Science participated. Then, a total of 100 sophomore
students majoring in English participated. A quasi-experimental study was
subsequently undertaken to compare students from two courses, which are
online programming and Chinese-English translation, assigning them to an
experimental group and a comparison group, respectively. Then, we conducted
independent samples t-tests to measure the dierence between the academic
performance of the two group of students from two courses. The results indicate
that both groups of students who were exposed to the intergroup competition
mechanism demonstrated considerably higher levels of academic performance
and engagement compared to the other group of students. The findings indicate
that the competition mechanism, has the potential to be a beneficial instrument
for enhancing both students’ learning performance and motivation.
KEYWORDS
online education, engagement, academic performance, cooperative learning,
intergroup competition
1 Introduction
Previous studies have regarded online learning as a valuable instrument for enhancing
face-to-face learning activities (Albors-Garrigos et al., 2011;Arnal et al., 2017;English et al.,
2022). In recent decades, Online learning has gained widespread acceptance as a viable
educational strategy across different levels of education in recent decades (Adedoyin and
Soykan, 2020;Almahasees et al., 2021;Alaali, 2022). This is primarily due to the potential
of online learning approaches to facilitate accelerated and enhanced developmental trends
(Campanella et al., 2008;Hodges et al., 2020;Tripon, 2022).
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Wan et al. 10.3389/fpsyg.2024.1268734
Numerous studies have been undertaken to investigate the
implementation of intergroup competition inside university
courses. In the work of Wood et al. (2005), it was observed that
university students were able to learn course-related knowledge
through their ability to adapt to competitive environments. In a
study conducted by Lee et al. (2022) and Yu (2001), it was shown
that fifth-grade kids exhibited improved learning outcomes after
adopting intergroup competition. However, the implementation of
intergroup competition as a cooperative method resulted in poorer
performance in science class for students of the same grade. To
note that current studies did not directly address the examination
of intergroup competitive mechanisms within an online learning
environment. Hence, further investigation needs to be conducted
to explore the impact of intergroup competition in the context of
online learning.
Despite several research have examined the various aspects
affecting online learning, particularly in relation to online
programming platforms, a significant gap remains in the literature
about the omission of intergroup competitive mechanisms
and their impact on these platforms. As elucidated in the
aforementioned literature, a range of intergroup competition
tactics can be utilized as a stimulating modality for fostering
collaboration. These studies have also demonstrated their potential
effectiveness in enhancing students’ learning performance and
engagement. Furthermore, the global emergence of the novel
Coronavirus in 2020 has resulted in a reduction of the traditional
offline learning period that typically coincides with online learning,
potentially exerting a substantial impact on the process of online
education. Hence, the purpose of this study was to investigate
the impact of incorporating intergroup competition into an
online platform on the academic performance and engagement of
university students.
2 Literature review
Over the past few decades, significant advancements in
information technologies have instigated transformative shifts
within universities globally (Jones and O’Shea, 2004;Kurbel et al.,
2009;Dwivedi et al., 2019;Liang and Cui, 2022). E-learning
and the Internet are widely recognized as suitable platforms for
delivering various sorts of courses to students, without spatial
or temporal limitations. Numerous studies have been undertaken
in the past to examine the impact of e-learning on university
students (Yu and Yu, 2010;Mese and Sevilen, 2021). For instance,
the study conducted by Chan and Waugh (2007) aimed to
examine the various factors influencing the level of student
engagement in the online learning environment (OLE) among
mathematics distance learning students at the Open Learning
University of Hong Kong (OUHK). Their objective was to identify
potential recommendations for enhancing students’ utilization of
the OLE. A survey instrument was developed with the purpose of
investigating the usage patterns of OLE among students. The results
of the statistical analysis indicated that students had a positive
inclination toward utilizing the OLE as a means of exchanging
knowledge and engaging in collaborative learning. In addition,
the students expressed a collective preference for receiving lesson
notes and assignment solutions concurrently. The work of Liu
et al. (2010) introduced the technology acceptance model as a
fundamental framework and expanded upon the external and
perceived variables inside their model. This study involved the
participation of 436 senior high school students from Taiwan,
who were engaged in an online learning community with a
specific emphasis on English language acquisition. The research
findings indicated that the inclusion of additional variables can
be a reliable predictor of user adoption in an online learning
community. In their study, Fan et al. (2021) examined the various
factors that influence the motivation of online learners. The
sample consisted of 93 participants, and the study considered
components such as learners, educators, curriculum, and platform,
as well as 13 subordinate factors. The findings of the study
indicate that several factors, such as learning demand, self-efficacy,
instructor personal traits, educational level, course material,
course assessment, technical support, learning interaction, and
incentive measures, have been observed to exert considerable
positive influences on the motivation to engage in online learning.
