Almulla, Cogent Education (2023), 10: 2172929
https://doi.org/10.1080/2331186X.2023.2172929
CURRICULUM & TEACHING STUDIES | RESEARCH ARTICLE
Received: 14 March 2022
Accepted: 22 January 2023
*Corresponding author: Mohammed
Abdullatif Almulla, Education Curricula and Instruction, King Faisal
University, Saudi Arabia
E-mail: maalmulla@kfu.edu.sa
Reviewing editor:
A.Y.M. Atiquil Islam, Department of
Education Information Technology,
East China Normal University,
Shanghai, China
Additional information is available at
the end of the article
Constructivism learning theory: A paradigm for
students’ critical thinking, creativity, and problem
solving to affect academic performance in higher
education
Mohammed Abdullatif Almulla1*
Abstract: This study looks at whether creativity and critical thinking help students
solve problems and improve their grades by mediating the link between 21st century
skills (learning motivation, cooperativity, and interaction with peers, engagement
with peers, and a smart classroom environment). The mediating relationship
between creativity and critical thinking was discovered using structural equation
modelling (SEM) research. The participants in the study were 297 postgraduate and
undergraduate students from four faculties at King Faisal University who consented
to take part. They were chosen using random sampling and volunteered to take
part. Learning motivation, cooperativity, peer interaction, peer engagement, and
a smart classroom environment all had a direct positive impact on students’ critical
thinking and creativity; their critical thinking and creativity had a direct positive
impact on their problem solving and academic performance; and their problem
solving had a direct positive impact on their academic performance. The hypotheses
developed a model for measuring students’ critical thinking and creativity, which
affect problem-solving skills and thus students’ academic performance in Saudi
Arabian higher education.
Subjects: Teaching & Learning - Education; Educational Research; Education Studies;
Teaching & Learning; Study Skills; Teachers & Teacher Education; Theory of Education;
ClassroomPractice; Curriculum Studies
ABOUT THE AUTHOR
Dr. Mohammed Abdullatif Almulla is an Associate Professor. He is the Dean of the College of Education
at King Faisal University, As well as, chairman of the graduate studies and scientific research commit­
tee in the College of Education at King Faisal University. He received the Ph.D. degree Leicester
University in United Kingdom, in 2017.
Also, he is currently an Associate Professor in the curriculum and instruction department at King Faisal
University, Al-ahsa, Saudi Arabia. His research focus on blended learning, online learning, flipped
classroom, social media networks, thinking development skills, and problem based learning, and
teaching methods.
Mohammed Abdullatif
Almulla
© 2023 The Author(s). This open access article is distributed under a Creative Commons
Attribution (CC-BY) 4.0 license.
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Keywords: critical thinking; problem solving; academic performance; higher education;
Structural Equation Modelling (SEM)
1. Introduction
Technological, sociological, and scientific advancements resulted in a variety of changes in society
and education. As a result, intellectual ability is no longer adequate for survival in the twenty-first
century (Kazemi et al., 2020). Educators, businesses, and policymakers all stressed the need for these
skills, dubbed “21st-century competences” (Benbow et al., .2020; Vista, 2020). While these skills are
considered essential for survival in the twenty-first century, they were always been necessary
throughout history (Kazemi et al., 2020). However, in today’s world, these skills must be updated
and taught to meet the demands of a globalizing globe (Dishon & Gilead, 2020).
These skills include the ability to adapt quickly to the digital world, the ability to learn outside of
the classroom, the adoption of a lifelong learning motivation approach, not viewing the teacher as
the sole source of information, and not overburdening the mind with unnecessary details as
a result of excessive information exposure (Lucas, 2019). The term “21st century skills” refers to
a wide range of skills (Vista, 2020). Individuals’ use of analysis, reasoning, and cooperativity skills in
understanding and resolving circumstances connected to their interests is referred to as 21stcentury skills in general (Ananiadou & Claro, 2009).
Theories of cognitive development among emerging adults posit that environmental and agere­
lated influences are responsible for individual differences in complex reasoning abilities (Orona
et al., 2022). Therefore, the concept of 21st century skills has gained popularity in higher education
and general education during the past few decades. The fundamental tenet of this concept is the
conviction that individuals who leave school to enter the workforce today need a specific skill set in
order to be successful and contribute to the advancement of the economy and society in an
environment that is both challenging and complicated (Tight, 2021).
The key contributions, according to Li et al. (2021), are on describing the critical skills and
subject-matter expertise in demand in the manufacturing sector and identifying possibilities for
worker training and upskilling to solve the growing skills and knowledge gap. However, Kocak et al.
(2021) examined if problem-solving and other 21st century skills (such as algorithmic thinking,
creativity, digital literacy, and effective communication) are related via the lens of cooperation and
critical thinking. Overall, the results show that critical thinking is an essential intermediary between
problem solving and other 21st-century skills.
One of the most important goals of today’s educational institutions is to guarantee that
students was these skills in order to succeed in social and commercial circumstances and to
fully participate in democratic societies (Dishon & Gilead, 2020; McGunagle & Zizka, 2020). As
a result, numerous studies where been conducted around the world to determine which of these
competencies educational institutions must provide. In addition, the advent of new and more
complicated skill needs may be influenced these categorization differences.
Despite this disparity, it is widely believed that children need teamwork, creativity, critical
thinking, and problem-solving skills to improve their academic success (Sayaf et al., 2022; 2018; AlRahmi et al., 2021a; Kazemi et al., 2020; Van Laar et al., 2017). In practically every circumstance,
these vital skills are required. Critical thinking, problem-solving, and academic success all been
crucial to humanity’s existence from the beginning of time to the present. Interpersonal, learning
motivation and engagement skills, as well as critical thinking and problem-solving talents, always
been prized by humans.
