Utilizing open-source programming environments and student clustering to foster K-12 STEAM learning outcomes. Modelling processes via simulation scenarios.

Utilizing open-source programming environments and student clustering to foster K-12 STEAM learning outcomes. Modelling processes via simulation scenarios. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Utilizing open-source programming environments and student clustering to foster K-12 STEAM learning outcomes. Modelling processes via simulation scenarios. Constantina Spyropoulou, Ph.D.C, Manolis Wallace, Associate professor, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6116171/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the impact of open-source programming environments and student clustering on K-12 STEAM learning outcomes. The research addresses the gap in understanding how knowledge acquisition is influenced by collaborative expert groups and digital instructional tools. A quasi-experimental design is implemented, assessing student performance through formative assessments and statistical analysis. Results indicate that clustering students into expert groups significantly enhances declarative, conceptual, procedural, and evaluative knowledge. The study also explores the effectiveness of modeling processes via simulation scenarios. Findings suggest that optimized student grouping and technology-enhanced learning contribute to improved learning efficiency. The implications note the importance of innovative instructional strategies in STEAM education. K-12 STEAM education open-source software collaborative learning student clustering modeling and simulation experimental design Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The nascent field of STEAM (science, technology, engineering, arts, and mathematics) represents ongoing attempts by educational researchers and policymakers to make sense of and potentially institutionalize the concept of a holistic approach (Mejias et al., 2021 ). The integration of technology in education has been extensively researched, particularly within the context of K-12 STEAM education. Open-source programming environments have emerged as tools that facilitate collaborative learning experiences among students. However, there remains a notable gap in the literature concerning the specific impact of these environments on structured student collaborations. Open-source programming environments promote collaborative learning by allowing students to engage in projects that require teamwork and communication. Participation in open-source projects can enhance students' professional skills, including coordination and collaboration, which are essential for success in real-world scenarios (Shernoff, 2017). This perspective aligns with the notion that open-source environments foster an atmosphere of shared knowledge and collective problem-solving. Engaging with open-source projects provides students with invaluable experiences that extend beyond technical skills. According to Chua ( 2018 ), exposure to open-source principles cultivates an open mindset among students, preparing them for future collaborative efforts in various industries. The collaborative nature of open-source projects encourages K-12 students to form relationships and develop a sense of community, which is crucial for their personal and professional growth (Goldey, 2021 ). Despite the potential benefits, several challenges hinder the effective implementation of open-source programming environments in K-12 STEAM educational settings. A systematic review by Gül et al. (2023) highlights that while STEM education fosters 21st-century skills, educators often find designing STEM activities challenging due to the complexity of integrating multiple disciplines. Similarly, Montiel and Gomez-Zermeño ( 2021 ) identify obstacles in adopting open-source tools like Scratch for teaching computational thinking. These challenges include a lack of teacher training, insufficient technological infrastructure, and difficulties in aligning new tools with existing curricula. This difference in goals can complicate collaboration efforts. While a wide range of remarkable research has investigated the role of technology and the importance of collaboration in education (Yang et al. 2020, Ackermans et al., 2019 ; Colliot & Jamet, 2018 ; Timmis et al., 2016 ; Yang et al., 2018 ), there remains a notable gap in the literature regarding the impact of open-source programming environments on structured student collaborations (Sweeney & Smith, 2024 ). This study aims to address this gap by evaluating whether and to what extent clustering students into expert groups within an open-source environment enhances their knowledge acquisition. The literature on K-12 STEAM education highlights several challenges (Milara & Orduña, 2024 ) in assessing knowledge acquisition (Jia et al., 2021 ). The integrated nature of STEAM, combined with the lack of suitable assessment tools and longitudinal studies, makes it difficult to obtain clear evidence of knowledge acquisition in K-12 STEAM education (Brown et al., 1989, Perkins & Salomon, 1992 , OECD, 2018). For example, in many integrated STEM courses, students do not engage in the construction of in-depth mathematics, engineering, or science concepts (Chalmers et al., 2017 ), lowering the overall challenge of their learning (Berland, 2013; English, 2016 ). These types of knowledge are declarative knowledge, conceptual knowledge, procedural knowledge (technical, contextual), evaluative knowledge, and metacognitive knowledge (Tani et al., 2022 ). While acknowledging that there are various types of knowledge, this research refers to these types in a more typical manner. In this study, procedural knowledge refers to know-how (techniques) and the criteria for determining when to use appropriate procedures (Carter & Pritchard, 2015 ): these subtypes are referred to as “technical” and “contextual”. Declarative knowledge encompasses, at a fundamental level, knowledge of terms and propositional aspects of the domain and pertains to statements such "something is the case" ( Adams, 2009 ) . Additional definitions of conceptual knowledge include the relationships between these domain-specific components as well as associated concepts, hypotheses, and models (Hiebert, 2013 ). Finally, a category has been added to identify what is known as "evaluative" knowledge, which is the capacity to use established criteria to assess assertions. Metacognitive knowledge refers to one's own cognitive processes (Pintrich, 2002 ). Research Gap While existing studies highlight the benefits of digital tools in STEM/STEAM education, limited research focuses on the interplay between student clustering and open-source environments. This study investigates how clustering students into expert groups within an open-source environment enhances their knowledge acquisition Research Purpose and Questions How does the integration of open-source programming environments impact K-12 STEAM learning outcomes? What is the effect of student clustering on knowledge acquisition? How do simulation scenarios model and optimize instructional strategies? Theoretical framework Researchers use the framework of cognitive load theory to investigate the effect of open-source programming environments on the acquisition of different types of knowledge. Cognitive load theory (CLT) is a learning and instruction theory that describes instructional design inferences of a model of human cognitive building design on the basis of long-term memory's permanent knowledge basis (LTM) and working memory's temporary conscious processor of information. Working memory is characterized by its short length and capacity. If these boundaries are crossed, learning is impeded, and working memory is overwhelmed (Kalyuga, 2011 ). This theory is selected because the demands on the student to acquire STEAM-related knowledge at the primary level may be complex. Cognitive load theory offers insights into the potential impact of open-source programming environments on processing capacity, especially when the requirements are considerable. It is based on several tenets and has been integrated through a variety of models, including the knowledge integration perspective, which is relevant to understanding how students integrate ideas (Pashler et al., 2007 ; Schneider & Stern, 2009). In STEAM education, the outcome of these cognitive processes is often measured by changes in the learner’s perceptual capacity to learn the key aspects in a transferable way and not to depend on the structure of the materials (Kaminski et al., 2009 ). In fact, absorbing complex knowledge can require substantial cognitive processing (Chi & Ohlsson, 2005 ), which can include reasoning, problem-solving, and model construction (Barbey & Barsalou, 2009 ). However, because the learning and transfer of knowledge depend in part on concreteness and abstraction, students' prior knowledge and working memory struggle to address the deep structure of a problem (Penner et al., 1997 ). Therefore, according to the success of the formative assessment intervention, which enables students to screen out surface-level aspects and investigate how deeper-level features work, they can attend to deeper structural features. As a result, the greater the cognitive load is, the greater the processing capacity that may be used to recognize the distinctions and better align the theoretical concepts with the data. In this study, the focus is on knowledge-based outcomes, in contrast to most investigations of programming environments, which focus on the characteristics of programming environments (Mutlu-Bayraktar et al., 2019 ). Background literature The transdisciplinary character of STEAM (Meeth, 1978) involves projects where the concern may initially start with a social problem (Aguilera and Ortiz-Revilla, 2021) that mirrors an issue within the local community (Guyotte et al. 2014). Several studies on STEAM education emphasize students' prior learning experiences and conceptual comprehension (Ginns & Leppink, 2019; Gulbin & Unsal, 2021). In some cases, students are recruited on the basis of their absence of prior knowledge (Mutlu-Bayraktar et al., 2019). As a result, research on STEAM instruction in this category involves procedural tasks with little to no connection with what students have been learning intentionally (Koning & Jarodzka, 2017). Other studies have attempted to build on students’ prior knowledge of the STEAM subjects they are already studying using digital instructional materials in the classroom. A large portion of this research focuses on teaching science or math concepts in primary education (Lin & Tsai, 2021). For example, there is some evidence that animations increase the probability of answering questions correctly on multiple-choice tests in elementary science contexts (Dalacosta et al., 2009). A variety of analyses have examined the purpose of cognitive processes related to working memory and spatial ability (Cheng, Lai, Cheng, & Huang, 2022). In the studies under evaluation, learning has also frequently been evaluated via achievement tests (e.g., Sung et al., 2019) or retention and transfer assessments (e.g., Colliot & Jamet, 2018; Craig & Schroeder, 2017). A number of the cited studies additionally examined prior knowledge and motivation-related learning outcomes (e.g., Park, 2015; Park et al., 2016). Within the studies in primary education contexts we reviewed, cases arising in STEM disciplines have often been explored (Leavy et al., 2023; Barnes et al., 2017; Chen and Huang, 2020; Grandl et al., 2020; Jailungka et al., 2020; Timotheou and Ioannou, 2019). The most frequently studied dependent variables in terms of cognitive load have been retention and transfer (Mayer, 2019). Only a few studies have examined the different types of knowledge acquired by primary education students in the STEAM discipline (Quigley et al., 2017). While research on the use of an open-source programming environment in primary STEAM education is evident, studies on cognitive processing are scarce. This study contributes to existing research in two significant ways: by extending research into STEAM learning in primary education and by further investigating learning outcomes related to different types of knowledge. Innovation: the use of open-source programming environments and students’ collaborative clustering Innovation took place in a total of 570 participants in a K-12 context after COVID-19 (Lin & Johnson, 2021). It consisted of engaging students with open-source programming software under a