TIMSS Science Achievement Profiles and Their Associations with Motivation, Instructional Quality Scales

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We investigated students' achievement patterns across science content and cognitive domain subscales using latent profile analysis. Based on the results of the analyses, the optimum number of profiles was determined as four. When the achievement profiles of the students were analyzed, it was determined that the mean scores of the life science and applying subscales of the students in the low achievement profiles were significantly higher than the other subscale scores. In high achievement profiles, it was observed that the life and physical science subscale scores were similar and higher than the Earth subscale. When the cognitive domains in high achievement profiles were investigated, the highest mean score was found for the knowing subscale, which was followed by reasoning and applying subscales. The study also conducted covariate analyses with demographical and teaching variables commonly assumed to be associated with achievement in the literature, such as gender, language, home resources for learning and instructional quality. We also investigated science motivation scales across the covariate analysis. According to the results of the study, students who sometimes or never speak German in their homes were classified in low achievement profiles compared to students who always speak German in their homes. Likewise, students who had few or some home resources for learning at home were also classified in lower achievement profiles. In addition, students who were very confident in science were categorized in high achievement profiles. Similarly, it was found that students with the high ratings of classroom management and constructive support were classified in higher achievement profiles. However, even though the students were classified in the low achievement profile, it was found that they rated the teaching highly cognitive activating and liked learning science. Profile Analysis Achievement Profiles Motivation Home Resources for Learning Instructional Quality Introduction Basic science competences among students are considered important in order to meet the various challenges of the 21st century. Student´s early science achievement is predictive for their science competences later on (Morgan et al., 2016 ). As basic science competences are relevant prerequisites for social participation promoting the development of science competences in school is important. Especially against the background that science achievement gaps begin very early and are persistent (Morgan et al., 2016 ), it requires a closer investigation of student´s science competences. For this reason, science achievement of students and causal relationships between science achievement and different background variables that may contribute to science achievement have been the focus of interest of many researchers. Many researchers have analyzed science achievement and the predictors of science achievement using a variety of approaches, especially with large-scale assessments using data from a wide range of heterogeneous groups. Traditional, variable-centered correlation approaches (e.g., ANOVAs, correlation) were used in most of these studies. However, these approaches have serious limitations in appropriately characterizing heterogeneity and complex, non-linear learning models (Hickendorff et al., 2018 ). These approaches, which emphasize the relationship between variables (Bergman et al., 2003 ), assume that the relationship between variables can be applied to all students in the same way (Hickendorff et al., 2018 ; Michaelides, 2019). In other words, these approaches assume that all individuals in the sample belong to a single profile or population and that there is no differentiation between latent subgroups (Ferguson et al., 2019). By contrast to variable-centered approaches, person-centered approaches are not constrained by linear patterns and the interactions between variables can be modeled as heterogeneous (Bergman & Magnusson, 1997 ; Hickendorff et al., 2018 ). Latent profile analysis (LPA) and latent class analysis (LCA) are person-centered approaches. These approaches include methods that identify latent groups in the data by examining the distribution of groups in the data and determining whether these distributions are significant based on the possibility that individuals belong to different groups (Ferguson et al., 2019). Various application frameworks for latent profile analysis have been developed by different researchers. For example, Bauer ( 2022 ) classified the implementation stages of latent profile analysis as follows: specification of the model, identification of latent classes, interpretation of the optimal profile solution and inclusion of predictors and/or outcomes of latent class membership. The last step is also the analysis of the latent profile using covariates and, once the optimal latent class solution has been obtained, the identification of variables (e.g. social background variables) that predict latent class membership (Vermunt & Magidson, 2002 ; Bauer, 2022 ). It has been highlighted in the literature that these person-centered methods can be used as an alternative to traditional variable-centered approaches, especially in the grouping of complex multidimensional data with heterogeneous patterns, with different application phases proposed by different researchers (e.g., Hickendorff et al., 2018 ; Vermunt & Magidson, 2002 ; Spurk et al., 2020 ; Bauer, 2022 ). Therefore, it has been particularly recommended to use these approaches in assessments with highly heterogeneous populations such as the Trends in International Mathematics and Science Study (TIMSS) and the Programme for International Student Assessment (PISA). For example, the LPA approach has been used in different studies to examine achievement profiles (e.g., Wendt & Kasper, 2016 ; Chen et al., 2021 ) and attitudinal profiles (e.g., Michaelides, 2019; Berger et al., 2020 ; Liou 2017 ) in large-scale assessment studies. According to the findings of these studies, for instance, background variables varied in terms of their predictive significance for various profiles. In this study, it is aimed to determine the achievement profiles of fourth grade German students in science using the content and cognitive domains in TIMSS 2019 and to analyze the relationship between different profiles and various variables whose impact on achievement is emphasized in the literature with variable or person-centered approaches. Various studies have already investigated different factors influencing students’ achievement and the results show that, in addition to individual background variables, characteristics of the home environment and teaching characteristics can have a positive influence on students’ achievement. In the home environment, these include, for example, the home learning environment (e.g., Melhuish, 2010 ; Morgan et al., 2016 ) and the quality of teaching for teaching characteristics (e.g., Baumert et al., 2010; Fauth et al., 2014 ). The students' home background has been examined by numerous researchers with TIMSS data for different countries and its clear impact on achievement has been emphasized by the results of these studies (e.g., Wiberg & Rolfsman, 2019 ; Erberber et al., 2015 ). The main finding from these studies was that students from high socio-economic backgrounds tend to perform better in school compared to students from low socio-economic backgrounds. In a similar study (Caponera & Losito, 2016 ), the relationship between context factors and achievement was examined by analyzing grade 8 students from 28 countries who participated in TIMSS 2011. According to the findings of the study, high socio-economic status had a positive effect on mathematics achievement. Students with lower socio-economic status tended to have lower mathematics achievement. Chen et al., ( 2021 ) examined the profile of the mathematics subscales in TIMSS 2011 and found that the U.S. students’ performance was poorer on the Geometry subscale than on other mathematics subscales. In the study they also found that the background variables (gender, age, home language, race, and preference for mathematics and science) significantly influenced the probability of being classified in the group with the lowest performance and the largest gap between geometry and other mathematics subscales. Bos et al. ( 2012a , b ) also examined model latent profiles of student achievement in reading, mathematics, and science for 4th-grade students in Germany. The authors found seven invariantly rank-ordered profiles based on the similarity of students’ cross-domain achievement and then further studied relationships with background variables, such as gender, cultural and socioeconomic characteristics. According to the results of the study, the authors found that gender differences only appeared in high achievement profiles and a significant relationship between achievement and socioeconomic background variables was found among almost all profiles. Motivation is one of the variables whose effect on achievement has been examined in various studies. Although it has been emphasized that students with high motivation tend to perform better, some studies have found that students with low motivational beliefs have high academic achievement. Other studies have emphasized that motivation and achievement have a differential relationship for high and low achievement profiles. Wang and Liou ( 2017 ), for example, used data on TIMSS 2011 Taiwanese eight-grade students and divided students into three student groups: total group, high-achieving group and low-achieving group. Self-concept positively predicted science achievement for the high-achieving group, but negatively predicted science achievement for the low-achieving group. In the total group, all motivational beliefs positively predicted science achievement. A similar study was conducted by Liou ( 2017 ). Liou ( 2017 ) examined the relationship between motivational beliefs in science learning and science achievement with TIMSS 2011 data from 26 countries. Despite the general tendency, Liou ( 2017 ) showed that the effect of motivational variables on science achievement differs between western and eastern cultures. Instructional quality profiles and variables are among the variables whose relationship with achievement and motivation in science has been highlighted in the literature with different TIMSS data. For example, Teig and Nilsen ( 2022 ) constructed different instructional quality profiles using Norwegian data from (TIMSS) 2015 (using Grades 5 and 9) and found that these profiles varied across different aspects of instructional quality in both grades, and that student characteristics, particularly language spoken at home and socioeconomic status, predicted student-level profile membership, whereas teacher efficacy (i.e., self-efficacy in science teaching) did not. Furthermore, the researchers emphasized that different teaching quality profiles are significantly related to motivation and, to a certain degree, to science achievement. Bellens et al. ( 2019 ) in a similar framework used TIMSS 2015 data from Belgium (Flanders), Germany, and Norway to examine the invariance of instructional quality variables across countries and the impact of students' socioeconomic status and language on achievement. In addition to this, they also stated that instructional quality variables can serve as a catalyst for increasing achievement in education systems. In addition to these studies that found the relationship between achievement and instructional quality to be positive and significant, Blömeke et al. ( 2016 ) also examined the relationship between the achievement of fourth grade students and the quality of teachers and instructional quality in TIMSS 2011 data (from 47 countries). According to the results of the study, teacher quality was significantly related to instructional quality and student achievement, whereas student achievement was not well predicted by instructional quality. Similarly, Scherer and Nilsen ( 2016 ) examined the relationships among school climate, instructional quality, and achievement, motivation in mathematics using TIMSS 2011 data from 50 countries. One of the findings from this study was that there was a significant positive relation between instructional quality and achievement motivation at the classroom level in mathematics. In summary, numerous studies have been conducted to examine the relationship between achievement and other variables and to identify which variables tend to be more predictive indicators of achievement. However, the extent to which these influencing factors lead to varying profiles has not yet been comprehensively researched and should therefore be investigated. In addition, the relationship between students’ science achievement and various variables is often addressed by statistical approaches based on correlational analyses, and these variable-centered analyses are usually applied assuming linearity in the relationships (Michaelides, 2019). For a helpful insight into students’ science achievement, it would be useful to also consider individual heterogeneity. Therefore, in-depth analyses are required here to get an individual perspective on each student. Against this background the present study aims to describe individual patterns of student achievement among the science content and cognitive domains subscales and to examine which background variables as well as students’ rating of instructional quality may contribute to student´s classifications in these profiles. Besides identifying students’ achievement profiles, because students’ academic competences are the results of a complex interplay between factors located at the country, the school, the classroom, and the student level and a lot of these factors shape inter-individual differences in student achievement in a rather similar way (Bergold et al. 2016 ), it is of great interest to find out why profiles may vary or why students may be grouped in different profiles and in which influencing factors or demographic variables do the student of different profiles differ. The implementation of LPAs requires a large sample and so far, there are few studies with data on students' science achievement, background variables, home learning environment and teaching quality. For instance, TIMSS provides detailed information about what students around the world know and can do in science in general and in specific science content areas such as physical science, life science and earth science (Martin et al., 2000 ) and also supply information about students’ background and ratings of teaching quality. Regarding science achievement, the assessment of TIMSS items offers the opportunity to compare the student's achievements in different content areas. Following the TIMSS conceptualization of science achievement, science achievement includes on the one hand the relevant content domains (e.g., life science, physical science, and Earth science), which assess science knowledge broadly with a focus on curricular content, and on the other hand central thought processes that form the basis for solving TIMSS tasks (Mullis et al., 2020 ). In TIMSS they are referred to as cognitive domain (Bloom, 1956) and describe the reproduction of knowledge, application of knowledge and problem solving. The cognitive skills were categorized into three broad domains knowing, applying, and reasoning in TIMSS (Mullis et al., 2020 ). The assessment of TIMSS items offers the opportunity to take a closer look at individual content areas and compare the student's achievements in different content areas. The results show that German students, who are the focus of this study, perform similarly to many other countries (Schwippert et al., 2020 ) and that the findings of this study could also apply to other countries. This study aims to examine the achievement and cognitive profiles in science using TIMSS 2019 4th grade data. The study also aims to determine how various student characteristics (students' demographic background variables, motivation) and instructional quality predict profile membership. Specifically, it is aimed to determine which variables used in the covariate analyses are meaningful for certain achievement profiles. The following questions are addressed in the study based on this aim. In which achievement profiles can German fourth graders be classified when using TIMSS 2019 science achievement subscales and science cognitive domains scores? What were the average scores of students in the life, physical and earth and knowing, applying and reasoning