Introduction

With the twenty-first century, the skill sets sought in students are changing rapidly, and among these sets, creative problem solving skills and computational thinking skills come to the fore (Bocconi et al., 2016). In recent years, especially, computational thinking skills have been identified as an essential factor in enhancing problem-solving skills (Barr & Stephenson, 2011; Gerosa et al., 2021; International Society for Technology in Education (ISTE) & Computer Science Teachers Association (CSTA), 2011; Tang et al., 2020; Tekdal, 2021). The reason behind this is that computational thinking facilitates the formulation and resolution of problems, the creation of systems, and an understanding of human behavior by leveraging the foundational principles of computer science, as exemplified by Grover and Pea in 2013 and Wing in 2008. Barr and Stephenson (2011) posited that computational thinking is a form of problem-solving. Czerkawski and Lyman (2015) argued that computational thinking skills are essential for nurturing individuals capable of identifying and resolving problems more effectively. This is because computational thinking divides a problem into smaller sub-problems, making the problem more tractable and the problem solving process manageable (Grover & Pea, 2018). Because computational thinking skills facilitate the solution of complex problems by breaking them down into smaller components based on the decomposition component, and enable the following of the necessary sequential steps in the problem-solving process through the algorithmic thinking component (Grover & Pea, 2018). In addition, algorithmic thinking includes the ability to analyse problems step by step and to proceed systematically with logical steps (Weese & Feldhausen, 2017). This feature also plays an important role in the creative problem solving process and facilitates the generation of different solutions by organising steps and determining solution strategies when faced with complex problems (Israel-Fishelson & Hershkovitz, 2022). Therefore, by providing a logical and analytical approach to a problem, computational thinking skills pave the way for understanding the root causes of the problem, determining the most appropriate solution and even producing creative solutions (Israel-Fishelson & Hershkovitz, 2022). As a result, individuals with computational thinking skills are believed to have an advantage when addressing real-life challenges (Kalelioğlu et al., 2016). While literature suggests that computational thinking skills significantly influence problem-solving abilities (Li et al., 2020; Nouri et al., 2020), it remains intriguing how these skills impact creative problem-solving specifically. This is because there is limited research on this particular subject.

Many countries have recognized the need to integrate skills such as problem-solving, computational thinking, and creativity, which are listed among the 21st-century skills, into the education system in order to raise productive individuals who can overcome challenges (Bocconi et al., 2016; Deschryver & Yadav, 2015; Nouri et al., 2020). Thus, one of the most important functions of education in recent years is to cultivate innovative and productive individuals who can produce creative solutions to problems in daily life (ISTE, 2015). To realize this aim and to train the creative problem solvers that societies need, computational thinking skills were utilized in this study. When the literature is reviewed, it is observed that there is a need for experimental studies that utilize computational thinking skills to help students, especially from a young age, acquire creative problem-solving skills (Abdulla & Cramond, 2018; Abdulla et al., 2020; Kong, 2022; Park & Green, 2019). Moreover, there is also a need for the integration of different teaching approaches that can be used to nurture children as creative problem solvers from early ages across various disciplines (Hutchins et al., 2020; Kong, 2022). Considering these gaps in the field, in this study, computational thinking skills have been integrated into science teaching, and two different teaching modules focused on plugged and unplugged computational thinking have been designed. In the activities of these teaching modules, students generate creative solutions to everyday problems by utilizing their conceptual knowledge from science classes. Through these modules, the impact of both plugged (implementations carried out with the aid of block-based visual programming tools, etc.) and unplugged (implementations carried out without a computer but with tangible materials) approaches on the development of middle school students' creative problem-solving skills are investigated. Furthermore, this study provides experimental evidence determining which teaching module, plugged or unplugged, is more effective in enhancing the creative problem-solving skills of middle school students.

Background and Literature Review

Computational Thinking (CT) and its Components

Shute et al. (2017) define computational thinking as the fundamental basis required for addressing and resolving real-world challenges using algorithmic approaches, applicable across diverse fields and situations. Computational thinking skills represent a universal skill set that can be used in different fields and disciplines (Voogt et al., 2015). Wing (2010) emphasizes the significance of computational thinking, noting its importance stems from being a high-level skill comprised of numerous sub-components. In the literature, different researchers present different views on the components of computational thinking. As an illustration, Shute et al. (2017) CT facets are defined as decomposition, abstraction, algorithms, debugging, ıteration, generalization. Csizmadia et al. (2015) identified components such as logical reasoning, algorithmic thinking, decomposition, generalization, abstraction, and evaluation, while other researchers highlighted components like data collection, data applications, data analysis, decomposition, abstraction, pattern recognition, algorithm creation, automation, simulation and modeling (Dagienė et al., 2017; ISTE & CSTA, 2011; Kalelioğlu et al., 2016; Sondakh et al., 2020).

While the components of CT lack unanimous agreement in existing literature (Fronza et al., 2017), this study adopts the widely recognized standards (ISTE & CSTA, 2011) and elements frequently endorsed by scholars (Dagienė et al., 2017; Kalelioğlu et al., 2016; Shute et al., 2017; Sondakh et al., 2020). In this study, the computational thinking elements, along with their descriptions, are presented in Table 1.

Table 1 CT components and their definitions
Full size table

CT Approaches

Within the scope of the study, the computational thinking elements in Table 1 were integrated into the developed teaching modules using Plugged and unplugged CT approaches.