collaborative approach. Focused on a real-world problem covered during two months in the face-to-face class. Two different ways of presenting this innovative programming environment were carried out, and the version applied was randomized. With respect to other features of engaging students in an open-source programming environment, this research followed the seven principles for collaborative learning. Open-source programming environments have the potential to enhance cognition and knowledge acquisition (Lajoie & Derry, 2013). It is thought to do so on the basis of these established principles of collaborative e-learning environments (Konstantinidis et al., 2009): The users who participate have different roles and privileges. The educational interactions in the environment transform the simple virtual space into a communication space. The information in the environment is represented in multiple ways that can vary from simple text to three-dimensional (3D) graphics. Students are not passive users but can interact with each other and with the virtual environment. The system that supports the environment integrates multiple technologies. The possibility of implementing multiple learning scenarios is supported. Recognizable elements from the real world are visualized. The program promoted the clustering of students into expert groups to collaborate when programming in Scratch software (principle 1 above). Each expert group exchanged ideas with one another and finally transferred them to the other groups, suggesting ways to implement the solution using the programming concepts into the software (principle 2). Images and words were presented before the student as a visualization (principle 3). All the expert groups were active users of the programming environment, as they interacted directly with it and between them (principle 4). The Scratch software provides supplemental tools and materials, and each expert group utilized it in several ways (e.g., a block-based programming environment, a tool to create animations) (principle 5). All the expert groups designed their solutions to the problem as a final scenario in Scratch (principle 6). Handling a real-world problem and illustrating the solution in the programming software (principles 7). Methodology Paradigm The empirical problem was to design a case study in which the impact of participation in expert groups in a programming environment could be identified and quantified statistically on student learning outcomes. To accomplish this, researchers followed an approach similar to a quasi-experimental design. This approach involves gathering information about students' skills before engaging with the programming environment and randomizing their exposure to it. To identify other observed or non-observed characteristics of the participants, we had to carry out the research design. Research design Researchers carried out a case study comprising thirty-four samples of K–12 STEAM programs: fourteen at non-profit organizations and twenty in the in-school context, both in urban centres. Non-profit organizations are concerned with postschool STEAM programs offered by laboratories or centres. There were 575 participants, including 75 adults and 500 students. In the school context, there were STEAM courses that focused on the disciplines of science and coding. In non-profit organizations, courses focus on all STEAM disciplines. At each research site, the projects were applied to students at the primary, junior (ages 4–6) and intermediate (ages 7–12) levels. Face‒to-face interviews were carried out with 500 students (participants and nonparticipants in programs A and B), individually. As mentioned above, the program randomly exposed students in a quasi-experimental research design by producing two distinct but closely related STEAM projects (A’ and B’). Both projects were covered in the period of the course. This strategy enabled the researchers to generate three groups of students: those who did not take part in the project, those who took part in project A, and those who took part in project B. Projects A and B were randomized between simultaneous sessions. Through this process, students and adult professionals volunteering to participate in the project did not know in advance whether they were participating in project A or B. By relying on the randomization of the projects, researchers were able to use the group of students who participated in project B as a reference group to measure the effect of participating in project A, and vice versa. Both projects had identical processes but covered different topics in their core parts. These topics were the energy footprint (EF) and climate change (CC), respectively. Both are important subjects and fundamental ideas that form the basis of a large portion of the K–12 STEAM curriculum. Assigning various evaluation questions to each category of knowledge was the researchers’ initial task. To accomplish this, researchers first divided different types of knowledge into four categories: declarative, conceptual, procedural, and evaluative. The researchers employed interview formats that were modified from other STEM/STEAM studies (Ghanbari, 2014 ). Both projects applied were included in some of the 20 questions on the midterm formative quiz, which was conducted with multiple-choice questions and a graded Likert scale, which was carried out the week after the projects. In addition to the formative assessment, researchers used an ad hoc survey to gather students' general opinions on their own learning experience after taking part in one of the projects to monitor any potential impact on their metacognitive knowledge. Participants The participants were selected from those who expressed an interest in voluntary group participation. To distinguish between participants and nonparticipants, Table 1 provides statistics on the unconditional means of key student demographic characteristics and the mean differences between demographic and age factors (4–12 years). Researchers use the Kruskal‒Wallis test to determine whether these unconditional means are significantly different and report the p value of the test in the last column of Table 1 . Table 1 Summary Statistics Variable Participants Non-Participants Difference Kruskal‒Wallis test Demographics Girls 240 42 198 p value<. oo1 Age 6.5 7 -0.5 p value<. oo1 Previous formative interview 5 4.5 0.5 p value<. oo1 Late formative quiz 7.2 6.7 0.5 p value<. oo1 Knowledge types: Declarative 399 51 Conceptual 361 41 Technical 317 38 Contextual 312 45 Evaluative 305 44 N 436 64 Sample selection and data availability For the analysis to be performed, the authors define both the school and out-of-school STEAM projects that form the sample for the analysis. Some of them are the most innovative education units. Innovation in STEAM-related projects is considerable in most of the above units. Consequently, over the course of two months of this project, researchers gathered data from 34 schools and non-profit organizations. These data are indicative of how students performed on STEAM projects. Therefore, researchers have used the metrics from the collected STEAM project learning outcomes as indicators of the students' performance and elaborated them via simulation through fuzzy cognitive mapping (FCM). This dataset will be subjected to statistical and simulation analysis to obtain effective results that will support STEAM projects’ need to reengineer their core activities for learning outcome orientation. Table 2 Definition of selected factors STEAM projects metrics Description Scaffolding Feedback It is a method designed to take advantage of the benefits of retrieval practice by providing incremental hints until the correct answer could be self-generated. (Finn & Metcalfe, 2010 ) Use of Technology Refers to Integration of technology to enhance the student learning experience. Frequency of Communication between Peers The exchange, imparting of information, ideas, or feelings between student groups who are at the same hierarchical level as each other for the purpose of fulfilling a common task or goal. Alternative Utilization of a Programming Learning Environment Refers to active and collaborative learning utilizing ways to improve student understanding of programming concepts, reduce the level of frustration, and better prepare students for jobs based on teamwork (Poindexter, S., 2003 ). Instructional Design A system of procedures for developing education and training curricula in a consistent and reliable fashion (Branch & Merrill, 2011 ). Student Correspondence Time It is essentially the amount of time the teacher waits before expecting a response from the students, express an idea or make a statement (Mary Budd Rowe, 1972 ) Collaborative Expert Group Referring to the cultivation of collaborative problem-solving skills. Students (1) establishing and maintaining shared understanding, (2) taking appropriate action, and (3) establishing and maintaining team organization as professionals (Graesser et al., 2018 ). Teacher Lectured for more than 15 minutes Modern best practices suggest limiting the length of a lecture to 15–20 minutes, breaking up a longer lecture with hands-on activities, as research shows that 20 minutes is about as long as humans can maintain their attention on one source of information (Bligh, 2000 ). Low Teacher's Technology Skills The lack of the capacity to both easily and effectively access knowledge in different codes and formats (e.g., text, videos, images) in a digital environment (Claro et al., 2018 ). Onerous Overuse of Technology Using technology excessively in education, can lead to feelings of tension, poor achievement, loss of interest, may cause withdrawal and fatigue as well as impair student learning (Strom A., 2021 ). Hypothesis The primary research hypotheses are stated in this section of the study, which is followed by a thorough examination. STEAM learning outcome planning has been shown to be highly beneficial in a variety of learning environments. This raises the question of whether it is beneficial to use open-source programming environments and student clustering in expert groups to enhance STEAM learning outcomes and reengineer STEAM curriculum activities to learning. To assess the efficiency of STEAM learning outcome planning, researchers emphasize students’ knowledge acquisition and how this affects important factors of STEAM learning activities, such as the use of technology, instructional design, student correspondence time, and scaffolding feedback. For this purpose, nine key research hypotheses are presented, with the aim of highlighting the STEAM learning planning process by analysing students’ knowledge acquisition. The correlations between the factors emerged throughout the quantitative research and the analysis of the statistical data. The hypotheses concern the interrelationships shown by the statistical analysis and the observation process (Tables 3 & 4 ). The authors performed correlation analysis in more detail. It is clear from the reported correlations below that the K–12 STEAM learning outcome indicators are strongly related. The research hypotheses are presented below: Hypothesis 1 (H1). “ Instructional design positively impacts students’ correspondence time”. The first research hypothesis is based on the concept of whether the use of educational methods and techniques in a certain instructional design could enhance the ability of students to respond to the formative quiz, make their first question or express their idea during the lesson in less time (students’ correspondence time). Researchers aim to answer that if the teacher increases, the instructional design could lessen the students’ correspondence time. Hypothesis 2 (H2). Low technology skills can affect the use of technology. The second hypothesis relies on the fact that when teachers cannot easily handle technology, the use of technology decreases. Thus, teachers need to make greater efforts to accomplish computer-supported STEAM lessons, resulting in a decrease in the engagement of students with the use of technology. This implies a decrease in the value of their teaching, which requires valuable time for the optimization of other parts (e.g., activity materials). In this way, they decrease the value of their students while their teaching is not optimized. Hypothesis 3 (H3). Scaffolding feedback influences student correspondence time. The third hypothesis of this paper concerns the time needed for students to express their ideas, ask their first question, or respond to the formative quiz. Scaffolding feedback should