subscales in the optimal achievement profiles? What were the characteristics of the students classified in the high and low achievement profiles when gender, language and home resources for learning were analyzed as covariates? How do students classified in high and low achievement profiles differ in their interest in science and their self-concept levels in science? When the instructional quality scales (cognitive activation, classroom management and constructive support) are considered as covariates, which sub-scales of instructional quality significantly predict students' achievement and cognitive profiles? This article begins with a review of recent research on influence factors for students’ science achievement on the individual and teaching level. It then investigates students' achievement patterns across science content and cognitive domain subscales using latent profile analysis. In addition, covariate analyses with demographical and teaching variables commonly assumed to be associated with achievement in the literature, such as gender, language, home resources for learning and teaching quality were carried out. Science Achievement and its association with background variables Researchers emphasize the importance of the home environment for young students’ (science) competences (Melhuish, 2010 ; Morgan et al., 2016 ; Junge et al., 2021 ). For young students, the most important learning environment for various educational processes is the family or home context (Melhuish, 2010 ). This includes, for example, the interplay between structural characteristics, parental beliefs and attitudes, and educational processes (Melhuish, 2010 ; Junge et al., 2021 ). The home learning environment has a significant influence on student´s emotional and intellectual growth, school readiness, and their subsequent academic achievement (Sammons et al., 2015 ). With regard to science, research regarding secondary school students showed that parents’ attitudes towards science appear to be stable over time and presumably shape high school students’ perspective towards learning science as well as later science-specific career choices (Chen, 2001 ; Ferry et al.,2000). At the same time, studies indicated that parents were less involved in their children’s science learning in elementary and secondary school as compared, for example, to math and reading (Kaya & Lundeen, 2017 ; Shymansky et al., 2000 ). This could result in a higher variance in children’s science learning at home. The variation in the home learning environment might result in difference in student’s science achievement. Although domain-specific competences are assumed to be especially important for the development of domain-specific competences, also other competences and factors have been shown to influence them (e.g., Morgan et al., 2016 ; Edele & Stanat, 2011 ; Melhuish, 2010 ). For example, with regard to young children, it has been hypothesized that children's language skills play a central role in the development of skills in all other domains, as children acquire domain-specific skills through verbal interactions with parents, family, preschool teachers and school teachers (Justice et al., 2018 ). Consequently, language is a prerequisite for learning and at the same time also an object of learning as well as a medium of instruction, as noted by Prediger et al. ( 2018 ). Many studies already indicate that the language spoken at home can be particularly important for domain-specific achievement (e.g., Liang, 2010 ). Besides the importance of language as a prerequisite for learning and a medium of instruction, links between the language spoken at home and achievement are often linked to the fact that families speaking another language at home often are more likely to have a low families' socio-economic status and parents' level of education (Edele & Stanat, 2011 ). Related to this, student’s individual characteristics such as gender can also play a role. Gender differences in science have been explained by gender stereotype in previous work (e.g., Robnett & Leaper, 2013 ; Berger et al., 2020 ) as these stereotypes can influence an individual’s self-perception, motivation and experience in classroom (Plante et al., 2018). With regard to research results, for instance, it was showed by Ma ( 2022 ) that boys displayed significantly higher intrinsic interest in science, perceived competence in science and instrumental value of science than girls. Gender differences were also reported in a study by Britner ( 2008 ), even if the results varied depending on the content area. It is therefore conceivable that there are also gender differences in the science achievement of elementary school students. Students' motivation towards science achievement LPA of achievement allow to classify groups of students beyond those who are “high achiever” or those who are “low achiever”. These profiles, in turn, can be not only evaluated in terms of the quality of the selected classifications but also analyzed according to their subject-specific strengths and weaknesses as well as according to their relationships with individual background characteristics (Schurig et al. 2015 ). However, a strong focus of previous professional analyses with large-scale assessment data sets is on motivational-affective competences. For instance, Ma ( 2022 ) investigate with a TIMSS data set students’ profiles of attitudes toward science. Results of the latent profile analysis showed five distinct profiles of student attitudes toward science: (1) Negative attitudes, particularly toward perceived competence in science; (2) Negative attitudes, particularly toward instrumental value of science and engaging science teaching; (3) Moderate attitudes toward science; (4) Positive attitudes toward science; and (5) High-positive attitudes toward science. The most frequently represented profile in the sample of over 4000 students was profile 3. Berger et al. ( 2020 ) examined relationship between attitudes towards mathematics and science and they found six profiles with using data from Australian Grade 8 students sampled by TIMSS 2015. The study emphasized that positive attitudes towards both subjects are mutually beneficial and that high attitudes towards both subjects are associated with high achievement. The study also explored attitudinal differences between genders in latent profiles and found that boys tended to be more positive. Overall, profile analyses in science education show a strong focus on motivational-affective competences. Furthermore, there is little differentiation with regard to different content areas, although there are indications that there may be differences in competences (Britner, 2008 ; NCES, 2001). Consequently, there is still a lack of insight into profiles for science achievement. To get a broader and more authentic picture of student’s achievement patterns in science it is also important to investigate why profiles may vary and in which influencing factors or demographic variables do the students of different profiles differ. With regard to influencing factors, it can be assumed that other domain-specific skills have an influence here. Motivational-affective science competences in particular can be mentioned here, which have been linked to achievement in other studies and where results indicate that children who have higher motivational-affective science competences also perform better. For instance, Ma ( 2022 ) showed that students in profiles with higher levels of science attitudes tended to show better science achievement. Even if the relation is not perfectly linear, this finding indicates that science-specific motivational-affective competences such as attitudes, interest and motivation are positively linked to science achievement. Instructional Quality in science achievement A large body of research points to the importance of instructional quality for student’s achievement (e.g. Fauth et al., 2014 ; Neumann, Kauertz & Fischer, 2012 ; Senden, Nilsen & Teig, 2023 ). Instructional quality reflects teachers’ behavior in the classroom and is thus measured at the classroom. Instructional quality is conceptualized and measured in different ways. In the context of TIMSS, instructional quality is differentiated into the three basic dimensions Classroom Management, Cognitive Activation and Constructive Support (Praetorius et al., 2018 ). Classroom Management is the most generic dimension of instructional quality and refers to the teachers’ behavior and time management (Praetorius & Charalambou, 2018). Cognitive Activation describes how the teachers stimulates students’ cognitive activity (Baumert et al., 2010). Constructive Support includes characteristics that contribute to a learning environment that is conducive to learning and student-oriented. Research findings show positive relations between the three basic dimensions and students’ achievement and motivation in science (e.g. Fauth et al., 2014 ; Neumann et al., 2012 ; Teig et al., 2018). For instance, classrooms with students with high levels of science achievement are positively related to teachers, who effectively manage their classrooms (Fauth et al., 2014 ; Senden et al., 2023 ). In addition, studies showed students’ (ratings of) cognitive activation predicted their development of science-related interest and science achievement (Fauth et al., 2014 ; Teig et al., 2018). Thereby students who were provided with more cognitive activating instruction, for example by conducting scientific inquiry activities, achieved higher levels of science achievement (Teig et al., 2018). However, findings by a study by Lindermayer et al. ( 2024 ) indicate that there are different classroom profiles for instructional quality and that the instructional quality profiles differed significantly regarding students’ mathematics-related interest, intrinsic motivation, and achievement. Consequently, it is therefore conceivable that (perceived) instructional quality also might have an influence on the formation of achievement profiles in science. Methods Data Analysis In the study we analyzed TIMSS (2019) fourth-grade data to examine German students’ profiles of the achievement in the three science content domains (life science, physical science and earth science). We used latent profile analysis to explore student´s achievement profiles using data from 3447 German Grade 4 students from TIMSS 2019. The descriptive statistics of the data sets were presented in Table 1 in the results section. TIMSS provides five plausible values per content subscale and cognitive domains. In the study we used five plausible values of the science subscales (life science, physical science and earth science) and five plausible values of the cognitive domains (knowing, applying, reasoning) for exploring the latent profiles. In addition to the achievement profiles, we examined the effect of the students background variables on the class membership using LPA with covariates. Since the achievement profiles were created using 4th grade TIMSS (2019) data, background variables (gender, language) and the scale home resources for learning scale and for the fourth grade were used in the study. The home resources for learning scale (HLR), which is often emphasized in the literature as one of the important predictors of achievement, is one of the scales used in the covariate analyses in the study. The HRL scale consists of five variables (number of books in the home (students), number of childrens books in the home (parents), number of home study supports (students), highest level of education of either parent (parents), highest level of occupation of either parent (parents) (Martin & Mullis, 2012). The scale was created using these variables and in addition to the scale scores, an index was created for each scale using different cut values. For example, with the HLR index, three different categories (many, some and few resources) were created using different cut values. In study we used also "students like learning science" and "student confident in science scales" as covariate. Similar to the HLR scale, the index of these scales were used. Instructional quality scales (classroom management, cognitive activation, constructive support) were also used as covariates in the study. In TIMSS, instructional quality was measured using international scales, such as instructional clarity and supportive classroom climate (Yin & Fischbein, 2019). However, some countries, including Germany, extend this framework with more detailed subscales, such as classroom management, cognitive activation, and constructive support, to provide a more in-depth analysis of teaching practices (Schwippert et al., 2020 ). For more detailed information on the reliability of these scales, refer to Yin and Fischbein (2019) for the international scales and Schwippert et al. ( 2020 ) for the national adaptations. In addition to the scales, the demographic variables were incorporated as covariates in all latent profile models. In the supplementary file, the descriptive statistics of the scales and variables that were used as covariates in the study were provided. Covariates In the last part of the study, we examined the effects of dummy variables that were constructed from the index categories of the home resource for learning scale and motivational scales, instructional quality scales and demographic variables (gender, language), on the classification of profiles. The first variable examined was the gender variable and male were used as the baseline category. Therefore, it was evaluated whether the categorization of female students in the profiles was statistically significant compared to male students. The second covariate variable was the language variable, where the category "always speak language" was created as the reference or baseline variable. In other words, we examined the classifications in the profiles of students who often, sometimes and never speak German compared to students who speak German always at home. For the "Home resources for learning" scale, the baseline category “many” was taken as the reference category. In other words, the HLR categories were analyzed in the profiles of students with few and some resources for learning. The instructional quality scales (cognitive activation, classroom management, constructive support) indices have also three categories (low, medium, high) and we were identified “high” category as baseline category and the “low” and “medium” categories have identified as reference categories. Similarly, for the "students like learning science" scale, the category "very much like learning science" and for the "student confident in science" scale, the category "very confident in science" were identified as baseline categories. Reference categories were determined for all these variables in accordance with the purpose of the study and to avoid the challenges of statistical interpretation and difficulties in estimation that the "rare" category may cause. Detailed descriptive statistics for the created dummy categories and variables were presented in the supplementary file. Results Descriptive statistics The results of the descriptive statistics of each subscale are displayed in Table 1 . According to the results of the statistics, the mean score of the life science subscale was substantially higher and the mean score of the earth science was lower than other subscales. In addition, when the scores related to cognitive domains were examined, it was seen that the average scores of German fourth grade students in knowing (519.653) and reasoning (518.469) were similar, but the average score in applying (515.952) was smaller compared to other cognitive domains. The plausible values we obtained with our analyses were consistent with the plausible values given in the TIMSS 2019 reports (Mullis et al., 2020 ). Table 1 Descriptive statistics among the life science, physical science and earth science subscales (weighted) Subscales (Science) N Weighted Mean Min. Max. Median SD Life 3437 708968.3 521.427 210.785 751.496 527.909 79.543 Physical 3437 708968.3 518.375 195.991 784.152 523.722 82.879 Earth 3437 708968.3 508.928 186.246 786.489 541.555 86.235 Knowing 3437 708968.3 519.653 176.695 780.531 525.384 82.997 Applying 3437 708968.3 515.952 225.775 735.963 521.394 77.494 Reasoning 3437 708968.3 518.469 171.937 750.521 524.324 83.077 Results of the Model Fit In addition to the results of the descriptive statistics, the results of the goodness of model and entropy indices were provided in the Table 2 . The smaller values of the fit statistics used for the appropriate number of the classes and also for entropy, values of 0.80 and above are considered acceptable for the classification of individual cases into