Plugged CT Approach

The “Plugged CT” approach refers to teaching computer science concepts with the aid of a computer (Pellas & Peroutseas, 2017). In the plugged CT approach, block-based programming platforms like Scratch, Alice, and App Inventor can transform abstract programming ideas into tangible concepts using animations or simulations, integrating diverse media elements like sound, images, and music (Wei et al., 2021). Within block-based visual programming settings, students grasp programming logic in a more straightforward and engaging manner by drag-and-drop actions with code blocks (Totan, 2021). Furthermore, students can easily create algorithms by dragging and dropping code blocks together, and they can use computational thinking elements such as branching with if-then-else code blocks, and iteration with repeat/forever/repeat until etc.code blocks (Sigayret et al., 2022). Therefore, the plugged CT approach helps develop computational thinking skills (Chen et al., 2017; Rodriguez-Martinez et al., 2020). For example, Romero et al. (2017) observed the development of computational thinking skills among undergraduate students by inviting them to a creative programming activity using the Scratch programming tool. Furthermore, Voon et al. (2023) and Zaharin et al. (2018) have found that visual programming-supported interactive games like Hopscotch, along with logo-based technologies such as LED and robotic coding agents, as well as the Tangiblek robotic coding program, also enhance computational thinking.

Unplugged CT Approach

The "Unplugged CT” approach proposed by Bell et al. in 2011, refers students learning computer science concepts using various methods in environments without computers. The "Unplugged CT" approach uses tangible teaching tools like pencils, paper, colored cartons, markers, and puzzle pieces, as well as games, physical activities, contests, mystery-enhancing tasks, role-playing, and artistic exercises (Jagust et al., 2018). Thus, active learning approach is implemented with activity-based teaching (Kalelioğlu & Keskinkılıç, 2020). Moreover, unplugged activities make the class fun (Bell & Vahrenhold, 2018), provide an effective source of motivation thanks to the gamification element (Apostolellis et al., 2014; Soviani et al., 2019), and increase classroom participation because they promote competition among students (Yuliana et al., 2021). In addition, low cost of unplugged CT activities, their independence from computer usage, and the lack of need for information and communication technology (ICT) skills provide ease of implementation and create advantages for the unplugged CT approach (Busuttil & Formosa, 2020; Chen et al., 2023; Minamide et al., 2020). The literature emphasises that unplugged computational thinking activities are necessary to introduce students to computational thinking skills at a young age and to teach these skills (Kuo & Hsu, 2020; Waterman et al., 2020). In this context, some researchers have developed or utilized new unplugged board games and blocks using the unplugged CT approach (Minamide et al., 2020; Torres-Torres et al., 2019; Tsarava et al., 2019), and various action research, quasi-experimental designs, and case studies have demonstrated that unplugged CT approaches significantly enhance CT skills (Chen et al., 2023).

CT and Creative Problem Solving Skills

Creative problem solving is the approach used to uncover fresh, imaginative concepts or to reconfigure existing ideas in innovative ways, promoting a mindset geared towards innovation (Rastogi et al., 2018). In this respect, creative problem solving is quite different from classic problem-solving processes (Park & Green, 2019). In classical problem solving, students can reach a solution using a single path by using their existing knowledge. But in creative problem solving, the outcomes of the problems are not definite and there is a complexity that students need to solve (Kong, 2022). Different steps and processes need to be used to solve this complexity. Taking into account the classic problem-solving and creative problem-solving processes and steps proposed by researchers in the literature (Isaksen et al., 2010; Jackson, 1991; Polya, 2004; Treffinger et al., 2023), definitions of problem-solving and creative problem-solving stages are provided in Table 2. Additionally, differences between problem-solving and creative problem-solving are illustrated through an example problem scenario in Table 2.

Table 2 A comparison of classic problem-solving and creative problem-solving steps and processes (adapted from Isaksen et al., 2010; Jackson, 1991; Polya, 2004; Treffinger et al., 2023)
Full size table

As seen in Table 2, the more liberal and flexible structure of creative problem-solving requires looking at problems from different perspectives and evaluating various solutions. Especially many problems encountered in daily life are complex and require the use of multi-dimensional thinking and creativity (Deschryver & Yadav, 2015). Therefore, it is extremely important to raise individuals who can solve problems creatively, which is a need of societies.

Park and Green (2019) have stated that applications carried out with computational thinking skills are critical for students to have creative problem-solving skills in the twenty-first century. This is because during the solution of a problem, students use their imagination while forming algorithms and patterns, they come up with creative and original designs, and through reasoning, they push their minds to produce unique and new ideas (Seo & Kim, 2016; Yadav & Cooper, 2017). Deschryver and Yadav (2015) emphasize that creativity is a significant component of problem-solving and represents an additional dimension within computational thinking. In particular, when people tackle problems in their everyday lives, the abstraction element aids in streamlining the solution by avoiding intricate details, while the decomposition aspect offers a lucid and comprehensible strategy by segmenting the issue into smaller parts (Kalelioğlu et al., 2016). Furthermore, they devise innovative solutions by recognizing that various outcomes stem from different solutions through the branching component. Reasoning enables the ability to analyze different options and choose the most appropriate one (Chang et al., 2018). This contributes to the process of combining various concepts that form the basis of creative problem solving skills and generating new and innovative ideas (Israel-Fishelson & Hershkovitz, 2022). Additionally, reasoning supports error debugging and improving solutions, thereby aiding in the development of creative solutions (Leonard et al., 2018; Ling et al., 2018). Algorithmic thinking involves solving problems step by step and systematically planning the solution process (Chen et al., 2017). This allows for managing complexity in the creative problem-solving process, considering different approaches, and enhancing the ability to generate innovative solutions (Seo & Kim, 2016). Thus, thanks to computational thinking skills, problems in everyday life are solved more easily and creatively. In this regard, ISTE (2015) has stated that the computational thinking approach helps students develop their logical skills and also expand their creative problem solving skills processes and innovative abilities. Therefore, this study will determine how the elements of computational thinking in plugged and unplugged environments will affect the creative problem-solving skills of middle school students.

