Investigating the brain wave activities of middle school students during the implementation of STEM-based digital creativity practices

203 Journal of Gifted Education and Creativity 12(2), 203-219, December 2025 e-ISSN: 2149-1410 jgedc.org dergipark.org.tr/jgedc Genc Bilge Publishing Ltd. (Young Wise) © 2025 gencbilgeyayincilik.com Research Article Investigating the brain wave activities of middle school students during the implementation of STEM-based digital creativity practices Cagla Bulut Ates1*, Hilal Aktamış2 and Furkan Aydın3 Graduate School of Natural and Applied Sciences, Aydin Adnan Menderes University, Aydin, Türkiye Article Info Abstract Received: 18 August 2025 Accepted: 5 October 2025 Online: 30 December 2025 Keywords Brain waves Educational neuroscience EEG Scientific creativity STEM 2149-1410/ © 2025 the JGEDC. Published by Genc Bilge (Young Wise) Pub. Ltd. This is an open access article under the CC BY-NC-ND license This study explores when students engage in creative thinking during STEM activities designed to foster such skills. The significance of this research lies in its focus on scientific creativity from a neurophysiological perspective, aiming to provide insights into the brain mechanisms underlying creative thinking in educational contexts. The purpose of the study is to examine the effectiveness of specially designed STEM activities in fostering students’ scientific creativity by analyzing their brain wave patterns during different stages of participation. A case study design was adopted to enable an indepth exploration of the phenomenon. To monitor brain activity throughout different stages of the activities, a wireless EEG headset was used. Specifically, a NeuroSky MindWave Mobile 2 wireless EEG headset and the EEGID Data-Record application were employed for brainwave measurement, while a semi-structured interview form was used to collect students’ views. Analysis focused on alpha frequency amplitudes as indicators of cognitive engagement during creative tasks. Quantitative data were analyzed using statistical tests in SPSS, and qualitative data were examined through content analysis to identify patterns in students’ experiences. Results showed increased alpha activity during the active phases of creativity-focused tasks, suggesting heightened cognitive involvement. Although statistical analyses were conducted on these patterns, no significant differences were observed—likely due to the small sample size. To better understand contextual factors influencing EEG data, students’ experiences were also gathered through semi-structured interviews. Students generally viewed the EEG device as suitable for individual use but found it less practical in classroom settings. Overall, the findings indicate that the developed STEM activities hold promise for supporting creative thinking in science education. The study underscores the value of integrating neurophysiological measurements with educational interventions to better understand learning processes. However, broader data collection—particularly in classroom environments—is needed to strengthen generalizability. To cite this article: Bulut Ates, C., Aktamış, H., and Aydın, F. (2025). Investigating the brain wave activities of middle school students during the implementation of STEM-based digital creativity practices. Journal of Gifted Education and Creativity, 12(2), 203-219. DOI: https://doi.org/10.5281/zenodo.17271022 Introduction In recent years, creativity has gained increasing attention for its cognitive and educational value. Moreover, research consistently highlights its role in fostering intrinsic motivation and enhancing students’ ability to solve problems in novel ways (Amabile, 1983). Furthermore, Beghetto and Kaufman (2014) argue that fostering creativity in education requires moving away from rigid, traditional teaching approaches that may limit students' potential. This shift, in particular, is 1 Corresponding author: Dr, Graduate School of Natural and Applied Sciences, Aydin Adnan Menderes University, Aydin, Türkiye. Email: [email protected] ORCID: 0000-0002-8397-2926 2 Prof.Dr., Aydin Adnan Menderes University, Aydin, Türkiye. Email: [email protected] ORCID: 0000-0003-0717-5770 3 MSc, Graduate School of Natural and Applied Sciences, Aydin Adnan Menderes University, Aydin, Türkiye. Email: [email protected] ORCID:0000-00021531-0138 Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 204 especially important in middle school science education, where encouraging original thinking and hypothesis generation has become a key objective (Davis, 1999; Osburn & Mumford, 2006). In parallel, with developments in other fields, education has increasingly embraced interdisciplinary approaches to better understand learning processes. For instance, techniques such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allow researchers to explore brain activity and cognitive responses more directly (Liu & Huang, 2016). Consequently, the field of educational neuroscience has emerged from this intersection, offering valuable insights by integrating neuroscientific findings into educational contexts (Mareschal et al., 2013; Geake, 2009). Although the field has existed for over a decade, its relevance and application