appropriate classes. According to the results, since the entropy values were similar and around 0.91, we decided to look at the AIC and BIC and SABIC goodness of fit statistics to decide on the optimum number of profiles. When the results were analyzed, there were decreasing trends in all goodness of fit statistics for classes 2 and 3. However, from class 4 onwards, the differences in the fit statistics were not very substantial. In addition to the fit statistics, we also examined the differences in the mean scores of the students in the profiles. Therefore, the optimum number of profiles was set to 4. Table 2 The results of the model fitting indices under each classification (weighted). Number of Classes AIC BIC SABIC Entropy 2 227678.602 227795.307 227734.935 0.912 3 221830.350 221990.051 221907.437 0.912 4 218651.137 218853.834 218748.978 0.907 5 217039.839 217285.533 217158.435 0.902 6 216048.161 216336.851 216187.510 0.893 7 215494.739 215826.426 215654.842 0.878 8 215148.103 215522.786 215328.960 0.867 9 214837.049 215254.729 215038.661 0.857 AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; SABIC: sample size-adjusted BIC In addition to these results, the means of the subscales from the three- to five-class models were reported in Table 3 . When the means of the subscales in the first three-class model were analyzed, it was observed that the life science subscale had the highest mean (387.677, 474.933, 546.603), but the mean of the physical science subscale in the fourth profile (615.731) was higher than the life and earth science subscales. In other words, the highest mean for the most successful profile belongs to the physical science subscale and the lowest to the earth science subscale. When the subscales of the cognitive domains were examined, it was observed that in the first profile, in other words, in the lowest achievement profile, the applying subscale (387.210) had the highest mean scores, while knowing (380.353) and reasoning (381.913) subscales had similar values. In the second profile, the mean scores of all subscales were similar (knowing: 470.964, applying 469.057, reasoning 470.807). When the mean scores of the cognitive domains subscales in the third profile were examined, it was found that the highest mean scores were obtained for the knowing subscale (545.487) and the lowest mean scores were obtained for the reasoning subscale (543.103) and the lowest mean scores were obtained for the applying subscale (539.875). When the mean scores of the cognitive domains’ subscales of the students of the most successful profile were investigated, it was seen that the highest mean scores were obtained for knowing (619.656) and reasoning (614.781) subscales. The lowest mean scores were observed for applying (609.949) subscale. The International Benchmark defined by TIMSS (Mullis et al., 2020 ) refers to achievement at four points along the science scale: Advanced International Benchmark (625), High International Benchmark (550), Intermediate International Benchmark (475) and Low International Benchmark (400). When the mean scores for the science subscales were examined, it was seen that the life, physical and earth science subscale scores in profile 1 were below the low international benchmark (400), subscale scores in profile 2 were below the intermediate international benchmark (475) and subscale scores in profile 3 were above the intermediate level. The subscale scores in profile 4 were above the high international benchmark (550). Students were mostly classified in the second (N = 1017) and third profiles (N = 1275). The highest proportion of the students has been classified in the profile 3 (N = 1275) and these members performed above the intermediate benchmark (475) and below the high international benchmark for all three science domains. Table 3 The means of the subscales from the three- to six-class models (weighted). Classes Subscales Class 1 Class 2 Class 3 Class 4 Class 5 Three Life 410.317 510.640 596.649 Physical 405.973 506.740 595.546 Earth 394.179 495.660 589.664 Knowing 404.807 507.544 599.270 Applying 408.481 503.954 590.762 Reasoning 405.453 506.572 595.188 N 698 1536 1203 Prop. 0.203 0.447 0.350 Four Life 387.677 474.933 546.603 615.265 Physical 382.849 471.462 542.682 615.731 Earth 374.115 457.518 534.419 610.025 Knowing 380.353 470.964 545.487 619.656 Applying 387.210 469.057 539.875 609.949 Reasoning 381.913 470.807 543.103 614.781 N 418 1017 1275 727 Prop. 0.122 0.296 0.371 0.212 Five Life 363.306 440.641 504.227 564.826 626.466 Physical 359.024 436.919 500.294 561.517 627.453 Earth 352.412 422.459 488.637 554.224 622.474 Knowing 355.239 435.327 501.253 564.552 632.620 Applying 363.981 435.957 497.891 558.009 622.098 Reasoning 355.672 436.475 500.166 561.715 627.087 N 226 622 1004 1083 502 Prop. 0.066 0.181 0.292 0.315 0.146 Results of Covariate Analysis Finally, covariate analyses were conducted and the student variables were included as covariates in the models. When the results of the covariate analysis examined, some variables showed statistically significant differences between the profiles. In the study, the results of covariate analyses based on reference categories as well as reference profiles were presented in separate tables. For example, Table 4 provides the estimates of regression coefficients and odds ratios for the variables considered as covariates when the reference profile is 4, in other words, when the most successful student profile was considered as the reference profile. In addition, predicted regression coefficients and odds ratios for the other profiles were presented in the supplementary file. In Table 4 , the first column shows the regression coefficients that were estimated for each categorical latent variable on the predictor variable. The second column provides odds ratios that allow a better interpretation of the results. We also examined the statistical significance of the estimated coefficients using the p-values provided in the Mplus output for the regression coefficients and the 95% confidence interval (CI) values provided in the Mplus output for the odds ratios. If the 95% confidence interval does not include 1, it is taken as evidence that the odds ratio is statistically significant. This is because odds ratios that do not include 1 indicate a significant change in probabilities. For example, an odds ratio of 1.5 with a 95% confidence interval of (1.2, 1.8) indicates that the event is 1.5 times more likely to occur, and this difference is statistically significant because the interval does not include 1. Table 4 shows the regression coefficients and odds ratios for profile 1 while the reference profile is 4. Firstly, when the gender variable was examined, it was observed that since the reference category was male, female students had positive regression coefficients in profiles 1, 2, and 3 compared to profile 4. In other words, female students were categorized more in other profiles while the reference profile was 4 compared to male students. However, this difference was statistically significant only when comparing profile 2 with profile 4. Similarly, odds ratios were 1.278 when comparing profile 1 with profile 4 and were 1.331 when comparing profile 2 with profile 4, respectively. When Profile 3 with Profile 4 compared odds ratios were obtained as 1.161. Table 4 The Latent profiles with covariate variables (weighted). Profil 1 vs. 4 Profil 2 vs. 4 Profil 3 vs. 4 Covariates \(\:{\varvec{\beta\:}}_{1}\) \(\:{\varvec{\beta\:}}_{1}\:\varvec{O}\varvec{R}\) \(\:{\varvec{\beta\:}}_{1}\) \(\:{\varvec{\beta\:}}_{1}\:\varvec{O}\varvec{R}\) \(\:{\varvec{\beta\:}}_{1}\) \(\:{\varvec{\beta\:}}_{1}\:\varvec{O}\varvec{R}\) Gender 0.245 1.278 0.286* 1.331 0.149 1.161 Language 1.308** 3.697 0.723** 2.060 0.240 1.272 Home Resource Learning 2.951 19.120 1.782** 5.944 0.865** 2.374 Students Like Learning Science -0.003 0.997 -0.413* 0.662 -0.371* 0.690 Students Confident in Science 1.873** 6.507 1.302** 3.678 0.789** 2.200 Cognitive Activation -0.799** 0.450 -0.391** 0.676 -0.275 0.760 Classroom Management 0.599** 1.820 0.267 1.306 0.094 1.099 Constructive Support 0.372 1.451 0.418* 1.518 0.252 1.286 \(\:{\beta\:}_{1}\) is indicated the regression coefficients and β 1 OR is indicated the odds ratios. Bold values indicate If the 95% confidence interval includes 1, it suggests that the effect is not statistically different from 1 (no effect). **p < .05, **p < .01. When the second variable, language, was examined, it was observed that since the reference category was students who always spoke German at home, students who sometimes and never spoke German at home had statistically significant regression coefficients and odds ratios between the profiles. Similarly, when the values given in Table 4 were evaluated, it was seen that the regression coefficient was 1.308 (p < .01) when profile 1 was compared with profile 4, it was 0.723 (p < .01) when profile 2 was compared with profile 4, and the coefficient was 0.240 when profile 3 was compared with profile 4. The only non-significant regression coefficient for the language variable was 0.240 (p > .05) when comparing profile 3 with profile 4, and there was no difference between profile 3 and 4. A similar situation was observed with the odds ratios for the language variable, where the odds ratio was 3.697 when comparing profile 1 with profile 4, 2.060 when comparing profile 2 with profile 4 and both of these odds ratios were statistically significant. Similar to the regression coefficient, when comparing profile 3 with profile 4, the odds ratio was 1.272, which was not statistically significant. Based on all these findings, it can be concluded that students who sometimes and never speak German at home were classified in less successful profiles and mostly in profile 1. In other words, the more students who sometimes and never speak German at home classified in the least successful profile, when profile 4, which was the most successful profile, was considered as the reference profile. As mentioned earlier, the reference category was "high" for the instructional quality (classroom management, cognitive activation, constructive support) subscales. In other words, the values presented in Table 4 indicate the classification of students who rated the low and medium the instructional quality levels in terms of their achievement profiles when compared to students who rated high. When the values given in the table were examined, it was observed that according to profile 4, students, who rated low and medium classroom management and constructive support subscales levels, were classified in profiles 1, 2 and 3 more than profile 4. However, the students reported a high cognitive activity level were more often classified in low achievement profiles when comparing with students who rated low and medium level of cognitive activity. However, these differences were only statistically significant when comparing profile 1 and profile 2 with profile 4 presented in the table. Unlike other findings, the regression coefficient was (-0.275) when comparing profile 3 with profile 4, but this value was not statistically significant. When the regression (0.599) and odds ratios coefficients (1.820) of the classroom management subscale were examined, the only significant difference was observed when comparing profile 1 with profile 4. Similarly, if we examined the regression (0.418) and odds ratio coefficients (1.518) of constructive support subscale, the only significant difference was observed when comparing profile 2 with profile 4. The home resources for learning scale, students who have few or some resources at home are categorized in lower achievement profiles students. When the home resource for learning scale examined, the regression coefficients were 2.951 (Profile 1 vs. Profile 4), 1.782 (Profile 2 vs. Profile 4), 0.865 (Profile 3 vs. Profile 4). Similarly, odds ratios were 19.120 (Profile 1 vs. Profile 4), 5.944 (Profile 2 vs. Profile 4), 2.374 (Profile 3 vs. Profile 4) when the reference category was the many resources. In other words, students who have few and some resources for learning at home are categorized in lower achievement profiles. As mentioned before, the category "very much like learning science" was taken as the reference category for the subscale "students like learning science". Another finding here was that students in low achievement profiles were more likely to be categorized in the "very much like learning science" category, in other words, they liked learning science even though they were categorized in low achievement profiles. When the negative regression coefficients (-0.003 (Profile 1 vs. Profile 4), -0.413* (Profile 2 vs. Profile 4), -0.371* (Profile 3 vs. Profile 4) and odds ratios (0. 744 (Profile 1 vs. Profile 4), 0.662 (Profile 2 vs. Profile 4), 0.690 (Profile 3 vs. Profile 4) were examined, the differences between second, third profile with fourth profile were statistically significant. For the "student confident in science" scale, the baseline category "very confident in science" was taken as the reference category. When the values given in Table 4 were investigated, it was seen that the students who felt somewhat confident and not confident were classified in lower profiles compared to the students who felt very confident in science. When the regression coefficients (1.873** (Profile 1 vs. Profile 4), 1.302** (Profile 2 vs. Profile 4), 0.789 ** (Profile 3 vs. Profile 4) and odds ratios (6.507 (Profile 1 vs. Profile 4), 3.678 (Profile 2 vs. Profile 4), 2.200 (Profile 3 vs. Profile 4) were examined, it was observed that the differences between the profiles were also statistically significant. According to the findings obtained here, it was observed that students with low levels of science confidence were significantly categorized in low profiles compared to high profiles. In other words, students categorized in low achievement profiles did not feel confident in science when compared to the highest achievement profile. Discussion The aim of this study was to use the TIMSS 2019 dataset to determine how German fourth grade students were categorized into different profiles across different science content and cognitive domains, as well as how these profiles were predicted by gender, language at home, resources at home for learning, science motivation and instructional quality. The overall result from the descriptive statistics of the study showed that German students had the highest mean on the life subscale and the lowest mean on the earth science subscale. These results have been reported in previous studies and published reports, and our results were consistent with these reports (e.g., Schwippert et al., 2020 ; Mullis et al., 2020 ). In addition, we identified four optimal profiles for German students' achievement in science and found that students had different achievement means in different content domains. For example, students classified in the highest achievement profile had the highest mean scores on the physical subscale. In other words, it was found that when the students' achievement profiles increased, their achievement in the physical science subscale also increased. This study's findings also highlight the importance of assessing TIMSS results in terms of different student profiles. In the optimum profiles identified in the study, it was determined that students in different achievement profiles had relatively high mean scores in different cognitive domains. In previous reports (e.g., Mullis et al., 2020 ), the results of Germany's average cognitive domain subscale scores in science were determined and it was reported that the highest score was on the knowing subscale (520), followed by reasoning (519) and application subscales (516). In this study, it was observed that the application subscale had the highest mean value in the lowest achievement profile, while in the second achievement profile, knowing, application and reasoning subscales had similar mean values. It was observed that students classified in the third and fourth achievement profiles had the highest mean in the knowing subscale. A similar pattern of the relationship of cognitive domain subscales to achievement has been reported for different countries in TIMSS (e.g, Perkins & Clerkin, 2020 ). For example, countries with the highest rankings in mathematics also have the highest average scores on the knowing subscale. Also, for science scores, some countries with high achievement rankings have the highest average scores on reasoning, while others have the highest average scores on the knowing subscale. In addition to the achievement profiles of the students in the study, we also examined important background variables and scales