Use of CT in Circulatory System

Computational thinking approach are considered an important component needed for individuals to learn science (Garneli & Chorianopoulos, 2019; Lee et al., 2020). Because integrating computational thinking elements into science provides applied learning experiences in learning scientific laws (Luo et al., 2020). In addition, incorporating computational thinking components into diverse curricula, especially in science education, is crucial for linking intricate scientific ideas with real-world scenarios.

In the current study, the incorporation of computational thinking elements into science instruction was implemented within the framework of the circulatory system topic. The objective was to empower students to generate innovative solutions to real-life challenges by applying scientific principles to their everyday experiences. Accordingly, the focus has been on the topic of the circulatory system, which includes a large number of abstract concepts, systems, and relational processes, and where students have difficulty learning in science (pulmonary and systemic circulation, veins carrying clean and dirty blood, veins related to the chambers of the heart, blood pressure, pulse, the roles of blood cells etc.) (Alkhawaldeh, 2012; Anisah & Saptono, 2023; Fadhilah et al., 2023; Fajriyanti & Sayekti, 2022; Cheng & Gilbert, 2015). Another reason for choosing the circulatory system as the subject is that it is an ideal topic to integrate computational thinking elements. For instance, the mechanism of how the heart works or the step-by-step processes of the pulmonary and systemic circulation allow for the creation of algorithms. The mechanism of how the heart works or the continuous (iterated) pattern of the pulmonary and systemic circulation can be noticed. The circulatory system topic can be taught by breaking it down into components such as the heart, vessels, and blood cells. One can make abstractions regarding the kind of blood (clean or dirty) transported by various vessels. The vessels entering and exiting the chambers of the heart can be taught using the branching (if the blood is carried in the superior and inferior vena cava, it pours into the right upper chamber of the heart, etc.) component. The reasoning component can be applied to determine actions for instances of elevated blood pressure and heart rate. Therefore, within the scope of the study, the elements of computational thinking were integrated with the circulatory system gains, thereby providing the teaching of the circulatory system topic. Consequently, students are presented with real-life challenges concerning the circulatory system through activities in both the plugged and unplugged instructional modules that were crafted. While students are performing module activities, they are both seeking creative solutions to these problem scenario and learning the scientific concepts about the circulatory system. Thus, a relationship has been established between scientific concepts and everyday life problems. The problem scenario in the modules consist of open-ended questions that allow students to generate entirely creative, new, and original ideas by using the elements of computational thinking. The examples of problem scenario given to the students on the circulatory system and the computational thinking elements to be used in the solution of the problem are seen in Table 3.

Table 3 CT Components and Problem Scenarios in plugged and unplugged teaching modules
Full size table

The objective is to foster the development of students' creative problem-solving skills as they engage in computational thinking processes when addressing these challenging situations.

Aim of the Study

This research seeks to assess the impact of computational thinking-focused teaching modules, both plugged and unplugged, designed around the circulatory system theme, on the creative problem-solving abilities of 6th graders. Guided by this, we aim to address the following research query:

  • How do plugged and unplugged teaching modules centered on the circulatory system influence the creative problem-solving skills of 6th grade students?

Methods

Research Methodology

In this research, a quantitative methodology was employed, utilizing the pre-test post-test control group design (Creswell, 2019). The groups were determined by random assignment. Table 3 provides an overview of the study's design, the groups involved, and the experimental procedures employed in the research. As seen in Table 4, while the activities in the plugged teaching module were used in the experimental 1 group and the activities in the unplugged teaching module were used in the experimental 2 group, inquiry-based science activities included in the Turkish science education program were applied in the control group.

Table 4 Study Methodology
Full size table

Participants

The current study was conducted with 90 sixth-grade students studying at a state middle school. In this middle school, there were six different sixth-grade branches, and these branches had been formed by the school administration to have similar levels of academic achievement scores. Additionally, the number of female and male students has been evenly distributed. Since the branches had similar academic achievement scores and gender distribution, three of these branches were randomly assigned, one to the control group, and the other two to the experimental groups. The experimental 1 group consists of 30 students (16 girls and 14 boys), the experimental 2 group consists of 30 students (15 girls and 15 boys), and the control group consists of 30 students (14 girls and 16 boys). The age range of students in all groups is between 11 and 12. Experiment 1, experiment 2, and the control groups do not have prior experience with computational thinking and the Scratch program. Additionally, students in the experimental 1, experimental 2, and control groups have not received any prior instruction in creative problem-solving skills. Furthermore, there is no statistically significant difference among the pre-test scores of creative problem-solving skills for all three groups.