provide tips for students to support them in expressing ideas, providing quick responses and resolving problems that arise. Hypothesis 4 (H4). Teacher lectures lasting more than 15 minutes are related to the frequency of communication between peers. The fourth hypothesis is related to the amount of time teachers need to lecture to provide students with full detail of the project. If teachers could lecture for less than 15 minutes, the value of their teaching, saving valuable time for the optimization of other parts, could be increased. Hypothesis 5 (H5). Scaffolding feedback can adequately support collaborative expert grouping. The fifth hypothesis practically means that when the teacher increases the amount of scaffolding feedback during the project, students collaborate more effectively in their group, making questions, discussing ideas and design thinking. In this way, students work together, focus on the goals of their group, and acquire knowledge more efficiently. Hypothesis 6 (H6). The tendency of students to abandon the use of technology depends on the overuse of technology. The sixth hypothesis indicates that the more the teacher uses technology resulting in an onerous overuse of technology, the more the students tend to feel tired and lose purpose. Consequently, students tend to abandon technology. Hypothesis 7 (H7). Collaborative expert grouping could stand out as a peer-to-peer teaching technique by increasing the frequency of communication between peers. The seventh hypothesis relies on the fact that when students collaborate easily in a STEAM course, they also communicate constantly, enabling themselves to gain knowledge from their peers. More specifically, communication between peers during a collaborative grouping of experts also functions as a learning technique, where knowledge acquisition occurs faster and more creatively. Hypothesis 8 (H8). Alternative utilization of a programming learning environment can influence collaborative expert grouping. The eighth hypothesis is related to an alternative and more holistic way of utilizing a programming learning environment when the students have different roles and privileges. Researchers aim to answer that if students engage in the project as active users of the programming environment, communicating and visualizing real-world problems, then the collaboration between them is optimized. Hypothesis 9 (H9). Alternative utilization of a programming learning environment can positively impact the frequency of communication between peers. The ninth hypothesis is based on the concept that the alternative utilization of the programming environment is a student-centred perspective, and this positively affects the frequency of communication between students. In this way, students engage in the project as active users of the programming environment, interact with each other, and their interactions in the environment transform the simple virtual space into a communication space. Statistical Results According to the results of the formative assessment, 78% of the students who took part in the project benefited from receiving constant scaffolding feedback. In addition, 97% of them appear to benefit from the use of technology. As indicated by the data, 82% of the students are more likely to engage with the course's concepts, as they communicate with each other frequently. As revealed by the ad hoc survey, 68% of the students cultivated their own metacognitive processes through alternative utilization of the programming environment. Furthermore, 96% of the participants argued that when there is a certain flow on the course, the learning environment is more understandable. During the formative quiz, 75% of the students responded to all 20 questions in less than 15 minutes. In addition, 78% of the students spent less than two minutes throughout the program. Furthermore, 93% of the students who clustered into collaborative groups with a certain goal achieved the group's goal. On the other hand, 94% of the students stated that when the teacher lectured for more than 15 minutes, they felt bored and tired. Moreover, 67% of the students lost interest when the teacher had low technology skills. Finally, 73% of the students reported a loss of focus and the goal of the project when there was an onerous overuse of technology. Table 3 Correlations with the main factor (Learning Outcomes) rthermore, during the observation, 77% of the students expressed their first question (student correspondence time) in less than one minute when the programming environment was presented to them via the educational strategy of building excitement (instructional design). Similarly, 65% of the students took advantage of the tips given to them (scaffolding feedback) to resolve problems that arose (student correspondence time). Additionally, 71% of the students frequently communicated (frequency of communication between peers) while collaborating in expert groups (collaborative expert grouping). Additionally, by using the programming software in an alternative way (alternative utilization of a programming learning environment), 66% of the students became experts working collaboratively (collaborative expert grouping). In addition, 72% of the students frequently communicated (frequency of communication between peers) while cooperating in an alternative way in the software (alternative utilization of a programming learning environment). Additionally, when the students collaborated in the expert groups (collaborative expert grouping), 83% of them benefited from constant feedback (scaffolding feedback). Similarly, when teachers showed lower skills in technology (low teacher's technology skills), 50% of the students were not fully engaged in the use of the software, and any other digital tool was utilized (use of technology). Moreover, when teachers lectured for more than fifteen minutes, 48% of the students stopped communicating with their peers (frequency of communication between peers). Finally, the students reported a loss of focus and the goal of the project when there was overuse of technology (Onerous Overuse of Technology); thus, 69% of them were not interested in using the software any longer (Use of Technology). Table 4 Correlations between factors Exploratory Model Deployment The current study's objective is to develop an FCM-based approach for planning STEAM learning outcomes. This approach is based on the FCM method, which enables the quantification of the deviation of the relationship between the relevant variables while simultaneously offering a systematic analysis to compare various scenarios (Hair, J.F., Jr., 2007). The authors performed and investigated three scenarios in the following stage of the paper to enhance the performance of K-12 STEAM implementation. The outcomes were determined via various key metrics by adjusting the analytic factors of the large-scale case study results. The authors identified the factors that influence most important variables after completing the quantitative and correlation analysis in the earlier phase. The FCM simulation analysis, which is used to demonstrate the immediate impact of the K–12 STEAM learning outcome performance indicators, is based on the quantitative and correlation coefficients of the variables. Fuzzy cognitive maps (FCMs) are fuzzy graph structures used to represent causal logic. Their ambiguity allows for blurred degrees of causality between blurred causal concepts. Their graph structure allows for systematic causal propagation, particularly in the chain forward and backwards, and allows the development of knowledge bases by linking different FCMs (Kosko, 1986). FCMs are particularly applicable to areas of mild knowledge, and causality is represented as a vague relationship with causal concepts (Glykas, 2010 ). They are a conclusive grid that uses a graph in a circular arrangement, through which the cause‒and‒effect relationship between the nodes that make up the system variables is represented. The nodes represent the variables of the system; the connections between the nodes are the relationships between them with different importance. A fuzzy cognitive map incorporates the accumulated experience and knowledge of system specialists modelling FCMs as the result of the method by which the system was created (Groumpos P.P. 2010). Mental Modeller is a software that facilitates the integration and analysis of knowledge in modelling. It is a participatory modelling tool based on FCM, which clarifies the mental models of stakeholders and provides the opportunity to integrate different types of knowledge into decision making. Furthermore, hypotheses to be tested and to execute scenarios for identifying the perceived effects of the proposed policies are defined (Gray et al., 2013). In addition, Mental Modeller is a decision support system (DSS) that provides the ability to solve problems and communications for semi-structured problems (Raymond McLeod, Jr. 1998). A DSS is a computer-based system consisting of components (components of language, knowledge and problem processing systems) that interact with one another (Bonczek, 1980). The Mental Modeller architecture allows researchers, scientists, and decision-makers to develop models and scenarios of future system statements on an equal footing (J.T. Peterson, and J.W. Evans. 2003). Specifically, the software was designed to enable stakeholders to (1) construct a quality conceptual model, (2) develop conditions, and (3) revise their model based on the model output. The mental modeller consists of three main user interfaces: (a) the concept mapping interface that provides space for model construction and configures the model construction in the format required for FCM analysis. (b) the matrix interface that allows the structural properties of the cognitive map to become clear by examining the pairwise relationships; and (c) the script interface that allows stakeholders to execute and compare changes within the system under different possible scenarios and reviews (Gray et al. 2013). By identifying various parameters, including positive and negative correlations among factors, stationary frameworks displaying information can be built. Researchers used Mental Modeller's online platform application for this purpose, where they could use the FCM modelling tool. The FCM offers a comprehensive analysis of the branch of education since it integrates the sector's statistical analysis. Compared with the whole curriculum content, it can anticipate optimization scenarios. This feature is immensely helpful for schools and nonprofit organizations in education since the employment of such an approach can offer valuable insights for obtaining improvements in learning outcomes. The referred examination of the sector can be seen in the implemented FCM model in Fig. 1 . Although many software and web-based editors focus on modelling fuzzy cognitive maps, such software is used to deploy numerous scenarios in addition to modelling. Thus, FCM enables a plethora of researchers to contribute to the development of their scientific works. Increasing/Decreasing Alternative Utilization of Programming Learning Environment Analytics To estimate the direct effects of the use of open-source software and students’ clustering results on the K-12 STEAM learning outcomes, researchers have created three scenarios. In the first scenario, the alternative utilization of programming learning environment metrics increased by 10% and then decreased by 10% to observe the variation caused by other key metrics, such as collaborative expert grouping and frequency of communication between peers. Figure 3 shows that the overall course of these metrics is anodic. Learning outcomes increase the most with an increase of 20% (point 1, Fig. 3 ), which is a positive outcome in terms of learning outcomes. The frequency of communication between peers increases by 12% (point 2, Fig. 3 ), and collaborative expert grouping increases by 7% (point 3, Fig. 3 ), which is also a good sign of STEAM program efficiency. On the other hand, by decreasing alternative utilization of programming learning environment metrics by 10%, we obtain the exact opposite results, meaning decreases in learning outcomes and the frequency of communication between peers and collaborative expert grouping by 20% (point 1, Fig. 4 ), 12% (point 2, Fig. 4 ), and 7% (point 3, Fig. 4 ), respectively (Fig. 4 ). This time, a decrease in all metrics leads to an increase in teacher lectures for more than 15 minutes and, finally, to a deterioration in the frequency of communication between peers (decrease) and collaborative expert grouping (decrease), which are metrics that show devaluation when kept at low values. To obtain further insights regarding the optimal strategy for the performance of STEAM education outcomes and provide reasons for the redesign of educational procedures, the authors deployed more FCM scenarios by adjusting the alternative utilization of programming learning environment analytics in different volumes with another key metric. Reducing Scaffolding Feedback and Increasing Alternative Utilization of a Programming Learning Environment This time, by utilizing the advantages of the hyperbolic tangent function, researchers designed Learning Outcomes, Student Correspondence Time, and the frequency of communication between peers and Collaborative Expert Grouping’s direct impacts by causing different variations in scaffolding feedback and alternative utilization of a programming learning environment’s metrics. The authors opted to investigate these STEAM learning outcome performance indicators by increasing the alternative utilization of a programming learning environment by 10% and, at the same time, by decreasing the scaffolding feedback by 10%. With this, the preceding scenario is intended to yield a variety of outcomes, and it also serves to emphasize the significance of each analytics area in the four performance indicators for the STEAM learning outcomes that have been examined. The direct effects of the various factors in the scenario are displayed in Fig. 5 . The frequency of communication between peers directly increased by 6% (point 3, Fig. 5 ), whereas learning outcomes, student correspondence time, and collaborative expert grouping decreased by 2% (point 1, Fig. 5 ), 6% (point 2, Fig. 5 ), and 2% (point 4, Fig. 5 ), respectively. Therefore, increasing the alternative utilization of a programming learning environment by 10% and decreasing the scaffolding feedback by 10% decreases the learning outcomes, student correspondence time, and collaborative expert grouping, which is not desirable. As a result, the reengineering processes of K-12 STEAM activities are triggered since adjusting metrics can alter the performance indicators of their K-12 STEAM implementation. Moreover, for greater clarity of results, another scenario in which a negative variable is decreased is performed. Reducing teacher lectures for more than 15 minutes and increasing alternative utilization of a programming learning environment With respect to the last scenario simulation, researchers have estimated the variation in learning outcomes. Frequency of communication and collaborative expert grouping when a decrease in a negative variable occurs. More specifically, in Fig. 6 , researchers examine how an increase of 10% in alternative utilization of a programming learning environment combined with a decrease in teacher lectures for more than 15 minutes by 10% affects the above key STEAM learning outcome performance metrics. As a result, the direct effect of enhancing the metrics is an increase in learning outcomes and the frequency of communication between peers and collaborative expert grouping by 31% (point 1, Fig. 6 ), 17% (point 2, Fig. 6 ) and 7% (point 3, Fig. 6 ), respectively. The effects of reducing teacher lectures for more than 15 minutes by 10% and increasing alternative utilization of a programming learning environment by 10% represent improvements, and STEAM stakeholders should consider the benefits of reducing lectures from teachers. Results and Discussion. The results of this study underscore the effectiveness of expert group clustering and open source programming environments in enhancing STEAM learning outcomes. By reducing teacher lecture time and increasing student collaboration, engagement and knowledge retention are significantly improved. These findings align with Cognitive Load Theory (CLT), suggesting that structured student interactions can minimize cognitive overload and optimize learning efficiency. The practical implications of this research include the integration of scaffolding feedback to enhance student engagement, the strategic use of open-source programming to foster peer interactions, and the reduction of teacher-led lectures to promote active learning and deeper understanding. However, the study has limitations, including limited generalizability and the need for longitudinal studies to assess long-term impacts. Future research should aim to expand the sample size to diverse populations and explore cross-disciplinary applications. In addition, this study highlights the importance of structured collaboration and digital learning tools in STEAM education, offering valuable insights for educators seeking to optimize instructional strategies and improve learning outcomes (Shin, Y., & Song, D. 2022 ). Furthermore, from the FCM simulation analysis, a crucial outcome regarding valuable classroom time is derived. From the overall process of the FCM scenario simulation, it can be deduced that the contribution of decision support systems (DSSs), such as the Mental Modeller platform software, is very important in estimating the efficiency of reengineering procedures. Reengineering procedures such as the proposed procedure for improving the K-12 STEAM learning outcomes through student formative quizzes and observational data. 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J Educational Comput Res 60(6):1490–1513 Strom A (2021) The negative effects of technology for students and educators Sungur Gul K, Kirmizigul S, Ates AS, H., Garzon J (2023) Advantages and challenges of STEM education in K-12: Systematic review and research synthesis International Journal of Research in Education Sci (IJRES), 9(2), 283–307 https://doi.org/10.46328/ijres.3127 Sweeney T, Smith D (2024) The prevalence and use of emerging technologies in STEAM education: A systematic review of the literature. J Comput Assist Learn. https://doi.org/10.1111/jcal.12806 Tani M, Manuguerra M, Khan S (2022) Can videos affect learning outcomes? Evidence from an actual learning environment. Educ Tech Res Dev 70:1675–1693. https://doi.org/10.1007/s11423-022-10147-3 Timmis S, Broadfoot P, Sutherland R, Oldfield A (2016) Rethinking assessment in a digital age: Opportunities, challenges and risks. Br Edu Res J 42(3):454–476. https://doi.org/10.1002/berj.3215 Yang D, Baldwin SJ (2020) Using technology to support student learning in an integrated STEM learning environment. Int J Technol Educ Sci (IJTES) 4(1):1–11. https://doi.org/10.46328/ijtes.v4i1.54 Yang X, Lin L, Cheng PY et al (2018) Examining creativity through a virtual reality support system. Educ Tech Res Dev 66:1231–1254. https://doi.org/10.1007/s11423-018-9604-z Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6116171","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":421467569,"identity":"7be5e8b0-308d-4c0c-9b5d-540631f39064","order_by":0,"name":"Constantina Spyropoulou, Ph.D.C","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACCQSTuYGBoYI0LYxALWdI1sLYRoQWyQb2Z49599jk8UskNj7mnXdYzpyB/doHfFqkGXjMjXmepRVLzkhsNubddtjYsoGneAY+LXIMPGzSPAcOJ264kdgmDdSSuOEATzJeh8kxsD9D0jKHCC3SDAxmSFoaQFrYD+PVItnMY2445wDQLz0Pmw3nHEs3tmzmYcarReJ4+7MHbw4AQ4w9+eCDNzXWcubs7Y/xamFgZmBj4mFgSIALGDDzGODXwsDAxvgDRQsD+wNCWkbBKBgFo2BkAQBerEXtZhzQGQAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Peoloponnese","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"Constantina","lastName":"Spyropoulou","suffix":"Ph.D"},{"id":421467769,"identity":"e1cf5488-5f99-4109-9a3b-6eb77bd12f96","order_by":1,"name":"Manolis Wallace, Associate professor","email":"","orcid":"","institution":"University of the Peoloponnese","correspondingAuthor":false,"prefix":"","firstName":"Associate","middleName":"professor Manolis","lastName":"Wallace","suffix":""},{"id":421467770,"identity":"ff40eb3a-2d11-40d4-a69a-3e893e8358d5","order_by":2,"name":"Vassileios Poulopoulos, Assistant Professor","email":"","orcid":"","institution":"University of the Peoloponnese","correspondingAuthor":false,"prefix":"","firstName":"Assistant","middleName":"Professor Vassileios","lastName":"Poulopoulos","suffix":""},{"id":421467771,"identity":"0301d95a-6026-4b44-80a5-9712c274290d","order_by":3,"name":"Costas Vasilakis, Professor","email":"","orcid":"","institution":"University of the Peoloponnese","correspondingAuthor":false,"prefix":"","firstName":"Professor","middleName":"Costas","lastName":"Vasilakis","suffix":""}],"badges":[],"createdAt":"2025-02-26 21:59:54","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6116171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6116171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81804270,"identity":"c62670fa-c557-438c-a17c-c7b80117aead","added_by":"auto","created_at":"2025-05-02 06:46:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/c048d52745e911f25ff6a7f9.png"},{"id":81804269,"identity":"5099a841-0007-40ea-b4c2-b240ce3c9aae","added_by":"auto","created_at":"2025-05-02 06:46:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/3777ef20dcda5324ab4f3110.png"},{"id":81804272,"identity":"16fe36a3-b931-4997-bf7b-d2ae9068721a","added_by":"auto","created_at":"2025-05-02 06:46:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/8969c19865cde7d88dde7442.png"},{"id":81804771,"identity":"d5539075-c9b8-44e5-a8c8-07593c1f8f4f","added_by":"auto","created_at":"2025-05-02 06:54:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/35e333ad15357d22f4147743.png"},{"id":81804281,"identity":"401b459e-f33a-4873-b2df-a4c5d967360b","added_by":"auto","created_at":"2025-05-02 06:46:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42746,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of decreasing scaffolding feedback and increasing alternative utilization of a programming learning environment. Source: \u003ca href=\"http://www.dev.mentalmodeler.com/\"\u003e\u003cstrong\u003ewww.dev.mentalmodeler.com\u003c/strong\u003e\u003c/a\u003e (accessed on 12 February 2023).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/96f3a6d4d6a5513fc91487bd.png"},{"id":81804275,"identity":"96cd764d-35cc-4bd1-ae6d-068e32205ed6","added_by":"auto","created_at":"2025-05-02 06:46:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38161,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of decreasing teacher lectures for more than 15 minutes and increasing alternative utilization of a programming learning environment. Source: \u003ca href=\"http://www.dev.mentalmodeler.com/\"\u003e\u003cstrong\u003ewww.dev.mentalmodeler.com\u003c/strong\u003e\u003c/a\u003e (accessed on 12 February 2023).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/7febc536bd0c02e3fa3eda55.png"},{"id":81805009,"identity":"2224dde0-0558-4d53-9523-a2c48df0f542","added_by":"auto","created_at":"2025-05-02 07:02:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1572435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6116171/v1/fdffac09-77dc-4c44-ac54-d41eab0d988b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUtilizing open-source programming environments and student clustering to foster K-12 STEAM learning outcomes. Modelling processes via simulation scenarios.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe nascent field of STEAM (science, technology, engineering, arts, and mathematics) represents ongoing attempts by educational researchers and policymakers to make sense of and potentially institutionalize the concept of a holistic approach (Mejias et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The integration of technology in education has been extensively researched, particularly within the context of K-12 STEAM education. Open-source programming environments have emerged as tools that facilitate collaborative learning experiences among students. However, there remains a notable gap in the literature concerning the specific impact of these environments on structured student collaborations. Open-source programming environments promote collaborative learning by allowing students to engage in projects that require teamwork and communication. Participation in open-source projects can enhance students' professional skills, including coordination and collaboration, which are essential for success in real-world scenarios (Shernoff, 2017). This perspective aligns with the notion that open-source environments foster an atmosphere of shared knowledge and collective problem-solving. Engaging with open-source projects provides students with invaluable experiences that extend beyond technical skills. According to Chua (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), exposure to open-source principles cultivates an open mindset among students, preparing them for future collaborative efforts in various industries. The collaborative nature of open-source projects encourages K-12 students to form relationships and develop a sense of community, which is crucial for their personal and professional growth (Goldey, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite the potential benefits, several challenges hinder the effective implementation of open-source programming environments in K-12 STEAM educational settings. A systematic review by G\u0026uuml;l et al. (2023) highlights that while STEM education fosters 21st-century skills, educators often find designing STEM activities challenging due to the complexity of integrating multiple disciplines. Similarly, Montiel and Gomez-Zerme\u0026ntilde;o (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) identify obstacles in adopting open-source tools like Scratch for teaching computational thinking. These challenges include a lack of teacher training, insufficient technological infrastructure, and difficulties in aligning new tools with existing curricula. This difference in goals can complicate collaboration efforts.