Experimental Research on Enhancing Effectiveness in Teaching the Nature of Seasons

Experimental Research on Enhancing Effectiveness in Teaching the Nature of Seasons | 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 Experimental Research on Enhancing Effectiveness in Teaching the Nature of Seasons Melike Güzin Semercioğlu, Hüseyin Kalkan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4625007/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In recent years, studies by education researchers indicate that, despite various instructional methods to enhance the conceptual understanding of seasons, the reasons for the formation of seasons and the processes involved are still not fully grasped. This research aims to investigate the fundamental reasons for the difficulties in comprehending the formation of seasons and to develop possible solutions. To achieve this goal, a physical model based on the "Change in the Amount of Energy Falling onto a Unit Surface (CAEFUS)" of parallel beams of sunlight as Earth orbits around the Sun with a tilted axis of approximately 23.5 degrees was developed and applied to participants, and data were collected. These instructional processes were applied to experimental and control groups. The research was conducted on 148 eighth-grade students in a state school in Samsun province. Exploratory factor analysis was used to analyze students' drawings and separate them into their most prominent elements. The quantitative data analysis, consisting of multiple-choice questions, utilized the SPSS 22.0 statistical program. The data analysis revealed that the CAEFUS-based model significantly impacted the meaningful teaching of the nature of seasons. These results were thoroughly evaluated through a detailed comparison with the literature. Nature of seasons astronomy education physical model mental model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction One of the goals of understanding the nature of science is to comprehend and internalize how events operate and the relationships between these events and concepts (Cho & Nam, 2017 ). Fundamental astronomy topics, which are among the subfields of science, are gaining increasing importance, accompanied by a significant surge in research and educational strategy implementations related to this field (De Paor et al., 2017 ; Kim, 2015 ; Plummer & Maynard, 2014 ). In science education, while some concepts can be concretely experienced in daily life, others are abstract and not directly observable. Fundamental astronomy topics can be directly observed—for instance, the sunrise and sunset, the phases of the Moon, and the seasons. However, the factors causing these natural phenomena in daily life are not directly observable, such as celestial bodies' rotation and revolution movements. The "Nature of Seasons," which is the subject of this study, can be directly observed in daily life; however, concrete tools are needed to comprehend the event process. The use of visual representations in scientific communication and science education has become well-established (Bublea et al., 2009). Nonetheless, the debate about visual representations in education has continued for many years (Hsu, 2008 ; Oh & Kim, 2006 ). In the pre-Internet era, numerous research studies found evidence supporting the effectiveness of graphics in the learning process (Starakis et al., 2017 ). The reflection of these results has influenced many current studies. According to studies in the literature on how students perceive the concept of seasons, a common misconception is that the Earth moves closer to the Sun during the summer months, causing an increase in temperature (Van Loon et al., 2015 ). Cary Sneider and colleagues reviewed 41 articles detailing teaching strategies, practices, and difficulties in learning and teaching the topic of seasons in different countries up to 2011. Since 2011, studies in this field have pointed out that the difficulties in teaching astronomy concepts stem from the complexity of astronomy concepts, the difficulty in perceiving them, the prevalence of abstract concepts, and the need to think in three dimensions (Corrochano et al., 2017 ; Torregrosa et al., 2018 ). To generate explanations about the seasons, it is necessary to coordinate the patterns of temperature changes observed on Earth with the movement patterns of Earth in space and the Sun's path, understand different frames of reference, and interpret how observations are affected by Earth's spherical shape (Plummer & Maynard, 2014 ). Additionally, grasping the fundamental mathematical knowledge that reveals the geometry of the distribution of solar radiation on Earth's surface and understanding the relationships between the absorption of light by the surface is fundamental to scientifically comprehending the seasons. Unfortunately, understanding and learning the academic knowledge that forms the basis of the geometric changes and absorption of light on the surface is quite challenging at the middle school level. However, it is possible to teach these two concepts and their relationships using a physical model, enabling each participant to make accurate scientific interpretations. Thus, using physical models to understand the nature of science proves to be highly advantageous. Therefore, a significant majority of such studies indicate that the seasons are an excellent tool for teaching the essence of science (Wang et al., 2021 ). At this point, models play an influential role in exploring the nature of science and, consequently, in the science education process. Models generally help understand how scientific theories and events interact, explain difficult-to-understand concepts, and concretize abstract concepts (Aseeva, 2021 ; Minaslı, 2009 ; Schwarz & Gwekwerere, 2007 ). Also, models allow learners to engage more in the process (Kolchin et al., 2022 ). Ananishnev ( 2010 ) stated that models help learners coordinate objects' integrity, usability, and functionality in their mental models and build bridges between events and concepts. However, some studies indicate that incorrectly scaled or developed models can lead to many misconceptions and that models used for purposes other than their intended can cause students to interpret events differently (Wilson et al., 2019 ). Numerous studies have shown that models and analogies, which have a solid potential to change mental models, can effectively prompt the re-evaluation of misconceptions and ingrained thoughts, creating new channels in mental maps for new conceptual models (Trumper, 2006b ). When examining international and national sources, it is evident that the application of models in science education has a high impact on process-oriented inquiry and is highly effective in teaching scientific concepts (Katarina & Pavlin, 2020 ; Taylor et al., 2010 ). Studies in the literature indicate that teaching science concepts, which primarily consist of empirical data, principles, laws, theories, etc., using models reduces complexity, facilitates comprehension, concretizes abstract concepts, enables three-dimensional thinking about events, accelerates understanding functions, increases interest and curiosity, and enhances the desire to learn (Katarina & Pavlin, 2020 ; Yun, 2020 ). López-Vargas and colleagues ( 2017 ) describe models as an activity and a therapeutic process involving multiple components such as conceptualization, representation, or simulation. Consequently, models play a significant role in astronomy education because they provide opportunities for tangible visualization, promote interaction, and actively encourage practical experiences. A review of the literature reveals that there are few studies explaining and understanding the process of "Change in Energy Amount on Unit Surface" (CAEFUS) based on the shape of the Earth, its revolution, and axial tilt, and their relationship to students' mental models. The starting point of this study is to determine the change in mental models after teaching CAEFUS using a model developed within the research. For this purpose, 8th-grade students were taught CAEFUS by exploring a physical model. The solutions developed by students regarding CAEFUS were discussed. In this study, the following questions were addressed with the use of models: RQ1: Have the students' explanations and visual representations of the nature of seasons been influenced by the CAEFUS model? RQ2: How has the CAEFUS model influenced the students' explanations and visual representations of the nature of seasons? RQ3: Which features of the models used most significantly affected students' ability to provide meaningful explanations and visual expressions of the nature of seasons? This study is designed to enhance the understanding of the CAEFUS concept in teaching seasons. Conceptual Framework Most studies on teaching the formation of seasons typically seek to answer the question, "Why do seasons occur?" The answer to this question is often expressed as "axial tilt," "Earth's movements (rotation and revolution)," and "Earth's distance from and proximity to the Sun." However, the real question that needs to be asked is, "How do seasons occur?" In the literature, there is a lack of research focusing on changes in students' mental models regarding the process based on CAEFUS, which explains the formation of seasons based on the angle of sunlight with the Earth's surface (Cho et al., 2017 ; Sneider et al., 2011 ). The fundamental problem in explaining the formation of seasons based on the angle of sunlight with the Earth's surface in students' mental models lies in the difficulty in perceiving the physical processes necessary for CAEFUS. The formation of seasons based on the angle of sunlight with the Earth's surface is illustrated in Figs. 1 and 2 . Figure 1 HERE Figure 2. HERE As seen in Fig. 2 , the relationship between the angle of incidence of rays and the spread occurring on the Earth's surface is as follows: (1) In Eq. (1), 𝑑k represents the area where solar rays spread on the Earth's surface, 𝑑g represents the area that solar rays have before encountering any surface, and θ is the angle of incidence. As shown in (a), when θ = 30° the area where solar rays spread is wider, while when θ = 90° the area where rays spread is narrower. Therefore, the amount of energy per unit area incident on the Earth's surface is inversely proportional to the area over which solar light spreads. For example, assuming that the energy of a beam of light is 10 J, and it spreads over surfaces of 10 units and 20 units separately; in the first case, 1 J (10/10) of energy falls on a unit area, while in the second case, 0.5 J (10/20) of energy falls on a unit area. The same situation can be applied to (b). Parallel incoming solar rays will spread over different areas at different angles depending on the shape of the surface they encounter, and accordingly, the energy transfer per unit area will vary inversely with the width of the surface. This research considers the difficulties and misconceptions in many studies conducted up to the present day and aims to create a different perspective on the teaching and learning the seasons topic to minimize misconceptions and learning difficulties in this area. In the study, explanations made regarding the teaching of the concept of seasons include: (a) explanations based on the tilt of the Earth's rotational axis, (b) explanations based on the Earth's revolution around the Sun, (c) explanations based on the Earth's spherical structure, (d) explanations using all three together, and (e) explanations based on the angle of incidence of solar rays parallel to the Earth's surface as it revolves around the Sun, taking into account CAEFUS on the Earth, have been separately and collectively examined. The study investigated the level of explanation students could provide at various stages before and after education with models and their relationships with scientifically accurate explanations of seasons. In this study, which aimed to ensure an understanding of how seasons occur rather than what they are, factors affecting CAEFUS, which is the fundamental reason for understanding seasons, were addressed. A physical model was used to teach these factors at the level of comprehension and higher cognitive domain stages. Figure 3 . HERE All factors contributing to the formation of seasons should be scientifically explored and students should be encouraged to ground them on a scientific basis. As illustrated in conceptual further framework Fig. 3 , drawn based on the angle of the Sun's rays with concerning's surface, in accordance CAEFUS. Method This study employed a quasi-experimental method with two samples (Sönmez & Alacapınar, 2013 ). The sample consisted of 148 (72 experimental group + 76 control group) 8th-grade students attending a public school affiliated