Implementation Process

The same science teacher executed the implementation process across three distinct groups. This teacher is employed by the Ministry of National Education (MEB) of the Republic of Turkey and has 10 years of experience in science teaching. Before the implementation, the teacher in charge underwent a one-month training (totaling 16 h) on computational thinking skills and the activities outlined in the teaching modules. The teacher who carried out the implementation received training from the responsible author (1st author). During 6 h of the training, a presentation was made to the teacher in charge about computational thinking skills and the activities in the developed teaching modules. Afterward, as an observer, the teacher in charge participated in the pilot implementation of the module (conducted by the responsible author) and observed its activities for 10 h. Thus, it was ensured that the responsible teacher has the necessary knowledge and experience on the implementation of module activities.

Implementation in Experiment 1 Group

Plugged teaching module was implemented in experimental group 1. 10 different activities were included in the module. Through the use of a computer and the visual programming tool Scratch, students finished the activities in the teaching module. In two stages and over 16 lesson hours (with each lesson being 40 min), the 10 developed activities were carried out. The 1st stage spanned 4 lesson hours, students had been introduced to the Scratch program installed on their computers. Initially, they were shown the code blocks, coding panel, and stage sections. Then, they were provided with brief information on creating code blocks via drag-and-drop in the Scratch program and about the code blocks themselves. In the next step, students assembled the code blocks they wanted to try, issuing various commands to puppets on the stage (such as turning 90 degrees to the right, moving 10 steps forward, changing costumes, etc.). Additionally, through the Scratch program, CT concepts such as branching (if-else), continuous repetition/repetition until (iteration), block creation (decomposition and abstraction), or small sequential tasks (algorithm creation) were introduced to students. Thus, over 2 class hours, students began to understand CT concepts and how to use the Scratch program. Later (within a 2 class hours), using the Scratch program, students applied computational thinking components to solve real-life problems related to the circulatory system. For example, creating an algorithm to ensure that Ayşe, who lost blood as a result of a traffic accident, receives the blood cell she needs to prevent her death. (Examples of creative problem scenarios can be found in Table 3).

In the second stage (12 lesson hours), students completed the 10 different activities in the teaching module and during this, they attempted to generate creative solutions using computational thinking skills for the 10 different problem scenario within the activity. While producing creative solutions to the problem scenarios, the students used the creative problem solving steps and processes specified in Table 2. While producing solutions to problem scenario in module activities, they completed the systemic and pulmonary circulation by sequencing the code blocks step by step (algorithm creation). Students learn the number of chambers of the heart using branching and iteration skills. They determine the type of blood carried in the right and left chambers of the heart using the branching element. They create algorithms to reach clean and dirty blood exits in the labyrinth of veins. In the case of high blood pressure and pulse, they produced solutions to the given problem situation using reasoning skills and expressed these solutions by creating a dialogue between puppets. By using branching and iteration skills, they determined which blood groups can exchange blood in case of emergency in daily life. By creating code blocks from blood cells (decomposition), they determined which blood cell increases the number of blood cells in case of illness or which blood cell provides blood clotting in case of injuries and expressed the functions of blood cells in summary form (abstraction). Students created patterns from cards of blood cells (pattern construction). They created a pattern path from coloured cardboard and found creative solutions to various problems for the blood cell to move along this pattern path. Through these activities, students can produce original designs that foster their creativity, while also crafting imaginative solutions to the module's issues. During the implementation, students actively participated in all activities, while the teacher guided the students. The sample activity is provided in Appendix 1.

Implementation in Experiment 2 Group

Unplugged teaching module was implemented in experimental group 2. 10 different activities were included in the module. Students completed the activities in this teaching module without using computers by using teaching materials (pencils, paper, markers, circulatory system puzzle pieces, coloured cartons, computer-free coding game Tospaa, etc.), kinesthetic activities, competitions, games, etc. In two stages and across 16 lessons (each 40 min in duration), the 10 developed activities were executed. During the 1st stage's 4 lesson hours, the students were divided into 5 groups of 6 students each. Each group was given a Tospaa game box and the game cards were introduced. The students created the Tospaa game scenario by using the first task card under the guidance of the teacher. Meanwhile, they created algorithms using Tospaa direction cards, used iteration skills with loop cards and explored branching with the help of condition cards (2 class hours). Afterwards (within a 2 class hours), the students used various game activities, kinesthetic activities, competitions and role-playing activities, coloured cards, papers to generate creative solutions to everyday life problem situations about the circulatory system For example, you should design an innovative circulation system model to reduce traffic congestion in the city and minimise environmental impacts (see Table 3). To find creative solutions to this problem situation, students used computational thinking skills such as reasoning and branching. In the end, the group that designed the most innovative circulatory system model won the competition.