have grown significantly in recent years (Bigdeli, 2012; Howard-Jones et al., 2016; Dündar & Ayvaz, 2016). Within this framework, science educational neuroscience extends these ideas further by combining scientific content, psychology, and biological data to inform both research and instructional design (Liu & Huang, 2016). Science Education and Creativity The transformation in science education reflects a broader educational shift toward fostering creative thinking. Over the years, educational paradigms have moved beyond rote memorization and standardized assessments, placing greater emphasis on developing students' ability to think critically and creatively. Scholars have long pointed out that conventional education systems may unintentionally limit students’ innate creativity, thereby impeding their problemsolving skills and diminishing motivation (Robinson, 2006; Cheng, 2010). Within the context of science education, nurturing creativity has gained particular importance. Scientific inquiry requires not only mastery of foundational knowledge but also the ability to think critically, generate new ideas, and propose innovative solutions to complex problems. Middle school represents a pivotal stage in students’ development, where creative thinking can be introduced and strengthened as a core competency. Educational researchers have highlighted the role of creativity in scientific learning. For example, Beghetto and Kaufman (2014) emphasize the value of "Big-C" creativity in science, referring to significant breakthroughs and original discoveries driven by imaginative thinking. They argue that science education should aim not only to transmit established knowledge but also to encourage the development of creative problem-solving and hypothesis-building skills. Various strategies have been proposed to integrate creativity into science curricula. One prominent example is STEM education, which supports the development of innovative thinking by combining science, technology, engineering, and mathematics. However, while numerous activities are designed to enhance creativity, their true impact is often measured by traditional criteria rather than their effectiveness in stimulating original thought (NRC, 2012; Hennessey & Amabile, 2010; Sawyer, 2011). Addressing this gap, the present study seeks to explore whether carefully designed instructional activities can genuinely foster students’ creative thinking in scientific contexts. In line with these ideas, we formulated our hypotheses as follows: Ø STEM activities developed in the context of this study to foster creative thinking skills enable students to use their creative thinking skills. Ø STEM activities developed in the context of this study to improve creative thinking skills can be used to support students' creative thinking skills. Ø The situations participants are in while using the EEG headset may affect the validity of the data obtained. Brain Wave Activities and Cognitive Processing The investigation of brainwave activity as a tool for understanding cognitive processes has gained increasing attention in recent years. Electroencephalography (EEG), a widely used non-invasive technique for tracking electrical activity in the brain, has become a valuable method in cognitive research. Notably, Klimesch (1999) associated distinct brainwave frequencies—such as alpha, beta, delta, and theta—with specific cognitive states. For example, alpha waves are typically linked to relaxed alertness and mental reflection, whereas beta waves are associated with focused attention and active problem-solving. Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 205 Moreover, EEG has moved beyond the confines of laboratory settings. It is now used in applied contexts such as brain-computer interfaces and neurofeedback training, as shown by Thibault et al. (2016). In addition, functional magnetic resonance imaging (fMRI), which tracks changes in blood flow as an indicator of neural activity, offers complementary data when combined with EEG. This integrative approach has significantly enriched the field of cognitive neuroscience by enabling a more comprehensive view of brain function across various tasks and environments. Beyond EEG, researchers have also adopted a range of other techniques to explore the neural foundations of creativity. For instance, studies using fMRI (Arden et al., 2010; Abraham & Bubic, 2015), transcranial magnetic stimulation (TMS) (Chi & Snyder, 2011; Schicktanz et al., 2020), and structural imaging tools like voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) (Jung et al., 2010; Takeuchi et al., 2012) have contributed valuable insights. While fMRI sheds light on brain networks activated during creative tasks, TMS allows researchers to investigate the causal roles of specific brain regions. Meanwhile, VBM and DTI provide information about brain structure and connectivity linked to creative thinking. Together, these methods broaden our understanding of how creativity is represented and supported in the brain, highlighting the value of interdisciplinary approaches in studying innovation. Divergent and Convergent Thinking Expanding upon this interdisciplinary perspective not only opens new directions for cognitive research