They are the foundation of cooperativity and other 21st-century talents since they are based on
social interaction (Bulus¸ et al., 2017; Gkemisi et al., 2016; W. M. Al-Rahmi et al., 2015a; Alhussain
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et al., 2020). Cooperation is now regarded as one of the most important corporate skills.
Individuals are expected to operate as part of a group or team because workloads were grown
dramatically (Lewin & Mcnicol, 2015; Marbach-Ad et al., 2019).
Collaboration is also necessary for the discovery of hidden skills (Neubert et al., 2015). It is
considered a vital talent since it allows for the abstract and logical selection of components with
the purpose of issue solving via analysis (Doleck et al., 2017). In today’s digital age, a smart
classroom environment is also a necessary skill (Günes¸ & Bahçivan, 2018).
It is made up of both cognitive and technical skills (Lewin & Mcnicol, 2015). It is a crucial talent
for addressing problems, cognitive, and social challenges in the smart classroom, in particular
(Eshet-Alkalai, 2004). A “smart classroom” is a physical learning motivation environment that
incorporates modern educational technologies. Students can participate in formal educational
learning motivation experiences that go beyond what can be delivered in regular classrooms in
such a setting (Macleod et al., 2018).
A smart classroom environment has also been shown to increase students’ enthusiasm to study,
enhance active learning motivation, and improve academic achievement in previous studies (Jena,
2013; Liu et al., 2011).Despite the prevalence of research on the use of peer review in the class­
room, the function of student interaction in impacting learning achievement is seldom investi­
gated. In terms of engagement with educational materials, student involvement is a measure of
learners’ dedication to their learning motivation (Bolliger & Armier, 2013; Cole, 2009).
Students’ interaction is a sort of active learning motivation in which students do self-study,
utilizing course materials to engage in active learning motivation. This research base our research
on the idea that peer review can improve student interaction. Additionally, students’ participation
in offering and reacting to peer feedback may influence learner interaction, which, in turn, can
influence learning outcomes and students’ academic success (Al-rahmi et al., 2015b; Goh et al.,
2019). To put it another way, learner interaction and students’ involvement may operate as
a buffer between students’ participation in offering peer feedback on critical thinking and creativity
and their problem-solving and academic achievement (Al-Rahmi et al., 2021b).
In education, motivation plays a crucial role in both teaching and learning, encouraging teachers
to be passionate about their work and fostering student engagement (Coates, 2007). Motivation is
regarded by researchers (Al-Bassam, 1987; Brophy, 2010) as one of the key elements for success in
teaching and learning. A vital and significant component of the learning process is motivation
(Brewer & Burgess, 2005). In order to obtain or attain learning when learning a new skill, the
learner must be a desire and/or a need to learn. Research on second language acquisition and
instruction has focused heavily on students’ motivation (Simmons & Page, 2010). Rahman and
Alhaisoni (2013) and Mitchell and Alfuraih (2017) assert that the Saudi government has made
various changes to the English language curriculum and made English a required subject in schools
and colleges as a sign of its growing appreciation for the value of the language.
The level of success in learning, however, is still below expectations. The majority of Saudi pupils
simply possess simple reading and writing skills and are unable to communicate in English. All of
these issues contribute to the occurrence of Saudi students having low levels of motivation to
learn; hence, it is crucial that language teachers receive training and instruction on how to include
motivating approaches into their daily teaching practices (Alrabai, 2014). As previously stated, one
of the motivating factors for students is the classroom delivery strategies and teaching philoso­
phies used by teachers. Therefore, motivation is the drive that propels students to pursue knowl­
edge, persevere through learning challenges, and improve their skills.
In the Middle East and Saudi Arabia, however, no study on model construction for exposing the
levels of 21st-century core competencies to each other was identified in the literature. In this
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sense, the study’s main goal is to build a structural model that analyses the link between 21stcentury talents and 21st-century talents. The validity of this research is demonstrated by the
creation of models that reflect expected levels of 21st-century skills. This study will also contribute
to the literature by emphasizing the importance of cooperativity and critical thinking as 21st
century skills for improving students’ academic success. As a result, this research should fill
a gap in the literature.
2. Research model and hypotheses development
Because it is difficult in today’s educational system to teach these talents to students, educational
policy-making institutions can supply 21st-century skills using the model provided. Furthermore,
governments require that higher education students’ progress in a world where information is king.
As a result, students in higher education must be equipped with 21st-century competencies (Kocak
et al., 2021). As a result, this study aimed to establish a new model by exploring the impact of
critical thinking and creativity in problem-solving and academic accomplishment among university
students using learning motivation, cooperativity, peer interaction, peer engagement, and a smart
classroom environment. Figure 1 depicts how the discovery enhances critical thinking and crea­
tivity, which in turn enhance problem solving and academic achievement.
2.1. Learning motivation (LM)
Throughout the learning process, learning motivation (LM) encourages people to perform activities
that will help them achieve a goal, satisfy a need, or meet an expectation (Gopalan et al., 2017,
October). Despite the fact that there is no unanimity on the subject, Pintrich et al. (1991) published
research that identified two basic motivating constructs: critical thinking and creativity.
Previous research has shown that a student’s LM is a critical relationship between their
performance and accomplishment in a variety of learning situations. In an online learning envir­
onment, Roberts and Dyer (2005) discovered that students’ learning motivation was linked to
critical thinking, a component of problem-solving and academic success. According to Gong et al.
(2020), students’ excitement for learning had a direct influence on their computational thinking
skills in the classroom, which included creativity, teamwork, critical thinking, and problem-solving,
according to Gong et al. (2020). Therefore, this research needs to develop and test the following
hypotheses in Saudi Arabia based on the discussion above:
Figure 1. Research Model with
Hypotheses
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H1: LM is positive with CR.
H2: LM is positive with CT.