In the second stage (12 lesson hours), students have completed the 10 different activities in the teaching module. While carrying out these activities, students tried to generate creative solutions to the 10 different problem scenario in the activity by using their computational thinking skills. While producing creative solutions to the problem scenarios, the students used the creative problem solving steps and processes specified in Table 2. During the module activities, the students created pathways of blood cells and made a heart model using colored cardboards. They created algorithms using Tospaa direction cards to reach this heart model. With the help of loop and conditional cards, they utilized looping and branching skills. They demonstrated clean and dirty blood in the heart model by painting the chambers of the heart in different colors (decomposition). Using reasoning skills, they wrote dialogues between the models they created. Using colored cardboards, colored pencils, glue, and magnet pieces, students created models of organs involved in the systemic and pulmonary circulation (such as the heart, lungs, etc.) and blood vessels, thus forming puzzle pieces for both systemic and pulmonary circulation (decomposition, abstraction). They used these puzzle pieces to construct pathways for systemic and pulmonary circulation through problem scenarios and completed the systemic and pulmonary circulation step by step (algorithm creation). Students, following the problem scenarios, classified blood vessels carrying dirty and clean blood by creating a blood vessels box using colored cardboards, colored pencils, glue, scissors, etc. (decomposition). For example, they placed the names of vessels carrying clean blood in the red box, the names of vessels carrying dirty blood in the blue box, and the names of vessels involved in substance exchange in the green box. They created blood cell cards and wrote the tasks of blood cells on these cards in the simplest form (abstraction). They formed blood group cards and arranged these cards in a pattern. Using colored cardboards, they created patterned pathways and advanced their blindfolded peers along this path as they provided solutions to problems (using commands like turn right, take a step forward, turn left, etc.), and the group reaching the first exit won the game. Through these activities, students can produce original designs that foster their creativity, while also crafting imaginative solutions to the module's issues. During the implementation, the students actively participated in all the activities, and the teacher provided guidance to the students. The sample activity is provided in Appendix 2.

Implementation in Control Group

Meanwhile, the control group students relied on the circulatory system textbook and pursued the current curriculum's inquiry-based science activities, excluding the use of computational thinking aspects. The implementation in the control group also lasted for a total of 16 lesson hours and was carried out in 2 stages. In the first stage, which lasted for 12 lesson hours, the circulatory system topic was explained through the textbook, supported by lesson videos available at the web addresses (https://ders.eba.gov.tr/ and https://v218.morpakampus.com/ogretmen.asp?page=ders). Meanwhile, an inquiry-based science experiment was conducted with active participation from the students. In this activity, a real heart model (bovine heart) was brought to the classroom to demonstrate the sections of the heart, allowing students to examine the chambers of the heart. Students explored the structure of the heart and meanwhile tried to answer some questions raised by the teacher: (1) can you describe structure of chambers of the heart, and (2) what are the functions of chambers of the heart and vessels entering and leaving the heart? Following the experiment, the students did also exercises consisting of true–false, fill-in-the-blank and closed-ended questions about these topics. Additionally, during the first 12 h, students examined the structure of blood under the microscope and determined blood groups in the laboratory. During this period students tried to answer some questions raised by the teacher: (1) what is your blood group? (2) which factors are effective in determining your blood group? Students used the internet, textbooks, and educational videos in order to address these questions. Furthermore, students conducted research on blood from various sources (internet, textbook, educational videos, etc.) and discussed their findings in class. As part of the final activity of the first stage, students conducted an end-of-chapter project activity. In this activity, students gathered information about the blood groups of their family and friends, and then created a blood exchange diagram for individuals within their social circle. Afterwards, they presented this diagram to their classmates for review and discussion.

In the second stage (4 class hours), students participated in the discussion activity located at the end of textbook chapters. Thus, students delved into the significance of blood donation within society. They researched this topic thoroughly and shared their findings with their peers, fostering a dialogue around the importance of blood donation. Additionally, students have engaged in classroom discussions on strategies aimed at increasing blood donation rates, and then shared the solutions they proposed with their broader social circles. During the implementation, while students actively participated in all activities, the teacher guided the students. The sample activity is provided in Appendix 3. Table 5 presents the implementation process conducted in three different groups.

Table 5 Implementation stages, durations and activities carried out for 3 different groups
Full size table

Data Gathering Instrument

In this research, students' creative problem-solving skills were assessed using the Creative Problem Solving Skills Inventory (CPPSI), a 5-point Likert-type inventory comprising 40 items and five factors. The ratings on the scale are as follows: "Never = 1", "Rarely = 2", "Sometimes = 3", "Often = 4", "Always = 5". According to student responses, a minimum of 40 and a maximum of 200 points can be obtained from this inventory. This inventory, developed by Lin (2010), is based on Cho's (2003) 'Creative Problem-Solving Skill Dynamic System Model' and aims to uncover students' creative problem-solving skills. According to Cho's (2003) model, creative problem-solving skills consists of five components: convergent thinking, divergent thinking, motivation, environment, and general knowledge and skills. In this context, the (CPPSI) scale includes these 5 sub-dimensions. The inventory includes 8 items for convergent thinking, 10 items for divergent thinking, 6 items for motivation, 11 items for environment, and 5 items for general knowledge and skills sub-dimensions. The inventory was initially developed by Lin (2010) with 49 items. Convergent Thinking was defined with items ranging from 13 to 24; Divergent Thinking from 1 to 12; Environment factor from 34 to 44; Motivation from 25 to 33; and General Knowledge and Skills from 45 to 49. Later, Lin (2010) reduced the scale from 49 to 40 items and defined 5 different factors with 40 items. Accordingly, items 13, 14, 15, and 16 were removed from the Convergent Thinking factor; items 1 and 6 were removed from the Divergent Thinking factor; and items 31, 32, and 33 were removed from the Motivation factor. Lin (2010) calculated the Cronbach's alpha reliability coefficient for both the overall scale and its sub-dimensions. As a result the overall Cronbach's alpha reliability coefficient for the scale is .85. For the inventory's sub-dimensions, the reliability coefficients are as follows: .84 for convergent thinking, .87 for divergent thinking, .79 for motivation, .89 for environment, and .85 for general knowledge and skills. Information indicating the compatibility of this model with the existing data includes χ2 = 2028 (sd = 730), RMSEA = 0.046 [.043,.048], CFI = .929, TLI = .924. As seen in Table 6, reported standardized factor loadings range from .343 to .812.