but also enhances our understanding of divergent thinking—an essential component of creativity characterized by the ability to produce a variety of ideas. Divergent thinking encourages individuals to move beyond conventional boundaries and consider multiple possibilities (Guilford, 1967). It supports flexible thinking and strengthens problem-solving skills that are vital to creative expression. Conversely, convergent thinking involves narrowing down those possibilities to identify the most effective or relevant ideas (Guilford, 1967). While divergent thinking allows for the generation of novel concepts, convergent thinking enables their evaluation and refinement. As Runco and Jaeger (2012) argue, creativity depends on a dynamic balance between these two cognitive processes. Moreover, Cropley (2006) highlights the importance of applying both thinking styles within educational contexts. He suggests that instructional programs combining open-ended activities—such as brainstorming—with structured decision-making exercises can significantly support the development of students’ creative potential. Ultimately, the interaction between divergent and convergent thinking forms a cornerstone in both creativity theory and its educational applications. Measuring Scientific Creativity In examining the relationship between divergent and convergent thinking as central components of creativity, it becomes clear that assessing scientific creativity requires a wide range of approaches. These include standardized tools aimed at evaluating innovative problem-solving, experimental design, and original research inquiries (Runco, 2014). Structured assessments offer a consistent means of measurement, yet they are only part of the broader picture. Additionally, qualitative research methods—such as interviews and case studies—allow for deeper exploration of how individuals approach complex scientific problems, shedding light on their thought processes and creative strategies (Sternberg & Lubart, 1996). Advances in neuroscience, particularly through technologies like EEG and fMRI, have also contributed significantly to understanding creativity at the neural level by revealing brain activity patterns associated with idea generation and problem-solving (Dietrich & Kanso, 2010). Moreover, recent approaches have incorporated digital creativity environments alongside EEG monitoring to analyze brainwave responses during tasks requiring divergent and convergent thinking (Liu & Huang, 2016). These methods provide a novel way to capture cognitive engagement in real time, offering valuable insight into how students interact with scientific challenges. Collectively, these diverse methodologies—ranging from standardized testing to neuroimaging and digital practices—expand our understanding of scientific creativity and how it can be supported in educational settings. The increasing integration of advanced technologies in scientific research has also transformed data collection practices. Notably, the shift from traditional wired systems to wireless EEG devices has improved both the flexibility and accessibility of cognitive research tools. Fink (2012) emphasizes the potential of cost-effective, user-friendly wireless EEG headsets in broadening the scope of neuroscience applications. In this context, tools like Neurosky, Muse, and Emotiv have emerged as popular options Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 206 for portable data acquisition in research settings. Building on this, recent work by Erat and Durdu (2021) offers a comparative evaluation of Neurosky and Emotiv devices, highlighting their strengths and limitations in terms of usability and performance. Their findings underscore the importance of selecting appropriate technology to ensure the reliability and practicality of cognitive data collection. As wireless systems continue to evolve, they hold the potential to enhance the precision and efficiency of research on scientific creativity. The EEG Method in Scientific Research As the use of wireless EEG devices gains momentum in cognitive research, their potential as alternative data collection tools becomes increasingly apparent. This development reflects a broader recognition of EEG’s value in scientific fields such as neuroscience, neurology, and psychology. Electroencephalography (EEG) is a widely used, non-invasive technique for monitoring brain activity by capturing electrical signals generated by neuronal firing. Due to its exceptional temporal resolution, EEG enables real-time tracking of neural processes (Michel & Murray, 2012). EEG data are collected through electrodes placed on the scalp, which detect and amplify electrical activity resulting from synchronized neuronal discharges (Niedermeyer & da Silva, 2005). These signals are filtered and analyzed into frequency bands—delta, theta, alpha, beta, and gamma—each associated with distinct cognitive and emotional functions (Onton & Makeig, 2009). For example, alpha waves are typically linked to calm, inward-focused states, while beta waves correspond to focused attention and problem-solving (Klimesch, 1999; Ward, 2003; Niedermeyer & da Silva, 2005). Such frequency-specific analysis offers researchers a meaningful window into cognitive functioning