with the Ministry of National Education in Samsun province. The students included in the experimental and control groups were determined using a simple random sampling method. a Pre-test and post-test measurements were administered to the sample groups (before and after instruction and the retention test). Both qualitative active and quantitative measurements were utilized during the implementation phase of the study. In the qualitative dimension of the research, in-depth examination was conducted using an open-ended question scale to identify situations and context-dependent themes. The study's main theme is to assist in structuring mental models related to CAEFUS. The change in mental models can be determined by measuring the development of practical problem-solving skills. Therefore, the data collection process used a mixed-method (MM) approach. The stages of the research process are outlined in Fig. 4 . Figure 4 . HERE Stages of Developing the Seasons Model During the development of the seasons model, the concept of "the amount of energy falling onto a unit surface depending on the angle of the Sun's rays with the Earth's surface" was planned to be taught. The model was constructed according to this plan. • Scientific information regarding the Sun and the Earth was collected, and the scaling in the model was calculated. Information regarding the distance between the Earth and the Sun is provided in Table 1 . • Different positions (significant dates) were determined on the model to illustrate the Earth's orbit around the Sun, and a fixed but practical model was chosen instead of a movable one. • Since it was impossible to create the actual sizes of the Sun and the Earth within the same model, three powerful light-emitting sources were used instead of the Sun. • Four Pilates balls with a diameter of 85 cm, representing the Earth's four seasons, were drawn with latitudes and longitudes, and fixed systems were created to determine Turkey's position on the Earth. •The axes of the Earth were positioned to indicate the North Star and were fixed on supports at a height of one meter. Essential dates were written on the supports, and the positions and approximate distances of the Earth on these crucial dates were calculated. • Three identical (with a capacity of 1000 meters) vertical lights were used in the same direction through the centers of the four Earth models (considering the distances to the Sun). There was a gap of 13 cm between the lights. • The lights were positioned so that on March 21 and September 23, they would fall on the Tropic of Cancer at the top, the Equator in the middle, and the Tropic of Capricorn at the bottom. Table 1 Actual and Approximate Distances between the Sun and the Earth Date Actual Distance (approximately) Scaled Distance Between Sun and Earth March 21 between 147,000,000–152,000,000 km 139 cm June 21 between 147,000,000–152,000,000 km 140 cm July 4 (Aphelion) 152,000,000 km 142 cm September 23 between 147,000,000–152,000,000 km 139 cm December 21 between 147,000,000–152,000,000 km 138 cm January 3 (Perihelion)) 147,000,000 km 137,3 cm Table 1 .HERE Data Collection Tools a) Solar and Earth Movements Achievement Test (SEMAT), b) Solar and Earth Movements Open-Ended Questionnaire (SEOUQ) One open-ended question was directed to each class for the achievement test administered to students. The analysis of these questions was conducted independently of the multiple-choice questions. The researchers developed the data collection tools used in the study. Pilot studies were conducted. The reliability coefficient (pj) was determined to be 0.64, and the validity coefficient (rjx) was 0.68. Accordingly, although the scale is easy, its discriminative power is excellent (Gönen et al., 2011; Özçelik, 2010; Karip, 2009). In addition, to determine the student's level of knowledge regarding the research topic and better explain the teaching process's effectiveness, the question "Explain how the seasons occur" was directed as an open-ended question. Data Analysis Analysis of Quantitative Data SEMAT was administered twice to the students: once before the application and then again 8 weeks after. Before the application, the classes were randomly classified as control and experimental groups. The same scale was repeated for both groups before and after the application with equal time intervals and conditions. The analyses were conducted using the SPSS 22.0 package program. As the number of students in the experimental and control groups was more than 30 and ensured normal distribution, parametric analysis methods were decided to be used (Büyüköztürk, 2011 ). In the study, the mixed-design ANOVA technique was used to determine the astronomy achievements of students in the experimental and control groups. Analysis of Qualitative Data The answers given by students to open-ended questions were categorized as scientifically correct, scientifically partially correct, and scientifically incorrect. Students who made an entirely correct explanation by drawing correctly on the diagram were coded as scientifically correct; students who made a partially correct explanation by providing incomplete explanations on the drawing or only the explanation were coded as scientifically partially correct; students who made both the drawing and the explanation wrong were coded as scientifically incorrect. The percentages and frequencies of the obtained data were determined. Changes in students' mental models, determined by their drawings for 3 tests before and after the application, were analyzed. Findings and Interpretation This section focuses on the findings resulting from the conducted analyses. The findings are organized and presented to address the sub-objectives. Visual elements such as tables, figures, and graphs are used in presenting the findings. 1. What is the effectiveness level of the Solar-Earth model developed within the scope of the research in teaching the concept of the formation of seasons to students? 2. What is the impact of the Solar-Earth model developed within the scope of the research on the sustainability of student achievement levels regarding seasons? Findings Related to SEMAT In this stage, the pre-test results of the experimental and control groups were evaluated using one-way ANOVA for unrelated samples. Descriptive statistics for the pre-test results of students' astronomy achievements are presented in Table 2 . Table 2 Descriptive Statistics for SEMAT Group N Arithmetic Mean SD Skewness Kurtosis Experimental 72 1,52 0,905 0,555 0,252 Control 76 1,82 1,016 0,256 -0,402 Table 2 . HERE Table 2 highlights the remarkably close pre-test mean scores of the experimental and control groups. This led to the conduct of one-way ANOVA to assess the score differences between the experimental and control groups formed according to students' grade levels. The results of the analysis are presented in detail in Table 3 . Table 3 One-way ANOVA Results for SEMAT Pre-test Scores of Experimental and Control Group Students by Class Source of Variance SS df MS F p Between Groups 1,741 4 0,435 0,517 0,724 Within Groups 58,979 70 0,843 Total 60,720 74 Table 3 . HERE According to Table 3 , there is no significant differentiation before experimental application (= 1.741, p > .05). This result indicates that the groups' achievements related to the concepts and events under study were equivalent before the intervention. To assess whether there is a significant difference in the achievement levels between the experimental and control groups, one-way analysis of variathe nce (ANOVA) technique was used for the measurements. Descriptive analysis A df the experimental group students' SEMAT pre-test, post-test, and retention test results is presented in Table 4 . This table will assist us in visually understanding the analysis results. Table 4 Descriptive Statistics for SEMAT Pre-test, Post-test, and Retention Test Results of Experimental Group Students Group Test N Arithmetic Mean SD Skewness Kurtosis Experimental Pre-test 75 1,52 0,90 0,562 0,264 Post-test 76 3,21 1,23 -0,073 -0,851 Retention 77 3,76 0,98 -0,618 0,281 Retention Retention 78 1,82 1,01 0,238 -0,439 Retention 80 2,87 1,17 -0,321 -0,278 Retention 79 3,30 1,13 -0,837 1,414 Table 4 . HERE According to Table 4 , it is observed that there is an increase in the SEMAT achievement of the experimental group in the post-test and a slight further increase in the retention test. The highest achievement score is determined in the retention test, followed by the post-test, and the lowest in the pre-test. When examining the averages of the control group, it is observed that student achievement increased in the post-test similar to the experimental group, and slightly more in the retention test. However, when looking at the averages, while the pre-test average of the experimental group was lower than that of the control group, the average of the experimental group showed a significant increase in favor of the experimental group in the post-test and retention test. Figure 5 . HERE According to Fig. 5 , the average score on the post-test and retention have increased in the experimental group of students. Five questions were asked. The average score was 1.o2 on the pre-test, which increased to 3.o1 on the post-test, and the average rose to 3.o6 on the retention test. Findings regarding the statistical significance of the data obtained from the tests are provided in Table 5 . Table 5 One-way ANOVA Results for Students' SEMAT Achievement Scores Group Source of Variance SS df MS F p η2 Experimental Pre-Post-Retention test 201,400 2 100,700 91,396 .000 0,449 Error 246,802 224 1,102 Total 448,203 226 Control Pre-Post-Retention test 90,891 2 45,445 36,809 0,000 0,240 Error 287,664 233 1,235 Total 378,555 235 The pre-test results of the control group students showed an average score of 1.82. This average increased to 2.87 in the post-test. The retention test average was 3.30. A one-way repeated measures ANOVA was conducted to determine whether there was a significant difference in the SEMAT achievement scores of the experimental group students. The data for this ANOVA are provided in Table 5 . Table 5 . HERE Table 5 shows a significant difference between the pre-test and post-test scores of the experimental group (F = 91.396, p < .05). These results indicate that the teaching model used was effective in instruction and led to a significant differentiation. They were additionally, based on the eta squared (η²) value, it can be said that the teaching with the Solar-Earth models developed within the scope of the research had a considerable impact on students' SEMAT achievements (η² = 0.449). In the control group, there is also a significant difference between the pre-test and post-test scores (F = 45.445, p < .05). These results also indicate that the teaching model used was effective in instruction and led to a significant differentiation. Moreover, according to the eta squared (η²) value, the impact of the teaching method used on students' SEMAT achievements can be considered moderate (η² = 0.240). Eta squared is a statistical value that measures the effect of an independent variable on the dependent variable. It expresses the percentage of the total variance in the dependent variable explained by the independent variable and usually falls within the range of 0.00 to 1.00. Eta squared helps us determine the effect size for each main effect, interaction, and error in ANOVA analyses. These statistics allow us to assess the magnitude of the independent variable's effect on the dependent variable without the need for linearity assumptions. Additionally, values such as .02, .13, and .26 typically represent small, medium, and large effect sizes, respectively (Büyüköztürk, 2011 ). Eta squared assists researchers in better understanding their data and the number of independent variables involved. The sources of differences in the mean scores of the pre-test and post-test students in the experimental group were determined through pairwise comparisons, and the results are presented in Table 6 . Pairwise comparisons (Bonferroni) were conducted after one-way ANOVA to identify this finding. Table 6 Pairwise Comparisons of SEMAT Achievement Scores of Experimental Group Students Grup Comparison Mean Difference Standard Error p Experimental Pre-test Post-test -1,730 0,143 0,000*** Retention -2,189 0,158 0,000*** Post-test Pre-test 1,730 0,143 0,000*** Retention -0,459 0,132 0,003** Retention Pre-test 2,189 0,158 0,000*** Post-test 0,459 0,132 0,003** Control Pre-test Post-test -1,013 0,147 0,000*** Retention -1,462 0,169 0,000*** Post-test Pre-test 1,013 0,147 0,000*** Retention -0,449 0,146 0,01* Retention Pre-test 1,462 0,169 0,000*** Post-test -0,449 0,146 0,01* Tablo 7. ANOVA Results for SEMAT Achievement Scores of Experimental and Control