regarding perceived instructional quality that may be associated with these achievement profiles through covariate analyses. Overall, the results of this study reflected results from correlative studies, however, the prediction of the variables on achievement differed significantly between the profiles. The importance of language spoken at home for subject-specific achievement was consistent with previous findings from large-scale studies (e.g. Edele & Stanat, 2011 ; Liang, 2010 ; OECD, 2022 ; Younes et al., 2023 ). However, in the present study, language was found to be a significant predictor only for students classified in the first and second achievement profiles. For example, language was not a significant predictor for students in the third and fourth profiles, who are classified in higher achievement profiles. This could be related to the fact that students with migration background were more likely to be classified in lower achievement profiles. Similarly, students who had few and some resources at home for learning were classified in lower achievement profiles than students who had resources at home. In contrast to language, however, resources at home were found to be significant for all achievement profiles. Similarly, these findings supported results in the literature (e.g., Ker et al., 2023 ; Wiberg & Rolfsman, 2023 ; Shala & Grajcevci, 2023 ) and emphasized the importance of the background variables. Previous studies using TIMSS data have found a strong and positive correlation between motivational beliefs in science and science achievement (Liou et al., 2020 ; Hooper et al., 2017). However, some studies have shown that although some countries perform below the OECD average in science, students have high motivational beliefs about science (e.g., Karakolidis et al., 2019 ). In particular, it is emphasized that the relationship between achievement and motivation cannot be completely linear, and that effective and individualized strategies should be applied (Michaelides et al., 2019 ; Ma, 2022 ). The importance of motivational patterns across time and age groups, the culture from which individuals come from, has been emphasized (Michaelides et al., 2019 ). Moreover, studies have shown that the strength of the relationships between science motivational beliefs and science achievement increases from 4th to 8th grade (Liou et al., 2020 ). The findings from this study also supported both of these views in the literature. It was determined that students classified in low achievement profiles were somewhat or not confident in science. In contrast, especially the students classified in high achievement profiles were very confident in science. Student´s confident in science was a significant predictor between low and high achievement profiles, this finding is consistent with previous studies conducted in several countries (e.g., Kaya & Rice, 2010). However, when the students like learning scale was analyzed, it was found that the students classified in low achievement profiles liked learning science. In other words, the students classified in low achievement profiles liked science even though they did not feel confident in science. When the subscales of instructional quality were examined, it was found that students, who reported a high classroom management and constructive support in their classroom were classified in higher achievement profiles. However, students who reported a high cognitive activity were categorized in low achievement profiles as well. These results were similar to those obtained with the “Students like learning” scale. Although students were classified in low achievement profiles, they liked learning science and reported a high cognitive activity. This can possibly be explained by the fact that students’ characteristics (such as prior knowledge, cognitive abilities, motivation and social background) have been shown to predict their ratings of instructional quality (Igler et al., 2019 ). It would be conceivable that students with lower achievement levels rate their own cognitive activation higher, as they might have a lower prior knowledge and therefore tasks in lessons might be perceived as more demanding (Fauth et al., 2014 ). The differences between students' mean scores on the sub-scales of content and cognitive domains and their achievement profiles emphasized the importance of the application of profile analysis based on the assumption of “individual heterogeneity”. It is noteworthy that the findings obtained across and between countries, especially in terms of subscale scores, vary in terms of achievement profiles. The achievement profiles of different student profiles in different domains and the associated background variables need to be considered in countries' educational policies and classroom activities. Overall, it should be emphasized that this study is a descriptive approach at the national level for Germany and that the reasons for the relationship between these variables and high or low academic achievement can be identified in different samples. With regard to our findings, the following limitations must be noted that the results are based on cross-sectional data and no causal inferences can be made. In addition, instructional quality was rated by students. Even though student ratings are commonly used and show positive correlations with students' achievement (Fauth et al., 2014 ; Teig et al., 2019), when interpreting the results, the level on which the ratings provide information must be borne in mind (e.g., Lüdtke et al., 2009 ). In this study the student ratings provide information at individual level and not at class or even school level. Furthermore, it should be emphasized that the concept of classroom management includes a wide range of sub-dimensions and this study is limited to the construct measured by the TIMSS German national sub-scales. However, it is conceivable that the present result could provide an important starting point for how the performance of low achievers can be increased. For instance, different aspects of teaching quality are discussed in research regarding their potential sufficiently in the development of motivational-affective competences. In addition, different forms of cognitive support, such as reduction of task complexity, individual support, and feedback might be important to develop competence-related beliefs (e.g. confidence) (Ryan & Deci, 2000 ; Eccles, 2009 ). However, no statement can be made on the basis of this study this assumption would need to be tested in further studies. Conclusion This study has both theoretical and practical implications. The study suggests that latent profile analysis is a proper and feasible method for identifying students’ achievement (or other variables of interest), and for exploring the associations among variables, particularly when variable-centered approaches do not apply due to nonlinear relationships among variables. In terms of practical implications, the results demonstrate that teachers, researchers and policymakers should take students’ different profiles into account, developing adaptive and individualized strategies to improve students’ science achievement. For example, for students with low achievement in science, who are more likely to feel less confident, it could be a useful to use an adaptive teaching strategy and assign specific tasks at which they will excel, provide flexible formative science assessment, and/or give genuine and positive feedback on their efforts rather than outcome. Moreover, the results also show that it is important for teachers to reduce gender stereotype in science, and in particular take effective measures to improve the participation and attitudes toward science of girls. Declarations Author Contribution GYT developed the study design, performed the data analyses and contributed to the writing and revision of the study, JB contributed to the development of the study design, writing and revision of the study, MS contributed to the development of the study design, writing and revision of the study. All authors made substantial contributions to the interpretation and discussion of the results. All authors have read and approved the final manuscript. Data Availability Some part of the datasets analyzed for this study cannot be available because this is an official data set and must have permission from “Institut zur Qualitätsentwicklung im Bildungswesen” (IQB): https://www.iqb.hu-berlin.de/institut/staff). Some of the datasets that support the conclusions of this study are open access and can be downloaded as public use files from the IEA website: https://www.iea.nl/data-tools/repository/timss References Bauer, J. (2022). A Primer to Latent Profile and Latent Class Analysis. In: Goller, M., Kyndt, E., Paloniemi, S., Damşa, C. (eds) Methods for Researching Professional Learning and Development. Professional and Practice-based Learning, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-08518-5_11 Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y.-M. (2017). 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Springer, Cham. https://doi.org/10.1007/978-3-319-26633-6_12 Perkins, R. & Clerkin, A. (2020). TIMSS 2019: Ireland’s results in mathematics and science. Dublin: Educational Research Centre. Praetorius, A. K., & Charalambous, C. Y. (2018). Classroom observation frameworks for studying instructional quality: looking back and looking forward. ZdM, 50, 535–553. Praetorius, A. K., Klieme, E., Herbert, B., & Pinger, P. (2018). Generic dimensions of teaching quality: The German framework of three basic dimensions. ZdM, 50, 407-426. Prediger, S., Erath, K., & Moser Opitz, E. (2018). Language challenges for students with mathematical difficulties – An overview on research results and instructional approaches. https://wwwold.mathematik.tu-dortmund.de/~prediger/veroeff/19-HB-PredErathMoser-MathDifficultiesLanguage.pdf Robnett, R. D., & Leaper, C. (2013). Friendship groups, personal motivation, and gender in relation to high school students’ STEM career interest. Journal of Research on Adolescence, 23(4), 652–664. https://doi.org/10.1111/jora.12013 Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037//0003-066X.55.1.68 Sammons, P., Toth, K., Sylva, K., Melhuish, E., Siraj, I., & Taggart, B. (2015). The long-term role of the home learning environment in shaping students’ academic attainment in secondary school. Journal of Children's Services, 10(3), 189–201. https://doi.org/10.1108/JCS-02-2015-0007 SAS Institute Inc. (2002–2012). SAS (Version 9.4). https://support.sas.com/documentation/94/. Schurig, M., Wendt, H., Kasper, D. & Bos, W. (2015). Fachspezifische Stärken und Schwächen von Viertklässlerinnen und Viertklässlern in Deutschland im europäischen Vergleich. In H. Wendt, T. C. Stubbe, K. Schwippert & W. Bos (Hrsg.), 10 Jahre international vergleichende Schulleistungsforschung in der Grundschule. Vertiefende Analysen zu IGLU und TIMSS 2001 bis 2011 (pp. 35–54). Münster: Waxmann. Scherer, R., & Nilsen, T. (2016). The relations among school climate, instructional quality, and achievement motivation in mathematics. In T. Nilsen & J.-E. Gustafsson (Eds.), Teacher quality, instructional quality and student outcomes: Relationships across countries, cohorts and time (pp. 51–80). Springer International Publishing. https://doi.org/10.1007/978-3-319-41252-8 Schwippert, K., Kasper, D., Köller, O., McElvany, N., Selter, C., Steffensky, M., & Wendt, H. (2020). TIMSS 2019: Mathematische und naturwissenschaftliche Kompetenzen von Grundschulkindern in Deutschland im internationalen Vergleich. Waxmann Verlag. Senden, B., Nilsen, T., & Teig, N. (2023). The validity of student ratings of teaching quality: Factorial structure, comparability, and the relation to achievement. Studies in Educational Evaluation, 78, 101274. Shala, A., & Grajcevci, A. (2023). Kosovar Students’ Performance in the 2019 TIMSS Assessment: Why School and Home Resources Drive Achievement? International Journal of Educational Reform, 32(3), 357-368. https://doi.org/10.1177/10567879231168372 Shymansky, J. A., Yore, L. D., & Hand, B. M. (2000). Empowering Families in Hands-on Science Programs. School Science and Mathematics, 100(1), 48–58. https://doi.org/10.1111/j.1949-8594.2000.tb17319.x Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, Article 103445. https://doi.org/10.1016/j.jvb.2020.103445. Teig, N., & Nilsen, T. (2022). Profiles of instructional quality in primary and secondary education: Patterns, predictors, and relations to student achievement and motivation in science. Studies in Educational Evaluation, 74, 101170. https://doi.org/10.1016/j.stueduc.2022.101170 Thomson, S., De Bortoli, L., Underwood, C., & Schmid, M. (2019). PISA 2018: Reporting Australia’s Results. Volume I Student Performance. Australian Council for Educational Research (ACER). https://research.acer.edu.au/ozpisa/35 Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. Applied latent class analysis, 11(89-106) , 60. Watt, H.M.G., Bucich, M. & Dacosta, L. (2019). Adolescents’ Motivational Profiles in Mathematics and Science: Associations With Achievement Striving, Career Aspirations and Psychological Wellbeing. Frontiers Psychology, 10:990. doi: 10.3389/fpsyg.2019.00990 Wendt, H., Kasper, D. (2016). Subject-specific strength and weaknesses of fourth-grade students in Europe: a comparative latent profile analysis of multidimensional proficiency patterns based on PIRLS/TIMSS combined 2011. Large-scale Assessments in Education, 4, 14 https://doi.org/10.1186/s40536-016-0026-2. Wiberg, M., & Rolfsman, E. (2019). The association between science achievement measures in schools and TIMSS science achievements in Sweden. International Journal of Science Education, 41(16), 2218–2232. https://doi.org/10.1080/09500693.2019.1666217 Wiberg, M., & Rolfsman, E. (2023). Students’ Self-reported Background SES Measures in TIMSS in Relation to Register SES Measures When Analysing Students’ Achievements in Sweden. Scandinavian Journal of Educational Research, 67(1), 69–82. https://doi.org/10.1080/00313831.2021.1983863 Yamashita, T., Smith, T. J., & Cummins, P. A. (2021). A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data. Journal of Educational and Behavioral Statistics, 46(4), 501–518. https://doi.org/10.3102/1076998620978554 Yin, L., & Fishbein, B. (2019). Creating and interpreting the TIMSS 2019 context questionnaire scales. Methods and procedures: TIMSS, 16(1), 1-331. Younes, R., Salloum, S. & Antoun, M. (2023). The effects of language and home factors on Lebanese students’ mathematics performance in TIMSS. Large-scale Assessment in Education,11, 30. https://doi.org/10.1186/s40536-023-00180-w Additional Declarations No competing interests reported. Supplementary Files supplementaryfileprofile105.10.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6984158","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488400141,"identity":"0dcc1d4a-cef3-4af5-b7c7-6a7fa81048a6","order_by":0,"name":"Güler Yavuz Temel","email":"data:image/png;base64,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","orcid":"","institution":"Universität Hamburg","correspondingAuthor":true,"prefix":"","firstName":"Güler","middleName":"Yavuz","lastName":"Temel","suffix":""},{"id":488400142,"identity":"4cdd1069-0b5b-4b11-b1da-91ce9ea38077","order_by":1,"name":"Julia Barenthien","email":"","orcid":"","institution":"Forschungs- und Entwicklungszentrum Fachhochschule Kiel (Germany)","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Barenthien","suffix":""},{"id":488400143,"identity":"883ebf06-a400-4bdd-b206-9394aed0406f","order_by":2,"name":"Mirjam Steffensky","email":"","orcid":"","institution":"Universität Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Mirjam","middleName":"","lastName":"Steffensky","suffix":""}],"badges":[],"createdAt":"2025-06-26 14:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6984158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6984158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87316721,"identity":"c16d3018-492b-43d9-8b36-a455d478373d","added_by":"auto","created_at":"2025-07-22 15:58:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":933273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6984158/v1/7459e967-2423-4a80-bbdc-4b43ee89effe.pdf"},{"id":87315711,"identity":"09918ad2-87b9-4858-973b-1e71237044ab","added_by":"auto","created_at":"2025-07-22 15:50:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33128,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfileprofile105.10.docx","url":"https://assets-eu.researchsquare.com/files/rs-6984158/v1/d0bb8fcccdb339c841d1256f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"TIMSS Science Achievement Profiles and Their Associations with Motivation, Instructional Quality Scales","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBasic science competences among students are considered important in order to meet the various challenges of the 21st century. Student\u0026acute;s early science achievement is predictive for their science competences later on (Morgan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As basic science competences are relevant prerequisites for social participation promoting the development of science competences in school is important. Especially against the background that science achievement gaps begin very early and are persistent (Morgan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), it requires a closer investigation of student\u0026acute;s science competences. For this reason, science achievement of students and causal relationships between science achievement and different background variables that may contribute to science achievement have been the focus of interest of many researchers.