2.2. Cooperativity (CO)
The association between critical thinking and cooperation skills has been studied in the literature.
In the bulk of this research, cooperation, or cooperative activities, is found to be strongly linked to
critical thinking skills or improved critical thinking skills (Chen & Swan, 2020; Duncan, 2020). Norris
and Ennis (1989) offer a four-stage structure for critical thinking, with the last phase being “a
critical examination of others’ opinions.” Individual viewpoints may also be best studied
in situations where they collaborate. Cooperative work lays the groundwork for discussions in
which students are better able to apply their key traits (Lucas et al., 2020; Osborne et al., 2018).
Therefore, this research needs to develop and test the following hypotheses in Saudi Arabia based
on the discussion above:
H3: CO is positive with CR.
H4: CO is positive with CT.
2.3. Peers interaction (PI)
Individual student interaction, individual student contact with a group of people, and individual
student contact with two groups of individuals are all possibilities. Sher (2009) (p. 104) defines
student interaction as the exchange of information and opinions about the course amongst
students in the presence or absence of the teacher. This type of contact might take the form of
group projects or debates, and it can benefit students by increasing involvement, engagement, and
knowledge sharing. It can also affect a student’s academic performance. According to Downing
et al., 2011), p. 85, students in an online learning environment miss out on the benefits of
organized conversation and the sense of community that might grow in a more traditional class­
room setting.
As a result, the absence of touch in an online educational environment should be avoided
in order to establish a smart classroom setting that is similar to traditional classrooms, which
are full of crucial learning engagement. According to Nair and Patil (2012), students’ engage­
ment with one another through the use of learning management system features was
reflected in their academic achievement since it drove them to continue their learning
activities.
According to Kang and Im (2013), instructional interaction components were more predictive
of participants’ perceived learning accomplishments than social interaction characteristics. They
discovered that factors linked to instructional engagement and the presence of an instructor had
stronger predictive value than those related to social contact in predicting learners’ subjective
pleasure. However, Koskey and Benson (2017) highlighted many challenges to adopting high levels
of student-student engagement in an online setting, including class size, time spent evaluating
student learning, learning motivation, and experience cooperating in the use of technology.
Therefore, this research needs to develop and test the following hypotheses in Saudi Arabia
based on the discussion above:
H5: PI is positive with CR.
H6: PI is positive with CT.
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2.4. Peers engagement (PE)
Academic development appears to be influenced by assessments of students’ engagement, con­
duct, and academic experiences. According to Kolb and Kolb (2005), the focus should be on
students’ experiences and attempts to incorporate them into the process in order to improve
higher education learning. Furthermore, interpersonal connections, according to Deci (1992), can
provide students with a sense of belonging (Masika & Jones, 2016; Sayaf et al., 2021), which can
lead to them viewing learning as a happy process (Kember, 2001) and therefore being more
engaged in their studies.
Academics was long argued over what defines involvement. The term is commonly used by
researchers working on the National Survey of Student Participation (2017) to define aspects like
effort quality and engagement in productive learning activities (Kuh, (2009). Coates (2007, p. 122)
defines active and collaborative learning as “a broad construct intended to encompass salient
academic as well as certain non-academic aspects of the student experience,” which includes
active and collaborative learning, participation in challenging academic activities, formative com­
munication with academic staff, participation in enriching educational experiences, and feeling
legitimated and supported by institutional learning communities, among other things. Therefore,
this research needs to develop and test the following hypotheses in Saudi Arabia based on the
discussion above:
H7: PE is positive with CR.
H8: PE is positive with CT.
2.5. Smart classroom environment (SCE)
It will be simpler to improve students’ learning motivation, promote active learning behavior, and
produce good learning results in a smart classroom setting (Liu et al., 2011). Digital cameras and
recorders, interactive whiteboards, mobile devices (such as tablets and/or smartphones), wireless
internet, virtual learning platforms, and other technology-rich classrooms are examples of smart
classrooms (Oca et al., 2014; Yau et al., 2003).
Learning becomes more engaging, exhilarating, and meaningful when these tools are used in
the classroom. The children’s excitement for learning has soared. The ability of students to
research topics and convey their opinions has also increased (Yau et al., 2003). Many studies
was indicated that in the smart classroom setting, students’ online attitudes, learning methods,
and spirits was altered (Shen et al., 2014; Taleb & Hassanzadeh, 2015), including creativity, critical
thinking, problem-solving, and learning performance (Shen et al., 2014; Taleb & Hassanzadeh,
2015). 2011; Liu et al., 2011). Therefore, this research needs to develop and test the following
hypotheses in Saudi Arabia based on the discussion above:
H9: SCE is positive with CR.
H10: SCE is positive with CT.
2.6. Creativity (CR)
A unique, creative, and effective creation is what most people think of when they think of
creativity. According to the definition, the concept of creativity has two dimensions: “originality”
and “effectiveness.” Individuality is important for creativity, but it is insufficient on its own.
Creativity must be useful, different, and practical (Runco & Jaeger, 2012). As a result, creativity
entails coming up with fresh ideas and seeing how others react to them, as well as creating final
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things (Sowden et al., 2015). Many studies were discovered that creative and critical thinking skills
interact (interdependence), with neither having an impact on the other (Paul & Elder, 2009).
Nonetheless, they are two complementing characteristics (Martinez, 2007), and these two
skills are inextricably linked (Paul & Elder, 2006). According to Giannakopoulos and Buckley (2009)
and Ulger (2009), creative thinking skills are necessary for using critical thinking skills (2016).
According to Whetten and Cameron (2011), creative thinking skills are an extension of problemsolving skills. As a result of creative and critical thinking skills, the problem-solving process may be
more flexible and faster. Therefore, this research needs to develop and test the following hypoth­
eses in Saudi Arabia based on the discussion above:
H11: CR is positive with CT.