Nevertheless, there is a limited amount of research that has
specifically examined the impact of competition mechanisms,
which often include comparing one’s performance to others who
are completing the same activity (Coakley, 2007), on students’
academic accomplishment and engagement. Interpretation of the
social interdependence theory posits that intergroup competition
emerges as a consequence of team members uniting to engage
in competitive activities against competing groups. According
to Deci et al. (1981), common consequences of engaging in
competition encompass achieving victory and fostering a sense of
collective pride within the group. Typically, the implementation
of point-based rewards and leaderboards is a prevalent approach
to stimulate competitiveness within various contexts (Chang et al.,
2018;Hudja et al., 2020;Seaborn and Fels, 2015). One perspective
suggests that the allocation of points by teachers or peers might
serve as a direct source of motivation within an educational setting.
On the contrary, leaderboards have their origins in the realm
of cybergames and can be integrated into the online educational
setting to augment students’ motivation through the provision of
immediate feedback. In the study conducted by Dreu et al. (2021),
the authors investigated the impact of intergroup competition on
the cooperative performance and interactive strategies of primary
school children. A total of 80 students were selected to participate
in a puzzle task, where they were divided into groups of four and
instructed to collaborate. A total of eight groups were randomly
allocated to the non-competitive condition, whereas twelve groups
were placed to the competitive condition. The findings of this study
indicated that intergroup competition had a suppressive effect
on the frequency of communication among groups consisting of
younger students, whereas it led to an expansion of communication
among groups consisting of older students (Chen and Chiu,
2016). The present study devised a mechanism for intergroup
competition and included it into a multi-touch platform designed
for collaborative learning in the context of design-based education.
The objective was to augment the engagement levels, learning
outcomes, and creative abilities of primary school pupils. The
results of the statistical study indicated that students who were
exposed to intergroup competition had considerably higher levels
of student engagement, learning accomplishment, and originality
compared to students who were not exposed to competition.
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Previously, many studies have examined the impact of
competition on the academic performance of the students in
middle schools. For instance, the work of Chan and Lam (2008)
compared the influence of intergroup competition on students’
writing self-efficacy in vicarious learning. In the competitive group,
the self-efficacy decreased when the students engaged in vicarious
learning. In the control group, the self-efficacy of students did not
have a significant difference in vicarious learning. Furthermore,
in a game-based learning context designed for the students of
seventh-grade, the study Chen et al. (2019) revealed the impact of
competition and engagement in games, as well as the associations
between them on performance of the students in science learning.
To note that this study did not conceive that competition alone
had a direct effect on the students’ performance. However, it was
indirectly related to performance along with engagement. Then,
in the study Chen et al. (2020), a meta-analysis was conducted
to investigate the impacts of competition on digital game-based
learning. From this research, it can be found that competition
was effective in digital game-based learning for math, science and
language except for social science. Meanwhile, competition was
effective for K12 students and students in the universities. Ho et al.
(2021) proposed a meta-analytical study, in which whether peer
competition and peer collaboration moderated the effectiveness
of gamification in learning performance was investigated. In their
study, a moderating effect of peer competition in gamification in
learning could be revealed, which suggested that competitive games
were better than non-competitive games for promoting learning
performance. Recently, Wang and Huang (2023) presented a
question bank practice game to offer a situation of inter-
group competition with intra-group collaboration. Meanwhile,
a research model was devised to investigate the correlations
between competition, collaboration, and learning performance. Its
findings indicated that competition is a more significant factor than
collaboration for learning performance.