during various mental activities. Moreover, EEG’s high temporal precision makes it particularly suitable for studying fast-paced cognitive phenomena such as attention shifts, working memory, and language processing (Michel & Murray, 2012). The emergence of wireless devices like the Neurosky Mindwave Mobile 2 has further broadened EEG’s utility by facilitating research in naturalistic settings and reducing logistical barriers (Liu & Huang, 2016). In this regard, EEG has proven to be a critical tool for interdisciplinary research aiming to capture the nuances of brain activity during real-world tasks. Notably, creativity research has consistently identified alpha wave modulation as a key neural marker during creative tasks (Fink et al., 2007; Dietrich & Kanso, 2010). These shifts in alpha activity are thought to reflect cognitive engagement and altered mental states commonly associated with idea generation and divergent thinking. For instance, Singh (2017) found that although no statistically significant differences were observed between creative and non-creative tasks, increases in both theta and alpha bands suggested greater cognitive involvement during creative exercises. This points to the complexity of neural dynamics underlying creativity, where meaningful changes can occur even in the absence of statistical significance. A growing body of literature has explored creativity through the lens of neuroscience, examining its underlying cognitive mechanisms in various settings (Borling, 1981; Haier & Jung, 2008; Sawyer, 2011; Wise, 1995; Boynton, 2001; Stroe, 2018; Stevens Jr. & Zabelina, 2019; Lustenberger et al., 2015; Abraham, 2013; Jaušovec, 2000; Shiu et al., 2011). These studies have contributed to a richer understanding of the brain’s role in creative thinking. However, despite this expanding research base, a noticeable gap remains in studies applying neuroscientific methods to assess creativity specifically within the context of science education at the middle school level. Addressing this gap may yield important insights into how creative thinking can be fostered and evaluated during early science learning. Purpose of the Study The objective of this study is to explore scientific creativity through a neurophysiological lens. It entails the monitoring of middle school students' brainwave activity while they engage in creativity-enhancing activities as part of their science curriculum. The central aim is to ascertain whether the specially designed activities have the potential to augment students' scientific creativity skills, as indicated by changes in brainwave patterns and to revise & refine the ones of which the creative potential is comparatively weak. The study specifically focuses on assessing amplitude changes within the alpha frequency band of recorded brainwave data during designated "active phases" and contrasting them with "passive phases." These active phases involve the application of creativity techniques within the science activities. Ultimately, this Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 207 research endeavors to provide a deeper understanding of the interplay between neurophysiology and scientific creativity within the middle school educational context. Method This study was designed as a multiple case study (Yin, 2009) to investigate scientific creativity through detailed observation of individual learning processes. The aim was to capture in-depth EEG-based data from 12 students (three each from 5th to 8th grade) during one-on-one implementations. Each student completed three separate instructional modules individually rather than in a classroom setting, ensuring controlled observation of personal cognitive engagement. The case study design was preferred to allow for deep analysis of each participant’s brain activity and learning behaviors in authentic educational tasks. Methodological triangulation was used, combining EEG recordings, observational video data, and semi-structured interviews (Gall, Borg, & Gall, 1996). This approach increased the trustworthiness of findings by capturing both quantitative and qualitative dimensions of the participants’ experiences. Brainwave data were collected using the NeuroSky MindWave Mobile-2, a single-channel wireless EEG device that has been previously validated in educational and cognitive neuroscience studies (Grierson & Kiefer, 2011). The EEG data were segmented by phases within each module, categorized as either active (involving creativity-based strategies such as design and open-ended tasks) or passive (involving factual recall or recognition tasks). The primary focus was on alpha wave activity, due to its association with creative cognitive states (Fink et al., 2011; Stevens & Zabelina, 2019). Given the small sample size and the structure of the EEG data, each student’s performance was analyzed individually, and results were interpreted on a within-case basis. Statistical tests were conducted to support descriptive findings, but generalization was not the goal. Rather, the study aimed to explore neurocognitive signals of scientific creativity in practical learning settings. Use of AI Language Tools During the manuscript writing process, the authors used ChatGPT (OpenAI, 2024) solely to