H12: CR is positive with PS.
H13: CR is positive with AP.
2.7. Critical thinking (CT)
Problem-solving skills are essential, but so are critical thinking and teamwork (Anderson-Levitt,
2020). A higher-order thinking talent is critical thinking, which helps you come up with a feasible
solution to a problem. It is regarded as an important ability that has an impact on problem-solving
cognitive processes (Aein et al., 2020; Saputro et al., 2018; Whitten & Brahmasrene, 2011).
Reasoning, judging, analyzing, and inferring are also considered cognitive skills (Whitten &
Brahmasrene, 2011; Ulger, 2016). As a result, critical thinking might be viewed as a cognitive
engine that propels knowledge acquisition (Khosravani et al., 2012). As a result, problem-solving
demands a combination of higher-order and critical-thinking talents (Kereluik et al., 2013).
Therefore, this research needs to develop and test the following hypotheses in Saudi Arabia
based on the discussion above:
H14: CT is positive with PS.
H15: CT is positive with AP.
2.8. Problem solving and critical thinking (PS)
The process of finding new solutions in response to a problem is known as “issue-solving” (Caliskan
et al., 2010). Critical thinking, on the other hand, is a cognitive process that entails reviewing and
rearranging information in a person’s mind map (Hu, 2011). Problem-solving is a complex process
that necessitates the use of critical thinking skills to provide a range of answers (Daud & Santoso,
2018; Giannakopoulos & Buckley, 2009). Critical thinking skills were shown to impact problemsolving skills in the study (Aein et al., 2020), with a positive link between the two variables (Stadler
et al., 2020; Tang et al., 2020). Therefore, this research needs to develop and test the following
hypotheses in Saudi Arabia based on the discussion above:
H16: PS is positive with AP.
2.9. Students’ Academic Performance (AP)
Academic achievement is frequently mentioned as an explicit component of student engagement
models (Astin, 1984; Nora, 2003). Nora goes even farther, claiming that academic achievement is
“probably the most crucial element” in Hispanic kids’ perseverance (Nora, 2003). Academic per­
formance has been linked to students’ sense of belonging and belief in their own skills to acquire
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a college diploma; course grades were found to impact Hispanic students’ drop-out decisions three
times more than non-minorities (Nora, 2003). The theoretical foundation for a link between
integration and performance is obvious in both Tinto’s and Nora’s models. Nora (2003), Tinto
(1975); Rizzuto et al. (2009); Dou et al., 2016) revealed a beneficial association between
a student’s academic performance and their social contacts with other students in the classroom.
3. Research methodology
This study employs correlational analytic methods using a quantitative approach (See question­
naire in the appendix.). Whether there is a relationship between one variable and another, whether
correlation does not demonstrate a functional relationship, or whether correlation analysis fails to
distinguish between dependent and independent variables is the goal of correlational research
(Ghozali, 2011). In this study, the researcher employed the product moment correlation analysis
approach to investigate the association between one independent variable and one dependent
variable (Hair et al., 2012). The goal of this study is to look into the relationship between all
variables, specifically the nine variables identified in Figure 1, and the 16 hypotheses. Analysis of
data processing performed using AMOS 23.0.
To examine the purpose of this research, this study employed a total of 297 postgraduate and
undergraduate students enrolled in the four faculties at King Faisal University. For two reasons,
both the course and the university were chosen with care. For starters, all first-year university
students must take the course as part of their general education requirements. As a consequence
of the course’s registration, this research was able to assemble the required number of participants
from diverse areas. Second, the institution prioritizes information technology and has created
several smart classrooms.
All university instructors are given the opportunity to learn how to utilize smart classroom
technology and are encouraged to use it in their classes. The majority of instructors in this
university’s educational technology department was taught the course’s subject for two years in
the smart classroom. The semester-long course is 14 weeks long. As a result, students and
teachers meet once or twice a week. All courses use the same learning materials and equipment
in the smart classrooms. For the learning assignments, the students were divided into groups. Each
group consisted of 6–7 students who sat in a cluster seating configuration, which allowed them to
readily speak and interact. Ethical review and approval were waived for this study due to the fact
that this research adopted a questionnaire from previous research. Please refer to Section 3.1,
“Instruments and Measurement Model”. Therefore, all the students who answered the question­
naire agreed once they responded. Those who did not agree to respond to the questionnaire were
excluded.
3.1. Instruments and measurement model
As stated in Table 1 and 3, a survey instrument was used to meet the study goals through an indepth analysis. There were nine constructs with thirty-two indicators. Learning motivation was
proposed with the establishment of three items as recommended by Gopalan et al., 2017,
October), cooperativity with the establishment of four items as recommended by Chen and
Swan (2020), peer interaction with the establishment of four items as recommended by AlRahmi and Alkhalaf (2021), peer engagement with the establishment of four items as recom­
mended by Al-Rahmi et al. (2015), and a smart classroom environment with the establishment of
three items as recommended by Liu et al. (2011). Furthermore, critical thinking was proposed with
the development of three items as suggested by Anderson-Levitt (2020), and creativity was
proposed with the establishment of four items as suggested by Runco and Jaeger (2012).
Additionally, the establishment of three items as indicated by Caliskan et al. (2010) was proposed
for students’ problem solving, and the establishment of four items was proposed for students’
academic performance (Doleck et al., 2017), see questionnaire in the appendix.
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4. Research analysis and results
The demographic data is presented in Table 1. Among the 297 useable questionnaires, 171 (57.6%)
were from male respondents, while 126 were from female respondents (42.4%). Additionally, 211
(71.0%) were 17–22 years old, 48 (16.2%) were 23–27 years old, 10 (3.4%) were 28–30 years old,
11 (3.7%) were 31–34 years old, and 17 (5.7%) were more than 35 years old. Also, level of study,
256 (86.2%) were undergraduate students, and 41 (13.8%) were postgraduate students. Finally,
the faculties, 98 (33.0%) were from the faculty of education, 65 (21.9%) were from the faculty of
art, 69 (23.2%) were from the faculty of law, and 65 (21.9%) were from the faculty of manage­
ment, see, Table 1.