\u003c/p\u003e\u003cp\u003eMany researchers have analyzed science achievement and the predictors of science achievement using a variety of approaches, especially with large-scale assessments using data from a wide range of heterogeneous groups. Traditional, variable-centered correlation approaches (e.g., ANOVAs, correlation) were used in most of these studies. However, these approaches have serious limitations in appropriately characterizing heterogeneity and complex, non-linear learning models (Hickendorff et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These approaches, which emphasize the relationship between variables (Bergman et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), assume that the relationship between variables can be applied to all students in the same way (Hickendorff et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Michaelides, 2019). In other words, these approaches assume that all individuals in the sample belong to a single profile or population and that there is no differentiation between latent subgroups (Ferguson et al., 2019).\u003c/p\u003e\u003cp\u003eBy contrast to variable-centered approaches, person-centered approaches are not constrained by linear patterns and the interactions between variables can be modeled as heterogeneous (Bergman \u0026amp; Magnusson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hickendorff et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Latent profile analysis (LPA) and latent class analysis (LCA) are person-centered approaches. These approaches include methods that identify latent groups in the data by examining the distribution of groups in the data and determining whether these distributions are significant based on the possibility that individuals belong to different groups (Ferguson et al., 2019). Various application frameworks for latent profile analysis have been developed by different researchers. For example, Bauer (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) classified the implementation stages of latent profile analysis as follows: specification of the model, identification of latent classes, interpretation of the optimal profile solution and inclusion of predictors and/or outcomes of latent class membership. The last step is also the analysis of the latent profile using covariates and, once the optimal latent class solution has been obtained, the identification of variables (e.g. social background variables) that predict latent class membership (Vermunt \u0026amp; Magidson, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bauer, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been highlighted in the literature that these person-centered methods can be used as an alternative to traditional variable-centered approaches, especially in the grouping of complex multidimensional data with heterogeneous patterns, with different application phases proposed by different researchers (e.g., Hickendorff et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vermunt \u0026amp; Magidson, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Spurk et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bauer, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, it has been particularly recommended to use these approaches in assessments with highly heterogeneous populations such as the Trends in International Mathematics and Science Study (TIMSS) and the Programme for International Student Assessment (PISA). For example, the LPA approach has been used in different studies to examine achievement profiles (e.g., Wendt \u0026amp; Kasper, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and attitudinal profiles (e.g., Michaelides, 2019; Berger et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liou \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in large-scale assessment studies. According to the findings of these studies, for instance, background variables varied in terms of their predictive significance for various profiles.\u003c/p\u003e\u003cp\u003eIn this study, it is aimed to determine the achievement profiles of fourth grade German students in science using the content and cognitive domains in TIMSS 2019 and to analyze the relationship between different profiles and various variables whose impact on achievement is emphasized in the literature with variable or person-centered approaches. Various studies have already investigated different factors influencing students\u0026rsquo; achievement and the results show that, in addition to individual background variables, characteristics of the home environment and teaching characteristics can have a positive influence on students\u0026rsquo; achievement. In the home environment, these include, for example, the home learning environment (e.g., Melhuish, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the quality of teaching for teaching characteristics (e.g., Baumert et al., 2010; Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe students' home background has been examined by numerous researchers with TIMSS data for different countries and its clear impact on achievement has been emphasized by the results of these studies (e.g., Wiberg \u0026amp; Rolfsman, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Erberber et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The main finding from these studies was that students from high socio-economic backgrounds tend to perform better in school compared to students from low socio-economic backgrounds. In a similar study (Caponera \u0026amp; Losito, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the relationship between context factors and achievement was examined by analyzing grade 8 students from 28 countries who participated in TIMSS 2011. According to the findings of the study, high socio-economic status had a positive effect on mathematics achievement. Students with lower socio-economic status tended to have lower mathematics achievement. Chen et al., (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the profile of the mathematics subscales in TIMSS 2011 and found that the U.S. students\u0026rsquo; performance was poorer on the Geometry subscale than on other mathematics subscales. In the study they also found that the background variables (gender, age, home language, race, and preference for mathematics and science) significantly influenced the probability of being classified in the group with the lowest performance and the largest gap between geometry and other mathematics subscales. Bos et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003eb\u003c/span\u003e) also examined model latent profiles of student achievement in reading, mathematics, and science for 4th-grade students in Germany. The authors found seven invariantly rank-ordered profiles based on the similarity of students\u0026rsquo; cross-domain achievement and then further studied relationships with background variables, such as gender, cultural and socioeconomic characteristics. According to the results of the study, the authors found that gender differences only appeared in high achievement profiles and a significant relationship between achievement and socioeconomic background variables was found among almost all profiles.\u003c/p\u003e\u003cp\u003eMotivation is one of the variables whose effect on achievement has been examined in various studies. Although it has been emphasized that students with high motivation tend to perform better, some studies have found that students with low motivational beliefs have high academic achievement. Other studies have emphasized that motivation and achievement have a differential relationship for high and low achievement profiles. Wang and Liou (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), for example, used data on TIMSS 2011 Taiwanese eight-grade students and divided students into three student groups: total group, high-achieving group and low-achieving group. Self-concept positively predicted science achievement for the high-achieving group, but negatively predicted science achievement for the low-achieving group. In the total group, all motivational beliefs positively predicted science achievement. A similar study was conducted by Liou (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Liou (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examined the relationship between motivational beliefs in science learning and science achievement with TIMSS 2011 data from 26 countries. Despite the general tendency, Liou (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that the effect of motivational variables on science achievement differs between western and eastern cultures.\u003c/p\u003e\u003cp\u003eInstructional quality profiles and variables are among the variables whose relationship with achievement and motivation in science has been highlighted in the literature with different TIMSS data. For example, Teig and Nilsen (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) constructed different instructional quality profiles using Norwegian data from (TIMSS) 2015 (using Grades 5 and 9) and found that these profiles varied across different aspects of instructional quality in both grades, and that student characteristics, particularly language spoken at home and socioeconomic status, predicted student-level profile membership, whereas teacher efficacy (i.e., self-efficacy in science teaching) did not. Furthermore, the researchers emphasized that different teaching quality profiles are significantly related to motivation and, to a certain degree, to science achievement. Bellens et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in a similar framework used TIMSS 2015 data from Belgium (Flanders), Germany, and Norway to examine the invariance of instructional quality variables across countries and the impact of students' socioeconomic status and language on achievement. In addition to this, they also stated that instructional quality variables can serve as a catalyst for increasing achievement in education systems.\u003c/p\u003e\u003cp\u003eIn addition to these studies that found the relationship between achievement and instructional quality to be positive and significant, Bl\u0026ouml;meke et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also examined the relationship between the achievement of fourth grade students and the quality of teachers and instructional quality in TIMSS 2011 data (from 47 countries). According to the results of the study, teacher quality was significantly related to instructional quality and student achievement, whereas student achievement was not well predicted by instructional quality. Similarly, Scherer and Nilsen (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) examined the relationships among school climate, instructional quality, and achievement, motivation in mathematics using TIMSS 2011 data from 50 countries. One of the findings from this study was that there was a significant positive relation between instructional quality and achievement motivation at the classroom level in mathematics.\u003c/p\u003e\u003cp\u003eIn summary, numerous studies have been conducted to examine the relationship between achievement and other variables and to identify which variables tend to be more predictive indicators of achievement. However, the extent to which these influencing factors lead to varying profiles has not yet been comprehensively researched and should therefore be investigated. In addition, the relationship between students\u0026rsquo; science achievement and various variables is often addressed by statistical approaches based on correlational analyses, and these variable-centered analyses are usually applied assuming linearity in the relationships (Michaelides, 2019). For a helpful insight into students\u0026rsquo; science achievement, it would be useful to also consider individual heterogeneity. Therefore, in-depth analyses are required here to get an individual perspective on each student. Against this background the present study aims to describe individual patterns of student achievement among the science content and cognitive domains subscales and to examine which background variables as well as students\u0026rsquo; rating of instructional quality may contribute to student\u0026acute;s classifications in these profiles. Besides identifying students\u0026rsquo; achievement profiles, because students\u0026rsquo; academic competences are the results of a complex interplay between factors located at the country, the school, the classroom, and the student level and a lot of these factors shape inter-individual differences in student achievement in a rather similar way (Bergold et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), it is of great interest to find out why profiles may vary or why students may be grouped in different profiles and in which influencing factors or demographic variables do the student of different profiles differ.\u003c/p\u003e\u003cp\u003eThe implementation of LPAs requires a large sample and so far, there are few studies with data on students' science achievement, background variables, home learning environment and teaching quality. For instance, TIMSS provides detailed information about what students around the world know and can do in science in general and in specific science content areas such as physical science, life science and earth science (Martin et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and also supply information about students\u0026rsquo; background and ratings of teaching quality. Regarding science achievement, the assessment of TIMSS items offers the opportunity to compare the student's achievements in different content areas. Following the TIMSS conceptualization of science achievement, science achievement includes on the one hand the relevant content domains (e.g., life science, physical science, and Earth science), which assess science knowledge broadly with a focus on curricular content, and on the other hand central thought processes that form the basis for solving TIMSS tasks (Mullis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In TIMSS they are referred to as cognitive domain (Bloom, 1956) and describe the reproduction of knowledge, application of knowledge and problem solving. The cognitive skills were categorized into three broad domains knowing, applying, and reasoning in TIMSS (Mullis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The assessment of TIMSS items offers the opportunity to take a closer look at individual content areas and compare the student's achievements in different content areas. The results show that German students, who are the focus of this study, perform similarly to many other countries (Schwippert et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and that the findings of this study could also apply to other countries. This study aims to examine the achievement and cognitive profiles in science using TIMSS 2019 4th grade data. The study also aims to determine how various student characteristics (students' demographic background variables, motivation) and instructional quality predict profile membership. Specifically, it is aimed to determine which variables used in the covariate analyses are meaningful for certain achievement profiles. The following questions are addressed in the study based on this aim.\u003c/p\u003e\u003cp\u003eIn which achievement profiles can German fourth graders be classified when using TIMSS 2019 science achievement subscales and science cognitive domains scores? What were the average scores of students in the life, physical and earth and knowing, applying and reasoning subscales in the optimal achievement profiles? What were the characteristics of the students classified in the high and low achievement profiles when gender, language and home resources for learning were analyzed as covariates? How do students classified in high and low achievement profiles differ in their interest in science and their self-concept levels in science? When the instructional quality scales (cognitive activation, classroom management and constructive support) are considered as covariates, which sub-scales of instructional quality significantly predict students' achievement and cognitive profiles?