\u003c/p\u003e\n\u003cp\u003eWhile a wide range of remarkable research has investigated the role of technology and the importance of collaboration in education (Yang et al. 2020, Ackermans et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Colliot \u0026amp; Jamet, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Timmis et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), there remains a notable gap in the literature regarding the impact of open-source programming environments on structured student collaborations (Sweeney \u0026amp; Smith, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study aims to address this gap by evaluating whether and to what extent clustering students into expert groups within an open-source environment enhances their knowledge acquisition.\u003c/p\u003e\n\u003cp\u003eThe literature on K-12 STEAM education highlights several challenges (Milara \u0026amp; Ordu\u0026ntilde;a, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) in assessing knowledge acquisition (Jia et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The integrated nature of STEAM, combined with the lack of suitable assessment tools and longitudinal studies, makes it difficult to obtain clear evidence of knowledge acquisition in K-12 STEAM education (Brown et al., 1989, Perkins \u0026amp; Salomon, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e, OECD, 2018). For example, in many integrated STEM courses, students do not engage in the construction of in-depth mathematics, engineering, or science concepts (Chalmers et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), lowering the overall challenge of their learning (Berland, 2013; English, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). These types of knowledge are declarative knowledge, conceptual knowledge, procedural knowledge (technical, contextual), evaluative knowledge, and metacognitive knowledge (Tani et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). While acknowledging that there are various types of knowledge, this research refers to these types in a more typical manner. In this study, procedural knowledge refers to know-how (techniques) and the criteria for determining when to use appropriate procedures (Carter \u0026amp; Pritchard, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e): these subtypes are referred to as \u0026ldquo;technical\u0026rdquo; and \u0026ldquo;contextual\u0026rdquo;. Declarative knowledge encompasses, at a fundamental level, knowledge of terms and propositional aspects of the domain and pertains to statements such \"something is the case\" \u003cstrong\u003e(\u003c/strong\u003eAdams, 2009\u003cstrong\u003e)\u003c/strong\u003e. Additional definitions of conceptual knowledge include the relationships between these domain-specific components as well as associated concepts, hypotheses, and models (Hiebert, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Finally, a category has been added to identify what is known as \"evaluative\" knowledge, which is the capacity to use established criteria to assess assertions. Metacognitive knowledge refers to one's own cognitive processes (Pintrich, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Gap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile existing studies highlight the benefits of digital tools in STEM/STEAM education, limited research focuses on the interplay between student clustering and open-source environments. This study investigates how clustering students into expert groups within an open-source environment enhances their knowledge acquisition\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Purpose and Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHow does the integration of open-source programming environments impact K-12 STEAM learning outcomes?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWhat is the effect of student clustering on knowledge acquisition?\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow do simulation scenarios model and optimize instructional strategies?\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers use the framework of cognitive load theory to investigate the effect of open-source programming environments on the acquisition of different types of knowledge. Cognitive load theory (CLT) is a learning and instruction theory that describes instructional design inferences of a model of human cognitive building design on the basis of long-term memory's permanent knowledge basis (LTM) and working memory's temporary conscious processor of information. Working memory is characterized by its short length and capacity. If these boundaries are crossed, learning is impeded, and working memory is overwhelmed (Kalyuga, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). This theory is selected because the demands on the student to acquire STEAM-related knowledge at the primary level may be complex. Cognitive load theory offers insights into the potential impact of open-source programming environments on processing capacity, especially when the requirements are considerable. It is based on several tenets and has been integrated through a variety of models, including the knowledge integration perspective, which is relevant to understanding how students integrate ideas (Pashler et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Schneider \u0026amp; Stern, 2009).\u003c/p\u003e\n\u003cp\u003eIn STEAM education, the outcome of these cognitive processes is often measured by changes in the learner\u0026rsquo;s perceptual capacity to learn the key aspects in a transferable way and not to depend on the structure of the materials (Kaminski et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). In fact, absorbing complex knowledge can require substantial cognitive processing (Chi \u0026amp; Ohlsson, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), which can include reasoning, problem-solving, and model construction (Barbey \u0026amp; Barsalou, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, because the learning and transfer of knowledge depend in part on concreteness and abstraction, students' prior knowledge and working memory struggle to address the deep structure of a problem (Penner et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e). Therefore, according to the success of the formative assessment intervention, which enables students to screen out surface-level aspects and investigate how deeper-level features work, they can attend to deeper structural features. As a result, the greater the cognitive load is, the greater the processing capacity that may be used to recognize the distinctions and better align the theoretical concepts with the data. In this study, the focus is on knowledge-based outcomes, in contrast to most investigations of programming environments, which focus on the characteristics of programming environments (Mutlu-Bayraktar et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transdisciplinary character of STEAM (Meeth, 1978) involves projects where the concern may initially start with a social problem (Aguilera and Ortiz-Revilla, 2021) that mirrors an issue within the local community (Guyotte et al. 2014).\u003c/p\u003e\n\u003cp\u003eSeveral studies on STEAM education emphasize students' prior learning experiences and conceptual comprehension (Ginns \u0026amp; Leppink, 2019; Gulbin \u0026amp; Unsal, 2021). In some cases, students are recruited on the basis of their absence of prior knowledge (Mutlu-Bayraktar et al., 2019). As a result, research on STEAM instruction in this category involves procedural tasks with little to no connection with what students have been learning intentionally (Koning \u0026amp; Jarodzka, 2017). Other studies have attempted to build on students\u0026rsquo; prior knowledge of the STEAM subjects they are already studying using digital instructional materials in the classroom. A large portion of this research focuses on teaching science or math concepts in primary education (Lin \u0026amp; Tsai, 2021). For example, there is some evidence that animations increase the probability of answering questions correctly on multiple-choice tests in elementary science contexts (Dalacosta et al., 2009).\u003c/p\u003e\n\u003cp\u003eA variety of analyses have examined the purpose of cognitive processes related to working memory and spatial ability (Cheng, Lai, Cheng, \u0026amp; Huang, 2022). In the studies under evaluation, learning has also frequently been evaluated via achievement tests (e.g., Sung et al., 2019) or retention and transfer assessments (e.g., Colliot \u0026amp; Jamet, 2018; Craig \u0026amp; Schroeder, 2017). A number of the cited studies additionally examined prior knowledge and motivation-related learning outcomes (e.g., Park, 2015; Park et al., 2016). Within the studies in primary education contexts we reviewed, cases arising in STEM disciplines have often been explored (Leavy et al., 2023; Barnes et al., 2017; Chen and Huang, 2020; Grandl et al., 2020; Jailungka et al., 2020; Timotheou and Ioannou, 2019). The most frequently studied dependent variables in terms of cognitive load have been retention and transfer (Mayer, 2019). Only a few studies have examined the different types of knowledge acquired by primary education students in the STEAM discipline (Quigley et al., 2017). While research on the use of an open-source programming environment in primary STEAM education is evident, studies on cognitive processing are scarce. This study contributes to existing research in two significant ways: by extending research into STEAM learning in primary education and by further investigating learning outcomes related to different types of knowledge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInnovation:\u003c/strong\u003e \u003cstrong\u003ethe use of open-source programming environments and students\u0026rsquo; collaborative clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInnovation took place in a total of 570 participants in a K-12 context after COVID-19 (Lin \u0026amp; Johnson, 2021). It consisted of engaging students with open-source programming software under a collaborative approach. Focused on a real-world problem covered during two months in the face-to-face class. Two different ways of presenting this innovative programming environment were carried out, and the version applied was randomized.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith respect to other features of engaging students in an open-source programming environment, this research followed the seven principles for collaborative learning. Open-source programming environments have the potential to enhance cognition and knowledge acquisition (Lajoie \u0026amp; Derry, 2013). It is thought to do so on the basis of these established principles of collaborative e-learning environments (Konstantinidis et al., 2009):\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe users who participate have different roles and privileges.\u003c/li\u003e\n\u003cli\u003eThe educational interactions in the environment transform the simple virtual space into a communication space.\u003c/li\u003e\n\u003cli\u003eThe information in the environment is represented in multiple ways that can vary from simple text to three-dimensional (3D) graphics.\u003c/li\u003e\n\u003cli\u003eStudents are not passive users but can interact with each other and with the virtual environment.\u003c/li\u003e\n\u003cli\u003eThe system that supports the environment integrates multiple technologies.