4.1. Structured equation modelling
Structured equation modelling was used to investigate the complex relationships between the
direct effects of various research variables (learning motivation, cooperativity, peer interaction,
peer engagement, smart classroom environment, critical thinking, creativity, students’ problem
solving, and students’ academic performance).
As a result, the suggested study model argues that learning motivation, cooperativity, peer
interaction, peer engagement, and a smart classroom environment directly affect critical thinking
and creativity, as well as indirectly affect students’ problem solving and academic achievement
through critical thinking and creativity. The model was constructed and tested using AMOS 23.0
after deleting outliers, missing data, and dishonest replies, which totalled 21 instances. Maximum
likelihood estimation was used to compute the route coefficients. By deconstructing the entire
effect of higher-order thinking into direct effects, this research was able to distinguish the effect of
an independent variable not directly influenced by intervening factors from the effect of a variable
directly influenced by intervening factors.
To find particular links among the dimensions in the structural model, the statistical significance
of total, direct effects were further investigated. For model evaluation, a variety of goodness-of-fit
indices for model fit were investigated. The validity and reliability of the measurement model were
confirmed using the Statistical Package for the Social Sciences (SPSS) and Structural Equation
Modelling (AMOS-SEM). Construct validity, composite reliability, Cronbach’s alpha, and convergence
validity for the model’s goodness of fit were established using factor loadings, as shown by Hair
et al. (2012). Cronbach’s alpha was found to be 0.927 based on standardized items. Table 1 shows
the reliability coefficient (Cronbach’s alpha) for both the pilot and final test structures; all variables
were judged appropriate and proper. For more details, see, Table 2.
4.2. Data collection and analysis
The information was gathered at the end of the semester. After their final test, a researcher from
this study explained the goal of the study to all 297 participants. Participants were assured that
their information would be utilized solely for educational purposes and that the survey findings
would was no bearing on their grades. All responses were given voluntarily and anonymously. The
survey was conducted online, and the results were analysed using SPSS 22.0 and Amos 23.0. An
examination of the linkages between the primary influencing elements and students’ critical
thinking and problem solving to influence their academic performance was undertaken using
structural equation modelling.
4.3. Model fit evaluation
The CMN/DF ratio in Table 2 is 2.451, which is lower than the necessary threshold (5.00). NFI (0.949)
is a valid value, RFI (0.941) is a valid value, IFI (0.965) is a valid value, TLI (0.959) is a valid value,
CFI (0.965) is a valid value, GFI (0.940) is a valid value, and AGFI (0.925) is a valid value. Also, the
RMR value is below the threshold of 0.32 (0.05), as suggested by Hair et al. (2012). Figure 2 shows
all items and their factor values. This shows that the measurement model was acceptable and
well-suited to the structural model. Table 3 and Figueroa 2 are examples.
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Age
Gender
48
10
11
17
297
28–30
31–34
>35
Total
297
Total
211
126
Female
23–27
171
Male
17–22
Frequency
Factors
Table 1. Demographic data
100.0
5.7
3.7
3.4
16.2
71.0
100.0
42.4
57.6
Percent
Faculty
Level of study
Factors
Total
Management
Law
Art
Education
Total
Postgraduate
Undergraduate
297
65
69
65
98
297
41
256
Frequency
100.0
21.9
23.2
21.9
33.0
100.0
13.8
86.2
Percent
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Table 2. The reliability coefficient for all variables
Code
Pilot Test
Final Test
Learning Motivation
Variables
LM
.758
.881
Cooperativity
CO
.801
.902
Peer Interaction
PI
.709
.872
Peer Engagement
PE
.700
.891
Smart Classroom
Environment
SCE
.710
.888
Critical Thinking
CT
.731
.901
Creativity
CR
.721
.877
Students’ Academic
Performance
AP
.705
.909
Students’ ProblemSolving
PS
.734
.927
Table 3. Model fit evaluation
Model Fit
NFI
RFI
IFI
TLI
CFI
GFI
AGFI
RMR
Default model
.949
.941
.965
.959
.965
.940
.925
.032
1.000
.000
1.000
.000
1.000
1.000
.000
.000
.000
.000
.000
.000
.000
.178
.125
.310
Saturated
model
Independence
model
4.4. Reliability, validity, and measurement model
The SEM-AMOS measurement model for each idea has its own set of characteristics, such as
reliability and validity. Confirmatory factor analysis (CFA) and model fit were utilized to examine
the intensity of the link direction using the structural model. Table 3 lists the factors of the
measurement. The items of factors analysis are at or above the required 0.700 level; the composite
reliability (CR) of factors analysis is at or above the required 0.800 level; the average variance
extracted (AVE) of factors analysis is at or above the required 0.500 level; and Cronbach’s alpha
(CA) of factors analysis is at or above the required 0.800 level. See, Table 4.
4.5. Measurement validity convergent
Discriminant validity refers to the distinctions between sets of concepts and their measures. As
stipulated by the authors, the discriminant validity of all constructs was examined with values
greater than 0.50 and significant at p = 0.001 (Hair et al., 2012). As shown in Table 5, the square
root shared by objects in a single construct should be less than the similarities between items in
the two constructs.
4.6. Structural model and path coefficient
Both the interaction and the effect of independent factors on the dependent variable are specified
in the structural model (path coefficient). The maximum likelihood approach, in particular, may be
used to extensively evaluate complicated models and find numerous connections between multiitem elements, as well as the impact of moderating variables (Hair et al., 2012). The direct impact
of the route coefficient on the latent predictor variable and expected variables is shown in
Figures 3 and 4.