It can be summarized that the current studies for revealing the
influence of competition on students’ performance are still limited
to some extent. To be specific, the experts and scholars in this are
have neglected the influence of competition on the students taking
online courses, which needs to be studied in-depth.
2.1 Research questions
Bearing the above-mentioned analysis in mind, in this study we
raised the following questions as:
•Is there a significant difference in academic performance
between sophomore students who collaborate with their
partners to complete a Python project or a Chinese-English
translation project in online platforms, with the experiment
group utilizing an intergroup competition mechanism,
compared to the control group without such a mechanism?
•Does the introduction of an intergroup competition strategy,
where sophomores collaborate with their partners to complete
a Python project or a Chinese-English translation project
in the same online platforms, result in higher levels of
engagement for students in the experimental group compared
to the control group without the intergroup competition
mechanism?
3 Methodology
This research endeavored to delve into the intricacies of online
learning dynamics by adopting a quasi-experimental design, which
is a robust framework for comparing the experiences of two distinct
groups—an experimental group subjected to a specific intervention
and a control group that was not. The crux of this intervention
was an independent variable, meticulously crafted to introduce a
competitive element into the online learning environment. The
study’s primary focus rested on two pivotal dependent variables:
academic performance and engagement. Academic performance
was gauged through the traditional yet effective measure of
testing results. These assessments were meticulously designed to
evaluate the students’ grasp of the course material and their ability
to apply theoretical concepts in practical scenarios. The tests
were administered at various intervals throughout the course to
capture a comprehensive view of the students’ learning trajectories.
Engagement, on the other hand, was a more nuanced construct
that required a different approach to measurement. To this end, a
questionnaire was employed, crafted with care to encompass a wide
array of factors that contribute to a student’s active participation
in the learning process. This included, but was not limited to, the
frequency of logins, the depth of interaction with course materials,
and the level of contribution to online discussions and group
activities.
The initial stage of the study involved a thorough assessment
of the students’ learning performance through these testing results.
This phase was critical in establishing a baseline from which the
effects of the intervention could be measured. The results were
meticulously analyzed to identify patterns, trends, and any potential
outliers that could provide deeper insights into the learning
process. To complement the quantitative data garnered from the
tests, the qualitative data collected through the questionnaires
offered a rich tapestry of information regarding the students’
attitudes, motivations, and behaviors in the online learning
environment. This dual-pronged approach allowed for a more
holistic understanding of the impact of intergroup competition on
learning outcomes.
For the statistical analysis, the study leveraged the capabilities
of SPSS Statistics 22.0, a powerful tool renowned for its
comprehensive suite of analytical features. This software facilitated
the processing and interpretation of both the quantitative test
scores and the qualitative questionnaire responses. The analysis
was conducted with a keen eye for detail, ensuring that all data
were accurately represented and that the findings were robust and
reliable. To maintain the highest standards of scientific rigor, a two-
tailed αthreshold of 0.05 was employed for all statistical tests. This
conservative approach to statistical significance ensured that any
observed effects were not merely the result of chance, but rather
indicative of a genuine impact of the intervention on the students’
academic performance and engagement.
In summary, this study meticulously orchestrated a quasi-
experimental design to explore the effects of intergroup
competition on students’ online learning. Through a combination
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of testing results and questionnaires, a comprehensive assessment
of academic performance and engagement was conducted. The
use of SPSS Statistics 22.0 and a stringent alpha threshold further
underscored the study’s commitment to producing meaningful,
actionable insights into the complex interplay between competition
and learning in the digital age.