\u003c/p\u003e\u003cp\u003eThis article begins with a review of recent research on influence factors for students\u0026rsquo; science achievement on the individual and teaching level. It then investigates students' achievement patterns across science content and cognitive domain subscales using latent profile analysis. In addition, covariate analyses with demographical and teaching variables commonly assumed to be associated with achievement in the literature, such as gender, language, home resources for learning and teaching quality were carried out.\u003c/p\u003e\n\u003ch3\u003eScience Achievement and its association with background variables\u003c/h3\u003e\n\u003cp\u003eResearchers emphasize the importance of the home environment for young students\u0026rsquo; (science) competences (Melhuish, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morgan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Junge et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For young students, the most important learning environment for various educational processes is the family or home context (Melhuish, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This includes, for example, the interplay between structural characteristics, parental beliefs and attitudes, and educational processes (Melhuish, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Junge et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The home learning environment has a significant influence on student\u0026acute;s emotional and intellectual growth, school readiness, and their subsequent academic achievement (Sammons et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With regard to science, research regarding secondary school students showed that parents\u0026rsquo; attitudes towards science appear to be stable over time and presumably shape high school students\u0026rsquo; perspective towards learning science as well as later science-specific career choices (Chen, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ferry et al.,2000). At the same time, studies indicated that parents were less involved in their children\u0026rsquo;s science learning in elementary and secondary school as compared, for example, to math and reading (Kaya \u0026amp; Lundeen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shymansky et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This could result in a higher variance in children\u0026rsquo;s science learning at home. The variation in the home learning environment might result in difference in student\u0026rsquo;s science achievement.\u003c/p\u003e\u003cp\u003eAlthough domain-specific competences are assumed to be especially important for the development of domain-specific competences, also other competences and factors have been shown to influence them (e.g., Morgan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Edele \u0026amp; Stanat, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Melhuish, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, with regard to young children, it has been hypothesized that children's language skills play a central role in the development of skills in all other domains, as children acquire domain-specific skills through verbal interactions with parents, family, preschool teachers and school teachers (Justice et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, language is a prerequisite for learning and at the same time also an object of learning as well as a medium of instruction, as noted by Prediger et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Many studies already indicate that the language spoken at home can be particularly important for domain-specific achievement (e.g., Liang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Besides the importance of language as a prerequisite for learning and a medium of instruction, links between the language spoken at home and achievement are often linked to the fact that families speaking another language at home often are more likely to have a low families' socio-economic status and parents' level of education (Edele \u0026amp; Stanat, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRelated to this, student\u0026rsquo;s individual characteristics such as gender can also play a role. Gender differences in science have been explained by gender stereotype in previous work (e.g., Robnett \u0026amp; Leaper, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Berger et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as these stereotypes can influence an individual\u0026rsquo;s self-perception, motivation and experience in classroom (Plante et al., 2018). With regard to research results, for instance, it was showed by Ma (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that boys displayed significantly higher intrinsic interest in science, perceived competence in science and instrumental value of science than girls. Gender differences were also reported in a study by Britner (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), even if the results varied depending on the content area. It is therefore conceivable that there are also gender differences in the science achievement of elementary school students.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudents' motivation towards science achievement\u003c/h2\u003e\u003cp\u003eLPA of achievement allow to classify groups of students beyond those who are \u0026ldquo;high achiever\u0026rdquo; or those who are \u0026ldquo;low achiever\u0026rdquo;. These profiles, in turn, can be not only evaluated in terms of the quality of the selected classifications but also analyzed according to their subject-specific strengths and weaknesses as well as according to their relationships with individual background characteristics (Schurig et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, a strong focus of previous professional analyses with large-scale assessment data sets is on motivational-affective competences. For instance, Ma (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigate with a TIMSS data set students\u0026rsquo; profiles of attitudes toward science. Results of the latent profile analysis showed five distinct profiles of student attitudes toward science: (1) Negative attitudes, particularly toward perceived competence in science; (2) Negative attitudes, particularly toward instrumental value of science and engaging science teaching; (3) Moderate attitudes toward science; (4) Positive attitudes toward science; and (5) High-positive attitudes toward science. The most frequently represented profile in the sample of over 4000 students was profile 3. Berger et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined relationship between attitudes towards mathematics and science and they found six profiles with using data from Australian Grade 8 students sampled by TIMSS 2015. The study emphasized that positive attitudes towards both subjects are mutually beneficial and that high attitudes towards both subjects are associated with high achievement. The study also explored attitudinal differences between genders in latent profiles and found that boys tended to be more positive. Overall, profile analyses in science education show a strong focus on motivational-affective competences. Furthermore, there is little differentiation with regard to different content areas, although there are indications that there may be differences in competences (Britner, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; NCES, 2001). Consequently, there is still a lack of insight into profiles for science achievement.\u003c/p\u003e\u003cp\u003eTo get a broader and more authentic picture of student\u0026rsquo;s achievement patterns in science it is also important to investigate why profiles may vary and in which influencing factors or demographic variables do the students of different profiles differ. With regard to influencing factors, it can be assumed that other domain-specific skills have an influence here. Motivational-affective science competences in particular can be mentioned here, which have been linked to achievement in other studies and where results indicate that children who have higher motivational-affective science competences also perform better. For instance, Ma (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that students in profiles with higher levels of science attitudes tended to show better science achievement. Even if the relation is not perfectly linear, this finding indicates that science-specific motivational-affective competences such as attitudes, interest and motivation are positively linked to science achievement.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInstructional Quality in science achievement\u003c/h3\u003e\n\u003cp\u003eA large body of research points to the importance of instructional quality for student\u0026rsquo;s achievement (e.g. Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Neumann, Kauertz \u0026amp; Fischer, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Senden, Nilsen \u0026amp; Teig, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Instructional quality reflects teachers\u0026rsquo; behavior in the classroom and is thus measured at the classroom. Instructional quality is conceptualized and measured in different ways. In the context of TIMSS, instructional quality is differentiated into the three basic dimensions Classroom Management, Cognitive Activation and Constructive Support (Praetorius et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Classroom Management is the most generic dimension of instructional quality and refers to the teachers\u0026rsquo; behavior and time management (Praetorius \u0026amp; Charalambou, 2018). Cognitive Activation describes how the teachers stimulates students\u0026rsquo; cognitive activity (Baumert et al., 2010). Constructive Support includes characteristics that contribute to a learning environment that is conducive to learning and student-oriented. Research findings show positive relations between the three basic dimensions and students\u0026rsquo; achievement and motivation in science (e.g. Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Neumann et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Teig et al., 2018). For instance, classrooms with students with high levels of science achievement are positively related to teachers, who effectively manage their classrooms (Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Senden et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, studies showed students\u0026rsquo; (ratings of) cognitive activation predicted their development of science-related interest and science achievement (Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Teig et al., 2018). Thereby students who were provided with more cognitive activating instruction, for example by conducting scientific inquiry activities, achieved higher levels of science achievement (Teig et al., 2018). However, findings by a study by Lindermayer et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicate that there are different classroom profiles for instructional quality and that the instructional quality profiles differed significantly regarding students\u0026rsquo; mathematics-related interest, intrinsic motivation, and achievement. Consequently, it is therefore conceivable that (perceived) instructional quality also might have an influence on the formation of achievement profiles in science.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eIn the study we analyzed TIMSS (2019) fourth-grade data to examine German students\u0026rsquo; profiles of the achievement in the three science content domains (life science, physical science and earth science). We used latent profile analysis to explore student\u0026acute;s achievement profiles using data from 3447 German Grade 4 students from TIMSS 2019. The descriptive statistics of the data sets were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in the results section. TIMSS provides five plausible values per content subscale and cognitive domains. In the study we used five plausible values of the science subscales (life science, physical science and earth science) and five plausible values of the cognitive domains (knowing, applying, reasoning) for exploring the latent profiles.\u003c/p\u003e\u003cp\u003eIn addition to the achievement profiles, we examined the effect of the students background variables on the class membership using LPA with covariates. Since the achievement profiles were created using 4th grade TIMSS (2019) data, background variables (gender, language) and the scale home resources for learning scale and for the fourth grade were used in the study.\u003c/p\u003e\u003cp\u003eThe home resources for learning scale (HLR), which is often emphasized in the literature as one of the important predictors of achievement, is one of the scales used in the covariate analyses in the study. The HRL scale consists of five variables (number of books in the home (students), number of childrens books in the home (parents), number of home study supports (students), highest level of education of either parent (parents), highest level of occupation of either parent (parents) (Martin \u0026amp; Mullis, 2012). The scale was created using these variables and in addition to the scale scores, an index was created for each scale using different cut values. For example, with the HLR index, three different categories (many, some and few resources) were created using different cut values. In study we used also \"students like learning science\" and \"student confident in science scales\" as covariate. Similar to the HLR scale, the index of these scales were used. Instructional quality scales (classroom management, cognitive activation, constructive support) were also used as covariates in the study. In TIMSS, instructional quality was measured using international scales, such as instructional clarity and supportive classroom climate (Yin \u0026amp; Fischbein, 2019). However, some countries, including Germany, extend this framework with more detailed subscales, such as classroom management, cognitive activation, and constructive support, to provide a more in-depth analysis of teaching practices (Schwippert et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For more detailed information on the reliability of these scales, refer to Yin and Fischbein (2019) for the international scales and Schwippert et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for the national adaptations. In addition to the scales, the demographic variables were incorporated as covariates in all latent profile models. In the supplementary file, the descriptive statistics of the scales and variables that were used as covariates in the study were provided.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIn the last part of the study, we examined the effects of dummy variables that were constructed from the index categories of the home resource for learning scale and motivational scales, instructional quality scales and demographic variables (gender, language), on the classification of profiles. The first variable examined was the gender variable and male were used as the baseline category. Therefore, it was evaluated whether the categorization of female students in the profiles was statistically significant compared to male students. The second covariate variable was the language variable, where the category \"always speak language\" was created as the reference or baseline variable. In other words, we examined the classifications in the profiles of students who often, sometimes and never speak German compared to students who speak German always at home. For the \"Home resources for learning\" scale, the baseline category \u0026ldquo;many\u0026rdquo; was taken as the reference category. In other words, the HLR categories were analyzed in the profiles of students with few and some resources for learning. The instructional quality scales (cognitive activation, classroom management, constructive support) indices have also three categories (low, medium, high) and we were identified \u0026ldquo;high\u0026rdquo; category as baseline category and the \u0026ldquo;low\u0026rdquo; and \u0026ldquo;medium\u0026rdquo; categories have identified as reference categories. Similarly, for the \"students like learning science\" scale, the category \"very much like learning science\" and for the \"student confident in science\" scale, the category \"very confident in science\" were identified as baseline categories. Reference categories were determined for all these variables in accordance with the purpose of the study and to avoid the challenges of statistical interpretation and difficulties in estimation that the \"rare\" category may cause. Detailed descriptive statistics for the created dummy categories and variables were presented in the supplementary file.