4.7. Hypotheses testing results
Based on the results shown in Figure 4 and Table 6, the relationship between learning motivation
and creativity (β = .160; C.R = 7.367, p < 0.000 was accepted), as well as, the relationship between
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Figure 2. Measurement Model
Fit
learning motivation and critical thinking (β = .116; C.R = 4.715, p < 0.000 was accepted). Similarly,
the relationship between cooperativity and creativity (β = .254; C.R = 10.457, p < 0.000 was
accepted), as well as, the relationship between cooperativity and critical thinking (β = .111; C.
R = 3.954, p < 0.000 was accepted). Moreover, the relationship between peer interaction and
creativity (β = .213; C.R = 8.973, p < 0.000 was accepted), as well as, the relationship between peer
interaction and critical thinking (β = .366; C.R = 13.478, p < 0.000 was accepted). Furthermore, the
relationship between peer engagement and creativity (β = .156; C.R = 7.929, p < 0.000 was
accepted), as well as, the relationship between peer engagement and critical thinking (β = .052;
C.R = 2.333, p < 0.000 was accepted).
Additionally, the relationship between smart classroom environment and creativity (β = .130; C.
R = 5.994, p < 0.000 was accepted), as well as, the relationship between smart classroom
environment and critical thinking (β = .095; C.R = 3.883, p < 0.000 was accepted).
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Table 4. Reliability, validity, and measurement model
No
Items
Factors
Learning
Motivation
Estimate
CR
CA
AVE
.776
.892
.881
.632
.830
.902
.599
.821
.872
.614
.907
.891
.620
.902
.888
.633
.912
.901
.602
.892
.877
.598
.911
.909
.611
.918
.927
.643
1
LM1
<—
2
LM2
<—
3
LM3
<—
4
CO1
<—
5
CO2
<—
.886
6
CO3
<—
.839
7
CO4
<—
.759
8
PI1
<—
9
PI2
<—
10
PI3
<—
11
PI4
<—
12
PE1
<—
13
PE2
<—
14
PE3
<—
15
PE4
<—
16
SCE1
<—
17
SCE2
<—
18
SCE3
<—
19
CT1
<—
20
CT2
<—
21
CT3
<—
22
CR1
<—
23
CR2
<—
.778
24
CR3
<—
.792
25
CR4
<—
26
AP1
<—
27
AP2
<—
28
AP3
<—
29
PS1
<—
30
PS2
<—
31
PS3
<—
.838
32
PS4
<—
.701
.710
.752
Cooperativity
Peer
Interaction
.868
.796
.837
.776
.769
Peer
Engagement
.715
.772
.826
.771
Smart
Classroom
Environment
.847
.890
.850
Critical
Thinking
.833
.892
.861
Creativity
.800
.723
Students’
Academic
Performance
.824
.777
.727
Students’
ProblemSolving
.800
.850
The mediators variables show the relationship between creativity and critical thinking (β = .230;
C.R = 7.523, p < 0.000 was accepted), and the relationship between creativity and students’
problem solving (β = .557; C.R = 22.469, p < 0.000 was accepted), as well as, the relationship
between creativity and students’ academic performance (β = .230; C.R = 7.498, p < 0.000 was
accepted). Moreover, the relationship between critical thinking and students’ problem solving
(β = .235; C.R = 11.106, p < 0.000 was accepted), as well as, the relationship between critical
thinking and students’ academic performance (β = .074; C.R = 2.965, p < 0.000 was accepted).
Finally, the relationship between students’ problem solving and students’ academic performance
(β = .534; C.R = 18.377, p < 0.000 was accepted).
5. Factors described and analysed
The standard deviation (SD) and mean (mean) are two statistics that describe how measurements
in a population deviate from the average (mean) or expected value. When the standard deviation
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is low, the bulk of the data points are near the mean. The data is more evenly distributed if the
standard deviation is high. As a consequence, as shown in Figure 5, all values were accepted, and
the majority agreed and strongly agreed, meaning that creativity and critical thinking, which
influence problem-solving skills and affect students’ academic achievement, were embraced.
See, Figure 5.
5.1. Discussion and implications
Students’ acquisition of creativity, critical thinking, and problem-solving skills in higher education
has an influence on their academic success. As a result, the connections between the nine
components are investigated in this research, and a structural model is proposed. Before model
testing, the level of association between the talents was investigated, and all of the skills were
found to be a substantial and positive relationship.
A lot of research in the literature backs this up (Chen & Swan, 2020; Kocak et al., 2021; Orona
et al., 2022; Qiang et al., 2020; Sim et al., 2020; Tight, 2021). As the demand for twenty-firstcentury skills grows, it appears that educational stakeholders must guarantee that professed
learning objectives, teaching methodologies, and evaluation methods are all in sync. If students
are to learn the computational thinking skills needed to flourish in today’s world, they must be
explicitly addressed in a well-designed and delivered curriculum.
Table 6 shows the statistical analysis findings, which demonstrate that all of the hypothesized
relationships were proven valid. Some of the hypothesis findings ran counter to prior studies, such
as Doleck et al. (2017), which found that cooperativity and students’ critical thinking had
a negative influence on students’ academic performance. Previous research (Al-Maatouk et al.,
2020; Chen & Swan, 2020; Gong et al., 2020; Li et al., 2021; Masika & Jones, 2016; Oca et al., 2014)
supports this result on learning motivation, cooperativity, peer interaction, peer engagement, and
the smart classroom environment.