3.1 Participants
For the online programming course, a total of 108 sophomore
students, consisting of 48 females and 60 males, with ages
ranging from 18 to 22 years [MEAN = 20.69, Standard Deviation
(SD) = 1.62], who were majoring in computer science, software
engineering, and information management at Shandong Normal
University, were selected as participants. For the Chinese-English
course, a total of 100 sophomore students, consisting of 72 females
and 28 males, with ages ranging from 18 to 22 years (MEAN =
20.57, SD = 1.42), who were majoring in English at Shandong
Normal University, were selected as participants. The students were
enrolled in a series of courses for a duration of one year, which
covered essential topics such as online programming and Chinese-
English translation. During the Spring semesters of 2020 and 2022,
a group of students enrolled in an online Python course and an
online Chinese-English translation course, respectively. To note
that they were unable to physically attend the in-person session as
a result of the outbreak of the new Coronavirus. In this research,
the participants were allocated to two separate groups, including
the group adopting the intergroup competition mechanism and the
group that did not adopt intergroup competition mechanism (or
the control group).
3.2 Ethical approval
Ethical approval for this research was granted by the
Institutional Review Board at Shandong Jiaotong University,
reinforcing the study’s commitment to ethical standards and
academic integrity. Written informed consent was obtained
for each participant according to institutional guidelines. The
informed consent explains the study’s purpose, the participants’
rights, and the confidentiality of their responses.
3.3 Materials
A pre-test and a post-test were conducted to evaluate the
difference between students who were exposed to the intergroup
competition mechanism and those who were not. On the other
hand, the previous assessment was employed to evaluate the
students’ foundational understanding of online programming
and Chinese-English translation. Conversely, the post-test was
administered as the culminating assessment for the online
programming and Chinese-English translation. The students were
mandated to finalize the examinations within a time frame of 60
min and 90 min, respectively.
3.4 Online programming and Chinese-
English translation projects using
intergroup competition mechanism
A group of three students was mandated to collaborate in order
to develop a software application using the Python programming
language. Prior to the commencement of this online learning
course, we facilitated an online programming platform utilizing
Visual C# 2015. This platform enabled students to effectively engage
with their software development assignments. In order to evaluate
the students’ knowledge, it was necessary for them to undergo a
preliminary examination. The evaluation process involved utilizing
a standardized test paper with predetermined answers, which
were graded accordingly. Throughout the duration of the 18-week
course, a teacher and two assistant teachers evaluated each team
by appraising the level of project completion and rating their
performance in terms of teamwork on a weekly basis. In order
to mitigate potential bias from the teachers, a method of ranking
was employed wherein the average of three grades was utilized.
Then, the students in each team were informed of their respective
positions on the leaderboard, which was made available on the
online learning platform (see to Figure 1). After the completion of
the entire project, the students were instructed to partake in a post-
test, wherein a test paper containing predetermined answers was
utilized.
In a Chinese-English translation course, a team of three
students was required to work together on a collaborative
translation project. The online learning platform facilitated their
engagement with the translation tasks effectively. To assess their
proficiency, students were required to take a preliminary exam,
which was conducted using a standardized test. Throughout the 18-
week course, a teacher and two teaching assistants evaluated each
team on a weekly basis. They assessed the completion level of the
project and rated the students’ teamwork. To ensure fairness and
reduce teacher bias, the grading was done by averaging the scores
from three different sources. The students were then informed
of their standing on a leaderboard, which was accessible on the
Chinese-English translation course’s online platform. At the end of
the project, students were asked to take a post-test, which used a
test paper with predetermined correct answers.
In order to evaluate the level of student involvement and the
impact of intergroup competition strategy on student engagement,
a Chinese version of the UK involvement Survey (Bokhove and
Muijs, 2019) was employed. The survey consists of 14 items
designed to evaluate the quality of the learning materials, utilizing
a 4-point Likert-type scale ranging from 1 (indicating minimal
impact) to 4 (indicating significant impact).
4 Results
Two independent samples t-tests were conducted to evaluate
the difference between the groups of students in the pre-
test and post-test. As depicted in Tables 1,2, the descriptive
statistics encompassing the MEANs and SD for the dependent
variables pertaining to both academic achievement and students’
engagement are presented.
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FIGURE 1
The leader-board in the online learning platform.