Table 6 Standardized Factor Loadings of the 40-Item Model of CPSSI (adopted from Baran-Bulut et al., 2018)
Full size table

The inventory, adapted into Turkish by Baran-Bulut et al. (2018), was utilized for the present study. The adaptation of the scale to Turkish was carried out by Baran-Bulut et al. (2018) with 856 middle school students. In the adaptation study, linguistic equivalence work, validity, and reliability analyses were conducted. As a result of the adaptation study, the reliability coefficients are as follows: .78 for convergent thinking, .79 for divergent thinking, .73 for motivation, .88 for environment, and .77 for general knowledge and skills. These values suggest that the scale possesses a high degree of reliability, as affirmed by Baran-Bulut et al. (2018). To establish the scale's validity, a confirmatory factor analysis was performed. In accordance with the findings, the standardized regression loads exhibited a range of values spanning from .32 to .78. In the context of the confirmatory factor analysis, various fit indices were scrutinized, yielding the following results: GFI = .871, AGFI = .858, CFI = .896, IFI = .876, NNFI = .856, RMSEA = .055, and SRMR = .073 (Baran-Bulut et al., 2018). The model thus formed was found to have acceptable fit indices (Pallant, 2020). Accordingly, it was understood that the inventory is a valid and reliable scale and can be used within the scope of the present research. The inventory is presented in Appendix 4.

Data Analysis

As the Creative Problem Solving Skills Inventory (CPSSI) is a 5-point Likert scale, student responses were scored with "strongly disagree" equating to 1 point, "disagree" to 2 points, "undecided" to 3 points, "agree" to 4 points, and "strongly agree" to 5 points. The total score obtained from the inventory for each student was calculated by scoring the points given by the students for each of the 40 items in this way. Accordingly, the minimum score a student can get from this inventory is 40, and the maximum score is 200. In this manner, the scores of the students in all groups (the experimental 1, the experimental 2, and the control group) were calculated and analyzed in the SPSS 21.0 package program. To compare the creative problem-solving skills of the three distinct groups (experimental 1, experimental 2, and control groups), a one-way analysis of covariance (ANCOVA) was employed. The dependent variable in this study was the post-test scores of creative problem-solving skills. The ANCOVA is the test to be used to minimize Type 1 error in multiple analyses (Pallant, 2020). Therefore, the ANCOVA was performed to minimize Type 1 error by controlling the differentiation in the pre-tests between the groups.

Findings

Before starting the ANCOVA the necessary assumptions were thoroughly reviewed and met. First, a normality analysis was conducted on the pre-test and post-test scores of the Creative Problem Solving Skills Inventory (CPSSI) of the groups. Table 7 shows that for the CPSSI pre and post-test scores of experimental group 1, experimental group 2, and the control group, the p-values from the Shapiro–Wilk normality test are above .05. Additionally, with skewness and kurtosis values lying between -1.5 and + 1.5, as noted by Tabachnick et al. (2013), this suggests a normal distribution for all groups based on the CPSSI test results.

Table 7 Normality analysis of the data obtained from the CPSSI
Full size table

Subsequently, the Levene test was conducted to assess the equality of variances, revealing that there was no significant distinction in the test outcomes [F(2, 87) = 3.110, p > .05]. According to this result, the variances related to the CPSSI post-test scores are homogeneous. Another assumption is that there is a linear relationship between the CPSSI pre-test and the CPSSI post-test variables. As the last assumption, it was determined that the joint effect of Group*Pre-test on the CPSSI post-test scores was not significant [F(2,84) = .856, p > .05]. Accordingly, it can be said that the slopes of the regression lines are homogeneous. Thus, all the necessary assumptions for the ANCOVA have been met.

The ANCOVA results of the groups are given in Tables 8 and 9.

Table 8 The post-test means of CPSSI, adjusted means, standard deviations, and standard error values of the groups
Full size table

The adjusted post-test means of the CPSSI for the groups, as indicated in Table 8, were determined as follows: experimental group 1 (M = 145.50, SD = 28.67), experimental group 2 (M = 148.45, SD = 31.21), and the control group (M = 131.00, SD = 19.65). Accordingly, the experimental groups have a higher mean than the control group according to the adjusted post-test scores of CPSSI. The intergroup effect test was performed to determine whether the obtained adjusted post-test means were statistically significant, and it was presented in Table 9.

Table 9 The results of ANCOVA on the adjusted post-test scores of CPSSI for the groups
Full size table

According to Table 9, when the CPSSI pre-test scores of the groups were controlled, a statistically significant difference was observed between the CPSSI adjusted post-test scores of the groups [F(2,86) = 3.520, p < .05, ηp2 = .076]. The Tukey multiple comparison test was applied to reveal the differences between the CPSSI adjusted post-test scores of the groups. The Bonferroni multiple comparison test was applied to reveal the differences between the CPSSI adjusted post-test scores of the groups (Table 10).