Other research backs up this study’s conclusion that students’ critical thinking and creativity was
a significant and direct relationship (Anderson-Levitt, 2020; Daud & Santoso, 2018; Dou et al.,
2016; Stadler et al., 2020; Tang et al., 2020). Furthermore, according to this research, students’
critical thinking and creativity, which in turn affect problem-solving skills, affect their academic
performance in higher education.
Additionally, it was shown that students’ inquiry-based learning styles, introspective thinking,
problem-solving abilities, and critical thinking abilities had a substantial impact on their learning
performance. Using learning motivation, cooperativity, peer interaction, peer engagement, and
a smart classroom environment, the study investigated the impact of critical thinking and crea­
tivity in problem-solving and academic achievement among university students. This study serves
as an example of how critical thinking and creativity may be used to learn. A validated tool that
combines creativity and critical thinking with problem-solving abilities and critical thinking skills
has also been developed as a result of this research to improve student performance in Saudi
Arabia’s higher education system.
Therefore, students were the opportunity to use a smart classroom environment to improve their
problem-solving skills and academic performance. Furthermore, the students’ critical thinking,
creativity, and problem solving affected academic performance in higher education, all of which
were outcomes of our research. Last but not least, here are the scientific contributions:
●
Regarding the independent factors, students’ critical thinking and problem solving skills affected
academic performance in higher education; learning motivation, cooperativity, peer interaction, peer
engagement, and the smart classroom environment were found to affect students’ critical thinking
and creativity in the smart classroom environment.
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Table 5. Discriminant validity
Factors/
Items
LM
CO
PI
PE
SCE
CT
CR
AP
Learning
Motivation
.822
Cooperativity
.303
.837
Peer
Interaction
.285
.239
.898
Peer
Engagement
.303
.343
.254
.816
Smart
Classroom
Environment
.333
.289
.279
.329
.888
Critical
Thinking
.326
.328
.272
.335
.330
.883
Creativity
.381
.313
.287
.325
.344
.348
.871
Students’
Academic
Performance
.357
.340
.282
.355
.367
.358
.339
.903
Students’
ProblemSolving
.322
.287
.281
.326
.342
.328
.304
.376
PS
.840
Figure 3. Path Coefficient
Results
●
Regarding the mediators’ factors hypothesis about how students’ critical thinking and problemsolving affect academic performance in higher education, students’ critical thinking and creativity
were found to affect students’ problem solving and academic performance in the smart classroom
environment.
●
Regarding the dependent factor’s hypothesis, students’ critical thinking and problem solving
affected academic performance in higher education; students’ problem solving was found to affect
students’ academic performance in the smart classroom environment.
The scientific contributions are as follows:
●
Students’ attitudes toward technology and their enthusiasm for using it for smart classroom
environments can be enhanced by examining the impact of critical thinking and creativity in
problem-solving.
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Figure 4. Path T-values Results
Table 6. Hypotheses testing results
Relationships
No
Estimate (β)
S.E.
C.R.
P
Results
H1
CR
<—
LM
.160
.022
7.367
.000
Accepted
H2
CT
<—
LM
.116
.025
4.715
.000
Accepted
H3
CR
<—
CO
.254
.024
10.457
.000
Accepted
H4
CT
<—
CO
.111
.028
3.954
.000
Accepted
H5
CR
<—
PI
.213
.024
8.973
.000
Accepted
H6
CT
<—
PI
.366
.027
13.478
.000
Accepted
H7
CR
<—
PE
.156
.020
7.929
.000
Accepted
H8
CT
<—
PE
.052
.022
2.333
.020
Accepted
H9
CR
<—
SCE
.130
.022
5.994
.000
Accepted
H10
CT
<—
SCE
.095
.024
3.883
.000
Accepted
H11
CT
<—
CR
.230
.031
7.523
.000
Accepted
H12
PS
<—
CR
.557
.025
22.469
.000
Accepted
H13
AP
<—
CR
.230
.031
7.498
.000
Accepted
H14
PS
<—
CT
.253
.023
11.106
.000
Accepted
H15
AP
<—
CT
.074
.025
2.965
.003
Accepted
H16
AP
<—
PS
.534
.029
18.377
.000
Accepted
●
Teachers and mentors should promote student motivation, peer contact, peer engagement, and
a smart classroom environment so that students may solve problems, share knowledge, and do
research to improve their ability to learn, succeed, and conduct research.
●
Rather than putting pressure on students who haven’t used smart classroom environments, schools
and universities should promote those who have. With this approach, students incorporate materials
and elements into their learning.
●
Students’ attitudes toward and intentions for adopting a smart classroom environment for digital
learning are influenced by technology and resources. Students should use digital learning options
that are centered on learning motivation, cooperativity, peer interaction, peer engagement, and
a smart classroom environment in Saudi Arabia.
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Figure 5. Factors described and
analysed
5.2. Conclusion, limitation, and future work
The purpose of this study was to empirically investigate the link between students’ critical
thinking and creativity, which influences their problem-solving skills and, hence, their academic
success in Saudi Arabia’s higher education. In this study, learning motivation, cooperativity,
peer contact, peer engagement, and a smart classroom environment were found to be favour­
ably associated with students’ critical thinking and creativity. Furthermore, pupils’ critical
thinking and creativity were favourably associated with problem-solving skills and academic
success.
As a result, our findings add to the literature on students’ critical thinking and creativity by
demonstrating the link between problem-solving skills and academic success. In conclusion, the
findings of this study show that lecturers should consider students’ learning motivation, coopera­
tivity, peer interaction, and peer engagement in the smart classroom setting in order to encourage
critical thinking and creativity. While the current study has substantial implications, it is not
without flaws. It should be mentioned that, using a structural equation modelling approach, this
research has only looked at seven critical aspects that impact students’ problem-solving and
academic performance. In addition, the context was limited to a single topic area in a smart
classroom setting. Other topic areas and associated qualities, such as students’ learning styles and
approaches to studying, as well as teaching procedures and tactics, should be included in future
research. Future studies should expand to additional topic areas with similar characteristics and
use a mixed-methods approach to help in the triangulation of quantitative data, such as adding
follow-up interviews or qualitative responses to capture students’ and lecturers’ viewpoints.