Group Students Source of Variance SS df MS F p η2 Between Groups 321,298 145 Group (Experimental-Control) 15,642 1 15,642 7,625 0,006 0,214 Error 305,656 149 2,051 Within Groups 458.193 300 Measurement (Pre-Post-Retention) 228,941 2 114,470 168,445 0,000 0,337 Group*Measurement 27,342 2 13,671 20,117 0,000 0,450 Error 202,512 298 0,680 Total 779,491 Table 6 .HERE .05 ≤ p ≤ .10 Marginal significance *.01 ≤ p ≤ .05 Statistically significant **.001 ≤ p ≤ .01 High level of statistical significance *** p < .001 Very high level of statistical significance (Akbulut, 2022 ) Table 6 shows a very high level of statistically significant difference between the pre-test and post-test scores of the experimental group (p < .001). Considering the average scores, this difference favors the post-test. In light of these results, teaching with the Sun-Earth model developed within the research scope effectively improves student achievement. When the difference between this group's pre-test and retention test scores is examined, a very high level of statistically significant difference is again determined (p < .001). Considering the averages, the difference favors the retention test. Additionally, a high level of statistically significant difference is found between the post-test scores and the retention scores of the students (001 ≤ p ≤ .01). Considering the averages, the difference favors retention. When all findings are considered, it can be said that the teaching with the Sun-Earth model developed within the scope of the research serves its purpose. In the control group, while a very high level of statistically significant difference is found between the pre-test and post-test scores (p < .001), considering the averages, this difference favors the post-test. When the difference between the pre-test and retention scores of this group is examined, a very high level of statistically significant difference is also found (p < .001). Considering the averages, the difference favors the retention test. Additionally, a statistically significant difference is found between the post-test and retention scores of the students (01 ≤ p ≤ .05). Considering the averages, the difference favors retention. When all findings are considered, it can be said that teaching with the Sun-Earth model developed within the scope of the research serves its purpose. The achievements of the experimental and control groups in the applied tests were examined within each group. Following this examination, comparisons were made between the groups and the measurements to determine (if any) the differentiation between the teaching method with the physical models developed within the scope of the research and the teaching methods used in the National Education curriculum. For this purpose, a two-way analysis of variance technique for mixed measurements was used to compare the measurements and the groups. We examined the findings related to the change in the groups' SEMAT scores, and the results of the two-way analysis of variance are given in Table 7. Tablo 7. HERE According to Table 7, the results obtained from the achievement tests applied to the groups show a significant difference between the groups without separating the achievement scores into pre-test, post-test, and retention test (F = 7.625, p < .05). Additionally, this difference is also determined separately for each of the three tests when evaluating achievement between the groups (F = 168.445, p < .05). These results indicate that the teaching methods and techniques used in both groups affect achievement. A post-hoc (Scheffe) multiple comparison analysis was conducted to determine which specific pairs of subgroup mean scores contribute to this difference. The results of this analysis are presented in Table 8 . Table 8 . HERE Table 8. Post-Hoc (Scheffe) Results for Experimental and Control Groups Measurement Group N 1 2 3 Pre-test Experimental 74 1,54 Control 78 1,82 Pos-test Experimental 79 3,21 Control 76 2,86 Retention testi Experimental 79 3,76 Control 77 3,03 When analyzing the data, a significant result favoring the experimental group is observed across all three applications. For a more precise interpretation of this change, refer to Fig. 6 . Figure 6 . HERE Upon examining Fig. 6 , an increase is observed in both groups in the comparisons between the pre-test and post-test and between the post-test and retention test. However, upon closer inspection, it is evident that although the experimental group had lower scores than the control group in the pre-test, they exhibited a higher increase in the post-test than the control group. Similarly, there was a more significant increase in the retention test in the experimental group compared to the control group. It can be inferred that the difference between the two groups arises from these instances. Findings of Descriptive Analysis of Students' Responses to Open-Ended Questions At the end of SEMAT, each class was asked an open-ended question related to the Science program's curriculum, which was the subject of the research. The primary aim of these questions was to evaluate the effectiveness of the teaching model from a different perspective. For this purpose, the responses of systematically selected students from each class to the open-ended questions were examined individually. Findings of Descriptive Analysis of Students' Responses to Open-Ended Questions The scope of the open-ended question prepared for students is about how the seasons are formed. To ensure objectivity in the study, responses given by ten students systematically selected from each group were meticulously analyzed for the pre-test (P), post-test (S), and retention test (R). Attention was paid to systematic selection by skipping every eighth student, starting from the first student selected. The responses provided by the students are presented in Table 9 (Ö: Pre-test, S: Post-test, K: Retention-test, YANITSIZ: UNANSWERED, Aralık: Dec, Eylül: Sep, Mart: March, Haziran: June, Güneş: Sun, Dünya: Earth, İlkbahar: Spring, Sonbahar: Autumn/fall, Kış: Winter, Yaz: Summer) Table 9 . HERE The data analysis was conducted with a systematic selection of students to avoid random errors. Upon careful examination of the data, it is observed that the readiness of the groups before the teaching process was equal. The most significant change observed in the experimental group after the teaching process was the inclination of axes in their drawings. Upon closer inspection of the drawings, it was determined that the drawings of the experimental group were scientifically more accurate. The inclination of the axes, not observed in the drawings of the pre-test, was reflected in the post-test and retention tests. The results obtained from the two separate teaching processes for the experimental and control groups, based on their achievements in the study, are as follows: 1. The descriptive statistics of the experimental and control groups appear to be similar. Additionally, it was found that the classes' readiness before the teaching process was low. In the control group, although students referring to the inclination of axes were identified, their drawings indicated that they could not think in three dimensions, only at the level of learning from two-dimensional sources. Additionally, in the experimental group, some students' explanations of the formation of seasons, mentioning "sphericity" and "orbital motion," indicate that they were able to reach levels of understanding, application, and even analysis beyond the level of recalling the formation of seasons, as they were able to reflect this in their drawings. Unlike multiple-choice test analyses, open-ended questions demonstrate that the explanations and drawings made by students in the experimental group on the formation of seasons were more straightforward compared to those of the control group. Moreover, in both groups, some students clearly did not understand the formation of seasons, but this number was higher in the control group. Discussion and Conclusion In the new curriculum of the Ministry of National Education, the use of models is recommended as one of the most effective methods for teaching astronomy concepts. However, the models used must be associated with reality as they significantly form students' mental schemas. In this context, a teaching process with models was conducted to determine the effect of the Sun and Earth model developed for the research on student achievement. Aslan and Doğdu ( 1993 ) stated that material usage facilitates students' perception of a subject, increases student engagement with exciting materials, and arouses a desire to conduct further research. Altıntaş ( 1998 ) determined that materials or models provide students with rich, colorful, lively visual and sensory learning environments. This research has shown that students quickly learned that the distance of the Earth from the Sun does not affect the formation of seasons through their explanations and visual representations of the nature of seasons. Galano (2016) similarly expressed that the most accessible distance does not affect the seasons. In another study, Trumper ( 2006a ) found that elementary-middle-high school-university students stated that the seasonal change on Earth is due to the inclination of the axis. However, they could not explain the temperature difference between summer and winter. In another study, Henriques ( 2000 ) found that many students, while stating that the distance of the Earth from the Sun does not affect the formation of seasons, could not explain the reason for it. In this research, however, the higher success level of students in the experimental group in understanding the relationships between the inclination of the axis and the Earth's orbital motion with the nature of the seasons and depicting it indicates that the model CAEFUS is effective in student learning. Galano (2016) in his study stated that although they could understand the effect of the inclination of the axis and the orbital motion of the Earth on the nature of the seasons, they struggled to establish a relationship between the energy received by the Earth and the sunlight hitting the Earth's surface at different angles. Consequently, regarding (RQ1), the CAEFUS model influences students' explanations and visual representations of the nature of seasons. In addition, after models were used in the experimental group and textbook images and text were used in the control group, there was an increase in the level of achievement in both groups; however, this increase was in favor of the experimental group. Our findings indicate that understanding the mechanism underlying the nature of seasons, a tilted axis with a fixed direction in space, and the Earth's orbit around the Sun is the most challenging concept to grasp. Students in the experimental group developed different perspectives when interpreting the nature of seasons compared to students in the control group, as the model used was presented to students as free from conceptual misconceptions as possible. Understanding CAEFUS and discovering how the varying amount of energy creates seasons is critical to understanding the nature of seasons. Students who understood the effect of energy amount on the nature of seasons found it easier to grasp the Earth's axial tilt, the orbit around the Sun, and even its sphericity, as they expressed and depicted. Consequently, regarding (RQ2), students' explanations and visual representations of the nature of seasons shed light on the relationship between the transfer of solar energy and the Earth's surface area, thus affecting student achievement. The simultaneous occurrence of many physical events shapes the nature of seasons. For example, answers to questions such as how seasons would be affected if the Earth were not spherical, if there were no axial tilt, or if the Earth did not orbit around the Sun were provided through these models. Searching for answers to these questions while implementing the models allowed students to establish relationships between situations and current conditions. In this regard, (RQ3) the models used measuring and observing different conditions, thus affecting students' ability to analyze and imagine, enabling them to explain the nature of seasons meaningfully and express it visually. The nature of seasons is one of the most challenging concepts to grasp scientifically. In a study by Martin et al. (2023), attempts were made to teach students about the nature of seasons using both augmented reality and physical models. It was found that understanding the scientific basis of the subject was the most challenging part of learning for students. Numerous studies have reported similar findings on the nature of seasons (Danaia & McKinnon, 2007 ; Frede, 2008 ; Kavanagh & Sneider, 2006 ; Tsai & Chang, 