\u003c/li\u003e\n\u003cli\u003eThe possibility of implementing multiple learning scenarios is supported.\u003c/li\u003e\n\u003cli\u003eRecognizable elements from the real world are visualized.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe program promoted the clustering of students into expert groups to collaborate when programming in Scratch software (principle 1 above). Each expert group exchanged ideas with one another and finally transferred them to the other groups, suggesting ways to implement the solution using the programming concepts into the software (principle 2). Images and words were presented before the student as a visualization (principle 3). All the expert groups were active users of the programming environment, as they interacted directly with it and between them (principle 4). The Scratch software provides supplemental tools and materials, and each expert group utilized it in several ways (e.g., a block-based programming environment, a tool to create animations) (principle 5). All the expert groups designed their solutions to the problem as a final scenario in Scratch (principle 6). Handling a real-world problem and illustrating the solution in the programming software (principles 7).\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003eParadigm\u003c/h2\u003e\n \u003cp\u003eThe empirical problem was to design a case study in which the impact of participation in expert groups in a programming environment could be identified and quantified statistically on student learning outcomes. To accomplish this, researchers followed an approach similar to a quasi-experimental design. This approach involves gathering information about students\u0026apos; skills before engaging with the programming environment and randomizing their exposure to it. To identify other observed or non-observed characteristics of the participants, we had to carry out the research design.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eResearch design\u003c/h3\u003e\n\u003cp\u003eResearchers carried out a case study comprising thirty-four samples of K\u0026ndash;12 STEAM programs: fourteen at non-profit organizations and twenty in the in-school context, both in urban centres. Non-profit organizations are concerned with postschool STEAM programs offered by laboratories or centres. There were 575 participants, including 75 adults and 500 students. In the school context, there were STEAM courses that focused on the disciplines of science and coding. In non-profit organizations, courses focus on all STEAM disciplines. At each research site, the projects were applied to students at the primary, junior (ages 4\u0026ndash;6) and intermediate (ages 7\u0026ndash;12) levels. Face‒to-face interviews were carried out with 500 students (participants and nonparticipants in programs A and B), individually.\u003c/p\u003e\n\u003cp\u003eAs mentioned above, the program randomly exposed students in a quasi-experimental research design by producing two distinct but closely related STEAM projects (A\u0026rsquo; and B\u0026rsquo;). Both projects were covered in the period of the course. This strategy enabled the researchers to generate three groups of students: those who did not take part in the project, those who took part in project A, and those who took part in project B.\u003c/p\u003e\n\u003cp\u003eProjects A and B were randomized between simultaneous sessions. Through this process, students and adult professionals volunteering to participate in the project did not know in advance whether they were participating in project A or B. By relying on the randomization of the projects, researchers were able to use the group of students who participated in project B as a reference group to measure the effect of participating in project A, and vice versa.\u003c/p\u003e\n\u003cp\u003eBoth projects had identical processes but covered different topics in their core parts. These topics were the energy footprint (EF) and climate change (CC), respectively. Both are important subjects and fundamental ideas that form the basis of a large portion of the K\u0026ndash;12 STEAM curriculum. Assigning various evaluation questions to each category of knowledge was the researchers\u0026rsquo; initial task. To accomplish this, researchers first divided different types of knowledge into four categories: declarative, conceptual, procedural, and evaluative. The researchers employed interview formats that were modified from other STEM/STEAM studies (Ghanbari, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBoth projects applied were included in some of the 20 questions on the midterm formative quiz, which was conducted with multiple-choice questions and a graded Likert scale, which was carried out the week after the projects. In addition to the formative assessment, researchers used an ad hoc survey to gather students\u0026apos; general opinions on their own learning experience after taking part in one of the projects to monitor any potential impact on their metacognitive knowledge.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe participants were selected from those who expressed an interest in voluntary group participation.\u003c/p\u003e\n\u003cp\u003eTo distinguish between participants and nonparticipants, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provides statistics on the unconditional means of key student demographic characteristics and the mean differences between demographic and age factors (4\u0026ndash;12 years). Researchers use the Kruskal‒Wallis test to determine whether these unconditional means are significantly different and report the p value of the test in the last column of Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Participants\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKruskal‒Wallis test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u0026lt;. oo1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u0026lt;. oo1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePrevious formative interview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u0026lt;. oo1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLate formative quiz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u0026lt;. oo1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eKnowledge types:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeclarative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConceptual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContextual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvaluative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eSample selection and data availability\u003c/h3\u003e\n\u003cp\u003eFor the analysis to be performed, the authors define both the school and out-of-school STEAM projects that form the sample for the analysis. Some of them are the most innovative education units. Innovation in STEAM-related projects is considerable in most of the above units. Consequently, over the course of two months of this project, researchers gathered data from 34 schools and non-profit organizations. These data are indicative of how students performed on STEAM projects. Therefore, researchers have used the metrics from the collected STEAM project learning outcomes as indicators of the students\u0026apos; performance and elaborated them via simulation through fuzzy cognitive mapping (FCM). This dataset will be subjected to statistical and simulation analysis to obtain effective results that will support STEAM projects\u0026rsquo; need to reengineer their core activities for learning outcome orientation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDefinition of selected factors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSTEAM projects metrics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScaffolding Feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt is a method designed to take advantage of the benefits of retrieval practice by providing incremental hints until the correct answer could be self-generated. (Finn \u0026amp; Metcalfe, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUse of Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefers to Integration of technology to enhance the student learning experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency of Communication between Peers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe exchange, imparting of information, ideas, or feelings between student groups who are at the same hierarchical level as each other for the purpose of fulfilling a common task or goal.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlternative Utilization of a Programming Learning Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefers to active and collaborative learning utilizing ways to improve student understanding of programming concepts, reduce the level of frustration, and better prepare students for jobs based on teamwork (Poindexter, S., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstructional Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA system of procedures for developing education and training curricula in a consistent and reliable fashion (Branch \u0026amp; Merrill, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent Correspondence Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt is essentially the amount of time the teacher waits before expecting a response from the students, express an idea or make a statement (Mary Budd Rowe, \u003cspan class=\"CitationRef\"\u003e1972\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollaborative Expert Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferring to the cultivation of collaborative problem-solving skills. Students (1) establishing and maintaining shared understanding, (2) taking appropriate action, and (3) establishing and maintaining team organization as professionals (Graesser et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeacher Lectured for more than 15 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModern best practices suggest limiting the length of a lecture to 15\u0026ndash;20 minutes, breaking up a longer lecture with hands-on activities, as research shows that 20 minutes is about as long as humans can maintain their attention on one source of information (Bligh, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow Teacher\u0026apos;s Technology Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe lack of the capacity to both easily and effectively access knowledge in different codes and formats (e.g., text, videos, images) in a digital environment (Claro et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnerous Overuse of Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing technology excessively in education, can lead to feelings of tension, poor achievement, loss of interest, may cause withdrawal and fatigue as well as impair student learning (Strom A., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eHypothesis\u003c/h2\u003e\n \u003cp\u003eThe primary research hypotheses are stated in this section of the study, which is followed by a thorough examination. STEAM learning outcome planning has been shown to be highly beneficial in a variety of learning environments. This raises the question of whether it is beneficial to use open-source programming environments and student clustering in expert groups to enhance STEAM learning outcomes and reengineer STEAM curriculum activities to learning. To assess the efficiency of STEAM learning outcome planning, researchers emphasize students\u0026rsquo; knowledge acquisition and how this affects important factors of STEAM learning activities, such as the use of technology, instructional design, student correspondence time, and scaffolding feedback. For this purpose, nine key research hypotheses are presented, with the aim of highlighting the STEAM learning planning process by analysing students\u0026rsquo; knowledge acquisition. The correlations between the factors emerged throughout the quantitative research and the analysis of the statistical data. The hypotheses concern the interrelationships shown by the statistical analysis and the observation process (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The authors performed correlation analysis in more detail. It is clear from the reported correlations below that the K\u0026ndash;12 STEAM learning outcome indicators are strongly related. The research hypotheses are presented below:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H1).\u003c/strong\u003e \u0026ldquo;\u003cem\u003eInstructional design positively impacts students\u0026rsquo; correspondence time\u0026rdquo;.