Constructivism learning theory: A paradigm for students’ critical thinking, creativity, and problem solving to affect academic performance in higher education

Abstract

This study looks at whether creativity and critical thinking help students solve problems and improve their grades by mediating the link between 21^st 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.

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 “21^st-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 “21^st 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 21^st-century skills in general (Ananiadou & Claro, 2009).

Theories of cognitive development among emerging adults posit that environmental and agerelated 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; Al-Rahmi 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 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 classroom, the function of student interaction in impacting learning achievement is seldom investigated. 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 philosophies used by teachers. Therefore, motivation is the drive that propels students to pursue knowledge, 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 21^st-century core competencies to each other was identified in the literature. In this sense, the study’s main goal is to build a structural model that analyses the link between 21^st-century talents and 21^st-century talents. The validity of this research is demonstrated by the creation of models that reflect expected levels of 21^st-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 21^st-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 21^st-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 creativity, which in turn enhance problem solving and academic achievement.

Figure 1. Research Model with Hypotheses

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 environment, 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:

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 classroom 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’ engagement 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.

2.4. Peers engagement (PE)

Academic development appears to be influenced by assessments of students’ engagement, conduct, 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 communication 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 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 problem-solving 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 hypotheses 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 problem-solving 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 performance has been linked to students’ sense of belonging and belief in their own skills to acquire 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 questionnaire 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 questionnaire 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 in-depth 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 Al-Rahmi and Alkhalaf (2021), peer engagement with the establishment of four items as recommended 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.

Table 1. Demographic data

Gender	Factors	Frequency	Percent	Level of study	Factors	Frequency	Percent
	Male	171	57.6		Undergraduate	256	86.2
	Female	126	42.4		Postgraduate	41	13.8
	Total	297	100.0		Total	297	100.0
Age	17–22	211	71.0	Faculty	Education	98	33.0
	23–27	48	16.2		Art	65	21.9
	28–30	10	3.4		Law	69	23.2
	31–34	11	3.7		Management	65	21.9
	>35	17	5.7		Total	297	100.0
	Total	297	100.0

Table 2. The reliability coefficient for all variables

Variables	Code	Pilot Test	Final Test
Learning Motivation	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’ Problem-Solving	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
Saturated model	1.000	.000	1.000	.000	1.000	1.000	.000	.000
Independence model	.000	.000	.000	.000	.000	.178	.125	.310

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 management, 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.

Figure 2. Measurement Model Fit

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.

Table 4. Reliability, validity, and measurement model

No	Items	Factors		Estimate	CR	CA	AVE
1	LM1	<—	Learning Motivation	.776	.892	.881	.632
2	LM2	<—		.710
3	LM3	<—		.752
4	CO1	<—	Cooperativity	.868	.830	.902	.599
5	CO2	<—		.886
6	CO3	<—		.839
7	CO4	<—		.759
8	PI1	<—	Peer Interaction	.796	.821	.872	.614
9	PI2	<—		.837
10	PI3	<—		.776
11	PI4	<—		.769
12	PE1	<—	Peer Engagement	.715	.907	.891	.620
13	PE2	<—		.772
14	PE3	<—		.826
15	PE4	<—		.771
16	SCE1	<—	Smart Classroom Environment	.847	.902	.888	.633
17	SCE2	<—		.890
18	SCE3	<—		.850
19	CT1	<—	Critical Thinking	.833	.912	.901	.602
20	CT2	<—		.892
21	CT3	<—		.861
22	CR1	<—	Creativity	.800	.892	.877	.598
23	CR2	<—		.778
24	CR3	<—		.792
25	CR4	<—		.723
26	AP1	<—	Students’ Academic Performance	.824	.911	.909	.611
27	AP2	<—		.777
28	AP3	<—		.727
29	PS1	<—	Students’ Problem-Solving	.800	.918	.927	.643
30	PS2	<—		.850
31	PS3	<—		.838
32	PS4	<—		.701

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.

Table 5. Discriminant validity

Factors/ Items	LM	CO	PI	PE	SCE	CT	CR	AP	PS
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’ Problem-Solving	.322	.287	.281	.326	.342	.328	.304	.376	.840

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 multi-item 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.

Figure 3. Path Coefficient Results

Figure 4. Path T-values Results

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 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).

Table 6. Hypotheses testing results

No	Relationships			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

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).

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 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.

Figure 5. Factors described and analysed

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-first-century 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 creativity 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.

• Regarding the mediators’ factors hypothesis about how students’ critical thinking and problem-solving 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.

• 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.

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 favourably 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, cooperativity, 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.

Author Contributions

This research was done by a single author.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

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].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Not applicable.

Additional information

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]

Notes on contributors

Mohammed Abdullatif Almulla

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 committee 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.

An Appendix “Questionnaire”

Table

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.
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

Table

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.
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.