Table 10 The results of the Bonferroni Post Hoc analysis for the groups
Full size table

The CPSS post-test post hoc analysis revealed, a significant difference was found between the post-test mean differences of the experiment 2 group and the control group in favour of the experiment 2 group (MD = 17.45, p < .05). Cohen's d was calculated to measure the effect size between these two groups. The Cohen's d value between the experimental 2 group and the control group is 0.668. According to Cohen (2013), a Cohen’s d value of 0.5 indicates a medium effect size, while a value of 0.8 indicates a large effect size. Since the value of 0.668 found in our study can be approximately considered as 0.7, this value is closer to a large effect size rather than a medium effect size. Therefore, we can say that there is a significant difference close to a large effect size between the experimental 2 and control groups. While the post-test mean differences between experiment 1 group and the control group weren't significant (MD = 14.58, p > .05), the post-test average score for the experiment 1 group (M = 145.50, SD = 28.67) exceeded that of the control group (M = 131.00, SD = 19.65). Although the post-test mean differences between the experiment 1 and experiment 2 groups are not significant (MD = 2.94, p > .05), the post-test average for the experiment 2 group (M = 148.45, SD = 31.21) surpasses that of the experiment 1 group (M = 145.50, SD = 28.67).

Discussion and Implications

ANCOVA results showed that there was a statistically significant difference between the posttest averages of the adjusted creative problem solving skills obtained from self-reports of the students in experimental group 1, experimental group 2 and control group. Based on the post-hoc analysis performed, only significant difference between the post-test mean differences was between the experimental group 2 and the control group. Moreover, the approximate Cohen's d value of 0.7 indicates a significant difference close to a large effect size between experiment 2 and control groups. The posttest averages of experiment 1 and the control group showed no statistically significant difference. However, the creative problem-solving posttest average score for the experiment 1 group is higher than that of the control group. The findings of the study have shown that, according to the self-report of the experimental groups, the post test averages of creative problem-solving skills were higher than those of the control group. This might be due to students applying their computational thinking abilities to devise creative solutions for daily issues associated with the circulatory system. Computational thinking aids in solving real-life problems and promotes learning through hands-on experience (Boulden et al., 2018). As a result, this might have caused the students in the experimental group to think that they had more creative problem solving skills. Peteranetz et al. (2018) found that when they conducted creativity activities with engineering students by utilising computational thinking skills, their creative problem solving skills improved. Paf and Dinçer (2021) conducted a study investigating the correlation between computational thinking skills and creative problem-solving skills in middle school students spanning grades 5 through 8. The research yielded a noteworthy and moderate correlation between students' computational thinking skills and their creative problem-solving abilities. Consequently, their findings indicated that with higher grade levels, students demonstrated an enhanced utilization of computational thinking skills, leading to a corresponding improvement in their creative problem-solving abilities. Xu et al. (2022) in their study, examined the relationship between computational thinking skills and creativity of children aged 5-6. As a result of the research, they found that computational thinking skills were significantly associated with reasoning ability and creative thinking. They also found that acquiring CT skills at an early age has a direct positive effect on the development of creativity. These research results are consistent with our current research results and reveal the positive effect of computational thinking skills on middle school students’ creative problem-solving skills.

In experimental group 2, where the unplugged teaching module was applied, the variety of materials (paper and pencil, coloured cardboard, coloured pencils, glue, scissors, Tospaa game box, cards, puzzle pieces, etc.) provided to students in experiment 2 group may have led them to develop more creative and diverse problem-solving methods in response to the given tasks. Additionally, when this material freedom was combined with the implementation of computational thinking components, it may have prompted them to express themselves as being in a better position in terms of their creative problem-solving skills. However, in the control group where inquiry-based activities were implemented, the lower diversity of materials compared to the experimental group 2 may have led the control group students to perceive themselves as having less creative problem-solving skills. in addition, the fact that the ct components were not integrated into the control group may have led the students not to express themselves as better in terms of creative problem solving skills. Additionally, kinesthetic activities, interesting games and competitions (they utilized games and competitions, such as the group that reaches the heart model they constructed on the paths of the circulatory system they created on the Tospaa game board, employing the fewest number of playing cards (utilizing algorithmic creation, branching iteration skills) to emerge victorious in the competition), which are unique to unplugged environments, might have encouraged fast, practical and creative solutions by focusing students' interest and attention on the problem. Furthermore, the creation of competition among students through games and competitions (Tsarava et al., 2019) might have increased participation in problem-solving in the classroom, leading to the generation of new, original, and different ideas. Furthermore, the opportunity to segregate students into groups during kinesthetic activities or games executed in unplugged settings could have amplified intra-group communication and collaboration (Kotini & Tzelepi, 2015). This enhanced cooperation may have facilitated students to mutually assist one another in generating creative ideas for problem resolution. Various researchers have pointed out in this regard, that due to the game-oriented structure of unplugged activities, it increases interest, curiosity, and motivation (Kotini & Tzelepi, 2015; Tsarava et al., 2019), makes the lesson fun (Bell & Vahrenhold, 2018), and thus keeps the mind constantly active, maintaining the effort process for problem-solving (Kukul & Karataş, 2016; Tsarava et al., 2019). Hufad et al. (2021) also found that the playful activities in unplugged environments increased students' interest and enthusiasm and enhanced their problem-solving skills as a result of their study conducted by organizing game activities with 5-6-year-old children in unplugged environments. Similarly, Lee et al. (2020) found in their study that unplugged activities increased interest and motivation and improved the creativity of elementary school students. This study also supports the literature by showing that creative problem solving skills are significantly improved in unplugged environments according to student self-reports, and reveals that unplugged environments can be integrated into curricula in different disciplines.