2005 ). Therefore, a body of existing literature supports our research focusing on this aspect. The data and results obtained in our study are expected to serve as a foundation for future research and contribute to developing new models. Declarations Data Availability Statement The data utilized in this study are not available in a digital format. However, these data can be made available to interested researchers upon reasonable request by contacting the corresponding author. Ethics & Informed Consent The protocol was approved by the Provincial Directorate of National Education in accordance with the "Ministry of National Education Directive on Permission and Implementation of Research and Research Support to be Conducted in Schools and Institutions" and the "approval of the relevant directorate dated 24.05.2019 and numbered 10294247." Informed Consent The need for informed consent was waived by the Directorate of National Education. References Akbulut, Ö. (2022). Current Approaches in Reporting Statistical Significance in Scientific Research: Myths and Realities. International Journal of Eastern Mediterranean Agricultural Research, 5(1). Altıntaş, G. E. (1998). The Contribution of Materials (Experiment Sheets) and Puzzle Technique in the Teaching of Science in Primary Schools 4th Grade to Students' Academic Achievement. [Doctoral Dissertation], Pamukkale University. Ananishnev, V. M. (2010). Modeling in the Field of Education. System Psychology and Sociology, 1(2), 67-84. Aseeva, O. M. (2021). Modeling as a Method of Understanding the Surrounding Reality. Young Researcher, 6(348), 403-404. Aslan, Z., & Doğdu, S. (1993). Educational Technology Applications and Educational Tools. Ankara: Tekışık Offset. Büyüköztürk, Ş. (2011). Experimental Designs: Pre-test-post-test Control Group, Design, and Data Analysis. Pegem Akademi. Cho, E., Kim, C., & Choe, S. (2017). An Investigation into the Secondary Science Teachers’ Perception on Scientific Models and Modeling. Journal of the Korean Association for Science Education, 37(5), 859-877. Cho, H. S., & Nam, J. (2017). Analysis of Trends of Model and Modeling-Related Research in Science Education in Korea. Journal of the Korean Association for Science Education, 37(4), 539-552. Corrochano, D., Gomez-Gonçalves, A., Sevilla, J., Pampin-Garcia, S. (2017). Ideas of high school and university students about the meaning and origin of tides. Eureka Journal on Teaching and Popularization of Sciences, 14(2), 353–366. Danaia, L., & McKinnon, D. H. (2007). Common Alternative Astronomical Conceptions Encountered in Junior Secondary Science Classes: Why Is This So?. Astronomy Education Review, 6(2), 32-53. Doi: 10.3847/AER2007017 De Paor DG, Drodevic MM, Karabinos P, Burgin S, Coba F, Whitmeyer SJ (2017). Exploring the Reasons for the Seasons Using Google Earth, 3D Models, and Plots. International Journal of Digital Earth, 10(6), 582–603. Frede, V. (2008). The Seasons Explained by Refutational Modeling Activities. Astronomy Education Review, 7(1), 44-56. Doi: 10.3847/AER2008005 Henriques, L. (2000). Children's Misconceptions About Weather: A Review of the Literature. The Annual Meeting of the National Association of Research in Science Teaching, National Association of Research in Science Teaching. Hsu, Y. (2008). Learning About Seasons in a Technologically Enhanced Environment: The Impact of Teacher-Guided and Student-Centered Instructional Approaches on the Process of Students’ Conceptual Change. Science Education, 92(2), pp. 320-344. Katarina, S., & Pavlin, J. (2020). Improvements in Teachers’ Knowledge and Understanding of Basic Astronomy Concepts through Didactic Games. Journal of Baltic Science Education, 19(6), 1020-1033. Kavanagh, C., & Sneider, C. (2006). Learning about gravity Part II. Trajectories and orbits. Astronomy Education Review, 5(2), 53-102. Doi: 10.3847/AER2006019 Kim, H. J. (2015). A comparative study of the Bohr atomic model and the spectrum of atomic hydrogen in chemistry curriculum and physics curriculum. The Korean Society for School Science, 9(2), 94-100. Kolchin, I. S., Miroshnichenko, A. S., Kadeeva, O. E., & Syritsyna, V. N. (2022). 3D modeling as a tool for gamification of the process of studying science disciplines. Sovremennyye Problemy Nauki i Obrazovaniya [Modern Problems of Science and Education], 6(1), 45. https://doi.org/10.17513/spno.32256 López-Vargas, O., Ibáñez-Ibáñez, J., & Racines-Prada, O. (2017). Students’ metacognition and cognitive style and their effect on cognitive load and learning achievement. Journal of Educational Technology & Society, 20(3), 145-157. https://doi.org/10.1177/. 1365480217704263 Minaslı, E. (2009). The effect of using simulation and modeling in teaching the structure and properties of matter unit in science and technology course on achievement, concept learning and retention [Unpublished Master's Thesis]. Marmara University. Oh, J. Y., & Kim, Y. S. (2006). Preservice elementary teacher mental models about astronomical phenomena: seasons and moon phases. Journal of the Korean Association for Science Education, 26(1), 68-87. Plummer, J.D. & Maynard, L. (2014). Building a learning progression for celestial motion: an exploration of students’ reasoning about the seasons. J Res Sci Teach, 51(7), 902–929. Schwarz, C. V., & Gwekwerere, Y. N. (2007). Using a guided inquiry and modeling instructional framework (EIMA) to support preservice K-8 science teaching. Science Education, 91(1), 158-186. Sneider, C. (2011). Learning about Seasons: A Guide for Teachers and Curriculum Developers. Astronomy Education Review, 10(1), 1-23. https://doi.org/10.3847/AER2010035 Sneider, C., Bar, V., & Kavanagh, C. (2011). Learning about Seasons: A Guide for Teachers and Curriculum Developers. Astronomy Education Review, 10(1). https://doi.org/10.3847/AER2010035. Sönmez, V., & Alacapınar, F. G. (2013). Illustrated Scientific Research Methods. Ankara: Anı. Starakis, I., Galani, A. & Angeliki, L. (2017, Nisan). The use of "Scratch" in Geography: The teaching of Seasons. 9th Panhellenic Conference of ICT Educators , 128-136. Taylor, I. J., Barker, M., & Jones, A. (2010). Promoting mental model building in astronomy education. International Journal of Science Education, 25(10), 1205-1225. Torregrosa, J. M., Liminana, R., Menargues, A., & Colomer, R. (2018). In-depth teaching as oriented-research about seasons and the sun/earth model: effects on content knowledge attained by pre-service primary teachers. Journal of Baltic Science Education, 17(1), 97–119. Trumper, R. (2006a). Teaching future teachers basic astronomy concepts—seasonal changes—at a time of reform in science education. Journal of Research in Science Teaching, 43(9), 879-906. http://doi.org/10.1002/tea.20138 Trumper, R. (2006b). Factors affecting students’ junior high school students’ interest in physics. Journal of Science Education and Technology, 15(1), 47-58. http://doi.org/10.1007/s10956-006-0355-6 Tsai, C., & Chang, C. (2005). Lasting Effects of Instruction Guided by the Conflict Map: Experimental Study of Learning about the Causes of Seasons. Journal of Research in Science Teaching, 42(10), 1089-1111. Doi: 10.1002/tea.10039 Van Loon, M. H., Dunlosky, J., Van Gog, T., Van Merriënboer, J. J., & De Bruin, A. B. (2015). Refutations in science texts lead to hypercorrection of misconceptions held with high confidence. Contemporary Educational Psychology, 42, 39–48. Wang, J., Guan, Y., Lixin, W., Guan, X., Cai, W., Huang, J., Wenjie, D., & Zhang, B. (2021). Changing Lengths of the Four Seasons by Global Warming. Geophysical Research Letters. https://doi.org/10.1029/2020GL091753. Wilson, M. D., Boag, R. J., & Strickland, L. (2019). All models are wrong, some are useful, but are they reproducible? Computational Brain & Behavior, 2, pp. 239–241. https://doi.org/10.1007/s42113-019-00054-x. Yun, E. (2020). Review of trends in physics education research using topic modeling. Journal of Baltic Science Education, 19(3), pp. 388-400. https://doi.org/10.33225/jbse/20.19.388 Table 9 Table 9 is available in the Supplementary Files section. Pictures Pictures are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files TABLE9.docx Pictures.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviews received at journal 22 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviewers agreed at journal 21 Aug, 2024 Reviews received at journal 04 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 03 Jul, 2024 Editor assigned by journal 03 Jul, 2024 Submission checks completed at journal 02 Jul, 2024 First submitted to journal 23 Jun, 2024 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. 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16:51:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGraph of Changes Based on Group and Measurement\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4625007/v1/38e1d93338c0a3bbb323b071.png"},{"id":61184740,"identity":"aa668754-01ea-4061-a370-89bc5fae1e32","added_by":"auto","created_at":"2024-07-26 17:15:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1955570,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4625007/v1/92776396-10f6-4916-a7f0-ad29a6d0bea3.pdf"},{"id":61181829,"identity":"5716c312-790e-404e-bf17-e7415e3503fe","added_by":"auto","created_at":"2024-07-26 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internalize how events operate and the relationships between these events and concepts (Cho \u0026amp; Nam, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Fundamental astronomy topics, which are among the subfields of science, are gaining increasing importance, accompanied by a significant surge in research and educational strategy implementations related to this field (De Paor et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Plummer \u0026amp; Maynard, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In science education, while some concepts can be concretely experienced in daily life, others are abstract and not directly observable. Fundamental astronomy topics can be directly observed\u0026mdash;for instance, the sunrise and sunset, the phases of the Moon, and the seasons. However, the factors causing these natural phenomena in daily life are not directly observable, such as celestial bodies' rotation and revolution movements.\u003c/p\u003e \u003cp\u003eThe \"Nature of Seasons,\" which is the subject of this study, can be directly observed in daily life; however, concrete tools are needed to comprehend the event process. The use of visual representations in scientific communication and science education has become well-established (Bublea et al., 2009). Nonetheless, the debate about visual representations in education has continued for many years (Hsu, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Oh \u0026amp; Kim, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In the pre-Internet era, numerous research studies found evidence supporting the effectiveness of graphics in the learning process (Starakis et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The reflection of these results has influenced many current studies.