TABLE 1 MEANs and standard deviations of the dependent variables for the online programming course.
Dependent variables Competition group Control group
MEAN SD MEAN SD
Academic performance 81.45 2.40 67.48 1.78
Engagement 47.20 2.55 39.10 2.20
4.1 Academic performance
The present study employed an analysis of covariance
(ANCOVA) to examine the impact of students’ programming
or Chinese-English translation proficiency on their post-test
performance. Specifically, the study aimed to compare the post-
test scores of students in the competition group with those in the
control group, while controlling for the influence of their prior
test scores as a covariate. For the online programming course,
a statistically significant distinction was observed between the
competition group and the control group in terms of their academic
performance, as indicated by the obtained F-value = 1.82 (p=0.02)
and effect size (ηp=0.96). Meanwhile, for the Chinese-English
translation course, a statistically significant distinction was also
observed between the competition group and the control group in
terms of their academic performance with the F-value = 1.95 (p=
0.01) and effect size (η2
p=0.86). For both courses, the post-test
results revealed a considerable improvement in the performance
of students in the competition environment compared to their
counterparts in the control group.
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TABLE 2 MEANs and standard deviations of the dependent variables for the Chinese-English translation course.
Dependent variables Competition group Control group
MEAN SD MEAN SD
Academic performance 87.31 2.65 79.57 1.90
Engagement 52.40 2.46 44.60 2.76
4.2 Engagement
The researchers utilized an independent samples t-test
to evaluate the difference in engagement levels between the
competition group and the control group for both the online
programming and Chinese-English translation courses. The
findings from the online programming course of the t-test indicated
a substantial difference in the level of student engagement [t(106) =
17.67, p<0.01, η2
p=0.86]. The findings of the research indicate
that the level of student engagement in the intergroup competition
condition (MEAN = 47.20, SD = 2.55) was considerably higher
compared to the control group (MEAN = 39.10, SD = 2.20).
Meanwhile, the findings from the Chinese-English translation
course of the t-test indicated a substantial difference in the level
of student engagement [t(98) = 14.92, p<0.01, η2
p=0.83]. And
the level of student engagement in the intergroup competition
condition (MEAN = 52.40, SD = 2.46) was also considerably higher
compared to the control group (MEAN = 44.60, SD = 2.76).
5 Discussion
The study’s findings, as outlined in Section 2.1, are remarkable.
They clearly indicate that there are substantial differences in both
academic performance and engagement between the students who
were part of the inter-competition group and those in the control
group. Moreover, the results suggest that students who engaged
in intergroup competition achieved higher academic performance
than their peers who did not participate in such competitive
activities.
The results presented in the study conducted by Wood
et al. (2005) support the notion that the implementation of
intergroup competition might positively impact participants’
learning performance and motivation. The findings of this study
align with the research conducted by Tauer and Harackiewicz
(2004), where they observed that the integration of competition
and cooperation (specifically intergroup competition) consistently
resulted in increased levels of intrinsic motivation. This study
provides support for the research conducted by Roncarati et al.
(2006), suggesting that inter-group competition, as opposed to
collaboration, may enhance assessment performance and learning
outcomes. The findings align with those of Akpinar et al.
(2015), who observed that cultural variations in attitudes toward
collaboration and competitiveness can potentially impact learning
outcomes to some extent. The international competition and
the opportunity to engage with real-life business problems serve
as catalysts for students’ active involvement and contribute to
improved academic performance. The implementation of an intra-
group collaboration framework, supplemented by an inter-group
mechanism, has the potential to foster student motivation and
active engagement in learning activities, while also facilitating
positive social contact within the context of a design project.