During the inquiry-based instruction applied to the control group, the lack of gaming, competition, and kinesthetic elements during internet research, textbook study, or laboratory investigations might have led to a deficiency in the necessary interest and motivation for problem-solving. This situation might have limited the development of students' self-reports of creative problem solving skills by reducing their desire and effort to generate ideas for problem solving. Additionally, individual participation in project tasks by students in the control group may have restricted their ability to notice different perspectives and produce new ideas for problem-solving by drawing inspiration from their peers. Similarly, the absence of collaboration among students during individual activities may have hindered the development of their thoughts on creative problem-solving skills by preventing them from supporting each other through brainstorming or motivating group members in generating ideas. In addition, the students in the control group did not use computational thinking skills during the experimental activity, laboratory investigations, closed-ended questions, open-ended questions, fill-in-the-blank and true-false tasks, poster creation, project activity or individual investigations and discussion activities which may be the reason why creative problem solving skills did not improve compared to the experimental groups. Because computational thinking skills teach to recognise and apply various problem solving strategies (Israel-Fishelson & Hershkovitz, 2022). This allows different and effective solutions to emerge in the creative problem solving process (Maharani et al., 2019). At the same time, the control group students may not have developed creative problem-solving skills because they were limitedly interested in problem scenarios that would activate higher-order thinking skills during the activities they carried out, unlike the experimental groups. Bicer et al. (2019) suggested that when we integrate projects and challenging tasks that require students to use different thinking processes into our lessons, we will train students as creative problem solvers. Similarly, Kong (2022) measured the problem formulation skills of primary school students by integrating computational thinking skills, one of the higher-order thinking styles, into the learning environment and found that students' creativity in problem solving improved. In line with the literature, in the present study, in the experimental groups, students were provided with problem scenario that would activate their higher-order thinking processes and students were enabled to use their computational thinking skills, while in the control group, computational thinking skills were not included and project tasks were included in a limited number, which can be considered as the reasons for the emergence of the current results.

According to the students' self-reports, the creative problem-solving skills of the students in experimental group 1, who received the plugged teaching module, did not show a statistically significant difference when compared to the control group. However, it is worth noting that the post-test averages for creative problem-solving skills in experimental group 1 were noticeably higher than those of the control group, which may be attributed to the students' use of computational thinking skills with the assistance of the Scratch programming tool. On this matter, Romero et al. (2017) have argued and confirmed with their study that the Scratch application enhances students' creativity not only through coding or algorithm creation, but also through design processes (visual elements, sound recordings, background, etc.). Similarly to this study, students improved their creative problem-solving skills by using computational thinking abilities while completing the pulmonary and systemic circulatory systems step by step with the Scratch program (algorithm creation), creating blood vessel blocks (decomposition), and showing different colors in the puppet's clean and dirty blood-carrying vessels using if/else code blocks (branching). However, the lack of utilization of programming tools and computational thinking skills by students in the control group while conducting individual research and preparing presentations using resources such as computers and the internet may explain the insufficient development of their creative problem-solving skills compared to the experiment 1 group. In a different study, Noh and Lee (2020) found in their study with 5th and 6th grade students that students' computational thinking skills and creativity significantly improved through robotic programming in a plugged learning environment. Additionally, the study conducted by Zaharin et al. (2018) revealed that students' computational thinking skills, which they call CompT (computational thinking skills), improved students' creative problem solving abilities in attached environments using programming tools such as Hopscotch or Scratch, as well as robotics, LED or logo-based tools. Bers et al. (2014) implemented the TangibleK Robotics Programming as a plugged instructional tool for kindergarten children. As a result, they found that students' creative problem-solving skills improved while creatively constructing their robots and programming using computational thinking skills. The results of this study demonstrate similarities with the current study in terms of the utilization of computational thinking skills in a plugged learning environment and the coding and various design processes conducted with these tools, contributing to the enhancement of creative problem-solving skills.

In the current study, according to the students' self-reports, when comparing plugged and unplugged approaches in terms of creative problem-solving skills, the higher success rate of unplugged approaches may be attributed to the presence of kinesthetic activities, games, competitions, or artistic activities that are unique to unplugged environments. The lack of games, competitions, and kinesthetic elements in plugged environments might lead to a shortfall in effort and motivation for problem-solving compared to unplugged environments. Therefore, it could be said that the experimental 2 group was more successful in improving creative problem-solving. Additionally, another reason for better creative problem-solving skills in unplugged environments might be that the numerous tangible materials available in these settings allow students to exhibit their creativity freely, without being tied to the opportunities provided by technology. Additionally, in plugged environments, difficulties in coding skills and inadequacies in using technology while employing computational thinking skills, compared to unplugged environments, could have diverted attention to different aspects during problem-solving. In line with these thoughts, Cortina (2015) found that unplugged activities were more successful in enhancing students' creative problem-solving processes by increasing motivation and interest compared to plugged applications. Although the results of this study support the results of the current study, we can recommend different experimental studies (plugged, unplugged, plugged + unplugged) to compare the creative problem solving skills of middle school students.

Conclusions

This research noted that both plugged and unplugged teaching modules, centered on the circulatory system and oriented towards computational thinking, enhanced the creative problem-solving abilities of middle school students based on the students' self-reports. The unplugged teaching module implemented in the experiment 2 group was significantly more successful in developing creative problem solving skills compared to the control group. Although the plugged teaching module implemented in experiment 1 group was more successful in improving creative problem solving skills compared to the control group, this difference was not statistically significant. In addition, another result of this study was that there was no statistically significant difference between experiment 1 group and experiment 2 group in improving creative problem solving skills.