enhance the clarity and fluency of the English language. No content related to research design, data analysis, interpretation, or scientific reasoning was generated by AI. All revisions were critically reviewed and finalized by the authors to ensure full compliance with research integrity and publishing ethics. Implementation Process We selected 12 middle school students from three different schools in western Türkiye, considering academic performance and socioeconomic background. While selecting the students, a total of 12 students were studied, from three schools: urban, suburban and city center, four students from each grade level, each with a different grade level, and four students with medium academic success. The research took place within school premises, in approved areas like guidance rooms. Interventions coincided with school hours or were conducted after school with permission. Participants received an explanation of the EEG device and study objectives. They completed three science-themed implementations, each lasting 20-30 minutes, with video recording for analysis. Researchers carefully reviewed the recorded sessions to document participants' activities (Figure 1). Figure 1. Implementation procedure Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 208 A standardized format was developed for all educational modules, and these modules were administered to the participants through Google Forms under the researcher's supervision. Each educational module was structured into six distinct sections, as outlined in Table 1. These sections encompassed the following components: video content viewing (1), multiple-choice questions (2), open-ended questions (3), design or project idea development (4), drawing (5), and evaluation, which includes true-false questions (6.1) and self-evaluation (6.2) (Table 1). Table 1. Sections of educational modules Section Task Phase 1 Watching video Passive 2 Multiple-choice questions Passive 3 Open-ended questions Active 4 Design or project idea development Active 5 Drawing (the design in 4th section) Active 6.1 True-false questions Passive 6.2 Self-evaluation Active The sections of the modules that stimulate creativity were referred to as "active phases," while the sections designed without a creativity trigger, utilizing traditional teaching content, were termed "passive phases." This structural division of modules into active and passive phases drawed inspiration from the framework devised by Qu et al. (2018) in their study titled "EEG markers of STEM learning," where the section involving STEM (Science, Technology, Engineering, and Mathematics) activities was labeled as "active learning," and the portion incorporating activities using traditional methods was labeled as "passive learning." In our study, Qu et al.'s study was utilized only as the names given to the stages. Additionally in this study, it was aimed to reveal whether STEM modules developed to trigger creative thinking in participants and to ensure the usability of the modules in science education. In this study, the active phases essentially encompass sections aligned with divergent thinking, while the passive phases correspond to sections rooted in convergent thinking. To be more specific, the creative components of the activity modules were grounded in diverse methodologies such as divergent thinking, problem-solving approaches, and engineering design techniques commonly associated with the STEM educational approach. Additionally, researchers drew upon strategies found within the CoRT-4 program developed by De Bono (1985). These methodological choices were made to enhance the reliability of the sections expected to foster creativity, as numerous researchers in literature have highlighted their potential to augment creative thinking. In this manner, the evaluation of whether the participants, as expected, applied their scientific creativity skills during their involvement in the creative segments of the activities, was undertaken through the observation of their brain activity from a neurophysiological perspective. Educational modules There are 12 online educational modules created for a doctoral thesis study, with three modules designed for each middle school grade in the science course. These modules blended divergent thinking (active phase) with convergent thinking (passive phase). In determining the subjects, they were chosen among the subjects included in the science curriculum, in line with the sustainability theme that are emphasized today. The topics were chosen based on the Turkish Ministry of National Education curriculum, emphasizing creativity, design, critical thinking, and project development. They followed a standardized template, incorporating CoRT-4 Creative thinking techniques and problem-based learning steps followed by STEM-based instructional tasks. In total, 36 implementations were conducted, with each student experiencing three different modules, accessible online via Google Forms. Links can be accessed in the researcher's doctoral thesis (Bulut Ates, 2023). Elaboration of educational modules Each module started with a video selected to match the Ministry of National Education (MoNE) subjects and gradelevel achievements, introducing a problem scenario