\u003c/p\u003e \u003cp\u003eAccording to studies in the literature on how students perceive the concept of seasons, a common misconception is that the Earth moves closer to the Sun during the summer months, causing an increase in temperature (Van Loon et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cary Sneider and colleagues reviewed 41 articles detailing teaching strategies, practices, and difficulties in learning and teaching the topic of seasons in different countries up to 2011. Since 2011, studies in this field have pointed out that the difficulties in teaching astronomy concepts stem from the complexity of astronomy concepts, the difficulty in perceiving them, the prevalence of abstract concepts, and the need to think in three dimensions (Corrochano et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Torregrosa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo generate explanations about the seasons, it is necessary to coordinate the patterns of temperature changes observed on Earth with the movement patterns of Earth in space and the Sun's path, understand different frames of reference, and interpret how observations are affected by Earth's spherical shape (Plummer \u0026amp; Maynard, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, grasping the fundamental mathematical knowledge that reveals the geometry of the distribution of solar radiation on Earth's surface and understanding the relationships between the absorption of light by the surface is fundamental to scientifically comprehending the seasons. Unfortunately, understanding and learning the academic knowledge that forms the basis of the geometric changes and absorption of light on the surface is quite challenging at the middle school level. However, it is possible to teach these two concepts and their relationships using a physical model, enabling each participant to make accurate scientific interpretations. Thus, using physical models to understand the nature of science proves to be highly advantageous. Therefore, a significant majority of such studies indicate that the seasons are an excellent tool for teaching the essence of science (Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt this point, models play an influential role in exploring the nature of science and, consequently, in the science education process. Models generally help understand how scientific theories and events interact, explain difficult-to-understand concepts, and concretize abstract concepts (Aseeva, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Minaslı, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Schwarz \u0026amp; Gwekwerere, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Also, models allow learners to engage more in the process (Kolchin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ananishnev (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) stated that models help learners coordinate objects' integrity, usability, and functionality in their mental models and build bridges between events and concepts. However, some studies indicate that incorrectly scaled or developed models can lead to many misconceptions and that models used for purposes other than their intended can cause students to interpret events differently (Wilson et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have shown that models and analogies, which have a solid potential to change mental models, can effectively prompt the re-evaluation of misconceptions and ingrained thoughts, creating new channels in mental maps for new conceptual models (Trumper, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e). When examining international and national sources, it is evident that the application of models in science education has a high impact on process-oriented inquiry and is highly effective in teaching scientific concepts (Katarina \u0026amp; Pavlin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Studies in the literature indicate that teaching science concepts, which primarily consist of empirical data, principles, laws, theories, etc., using models reduces complexity, facilitates comprehension, concretizes abstract concepts, enables three-dimensional thinking about events, accelerates understanding functions, increases interest and curiosity, and enhances the desire to learn (Katarina \u0026amp; Pavlin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yun, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). L\u0026oacute;pez-Vargas and colleagues (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) describe models as an activity and a therapeutic process involving multiple components such as conceptualization, representation, or simulation. Consequently, models play a significant role in astronomy education because they provide opportunities for tangible visualization, promote interaction, and actively encourage practical experiences.\u003c/p\u003e \u003cp\u003eA review of the literature reveals that there are few studies explaining and understanding the process of \"Change in Energy Amount on Unit Surface\" (CAEFUS) based on the shape of the Earth, its revolution, and axial tilt, and their relationship to students' mental models. The starting point of this study is to determine the change in mental models after teaching CAEFUS using a model developed within the research. For this purpose, 8th-grade students were taught CAEFUS by exploring a physical model. The solutions developed by students regarding CAEFUS were discussed. In this study, the following questions were addressed with the use of models:\u003c/p\u003e \u003cp\u003eRQ1: Have the students' explanations and visual representations of the nature of seasons been influenced by the CAEFUS model?\u003c/p\u003e \u003cp\u003eRQ2: How has the CAEFUS model influenced the students' explanations and visual representations of the nature of seasons?\u003c/p\u003e \u003cp\u003eRQ3: Which features of the models used most significantly affected students' ability to provide meaningful explanations and visual expressions of the nature of seasons?\u003c/p\u003e \u003cp\u003eThis study is designed to enhance the understanding of the CAEFUS concept in teaching seasons.\u003c/p\u003e"},{"header":"Conceptual Framework","content":"\u003cp\u003eMost studies on teaching the formation of seasons typically seek to answer the question, \"Why do seasons occur?\" The answer to this question is often expressed as \"axial tilt,\" \"Earth's movements (rotation and revolution),\" and \"Earth's distance from and proximity to the Sun.\" However, the real question that needs to be asked is, \"How do seasons occur?\" In the literature, there is a lack of research focusing on changes in students' mental models regarding the process based on CAEFUS, which explains the formation of seasons based on the angle of sunlight with the Earth's surface (Cho et al., \u003cspan\u003e2017\u003c/span\u003e; Sneider et al., \u003cspan\u003e2011\u003c/span\u003e). The fundamental problem in explaining the formation of seasons based on the angle of sunlight with the Earth's surface in students' mental models lies in the difficulty in perceiving the physical processes necessary for CAEFUS. The formation of seasons based on the angle of sunlight with the Earth's surface is illustrated in Figs.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e and \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan\u003e1\u003c/span\u003e HERE\u003c/p\u003e\n\u003cp\u003eFigure 2. HERE\u003c/p\u003e\n\u003cp\u003eAs seen in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, the relationship between the angle of incidence of rays and the spread occurring on the Earth's surface is as follows:\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1721831281.png\"\u003e(1)\u003c/h2\u003e\n \u003cp\u003eIn Eq.\u0026nbsp;(1), \u003cem\u003e𝑑k\u003c/em\u003e represents the area where solar rays spread on the Earth's surface, \u003cem\u003e𝑑g\u003c/em\u003e represents the area that solar rays have before encountering any surface, and θ is the angle of incidence. As shown in (a), when θ = 30° the area where solar rays spread is wider, while when θ = 90° the area where rays spread is narrower. Therefore, the amount of energy per unit area incident on the Earth's surface is inversely proportional to the area over which solar light spreads. For example, assuming that the energy of a beam of light is 10 J, and it spreads over surfaces of 10 units and 20 units separately; in the first case, 1 J (10/10) of energy falls on a unit area, while in the second case, 0.5 J (10/20) of energy falls on a unit area. The same situation can be applied to (b). Parallel incoming solar rays will spread over different areas at different angles depending on the shape of the surface they encounter, and accordingly, the energy transfer per unit area will vary inversely with the width of the surface.\u003c/p\u003e\n \u003cp\u003eThis research considers the difficulties and misconceptions in many studies conducted up to the present day and aims to create a different perspective on the teaching and learning the seasons topic to minimize misconceptions and learning difficulties in this area. In the study, explanations made regarding the teaching of the concept of seasons include: (a) explanations based on the tilt of the Earth's rotational axis, (b) explanations based on the Earth's revolution around the Sun, (c) explanations based on the Earth's spherical structure, (d) explanations using all three together, and (e) explanations based on the angle of incidence of solar rays parallel to the Earth's surface as it revolves around the Sun, taking into account CAEFUS on the Earth, have been separately and collectively examined.\u003c/p\u003e\n \u003cp\u003eThe study investigated the level of explanation students could provide at various stages before and after education with models and their relationships with scientifically accurate explanations of seasons. In this study, which aimed to ensure an understanding of how seasons occur rather than what they are, factors affecting CAEFUS, which is the fundamental reason for understanding seasons, were addressed. A physical model was used to teach these factors at the level of comprehension and higher cognitive domain stages.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e3\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eAll factors contributing to the formation of seasons should be scientifically explored and students should be encouraged to ground them on a scientific basis. As illustrated in conceptual further framework Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e, drawn based on the angle of the Sun's rays with concerning's surface, in accordance CAEFUS.\u003c/p\u003e\u003c/div\u003e\n\n"},{"header":"Method","content":"\u003cp\u003eThis study employed a quasi-experimental method with two samples (Sönmez \u0026amp; Alacapınar, \u003cspan\u003e2013\u003c/span\u003e). The sample consisted of 148 (72 experimental group + 76 control group) 8th-grade students attending a public school affiliated with the Ministry of National Education in Samsun province. The students included in the experimental and control groups were determined using a simple random sampling method. a\u003c/p\u003e\u003cp\u003ePre-test and post-test measurements were administered to the sample groups (before and after instruction and the retention test). Both qualitative active and quantitative measurements were utilized during the implementation phase of the study. In the qualitative dimension of the research, in-depth examination was conducted using an open-ended question scale to identify situations and context-dependent themes.\u003c/p\u003e\u003cp\u003eThe study's main theme is to assist in structuring mental models related to CAEFUS. The change in mental models can be determined by measuring the development of practical problem-solving skills. Therefore, the data collection process used a mixed-method (MM) approach. The stages of the research process are outlined in Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFigure \u003cspan\u003e4\u003c/span\u003e. HERE\u003c/p\u003e\u003ch2\u003eStages of Developing the Seasons Model\u003c/h2\u003e\u003cp\u003eDuring the development of the seasons model, the concept of \"the amount of energy falling onto a unit surface depending on the angle of the Sun's rays with the Earth's surface\" was planned to be taught. The model was constructed according to this plan.\u003c/p\u003e\u003cp\u003e• Scientific information regarding the Sun and the Earth was collected, and the scaling in the model was calculated. Information regarding the distance between the Earth and the Sun is provided in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e• Different positions (significant dates) were determined on the model to illustrate the Earth's orbit around the Sun, and a fixed but practical model was chosen instead of a movable one.\u003c/p\u003e\u003cp\u003e• Since it was impossible to create the actual sizes of the Sun and the Earth within the same model, three powerful light-emitting sources were used instead of the Sun.\u003c/p\u003e\u003cp\u003e• Four Pilates balls with a diameter of 85 cm, representing the Earth's four seasons, were drawn with latitudes and longitudes, and fixed systems were created to determine Turkey's position on the Earth.\u003c/p\u003e\u003cp\u003e•The axes of the Earth were positioned to indicate the North Star and were fixed on supports at a height of one meter. Essential dates were written on the supports, and the positions and approximate distances of the Earth on these crucial dates were calculated.\u003c/p\u003e\u003cp\u003e• Three identical (with a capacity of 1000 meters) vertical lights were used in the same direction through the centers of the four Earth models (considering the distances to the Sun). There was a gap of 13 cm between the lights.\u003c/p\u003e\u003cp\u003e• The lights were positioned so that on March 21 and September 23, they would fall on the Tropic of Cancer at the top, the Equator in the middle, and the Tropic of Capricorn at the bottom.