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\u003cp\u003eThe results of the descriptive statistics of each subscale are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the results of the statistics, the mean score of the life science subscale was substantially higher and the mean score of the earth science was lower than other subscales. In addition, when the scores related to cognitive domains were examined, it was seen that the average scores of German fourth grade students in knowing (519.653) and reasoning (518.469) were similar, but the average score in applying (515.952) was smaller compared to other cognitive domains. The plausible values we obtained with our analyses were consistent with the plausible values given in the TIMSS 2019 reports (Mullis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDescriptive statistics among the life science, physical science and earth science subscales (weighted)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubscales\u003c/p\u003e\u003cp\u003e(Science)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeighted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e708968.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e521.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e210.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e751.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e527.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e79.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e708968.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e518.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e195.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e784.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e523.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e708968.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e508.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e186.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e786.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e541.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e86.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e708968.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e519.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e176.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e780.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e525.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e82.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApplying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e708968.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e515.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e225.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e735.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e521.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e77.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReasoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e708968.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e518.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e171.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e750.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e524.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e83.077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eResults of the Model Fit\u003c/h2\u003e\u003cp\u003eIn addition to the results of the descriptive statistics, the results of the goodness of model and entropy indices were provided in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The smaller values of the fit statistics used for the appropriate number of the classes and also for entropy, values of 0.80 and above are considered acceptable for the classification of individual cases into appropriate classes. According to the results, since the entropy values were similar and around 0.91, we decided to look at the AIC and BIC and SABIC goodness of fit statistics to decide on the optimum number of profiles. When the results were analyzed, there were decreasing trends in all goodness of fit statistics for classes 2 and 3. However, from class 4 onwards, the differences in the fit statistics were not very substantial. In addition to the fit statistics, we also examined the differences in the mean scores of the students in the profiles. Therefore, the optimum number of profiles was set to 4.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eThe results of the model fitting indices under each classification (weighted).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Classes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSABIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227678.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e227795.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e227734.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e221830.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e221990.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e221907.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e218651.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218853.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e218748.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e217039.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e217285.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e217158.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216048.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e216336.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e216187.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e215494.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e215826.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e215654.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e215148.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e215522.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e215328.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e214837.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e215254.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e215038.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; SABIC: sample size-adjusted BIC\u003c/h2\u003e\u003cp\u003eIn addition to these results, the means of the subscales from the three- to five-class models were reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. When the means of the subscales in the first three-class model were analyzed, it was observed that the life science subscale had the highest mean (387.677, 474.933, 546.603), but the mean of the physical science subscale in the fourth profile (615.731) was higher than the life and earth science subscales. In other words, the highest mean for the most successful profile belongs to the physical science subscale and the lowest to the earth science subscale.\u003c/p\u003e\u003cp\u003eWhen the subscales of the cognitive domains were examined, it was observed that in the first profile, in other words, in the lowest achievement profile, the applying subscale (387.210) had the highest mean scores, while knowing (380.353) and reasoning (381.913) subscales had similar values. In the second profile, the mean scores of all subscales were similar (knowing: 470.964, applying 469.057, reasoning 470.807). When the mean scores of the cognitive domains subscales in the third profile were examined, it was found that the highest mean scores were obtained for the knowing subscale (545.487) and the lowest mean scores were obtained for the reasoning subscale (543.103) and the lowest mean scores were obtained for the applying subscale (539.875). When the mean scores of the cognitive domains\u0026rsquo; subscales of the students of the most successful profile were investigated, it was seen that the highest mean scores were obtained for knowing (619.656) and reasoning (614.781) subscales. The lowest mean scores were observed for applying (609.949) subscale.\u003c/p\u003e\u003cp\u003eThe International Benchmark defined by TIMSS (Mullis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) refers to achievement at four points along the science scale: Advanced International Benchmark (625), High International Benchmark (550), Intermediate International Benchmark (475) and Low International Benchmark (400). When the mean scores for the science subscales were examined, it was seen that the life, physical and earth science subscale scores in profile 1 were below the low international benchmark (400), subscale scores in profile 2 were below the intermediate international benchmark (475) and subscale scores in profile 3 were above the intermediate level. The subscale scores in profile 4 were above the high international benchmark (550). Students were mostly classified in the second (N\u0026thinsp;=\u0026thinsp;1017) and third profiles (N\u0026thinsp;=\u0026thinsp;1275). The highest proportion of the students has been classified in the profile 3 (N\u0026thinsp;=\u0026thinsp;1275) and these members performed above the intermediate benchmark (475) and below the high international benchmark for all three science domains.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eThe means of the subscales from the three- to six-class models (weighted).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClasses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubscales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClass 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClass 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eClass 5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e510.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e596.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e405.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e506.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e595.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e495.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e589.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKnowing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e404.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e507.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e599.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApplying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e408.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e503.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e590.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReasoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e405.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e506.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e595.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eProp.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e474.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e546.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e615.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e382.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e471.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e542.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e615.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e374.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e457.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e534.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e610.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKnowing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e380.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e470.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e545.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e619.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApplying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e387.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e469.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e539.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e609.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReasoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e381.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e470.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e543.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e614.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eProp.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eFive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e363.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e440.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e504.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e564.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e626.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e359.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e436.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e500.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e561.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e627.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e422.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e488.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e554.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e622.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKnowing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e355.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e435.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e501.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e564.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e632.620\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApplying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e363.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e435.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e497.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e558.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e622.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReasoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e355.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e436.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e500.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e561.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e627.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eProp.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eResults of Covariate Analysis\u003c/h2\u003e\u003cp\u003eFinally, covariate analyses were conducted and the student variables were included as covariates in the models. When the results of the covariate analysis examined, some variables showed statistically significant differences between the profiles. In the study, the results of covariate analyses based on reference categories as well as reference profiles were presented in separate tables. For example, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the estimates of regression coefficients and odds ratios for the variables considered as covariates when the reference profile is 4, in other words, when the most successful student profile was considered as the reference profile. In addition, predicted regression coefficients and odds ratios for the other profiles were presented in the supplementary file.\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the first column shows the regression coefficients that were estimated for each categorical latent variable on the predictor variable. The second column provides odds ratios that allow a better interpretation of the results. We also examined the statistical significance of the estimated coefficients using the p-values provided in the Mplus output for the regression coefficients and the 95% confidence interval (CI) values provided in the Mplus output for the odds ratios. If the 95% confidence interval does not include 1, it is taken as evidence that the odds ratio is statistically significant. This is because odds ratios that do not include 1 indicate a significant change in probabilities. For example, an odds ratio of 1.5 with a 95% confidence interval of (1.2, 1.8) indicates that the event is 1.5 times more likely to occur, and this difference is statistically significant because the interval does not include 1.