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe first research hypothesis is based on the concept of whether the use of educational methods and techniques in a certain instructional design could enhance the ability of students to respond to the formative quiz, make their first question or express their idea during the lesson in less time (students\u0026rsquo; correspondence time). Researchers aim to answer that if the teacher increases, the instructional design could lessen the students\u0026rsquo; correspondence time.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H2).\u003c/strong\u003e \u003cem\u003eLow technology skills can affect the use of technology.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe second hypothesis relies on the fact that when teachers cannot easily handle technology, the use of technology decreases. Thus, teachers need to make greater efforts to accomplish computer-supported STEAM lessons, resulting in a decrease in the engagement of students with the use of technology. This implies a decrease in the value of their teaching, which requires valuable time for the optimization of other parts (e.g., activity materials). In this way, they decrease the value of their students while their teaching is not optimized.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H3).\u003c/strong\u003e \u003cem\u003eScaffolding feedback influences student correspondence time.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe third hypothesis of this paper concerns the time needed for students to express their ideas, ask their first question, or respond to the formative quiz. Scaffolding feedback should provide tips for students to support them in expressing ideas, providing quick responses and resolving problems that arise.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H4).\u003c/strong\u003e \u003cem\u003eTeacher lectures lasting more than 15 minutes are related to the frequency of communication between peers.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe fourth hypothesis is related to the amount of time teachers need to lecture to provide students with full detail of the project. If teachers could lecture for less than 15 minutes, the value of their teaching, saving valuable time for the optimization of other parts, could be increased.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H5).\u003c/strong\u003e \u003cem\u003eScaffolding feedback can adequately support collaborative expert grouping.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe fifth hypothesis practically means that when the teacher increases the amount of scaffolding feedback during the project, students collaborate more effectively in their group, making questions, discussing ideas and design thinking. In this way, students work together, focus on the goals of their group, and acquire knowledge more efficiently.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H6).\u003c/strong\u003e \u003cem\u003eThe tendency of students to abandon the use of technology depends on the overuse of technology.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe sixth hypothesis indicates that the more the teacher uses technology resulting in an onerous overuse of technology, the more the students tend to feel tired and lose purpose. Consequently, students tend to abandon technology.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 7\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H7).\u003c/strong\u003e \u003cem\u003eCollaborative expert grouping could stand out as a peer-to-peer teaching technique by increasing the frequency of communication between peers.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe seventh hypothesis relies on the fact that when students collaborate easily in a STEAM course, they also communicate constantly, enabling themselves to gain knowledge from their peers. More specifically, communication between peers during a collaborative grouping of experts also functions as a learning technique, where knowledge acquisition occurs faster and more creatively.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 8\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H8).\u003c/strong\u003e \u003cem\u003eAlternative utilization of a programming learning environment can influence collaborative expert grouping.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe eighth hypothesis is related to an alternative and more holistic way of utilizing a programming learning environment when the students have different roles and privileges. Researchers aim to answer that if students engage in the project as active users of the programming environment, communicating and visualizing real-world problems, then the collaboration between them is optimized.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis 9\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(H9).\u003c/strong\u003e \u003cem\u003eAlternative utilization of a programming learning environment can positively impact the frequency of communication between peers.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe ninth hypothesis is based on the concept that the alternative utilization of the programming environment is a student-centred perspective, and this positively affects the frequency of communication between students. In this way, students engage in the project as active users of the programming environment, interact with each other, and their interactions in the environment transform the simple virtual space into a communication space.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStatistical Results\u003c/h3\u003e\n\u003cp\u003eAccording to the results of the formative assessment, 78% of the students who took part in the project benefited from receiving constant scaffolding feedback. In addition, 97% of them appear to benefit from the use of technology. As indicated by the data, 82% of the students are more likely to engage with the course\u0026apos;s concepts, as they communicate with each other frequently. As revealed by the ad hoc survey, 68% of the students cultivated their own metacognitive processes through alternative utilization of the programming environment. Furthermore, 96% of the participants argued that when there is a certain flow on the course, the learning environment is more understandable. During the formative quiz, 75% of the students responded to all 20 questions in less than 15 minutes. In addition, 78% of the students spent less than two minutes throughout the program. Furthermore, 93% of the students who clustered into collaborative groups with a certain goal achieved the group\u0026apos;s goal. On the other hand, 94% of the students stated that when the teacher lectured for more than 15 minutes, they felt bored and tired. Moreover, 67% of the students lost interest when the teacher had low technology skills. Finally, 73% of the students reported a loss of focus and the goal of the project when there was an onerous overuse of technology.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Correlations with the main factor (Learning Outcomes)\n\u003c/div\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003erthermore, during the observation, 77% of the students expressed their first question (student correspondence time) in less than one minute when the programming environment was presented to them via the educational strategy of building excitement (instructional design). Similarly, 65% of the students took advantage of the tips given to them (scaffolding feedback) to resolve problems that arose (student correspondence time). Additionally, 71% of the students frequently communicated (frequency of communication between peers) while collaborating in expert groups (collaborative expert grouping). Additionally, by using the programming software in an alternative way (alternative utilization of a programming learning environment), 66% of the students became experts working collaboratively (collaborative expert grouping). In addition, 72% of the students frequently communicated (frequency of communication between peers) while cooperating in an alternative way in the software (alternative utilization of a programming learning environment). Additionally, when the students collaborated in the expert groups (collaborative expert grouping), 83% of them benefited from constant feedback (scaffolding feedback). Similarly, when teachers showed lower skills in technology (low teacher\u0026apos;s technology skills), 50% of the students were not fully engaged in the use of the software, and any other digital tool was utilized (use of technology). Moreover, when teachers lectured for more than fifteen minutes, 48% of the students stopped communicating with their peers (frequency of communication between peers). Finally, the students reported a loss of focus and the goal of the project when there was overuse of technology (Onerous Overuse of Technology); thus, 69% of them were not interested in using the software any longer (Use of Technology).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Correlations between factors\u003c/p\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eExploratory Model Deployment\u003c/h3\u003e\n\u003cp\u003eThe current study\u0026apos;s objective is to develop an FCM-based approach for planning STEAM learning outcomes. This approach is based on the FCM method, which enables the quantification of the deviation of the relationship between the relevant variables while simultaneously offering a systematic analysis to compare various scenarios (Hair, J.F., Jr., 2007).\u003c/p\u003e\n\u003cp\u003eThe authors performed and investigated three scenarios in the following stage of the paper to enhance the performance of K-12 STEAM implementation. The outcomes were determined via various key metrics by adjusting the analytic factors of the large-scale case study results. The authors identified the factors that influence most important variables after completing the quantitative and correlation analysis in the earlier phase. The FCM simulation analysis, which is used to demonstrate the immediate impact of the K\u0026ndash;12 STEAM learning outcome performance indicators, is based on the quantitative and correlation coefficients of the variables.\u003c/p\u003e\n\u003cp\u003eFuzzy cognitive maps (FCMs) are fuzzy graph structures used to represent causal logic. Their ambiguity allows for blurred degrees of causality between blurred causal concepts. Their graph structure allows for systematic causal propagation, particularly in the chain forward and backwards, and allows the development of knowledge bases by linking different FCMs (Kosko, 1986). FCMs are particularly applicable to areas of mild knowledge, and causality is represented as a vague relationship with causal concepts (Glykas, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). They are a conclusive grid that uses a graph in a circular arrangement, through which the cause‒and‒effect relationship between the nodes that make up the system variables is represented. The nodes represent the variables of the system; the connections between the nodes are the relationships between them with different importance. A fuzzy cognitive map incorporates the accumulated experience and knowledge of system specialists modelling FCMs as the result of the method by which the system was created (Groumpos P.P. 2010).\u003c/p\u003e\n\u003cp\u003eMental Modeller is a software that facilitates the integration and analysis of knowledge in modelling. It is a participatory modelling tool based on FCM, which clarifies the mental models of stakeholders and provides the opportunity to integrate different types of knowledge into decision making. Furthermore, hypotheses to be tested and to execute scenarios for identifying the perceived effects of the proposed policies are defined (Gray et al., 2013). In addition, Mental Modeller is a decision support system (DSS) that provides the ability to solve problems and communications for semi-structured problems (Raymond McLeod, Jr. 1998). A DSS is a computer-based system consisting of components (components of language, knowledge and problem processing systems) that interact with one another (Bonczek, 1980). The Mental Modeller architecture allows researchers, scientists, and decision-makers to develop models and scenarios of future system statements on an equal footing (J.T. Peterson, and J.W. Evans. 