\u003c/p\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eActual and Approximate Distances between the Sun and the Earth\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eActual Distance (approximately)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eScaled Distance Between Sun and Earth\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMarch 21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ebetween 147,000,000–152,000,000 km\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e139 cm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eJune 21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ebetween 147,000,000–152,000,000 km\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e140 cm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eJuly 4 (Aphelion)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e152,000,000 km\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e142 cm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSeptember 23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ebetween 147,000,000–152,000,000 km\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e139 cm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDecember 21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ebetween 147,000,000–152,000,000 km\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e138 cm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 3 (Perihelion))\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e147,000,000 km\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e137,3 cm\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.HERE\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Collection Tools\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ea) Solar and Earth Movements Achievement Test (SEMAT),\u003c/p\u003e\u003cp\u003eb) Solar and Earth Movements Open-Ended Questionnaire (SEOUQ)\u003c/p\u003e\u003cp\u003eOne open-ended question was directed to each class for the achievement test administered to students. The analysis of these questions was conducted independently of the multiple-choice questions. The researchers developed the data collection tools used in the study. Pilot studies were conducted. The reliability coefficient (pj) was determined to be 0.64, and the validity coefficient (rjx) was 0.68. Accordingly, although the scale is easy, its discriminative power is excellent (Gönen et al., 2011; Özçelik, 2010; Karip, 2009).\u003c/p\u003e\u003cp\u003eIn addition, to determine the student's level of knowledge regarding the research topic and better explain the teaching process's effectiveness, the question \"Explain how the seasons occur\" was directed as an open-ended question.\u003c/p\u003e"},{"header":"Data Analysis","content":"\u003ch2\u003eAnalysis of Quantitative Data\u003c/h2\u003e\n\u003cp\u003eSEMAT was administered twice to the students: once before the application and then again 8 weeks after. Before the application, the classes were randomly classified as control and experimental groups. The same scale was repeated for both groups before and after the application with equal time intervals and conditions. The analyses were conducted using the SPSS 22.0 package program. As the number of students in the experimental and control groups was more than 30 and ensured normal distribution, parametric analysis methods were decided to be used (B\u0026uuml;y\u0026uuml;k\u0026ouml;zt\u0026uuml;rk, \u003cspan\u003e2011\u003c/span\u003e). In the study, the mixed-design ANOVA technique was used to determine the astronomy achievements of students in the experimental and control groups.\u003c/p\u003e\n\u003ch2\u003eAnalysis of Qualitative Data\u003c/h2\u003e\n\u003cp\u003eThe answers given by students to open-ended questions were categorized as scientifically correct, scientifically partially correct, and scientifically incorrect. Students who made an entirely correct explanation by drawing correctly on the diagram were coded as scientifically correct; students who made a partially correct explanation by providing incomplete explanations on the drawing or only the explanation were coded as scientifically partially correct; students who made both the drawing and the explanation wrong were coded as scientifically incorrect. The percentages and frequencies of the obtained data were determined. Changes in students\u0026apos; mental models, determined by their drawings for 3 tests before and after the application, were analyzed.\u003c/p\u003e\n\u003ch3\u003eFindings and Interpretation\u003c/h3\u003e\n\u003cp\u003eThis section focuses on the findings resulting from the conducted analyses. The findings are organized and presented to address the sub-objectives. Visual elements such as tables, figures, and graphs are used in presenting the findings.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e1. What is the effectiveness level of the Solar-Earth model developed within the scope of the research in teaching the concept of the formation of seasons to students?\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2. What is the impact of the Solar-Earth model developed within the scope of the research on the sustainability of student achievement levels regarding seasons?\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eFindings Related to SEMAT\u003c/h2\u003e\n \u003cp\u003eIn this stage, the pre-test results of the experimental and control groups were evaluated using one-way ANOVA for unrelated samples. Descriptive statistics for the pre-test results of students\u0026apos; astronomy achievements are presented in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive Statistics for SEMAT\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArithmetic Mean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\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\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e highlights the remarkably close pre-test mean scores of the experimental and control groups. This led to the conduct of one-way ANOVA to assess the score differences between the experimental and control groups formed according to students\u0026apos; grade levels. The results of the analysis are presented in detail in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOne-way ANOVA Results for SEMAT Pre-test Scores of Experimental and Control Group Students by Class\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource of Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\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\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58,979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,843\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\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60,720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\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 \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e, there is no significant differentiation before experimental application (=\u0026thinsp;1.741, p\u0026thinsp;\u0026gt;\u0026thinsp;.05). This result indicates that the groups\u0026apos; achievements related to the concepts and events under study were equivalent before the intervention.\u003c/p\u003e\n \u003cp\u003eTo assess whether there is a significant difference in the achievement levels between the experimental and control groups, one-way analysis of variathe nce (ANOVA) technique was used for the measurements. Descriptive analysis A df the experimental group students\u0026apos; SEMAT pre-test, post-test, and retention test results is presented in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. This table will assist us in visually understanding the analysis results.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive Statistics for SEMAT Pre-test, Post-test, and Retention Test Results of Experimental Group Students\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArithmetic Mean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\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\" rowspan=\"3\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e, it is observed that there is an increase in the SEMAT achievement of the experimental group in the post-test and a slight further increase in the retention test. The highest achievement score is determined in the retention test, followed by the post-test, and the lowest in the pre-test.\u003c/p\u003e\n \u003cp\u003eWhen examining the averages of the control group, it is observed that student achievement increased in the post-test similar to the experimental group, and slightly more in the retention test. However, when looking at the averages, while the pre-test average of the experimental group was lower than that of the control group, the average of the experimental group showed a significant increase in favor of the experimental group in the post-test and retention test.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e5\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, the average score on the post-test and retention have increased in the experimental group of students. Five questions were asked. The average score was 1.o2 on the pre-test, which increased to 3.o1 on the post-test, and the average rose to 3.o6 on the retention test. Findings regarding the statistical significance of the data obtained from the tests are provided in Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eOne-way ANOVA Results for Students\u0026apos; SEMAT Achievement Scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource of Variance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026eta;2\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\" rowspan=\"3\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-Post-Retention test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100,700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91,396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246,802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,102\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448,203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226\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\" rowspan=\"3\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-Post-Retention test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90,891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45,445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36,809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e287,664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,235\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e235\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 \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe pre-test results of the control group students showed an average score of 1.82. This average increased to 2.87 in the post-test. The retention test average was 3.30.\u003c/p\u003e\n \u003cp\u003eA one-way repeated measures ANOVA was conducted to determine whether there was a significant difference in the SEMAT achievement scores of the experimental group students. The data for this ANOVA are provided in Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e shows a significant difference between the pre-test and post-test scores of the experimental group (F\u0026thinsp;=\u0026thinsp;91.396, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). These results indicate that the teaching model used was effective in instruction and led to a significant differentiation. They were additionally, based on the eta squared (\u0026eta;\u0026sup2;) value, it can be said that the teaching with the Solar-Earth models developed within the scope of the research had a considerable impact on students\u0026apos; SEMAT achievements (\u0026eta;\u0026sup2; = 0.449).\u003c/p\u003e\n \u003cp\u003eIn the control group, there is also a significant difference between the pre-test and post-test scores (F\u0026thinsp;=\u0026thinsp;45.445, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). These results also indicate that the teaching model used was effective in instruction and led to a significant differentiation. Moreover, according to the eta squared (\u0026eta;\u0026sup2;) value, the impact of the teaching method used on students\u0026apos; SEMAT achievements can be considered moderate (\u0026eta;\u0026sup2; = 0.240).\u003c/p\u003e\n \u003cp\u003eEta squared is a statistical value that measures the effect of an independent variable on the dependent variable. It expresses the percentage of the total variance in the dependent variable explained by the independent variable and usually falls within the range of 0.00 to 1.00. Eta squared helps us determine the effect size for each main effect, interaction, and error in ANOVA analyses. These statistics allow us to assess the magnitude of the independent variable\u0026apos;s effect on the dependent variable without the need for linearity assumptions. Additionally, values such as .02, .13, and .26 typically represent small, medium, and large effect sizes, respectively (B\u0026uuml;y\u0026uuml;k\u0026ouml;zt\u0026uuml;rk, \u003cspan\u003e2011\u003c/span\u003e). Eta squared assists researchers in better understanding their data and the number of independent variables involved.