that begins the passive phase for students. After the video, students moved to the second section with multiple-choice questions about the video's content, focusing on passive learning Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 209 without creative elements. The third section used open-ended questions to have students define problem-solving criteria, evaluate project ideas (if any), and rethink the scenario from their perspective, promoting creativity and transitioning them to the active phase. After the third section, students created unique projects related to the module's subject, guided through inquiries about problem-solving and device mechanics if they're doing design work. This aimed to stimulate their creativity. After the design phase, students, still in the active phase, illustrated their designs. In face-toface settings, paper and pencils were provided. In remote learning, an "upload your design" option allowed students to take photos and upload them via Google Forms, ensuring smooth remote implementation. Brainwave recordings when students photograph and upload their work were synchronized with video recordings to determine timing. The sixth section, after the drawing phase, had two parts. The first included true-false questions related to the video content, representing a passive phase. In contrast, the second part required students to evaluate their project ideas, suggest improvements, and make predictions about the module's problem scenario's future, involving active learning and creativity strategies. Picturesque landscapes or nature images briefly broke between sections to aid transitions and monitor brainwave data effectively. Students spent 30-40 seconds gazing at these images before moving to the next section. After completing all sections, brainwave and video recordings end, and this sequence was repeated in 36 applications. Data collection tools EEG Headset The brainwave sensor system developed by NeuroSky is a versatile product with applications spanning gaming, personal training enhancement, relaxation, and research and development. It employs Bluetooth technology for seamless data transmission. For research and development endeavors, computer software is utilized to interpret and analyze EEG signals. This device possesses the capability to capture raw EEG signals through a single sensor (channel) and real-time decomposition into gamma, beta, alpha, theta, and delta frequency bands. During the implementation of the activities, the EEG device was employed to gather brainwave data. Numerous studies affirm the suitability of the Neurosky Mindwave 2 device for scientific research, underscoring its validity and reliability as a research tool (Fong et al., 2015; Bitner et al., 2020; Ringtved et al., 2017; Sezer, 2015). EEG data-record application/interface EEGID represents software designed to capture EEG data transmitted via Bluetooth from either a mobile phone or computer. In this study, it was employed for the collection and recording of participants' brainwave data. This software is fully compatible with the Neurosky device and stores the EEG signal in its raw form, along with data segmented into gamma, beta, alpha, theta, and delta frequency bands, all in CSV format. Its suitability for scientific research has been corroborated by prior studies (Sahu et al., 2021; Singala & Trivedi, 2016; Sulaiman et al., 2018). Semi-structured interview form The interview questionnaire was created to assess participants' opinions concerning the functionality and compatibility of the wireless EEG device as a data collection tool within the learning process. A sample question is as follows: " Has the use of EEG device affected the participants in a negative manner while performing the activities?" Data Analysis The brainwave data collected with the EEG headset were analyzed by comparing the average alpha wave amplitudes recorded during the passive and active phases of each activity. This helped to observe how students' brain activity changed throughout different stages of the implementation. In addition to the EEG data, interviews were conducted to understand students’ experiences and opinions. These responses were examined through qualitative content analysis. The researchers reviewed each response, grouped similar ideas, and identified key themes related to the use of the EEG device during the learning process. To support the EEG findings with statistical evidence, additional analysis was conducted using SPSS 26. Given the small sample size (N = 12), non-parametric methods were applied. In particular, the Wilcoxon signed-rank test was used to compare alpha wave activity between passive and active phases across different modules. This test was chosen because Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 210 alpha waves are widely associated with creative thinking in the literature, and the data did not meet the assumptions required for parametric tests. The significance level for all statistical tests was set at p < .05 (Pallant, 2007; Baykul, 2010). esults Findings of participants’ brain waves data In this section, we analyzed alpha frequency band enhancements by examining brainwave data from participants' practice sessions, categorizing them as active or