Acknowledgements
This work was supported through the Annual Funding
track by the Deanship of Scientific Research, Vice
Presidency for Graduate Studies and Scientific Research,
King Faisal University, Saudi Arabia [Project No. GRANT
521].
Author details
Mohammed Abdullatif Almulla1
E-mail: maalmulla@kfu.edu.sa
ORCID ID: http://orcid.org/0000-0002-7846-8098
1
Department of Curriculum and Instruction, Faculty of
Education, King Faisal University, Al Ahsa, Saudi Arabia.
Funding
This work was supported through the Annual Funding
track by the Deanship of Scientific Research, Vice
Presidency for Graduate Studies and Scientific Research,
King Faisal University, Saudi Arabia [Project No.GRANT521]
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author Contributions
This research was done by a single author.
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Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Citation information
Cite this article as: Constructivism learning theory:
A paradigm for students’ critical thinking, creativity, and
problem solving to affect academic performance in higher
education, Mohammed Abdullatif Almulla, Cogent
Education (2023), 10: 2172929.
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An Appendix “Questionnaire”
Learning Motivation
1
I work hard to get a good grade even when I cooperativity in a class.
2
When Interaction on studying a topic, I try to make everything fit together.
3
Before I begin studying I think about the things I will need to do to learn.
Cooperativity
4
I believe our team can cooperate successfully when I conduct collaborative learning
5
I try to provide useful and sufficient information when I conduct collaborative learning.
6
I can finish my work efficiently when I conduct collaborative learning.
7
Work is split based on our abilities when I conduct collaborative learning.
Peer Interaction
8
During Interaction, I try to be warm when Interaction with others.
9
During Interaction, I try to make the other person feel good.
10
During Interaction, While I’m talking I think about how the other person feels.
11
During Interaction, I prefer that they can provide the tools to continue with my learning.
Peer Engagement
12
I have good Engagement with my team members when I conduct collaborative learning.
13
I am verbally and nonverbally supportive of other people.
14
By Engagement, I try to make the other person feel important.
15
By Engagement, I disclose at the same level that others disclose to me.
Smart Classroom Environment
16
When navigating smart classroom environments, I prefer that they can provide information which
I need, e.g., documents, images, voice, etc.
17
When navigating smart classroom environments, I prefer that they can provide a correct way to
learn what I need to know.
18
When navigating smart classroom environments, I prefer that they can discuss a learning topic
through various perspectives.
Critical Thinking
19
I ask myself periodically if I am meeting my goals.
20
I consider several alternatives to a problem before I answer.
21
I ask myself questions about how well I am doing once I finish a task.
Creativity
22
I like to observe something I haven’t seen before and understand it in detail.
23
I find myself pausing regularly to check my comprehension.
24
I like to try something new.
25
I like to do something by myself.
Students’ Academic Performance
26
Since starting critical thinking, I have never ever failed an examination.
(Continued)
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27
During critical thinking, I perform strongly in my semester examinations.
28
During critical thinking, I am able to achieve the academic goal that I have set.
Students’ Problem-Solving
29
When facing problems, I believe I have the ability to solve them.
30
I believe I can put effort into solving problems.
31
I can solve problems that I have met before.
32
I am willing to face problems and make an effort to solve them.
Questionnaire
Learning Motivation
1
I work hard to get a good grade even when I cooperativity in a class.
2
When Interaction on studying a topic, I try to make everything fit together.
3
Before I begin studying I think about the things I will need to do to learn.
Cooperativity
4
I believe our team can cooperate successfully when I conduct collaborative learning
5
I try to provide useful and sufficient information when I conduct collaborative learning.
6
I can finish my work efficiently when I conduct collaborative learning.
7
Work is split based on our abilities when I conduct collaborative learning.
Peer Interaction
8
During Interaction, I try to be warm when Interaction with others.
9
During Interaction, I try to make the other person feel good.
10
During Interaction, While I’m talking I think about how the other person feels.
11
During Interaction, I prefer that they can provide the tools to continue with my learning.
Peer Engagement
12
I have good Engagement with my team members when I conduct collaborative learning.
13
I am verbally and nonverbally supportive of other people.
14
By Engagement, I try to make the other person feel important.
15
By Engagement, I disclose at the same level that others disclose to me.
Smart Classroom Environment
16
When navigating smart classroom environments, I prefer that they can provide information which
I need, e.g., documents, images, voice, etc.
17
When navigating smart classroom environments, I prefer that they can provide a correct way to
learn what I need to know.
18
When navigating smart classroom environments, I prefer that they can discuss a learning topic
through various perspectives.
Critical Thinking
19
I ask myself periodically if I am meeting my goals.
(Continued)
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(Continued)
20
I consider several alternatives to a problem before I answer.
21
I ask myself questions about how well I am doing once I finish a task.
Creativity
22
I like to observe something I haven’t seen before and understand it in detail.
23
I find myself pausing regularly to check my comprehension.
24
I like to try something new.
25
I like to do something by myself.
Students’ Academic Performance
26
Since starting critical thinking, I have never ever failed an examination.
27
During critical thinking, I perform strongly in my semester examinations.
28
During critical thinking, I am able to achieve the academic goal that I have set.
Students’ Problem-Solving
29
When facing problems, I believe I have the ability to solve them.
30
I believe I can put effort into solving problems.
31
I can solve problems that I have met before.
32
I am willing to face problems and make an effort to solve them.
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Almulla, Cogent Education (2023), 10: 2172929
https://doi.org/10.1080/2331186X.2023.2172929
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