According to Chen and Chiu (2016), the practice of assigning
points to students based on their behaviors is widely regarded as a
potent motivator that has a positive impact on both their academic
achievement and level of involvement. Consequently, it can be
inferred that the students who were exposed to the intergroup
competition condition would have achieved higher levels of
academic performance compared to the students in the control
group. In contrast, the study conducted by Yu (1998) investigated
the comparative impacts of collaboration with and without inter-
group competition on students’ academic performance in science
and their attitudes toward science within a Computer-assisted
instruction (CAI) setting. The data that was acquired revealed
notable disparities between the two situations in terms of student
academic performance and student attitudes toward the field
of science. Nevertheless, based on the analysis of the findings,
it was recommended that the instructional strategy of intra-
group cooperation without inter-group competition be used as the
optimal approach. In contrast to the study conducted by Lam et al.
(2001), which encompassed a broader range of dependent variables
such as task enjoyment, achievement attribution, and test anxiety,
our analysis focused solely on examining the impact of intergroup
competition mechanism. Specifically, we evaluated this influence by
measuring learning performance and engagement as the dependent
variables. In the subsequent phase, we will proceed to incorporate
additional dependent variables into the investigation. Recently,
the work of Fernández Fernández et al. (2021) investigated the
influence of transition from face-to-face teaching to online learning
on students in college and the corresponding sustainability. It
can be observed from the outcome of this study that despite
being sustainable from an environmental, social, and economic
perspective.
Despite the numerous discoveries from this study, it is crucial
to acknowledge its limitations. First of all, the selection of the study
population might have introduced subjectivity. To be specific, only
the students majoring in Computer Science were selected partially
because one of the researchers is majoring in Computer Science.
In addition, due to the exclusive utilization of online learning and
the absence of offline activities in this study, it is plausible that
the implementation of this technique by classroom teachers may
be limited. Hence, it is possible that the results obtained may not
be applicable to the traditional face-to-face learning environment.
Furthermore, it is important to note that this research was only
carried out on a small group of sophomore students from one single
university. Therefore, caution should be exercised when attempting
to generalize the findings to a wider population encompassing other
majors, grade levels, districts, and nations.
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6 Conclusion
This study aimed to examine the effects of the intergroup
competition mechanism on students’ learning performance and
engagement in the context of intra-group cooperation. This study
represents an initial exploration of the online learning environment
by integrating an intergroup competition mechanism into an online
programming course and an online Chinese-English translation
course. A quasi-experimental design was employed to compare the
performance of students in the competition group with those in the
control group.
From the research and literature review in this study, it
can be observed that the introduced intergroup competition
mechanism has proven its capability for enhancing student
performance in online programming and Chinese-English
translation contexts. Meanwhile, it can also be observed that
competition is a potentially valuable instrument for improving
the students’ engagement. However, there are still several
limitation of this study need to be acknowledged. First of all,
the number of samples collected in this study is still limited,
and more samples should be provided in our next studies.
Moreover, since this is a case study, we did not take more
influence factors into consideration, which might have neglected
the impact of various factors on the students’ performance
and engagement.
In the future, to enhance the comprehensiveness of the analysis
regarding the impact of the intergroup competition mechanism,
it is recommended that further studies incorporate large number
of data samples and more different types of dependent variables.
In addition, we will also continue to study the other types of
influencing factors.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Shandong
Management University, School of Intelligence Engineering Ethics
Committee. The studies were conducted in accordance with the
local legislation and institutional requirements. The participants
provided their written informed consent to participate in this study.
Written informed consent was obtained from the individual(s)
for the publication of any potentially identifiable images or data
included in this article.
Author contributions
YW: Writing – review & editing, Data curation, Supervision,
Formal analysis, Validation. JL: Writing – original draft, Validation,
Supervision, Project administration, Methodology, Investigation,
Formal analysis, Conceptualization. YZ: Writing – original draft.
Funding
The author(s) declare financial support was received for the
research, authorship, and/or publication of this article. This work
was made possible through support from the Natural Science
Foundation of Shandong Province (No. ZR2020MF133), Key
Laboratory of public safety management technology of scientific
research and innovation platform in Shandong Universities during
the 13th Five Year Plan Period, Collaborative innovation center of
“Internet plus intelligent manufacturing” of Shandong Universities,
and intelligent manufacturing and data application engineering
laboratory of Shandong Province.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.
1268734/full#supplementary-material
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