2003). Specifically, the software was designed to enable stakeholders to (1) construct a quality conceptual model, (2) develop conditions, and (3) revise their model based on the model output. The mental modeller consists of three main user interfaces: (a) the concept mapping interface that provides space for model construction and configures the model construction in the format required for FCM analysis. (b) the matrix interface that allows the structural properties of the cognitive map to become clear by examining the pairwise relationships; and (c) the script interface that allows stakeholders to execute and compare changes within the system under different possible scenarios and reviews (Gray et al. 2013).\u003c/p\u003e\n\u003cp\u003eBy identifying various parameters, including positive and negative correlations among factors, stationary frameworks displaying information can be built. Researchers used Mental Modeller\u0026apos;s online platform application for this purpose, where they could use the FCM modelling tool.\u003c/p\u003e\n\u003cp\u003eThe FCM offers a comprehensive analysis of the branch of education since it integrates the sector\u0026apos;s statistical analysis. Compared with the whole curriculum content, it can anticipate optimization scenarios. This feature is immensely helpful for schools and nonprofit organizations in education since the employment of such an approach can offer valuable insights for obtaining improvements in learning outcomes. The referred examination of the sector can be seen in the implemented FCM model in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough many software and web-based editors focus on modelling fuzzy cognitive maps, such software is used to deploy numerous scenarios in addition to modelling. Thus, FCM enables a plethora of researchers to contribute to the development of their scientific works.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eIncreasing/Decreasing Alternative Utilization of Programming Learning Environment Analytics\u003c/h2\u003e\n \u003cp\u003eTo estimate the direct effects of the use of open-source software and students\u0026rsquo; clustering results on the K-12 STEAM learning outcomes, researchers have created three scenarios. In the first scenario, the alternative utilization of programming learning environment metrics increased by 10% and then decreased by 10% to observe the variation caused by other key metrics, such as collaborative expert grouping and frequency of communication between peers. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the overall course of these metrics is anodic. Learning outcomes increase the most with an increase of 20% (point 1, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which is a positive outcome in terms of learning outcomes. The frequency of communication between peers increases by 12% (point 2, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), and collaborative expert grouping increases by 7% (point 3, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which is also a good sign of STEAM program efficiency.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eOn the other hand, by decreasing alternative utilization of programming learning environment metrics by 10%, we obtain the exact opposite results, meaning decreases in learning outcomes and the frequency of communication between peers and collaborative expert grouping by 20% (point 1, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), 12% (point 2, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and 7% (point 3, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This time, a decrease in all metrics leads to an increase in teacher lectures for more than 15 minutes and, finally, to a deterioration in the frequency of communication between peers (decrease) and collaborative expert grouping (decrease), which are metrics that show devaluation when kept at low values. To obtain further insights regarding the optimal strategy for the performance of STEAM education outcomes and provide reasons for the redesign of educational procedures, the authors deployed more FCM scenarios by adjusting the alternative utilization of programming learning environment analytics in different volumes with another key metric.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eReducing Scaffolding Feedback and Increasing Alternative Utilization of a Programming Learning Environment\u003c/h2\u003e\n \u003cp\u003eThis time, by utilizing the advantages of the hyperbolic tangent function, researchers designed Learning Outcomes, Student Correspondence Time, and the frequency of communication between peers and Collaborative Expert Grouping\u0026rsquo;s direct impacts by causing different variations in scaffolding feedback and alternative utilization of a programming learning environment\u0026rsquo;s metrics. The authors opted to investigate these STEAM learning outcome performance indicators by increasing the alternative utilization of a programming learning environment by 10% and, at the same time, by decreasing the scaffolding feedback by 10%. With this, the preceding scenario is intended to yield a variety of outcomes, and it also serves to emphasize the significance of each analytics area in the four performance indicators for the STEAM learning outcomes that have been examined. The direct effects of the various factors in the scenario are displayed in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The frequency of communication between peers directly increased by 6% (point 3, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), whereas learning outcomes, student correspondence time, and collaborative expert grouping decreased by 2% (point 1, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), 6% (point 2, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), and 2% (point 4, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), respectively. Therefore, increasing the alternative utilization of a programming learning environment by 10% and decreasing the scaffolding feedback by 10% decreases the learning outcomes, student correspondence time, and collaborative expert grouping, which is not desirable. As a result, the reengineering processes of K-12 STEAM activities are triggered since adjusting metrics can alter the performance indicators of their K-12 STEAM implementation. Moreover, for greater clarity of results, another scenario in which a negative variable is decreased is performed.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReducing teacher lectures for more than 15 minutes and increasing alternative utilization of a programming learning environment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWith respect to the last scenario simulation, researchers have estimated the variation in learning outcomes. Frequency of communication and collaborative expert grouping when a decrease in a negative variable occurs. More specifically, in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, researchers examine how an increase of 10% in alternative utilization of a programming learning environment combined with a decrease in teacher lectures for more than 15 minutes by 10% affects the above key STEAM learning outcome performance metrics. As a result, the direct effect of enhancing the metrics is an increase in learning outcomes and the frequency of communication between peers and collaborative expert grouping by 31% (point 1, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), 17% (point 2, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) and 7% (point 3, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), respectively. The effects of reducing teacher lectures for more than 15 minutes by 10% and increasing alternative utilization of a programming learning environment by 10% represent improvements, and STEAM stakeholders should consider the benefits of reducing lectures from teachers.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and Discussion.","content":"\u003cp\u003eThe results of this study underscore the effectiveness of expert group clustering and open source programming environments in enhancing STEAM learning outcomes. By reducing teacher lecture time and increasing student collaboration, engagement and knowledge retention are significantly improved. These findings align with Cognitive Load Theory (CLT), suggesting that structured student interactions can minimize cognitive overload and optimize learning efficiency. The practical implications of this research include the integration of scaffolding feedback to enhance student engagement, the strategic use of open-source programming to foster peer interactions, and the reduction of teacher-led lectures to promote active learning and deeper understanding. However, the study has limitations, including limited generalizability and the need for longitudinal studies to assess long-term impacts. Future research should aim to expand the sample size to diverse populations and explore cross-disciplinary applications. In addition, this study highlights the importance of structured collaboration and digital learning tools in STEAM education, offering valuable insights for educators seeking to optimize instructional strategies and improve learning outcomes (Shin, Y., \u0026amp; Song, D. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, from the FCM simulation analysis, a crucial outcome regarding valuable classroom time is derived. From the overall process of the FCM scenario simulation, it can be deduced that the contribution of decision support systems (DSSs), such as the Mental Modeller platform software, is very important in estimating the efficiency of reengineering procedures. Reengineering procedures such as the proposed procedure for improving the K-12 STEAM learning outcomes through student formative quizzes and observational data. This is due to the DSS\u0026rsquo;s ability to depict the direct effects of various factors\u0026rsquo; variations in key metrics that promote reengineering assessment. The use and modelling of data through DSS simulation software in this study has proven to be a decisive factor in illustrating the direction of reengineering procedures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAckermans K, Rusman E, Brand-Gruwel S, Specht M (2019) Solving instructional design dilemmas to develop a Video Enhanced Rubric with modelling examples to support mental model development of complex skills: The Viewbrics-project use case. Education Tech Research Dev 67(4):983\u0026ndash;1002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams MP Empirical evidence and the knowledge-that/knowledge-how distinction. 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Educ Tech Res Dev 66:1231\u0026ndash;1254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11423-018-9604-z\u003c/span\u003e\u003cspan address=\"10.1007/s11423-018-9604-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Peloponnese","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"K-12 STEAM education, open-source software, collaborative learning, student clustering, modeling and simulation, experimental design","lastPublishedDoi":"10.21203/rs.3.rs-6116171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6116171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of open-source programming environments and student clustering on K-12 STEAM learning outcomes. The research addresses the gap in understanding how knowledge acquisition is influenced by collaborative expert groups and digital instructional tools. A quasi-experimental design is implemented, assessing student performance through formative assessments and statistical analysis. Results indicate that clustering students into expert groups significantly enhances declarative, conceptual, procedural, and evaluative knowledge. The study also explores the effectiveness of modeling processes via simulation scenarios. Findings suggest that optimized student grouping and technology-enhanced learning contribute to improved learning efficiency. The implications note the importance of innovative instructional strategies in STEAM education.\u003c/p\u003e","manuscriptTitle":"Utilizing open-source programming environments and student clustering to foster K-12 STEAM learning outcomes. Modelling processes via simulation scenarios.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 06:46:45","doi":"10.21203/rs.3.rs-6116171/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a4e6a4e-75d9-442e-8ba8-272b2bd4bee3","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-02T06:46:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-02 06:46:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6116171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6116171","identity":"rs-6116171","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}