\u003c/p\u003e\n \u003cp\u003eThe sources of differences in the mean scores of the pre-test and post-test students in the experimental group were determined through pairwise comparisons, and the results are presented in Table\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e. Pairwise comparisons (Bonferroni) were conducted after one-way ANOVA to identify this finding.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePairwise Comparisons of SEMAT Achievement Scores of Experimental Group Students\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\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\" rowspan=\"6\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,003**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0,146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTablo 7. ANOVA Results for SEMAT Achievement Scores of Experimental and Control Group Students\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026eta;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e321,298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eGroup (Experimental-Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e15,642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e15,642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003cp\u003e7,625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e0,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e0,214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e305,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e2,051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e458.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eMeasurement (Pre-Post-Retention)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e228,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e114,470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003cp\u003e168,445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e0,337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eGroup*Measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e27,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e13,671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003cp\u003e20,117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e0,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003cp\u003e0,450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e202,512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e0,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.58940397350993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003cp\u003e779,491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119205298013245%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.079470198675496%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.920529801324504%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.105960264900663%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable \u003cspan\u003e6\u003c/span\u003e.HERE\u003c/p\u003e\n \u003cp\u003e.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026le;\u0026thinsp;.10 Marginal significance\u003c/p\u003e\n \u003cp\u003e*.01\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026le;\u0026thinsp;.05 Statistically significant\u003c/p\u003e\n \u003cp\u003e**.001\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026le;\u0026thinsp;.01 High level of statistical significance\u003c/p\u003e\n \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.001 Very high level of statistical significance (Akbulut, \u003cspan\u003e2022\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e shows a very high level of statistically significant difference between the pre-test and post-test scores of the experimental group (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Considering the average scores, this difference favors the post-test. In light of these results, teaching with the Sun-Earth model developed within the research scope effectively improves student achievement. When the difference between this group\u0026apos;s pre-test and retention test scores is examined, a very high level of statistically significant difference is again determined (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Considering the averages, the difference favors the retention test. Additionally, a high level of statistically significant difference is found between the post-test scores and the retention scores of the students (001\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026le;\u0026thinsp;.01). Considering the averages, the difference favors retention. When all findings are considered, it can be said that the teaching with the Sun-Earth model developed within the scope of the research serves its purpose.\u003c/p\u003e\n \u003cp\u003eIn the control group, while a very high level of statistically significant difference is found between the pre-test and post-test scores (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), considering the averages, this difference favors the post-test. When the difference between the pre-test and retention scores of this group is examined, a very high level of statistically significant difference is also found (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Considering the averages, the difference favors the retention test. Additionally, a statistically significant difference is found between the post-test and retention scores of the students (01\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026le;\u0026thinsp;.05). Considering the averages, the difference favors retention. When all findings are considered, it can be said that teaching with the Sun-Earth model developed within the scope of the research serves its purpose.\u003c/p\u003e\n \u003cp\u003eThe achievements of the experimental and control groups in the applied tests were examined within each group. Following this examination, comparisons were made between the groups and the measurements to determine (if any) the differentiation between the teaching method with the physical models developed within the scope of the research and the teaching methods used in the National Education curriculum. For this purpose, a two-way analysis of variance technique for mixed measurements was used to compare the measurements and the groups.\u003c/p\u003e\n \u003cp\u003eWe examined the findings related to the change in the groups\u0026apos; SEMAT scores, and the results of the two-way analysis of variance are given in Table\u0026nbsp;7.\u003c/p\u003e\n \u003cp\u003eTablo 7. HERE\u003c/p\u003e\n \u003cp\u003eAccording to Table\u0026nbsp;7, the results obtained from the achievement tests applied to the groups show a significant difference between the groups without separating the achievement scores into pre-test, post-test, and retention test (F\u0026thinsp;=\u0026thinsp;7.625, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). Additionally, this difference is also determined separately for each of the three tests when evaluating achievement between the groups (F\u0026thinsp;=\u0026thinsp;168.445, p\u0026thinsp;\u0026lt;\u0026thinsp;.05). These results indicate that the teaching methods and techniques used in both groups affect achievement. A post-hoc (Scheffe) multiple comparison analysis was conducted to determine which specific pairs of subgroup mean scores contribute to this difference. The results of this analysis are presented in Table\u0026nbsp;\u003cspan\u003e8\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan\u003e8\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eTable 8. Post-Hoc (Scheffe) Results for Experimental and Control Groups\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.306930693069308%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.646864686468646%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.656765676567655%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.006600660066006%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.306930693069308%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.646864686468646%\" valign=\"top\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.656765676567655%\" valign=\"top\"\u003e\n \u003cp\u003e1,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.006600660066006%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.10838445807771%\" valign=\"top\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.881390593047033%\" valign=\"top\"\u003e\n \u003cp\u003e1,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.83640081799591%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.631901840490798%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.306930693069308%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePos-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.646864686468646%\" valign=\"top\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.656765676567655%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.006600660066006%\" valign=\"top\"\u003e\n \u003cp\u003e3,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.10838445807771%\" valign=\"top\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.881390593047033%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.83640081799591%\" valign=\"top\"\u003e\n \u003cp\u003e2,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.631901840490798%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.306930693069308%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetention testi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.646864686468646%\" valign=\"top\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.656765676567655%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.006600660066006%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.841584158415841%\" valign=\"top\"\u003e\n \u003cp\u003e3,76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.10838445807771%\" valign=\"top\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.541922290388548%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.881390593047033%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.83640081799591%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.631901840490798%\" valign=\"top\"\u003e\n \u003cp\u003e3,03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eWhen analyzing the data, a significant result favoring the experimental group is observed across all three applications. For a more precise interpretation of this change, refer to Fig. \u003cspan\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan\u003e6\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eUpon examining Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e, an increase is observed in both groups in the comparisons between the pre-test and post-test and between the post-test and retention test. However, upon closer inspection, it is evident that although the experimental group had lower scores than the control group in the pre-test, they exhibited a higher increase in the post-test than the control group. Similarly, there was a more significant increase in the retention test in the experimental group compared to the control group. It can be inferred that the difference between the two groups arises from these instances.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eFindings of Descriptive Analysis of Students\u0026apos; Responses to Open-Ended Questions\u003c/h2\u003e\n \u003cp\u003eAt the end of SEMAT, each class was asked an open-ended question related to the Science program\u0026apos;s curriculum, which was the subject of the research. The primary aim of these questions was to evaluate the effectiveness of the teaching model from a different perspective. For this purpose, the responses of systematically selected students from each class to the open-ended questions were examined individually.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eFindings of Descriptive Analysis of Students\u0026apos; Responses to Open-Ended Questions\u003c/h2\u003e\n \u003cp\u003eThe scope of the open-ended question prepared for students is about how the seasons are formed. To ensure objectivity in the study, responses given by ten students systematically selected from each group were meticulously analyzed for the pre-test (P), post-test (S), and retention test (R). Attention was paid to systematic selection by skipping every eighth student, starting from the first student selected. The responses provided by the students are presented in Table\u0026nbsp;\u003cspan\u003e9\u003c/span\u003e (\u0026Ouml;: Pre-test, S: Post-test, K: Retention-test, YANITSIZ: UNANSWERED, Aralık: Dec, Eyl\u0026uuml;l: Sep, Mart: March, Haziran: June, G\u0026uuml;neş: Sun, D\u0026uuml;nya: Earth, İlkbahar: Spring, Sonbahar: Autumn/fall, Kış: Winter, Yaz: Summer)\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e9\u003c/span\u003e. HERE\u003c/p\u003e\n \u003cp\u003eThe data analysis was conducted with a systematic selection of students to avoid random errors. Upon careful examination of the data, it is observed that the readiness of the groups before the teaching process was equal. The most significant change observed in the experimental group after the teaching process was the inclination of axes in their drawings. Upon closer inspection of the drawings, it was determined that the drawings of the experimental group were scientifically more accurate. The inclination of the axes, not observed in the drawings of the pre-test, was reflected in the post-test and retention tests.