passive phases. Time-dependent graphs were created to show amplitude changes, using fast Fourier transform (FFT) on the raw EEG signal acquired by the Neurosky device. The device is known for its reliability, backed by validity and reliability studies (Grierson & Kiefer, 2011) and includes a common mode rejection process to eliminate ambient noise. See Figure 2 for a graphical sample of brainwave data from a pilot study participant. Figure 2. Time-dependent amplitude change at the alpha frequency band of a participant brain wave data When examining the graph of time-dependent amplitude changes in the alpha frequency band for a participant, noticeable peaks are visible. To interpret these peaks, video recordings were meticulously reviewed to identify the start and end times of each section in the participant's data. For instance, in this graph, the highest peak was at the 15th minute, corresponding to the 4th section focused on project idea development. This approach was applied to all data, comparing peaks in active phases to passive phases and calculating average values within sections. It's important to note that each implementation was unique, and average values between implementations couldn’t be compared due to the lack of specific units or a common scale in frequency band values. The study's goal wasn’t to determine which educational modules make students more creative, but rather which sections within a module exhibited more scientific creativity skills in a single session. Therefore, conclusions can only be made on a case-by-case basis and cannot be generalized. For instance, data from the pilot study is shown below, representing mean scores in active and passive phases separately (Table 2). Table 2. Mean scores at active and passive phases (pilot study) Section Task Phase Mean score (Alpha frequency) 1 Watching video Passive 2220927 2 Multiple-choice questions Passive 1074163 3 Open-ended questions Active 19661522 4 Design or project idea development Active 3021913 5 Drawing (the design in 4th section) Active 109934 6.1 True-false questions Passive 175021 6.2 Self-evaluation Active 3546822 Table 2 shows a 6th-grade-student’s EEG data at active and passive phases obtained in the pilot study. These values represent the distribution of brain wave values at alpha frequency spectrum. The value obtained as a result of the process applied by the Neurosky device is a proportional value consisting of an average of approximately 7 digits without a specific unit such as Hertz (Hz). Namely, this value consists of unitless integer representations of the percent range of an adaptive fast fourier transform (FFT) exclusive to NeuroSky, with a full range from -(2^31) to (2^31)-1 (Neurosky, n.d.). Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 211 Figure 3. Average of mean scores at active and passive phases (Pilot study) When the figure of a 6th grader’s data is examined, it is seen that the increase in amplitude changes at active phases were higher than passive phases for three implementations all (Figure 3). It can be commented that creativity potential of active parts of the educational modules is better with the strategies used based on divergent thinking depending on the data of this participant. This computation process was carried out for each implementation in order to detect and compare mean scores at active and passive phases for every participant. Table 3 demonstrates overall mean scores added by participant and module topic information. Table 3. Mean score data by active and passive phases with module topics Level Module Topic S C MSAP MSPP SHV/A-P 5 1: Environmental Pollution 5.1 3429663 2867595 4-Active 5.2 3594105 3558852 6.2-Active 5.3 2827729 3151259 2-Passive 2: Life on Moon 5.1 3207268 3358239 4-Active 5.2 2407496 3453107 5-Active 5.3 3746637 2402044 3-Active 3: Force 5.1 2316495 3445620 6.1-Passive 5.2 6657125 4924780 6.2-Active 5.3 2703348 3344376 2-Passive 6 1: Matter and Heat 6.1 2728811 2847635 6.1-Passive 6.2 2966347 2821378 6.2-Active 6.3 3639581 3429787 6.2-Active 2: Fuels 6.1 5254688 3861424 4-Active 6.2 3136656 3228490 2-Passive 6.3 3620132 3249710 5-Active 3: Sense Organs 6.1 3575170 3687249 2-Passive 6.2 3940727 3228490 6.2-Active 6.3 2569012 2329409 4-Active 7 1: Space 7.1 2887134 1658345 5-Active 7.2 2993575 1835166 5-Active 7.3 3214523 2978432 6.2-Active 2: Force and Energy 7.1 3757265 3726374 3-Active 7.2 4432938 4308256 5-Active 7.3 3091262 2139781 4-Active 3: Waste Management 7.1 1837229 1520148 5-Active 7.2 2782312 3037524 2-Passive 7.3 3026923 3617057 1-Passive 8 1:Sustainable Development 8.1 3512857 2980021 6.2-Active 8.2 2766585 3449967 6.1-Passive 8.3 6466677 6905145 2-Passive 2: Global Warming 8.1 3199360 3186702 3-Active 8.2 2880588 1758878 4-Active 8.3 3643748 5104068 6.1-Passive 3: Energy 8.1 2837555 2210342 6.2-Active 8.2 2574874 2045085 6.2-Active 8.3 2844687 5196662 2-Passive SC: Student Code MSAP: Mean Score Active Phases MSPP: Mean Score Passive Phases SHV/A-P: Section with Highest Value/A-P 0 100000 200000 300000 Phases Active Phase Passive Phase Bulut Ates, Aktamış &Aydın Journal of Gifted Education and Creativity 12(2) (2025) 203-219 218 Fink, A., Schwab, D., & Papousek, I. 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