\u003c/p\u003e\n \u003cp\u003eThe results obtained from the two separate teaching processes for the experimental and control groups, based on their achievements in the study, are as follows:\u003c/p\u003e\n \u003cp\u003e1. The descriptive statistics of the experimental and control groups appear to be similar. Additionally, it was found that the classes\u0026apos; readiness before the teaching process was low.\u003c/p\u003e\n \u003cp\u003eIn the control group, although students referring to the inclination of axes were identified, their drawings indicated that they could not think in three dimensions, only at the level of learning from two-dimensional sources. Additionally, in the experimental group, some students\u0026apos; explanations of the formation of seasons, mentioning \u0026quot;sphericity\u0026quot; and \u0026quot;orbital motion,\u0026quot; indicate that they were able to reach levels of understanding, application, and even analysis beyond the level of recalling the formation of seasons, as they were able to reflect this in their drawings. Unlike multiple-choice test analyses, open-ended questions demonstrate that the explanations and drawings made by students in the experimental group on the formation of seasons were more straightforward compared to those of the control group. Moreover, in both groups, some students clearly did not understand the formation of seasons, but this number was higher in the control group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eIn the new curriculum of the Ministry of National Education, the use of models is recommended as one of the most effective methods for teaching astronomy concepts. However, the models used must be associated with reality as they significantly form students' mental schemas. In this context, a teaching process with models was conducted to determine the effect of the Sun and Earth model developed for the research on student achievement.\u003c/p\u003e\u003cp\u003eAslan and Doğdu (\u003cspan\u003e1993\u003c/span\u003e) stated that material usage facilitates students' perception of a subject, increases student engagement with exciting materials, and arouses a desire to conduct further research. Altıntaş (\u003cspan\u003e1998\u003c/span\u003e) determined that materials or models provide students with rich, colorful, lively visual and sensory learning environments.\u003c/p\u003e\u003cp\u003eThis research has shown that students quickly learned that the distance of the Earth from the Sun does not affect the formation of seasons through their explanations and visual representations of the nature of seasons. Galano (2016) similarly expressed that the most accessible distance does not affect the seasons. In another study, Trumper (\u003cspan\u003e2006a\u003c/span\u003e) found that elementary-middle-high school-university students stated that the seasonal change on Earth is due to the inclination of the axis. However, they could not explain the temperature difference between summer and winter. In another study, Henriques (\u003cspan\u003e2000\u003c/span\u003e) found that many students, while stating that the distance of the Earth from the Sun does not affect the formation of seasons, could not explain the reason for it. In this research, however, the higher success level of students in the experimental group in understanding the relationships between the inclination of the axis and the Earth's orbital motion with the nature of the seasons and depicting it indicates that the model CAEFUS is effective in student learning. Galano (2016) in his study stated that although they could understand the effect of the inclination of the axis and the orbital motion of the Earth on the nature of the seasons, they struggled to establish a relationship between the energy received by the Earth and the sunlight hitting the Earth's surface at different angles. Consequently, regarding (RQ1), the CAEFUS model influences students' explanations and visual representations of the nature of seasons.\u003c/p\u003e\u003cp\u003eIn addition, after models were used in the experimental group and textbook images and text were used in the control group, there was an increase in the level of achievement in both groups; however, this increase was in favor of the experimental group. Our findings indicate that understanding the mechanism underlying the nature of seasons, a tilted axis with a fixed direction in space, and the Earth's orbit around the Sun is the most challenging concept to grasp. Students in the experimental group developed different perspectives when interpreting the nature of seasons compared to students in the control group, as the model used was presented to students as free from conceptual misconceptions as possible. Understanding CAEFUS and discovering how the varying amount of energy creates seasons is critical to understanding the nature of seasons. Students who understood the effect of energy amount on the nature of seasons found it easier to grasp the Earth's axial tilt, the orbit around the Sun, and even its sphericity, as they expressed and depicted. Consequently, regarding (RQ2), students' explanations and visual representations of the nature of seasons shed light on the relationship between the transfer of solar energy and the Earth's surface area, thus affecting student achievement.\u003c/p\u003e\u003cp\u003eThe simultaneous occurrence of many physical events shapes the nature of seasons. For example, answers to questions such as how seasons would be affected if the Earth were not spherical, if there were no axial tilt, or if the Earth did not orbit around the Sun were provided through these models. Searching for answers to these questions while implementing the models allowed students to establish relationships between situations and current conditions. In this regard, (RQ3) the models used measuring and observing different conditions, thus affecting students' ability to analyze and imagine, enabling them to explain the nature of seasons meaningfully and express it visually.\u003c/p\u003e\u003cp\u003eThe nature of seasons is one of the most challenging concepts to grasp scientifically. In a study by Martin et al. (2023), attempts were made to teach students about the nature of seasons using both augmented reality and physical models. It was found that understanding the scientific basis of the subject was the most challenging part of learning for students. Numerous studies have reported similar findings on the nature of seasons (Danaia \u0026amp; McKinnon, \u003cspan\u003e2007\u003c/span\u003e; Frede, \u003cspan\u003e2008\u003c/span\u003e; Kavanagh \u0026amp; Sneider, \u003cspan\u003e2006\u003c/span\u003e; Tsai \u0026amp; Chang, \u003cspan\u003e2005\u003c/span\u003e). Therefore, a body of existing literature supports our research focusing on this aspect. The data and results obtained in our study are expected to serve as a foundation for future research and contribute to developing new models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study are not available in a digital format. However, these data can be made available to interested researchers upon reasonable request by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics \u0026amp; Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The protocol was approved by the Provincial Directorate of National Education in accordance with the \u0026quot;Ministry of National Education Directive on Permission and Implementation of Research and Research Support to be Conducted in Schools and Institutions\u0026quot; and the \u0026quot;approval of the relevant directorate dated 24.05.2019 and numbered 10294247.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe need for informed consent was waived by the Directorate of National Education.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkbulut, \u0026Ouml;. (2022). 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Doi: 10.1002/tea.10039\u003c/li\u003e\n\u003cli\u003eVan Loon, M. H., Dunlosky, J., Van Gog, T., Van Merri\u0026euml;nboer, J. J., \u0026amp; De Bruin, A. B. (2015). Refutations in science texts lead to hypercorrection of misconceptions held with high confidence. Contemporary Educational Psychology, 42, 39\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eWang, J., Guan, Y., Lixin, W., Guan, X., Cai, W., Huang, J., Wenjie, D., \u0026amp; Zhang, B. (2021). Changing Lengths of the Four Seasons by Global Warming. Geophysical Research Letters. https://doi.org/10.1029/2020GL091753.\u003c/li\u003e\n\u003cli\u003eWilson, M. D., Boag, R. J., \u0026amp; Strickland, L. (2019). All models are wrong, some are useful, but are they reproducible? Computational Brain \u0026amp; Behavior, 2, pp. 239\u0026ndash;241. https://doi.org/10.1007/s42113-019-00054-x.\u003c/li\u003e\n\u003cli\u003eYun, E. (2020). Review of trends in physics education research using topic modeling. Journal of Baltic Science Education, 19(3), pp. 388-400. https://doi.org/10.33225/jbse/20.19.388\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 9","content":"\u003cp\u003eTable 9 is available in the Supplementary Files section.\u003c/p\u003e"},{"header":"Pictures","content":"\u003cp\u003ePictures are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nature of seasons, astronomy education, physical model, mental model","lastPublishedDoi":"10.21203/rs.3.rs-4625007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4625007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, studies by education researchers indicate that, despite various instructional methods to enhance the conceptual understanding of seasons, the reasons for the formation of seasons and the processes involved are still not fully grasped. This research aims to investigate the fundamental reasons for the difficulties in comprehending the formation of seasons and to develop possible solutions. To achieve this goal, a physical model based on the \"Change in the Amount of Energy Falling onto a Unit Surface (CAEFUS)\" of parallel beams of sunlight as Earth orbits around the Sun with a tilted axis of approximately 23.5 degrees was developed and applied to participants, and data were collected. These instructional processes were applied to experimental and control groups. The research was conducted on 148 eighth-grade students in a state school in Samsun province. Exploratory factor analysis was used to analyze students' drawings and separate them into their most prominent elements. The quantitative data analysis, consisting of multiple-choice questions, utilized the SPSS 22.0 statistical program. The data analysis revealed that the CAEFUS-based model significantly impacted the meaningful teaching of the nature of seasons. These results were thoroughly evaluated through a detailed comparison with the literature.\u003c/p\u003e","manuscriptTitle":"Experimental Research on Enhancing Effectiveness in Teaching the Nature of Seasons","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-26 16:51:47","doi":"10.21203/rs.3.rs-4625007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-05T06:37:25+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"50677536051483764595913187536995793704","date":"2024-08-23T08:11:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227034834496468047758123658620736159584","date":"2024-08-23T06:15:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-22T09:21:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330694892317223569166333791937095944140","date":"2024-08-21T06:47:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278768670612966089600357275177793453581","date":"2024-08-21T05:49:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-05T00:38:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23971815076528103924351461624552898057","date":"2024-07-29T05:05:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-03T10:44:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-03T10:34:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-02T10:21:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2024-06-23T11:19:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"891c6b8d-dd28-426b-a5af-a5d6b375ed86","owner":[],"postedDate":"July 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-30T09:08:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-26 16:51:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4625007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4625007","identity":"rs-4625007","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}