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the regression coefficients and odds ratios for profile 1 while the reference profile is 4. Firstly, when the gender variable was examined, it was observed that since the reference category was male, female students had positive regression coefficients in profiles 1, 2, and 3 compared to profile 4. In other words, female students were categorized more in other profiles while the reference profile was 4 compared to male students. However, this difference was statistically significant only when comparing profile 2 with profile 4. Similarly, odds ratios were 1.278 when comparing profile 1 with profile 4 and were 1.331 when comparing profile 2 with profile 4, respectively. When Profile 3 with Profile 4 compared odds ratios were obtained as 1.161.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eThe Latent profiles with covariate variables (weighted).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eProfil 1 vs. 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eProfil 2 vs. 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eProfil 3 vs. 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{1}\\:\\varvec{O}\\varvec{R}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{1}\\:\\varvec{O}\\varvec{R}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\beta\\:}}_{1}\\:\\varvec{O}\\varvec{R}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.286*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.331\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLanguage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.308**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.697\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.723**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.060\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome Resource Learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e19.120\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.782**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e5.944\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.865**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2.374\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents Like Learning Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.413*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.662\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.371*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.690\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents Confident in Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.873**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.507\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.302**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3.678\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.789**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2.200\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognitive Activation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.799**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.450\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.391**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.676\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom Management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.599**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.820\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstructive Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.418*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.518\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eis indicated the regression coefficients and β\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eOR is indicated the odds ratios. Bold values indicate If the 95% confidence interval includes 1, it suggests that the effect is not statistically different from 1 (no effect). **p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhen the second variable, language, was examined, it was observed that since the reference category was students who always spoke German at home, students who sometimes and never spoke German at home had statistically significant regression coefficients and odds ratios between the profiles. Similarly, when the values given in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e were evaluated, it was seen that the regression coefficient was 1.308 (p\u0026thinsp;\u0026lt;\u0026thinsp;.01) when profile 1 was compared with profile 4, it was 0.723 (p\u0026thinsp;\u0026lt;\u0026thinsp;.01) when profile 2 was compared with profile 4, and the coefficient was 0.240 when profile 3 was compared with profile 4. The only non-significant regression coefficient for the language variable was 0.240 (p\u0026thinsp;\u0026gt;\u0026thinsp;.05) when comparing profile 3 with profile 4, and there was no difference between profile 3 and 4. A similar situation was observed with the odds ratios for the language variable, where the odds ratio was 3.697 when comparing profile 1 with profile 4, 2.060 when comparing profile 2 with profile 4 and both of these odds ratios were statistically significant. Similar to the regression coefficient, when comparing profile 3 with profile 4, the odds ratio was 1.272, which was not statistically significant. Based on all these findings, it can be concluded that students who sometimes and never speak German at home were classified in less successful profiles and mostly in profile 1. In other words, the more students who sometimes and never speak German at home classified in the least successful profile, when profile 4, which was the most successful profile, was considered as the reference profile.\u003c/p\u003e\u003cp\u003eAs mentioned earlier, the reference category was \"high\" for the instructional quality (classroom management, cognitive activation, constructive support) subscales. In other words, the values presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate the classification of students who rated the low and medium the instructional quality levels in terms of their achievement profiles when compared to students who rated high. When the values given in the table were examined, it was observed that according to profile 4, students, who rated low and medium classroom management and constructive support subscales levels, were classified in profiles 1, 2 and 3 more than profile 4. However, the students reported a high cognitive activity level were more often classified in low achievement profiles when comparing with students who rated low and medium level of cognitive activity. However, these differences were only statistically significant when comparing profile 1 and profile 2 with profile 4 presented in the table. Unlike other findings, the regression coefficient was (-0.275) when comparing profile 3 with profile 4, but this value was not statistically significant. When the regression (0.599) and odds ratios coefficients (1.820) of the classroom management subscale were examined, the only significant difference was observed when comparing profile 1 with profile 4. Similarly, if we examined the regression (0.418) and odds ratio coefficients (1.518) of constructive support subscale, the only significant difference was observed when comparing profile 2 with profile 4.\u003c/p\u003e\u003cp\u003eThe home resources for learning scale, students who have few or some resources at home are categorized in lower achievement profiles students. When the home resource for learning scale examined, the regression coefficients were 2.951 (Profile 1 vs. Profile 4), 1.782 (Profile 2 vs. Profile 4), 0.865 (Profile 3 vs. Profile 4). Similarly, odds ratios were 19.120 (Profile 1 vs. Profile 4), 5.944 (Profile 2 vs. Profile 4), 2.374 (Profile 3 vs. Profile 4) when the reference category was the many resources. In other words, students who have few and some resources for learning at home are categorized in lower achievement profiles.\u003c/p\u003e\u003cp\u003eAs mentioned before, the category \"very much like learning science\" was taken as the reference category for the subscale \"students like learning science\". Another finding here was that students in low achievement profiles were more likely to be categorized in the \"very much like learning science\" category, in other words, they liked learning science even though they were categorized in low achievement profiles. When the negative regression coefficients (-0.003 (Profile 1 vs. Profile 4), -0.413* (Profile 2 vs. Profile 4), -0.371* (Profile 3 vs. Profile 4) and odds ratios (0. 744 (Profile 1 vs. Profile 4), 0.662 (Profile 2 vs. Profile 4), 0.690 (Profile 3 vs. Profile 4) were examined, the differences between second, third profile with fourth profile were statistically significant.\u003c/p\u003e\u003cp\u003eFor the \"student confident in science\" scale, the baseline category \"very confident in science\" was taken as the reference category. When the values given in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e were investigated, it was seen that the students who felt somewhat confident and not confident were classified in lower profiles compared to the students who felt very confident in science. When the regression coefficients (1.873** (Profile 1 vs. Profile 4), 1.302** (Profile 2 vs. Profile 4), 0.789 ** (Profile 3 vs. Profile 4) and odds ratios (6.507 (Profile 1 vs. Profile 4), 3.678 (Profile 2 vs. Profile 4), 2.200 (Profile 3 vs. Profile 4) were examined, it was observed that the differences between the profiles were also statistically significant. According to the findings obtained here, it was observed that students with low levels of science confidence were significantly categorized in low profiles compared to high profiles. In other words, students categorized in low achievement profiles did not feel confident in science when compared to the highest achievement profile.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to use the TIMSS 2019 dataset to determine how German fourth grade students were categorized into different profiles across different science content and cognitive domains, as well as how these profiles were predicted by gender, language at home, resources at home for learning, science motivation and instructional quality. The overall result from the descriptive statistics of the study showed that German students had the highest mean on the life subscale and the lowest mean on the earth science subscale. These results have been reported in previous studies and published reports, and our results were consistent with these reports (e.g., Schwippert et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mullis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, we identified four optimal profiles for German students' achievement in science and found that students had different achievement means in different content domains. For example, students classified in the highest achievement profile had the highest mean scores on the physical subscale. In other words, it was found that when the students' achievement profiles increased, their achievement in the physical science subscale also increased. This study's findings also highlight the importance of assessing TIMSS results in terms of different student profiles. In the optimum profiles identified in the study, it was determined that students in different achievement profiles had relatively high mean scores in different cognitive domains. In previous reports (e.g., Mullis et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the results of Germany's average cognitive domain subscale scores in science were determined and it was reported that the highest score was on the knowing subscale (520), followed by reasoning (519) and application subscales (516). In this study, it was observed that the application subscale had the highest mean value in the lowest achievement profile, while in the second achievement profile, knowing, application and reasoning subscales had similar mean values. It was observed that students classified in the third and fourth achievement profiles had the highest mean in the knowing subscale. A similar pattern of the relationship of cognitive domain subscales to achievement has been reported for different countries in TIMSS (e.g, Perkins \u0026amp; Clerkin, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, countries with the highest rankings in mathematics also have the highest average scores on the knowing subscale. Also, for science scores, some countries with high achievement rankings have the highest average scores on reasoning, while others have the highest average scores on the knowing subscale.\u003c/p\u003e\u003cp\u003eIn addition to the achievement profiles of the students in the study, we also examined important background variables and scales regarding perceived instructional quality that may be associated with these achievement profiles through covariate analyses. Overall, the results of this study reflected results from correlative studies, however, the prediction of the variables on achievement differed significantly between the profiles. The importance of language spoken at home for subject-specific achievement was consistent with previous findings from large-scale studies (e.g. Edele \u0026amp; Stanat, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; OECD, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Younes et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in the present study, language was found to be a significant predictor only for students classified in the first and second achievement profiles. For example, language was not a significant predictor for students in the third and fourth profiles, who are classified in higher achievement profiles. This could be related to the fact that students with migration background were more likely to be classified in lower achievement profiles. Similarly, students who had few and some resources at home for learning were classified in lower achievement profiles than students who had resources at home. In contrast to language, however, resources at home were found to be significant for all achievement profiles. Similarly, these findings supported results in the literature (e.g., Ker et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wiberg \u0026amp; Rolfsman, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shala \u0026amp; Grajcevci, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and emphasized the importance of the background variables.\u003c/p\u003e\u003cp\u003ePrevious studies using TIMSS data have found a strong and positive correlation between motivational beliefs in science and science achievement (Liou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hooper et al., 2017). However, some studies have shown that although some countries perform below the OECD average in science, students have high motivational beliefs about science (e.g., Karakolidis et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In particular, it is emphasized that the relationship between achievement and motivation cannot be completely linear, and that effective and individualized strategies should be applied (Michaelides et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ma, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The importance of motivational patterns across time and age groups, the culture from which individuals come from, has been emphasized (Michaelides et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, studies have shown that the strength of the relationships between science motivational beliefs and science achievement increases from 4th to 8th grade (Liou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The findings from this study also supported both of these views in the literature. It was determined that students classified in low achievement profiles were somewhat or not confident in science. In contrast, especially the students classified in high achievement profiles were very confident in science. Student\u0026acute;s confident in science was a significant predictor between low and high achievement profiles, this finding is consistent with previous studies conducted in several countries (e.g., Kaya \u0026amp; Rice, 2010). However, when the students like learning scale was analyzed, it was found that the students classified in low achievement profiles liked learning science. In other words, the students classified in low achievement profiles liked science even though they did not feel confident in science.\u003c/p\u003e\u003cp\u003eWhen the subscales of instructional quality were examined, it was found that students, who reported a high classroom management and constructive support in their classroom were classified in higher achievement profiles. However, students who reported a high cognitive activity were categorized in low achievement profiles as well. These results were similar to those obtained with the \u0026ldquo;Students like learning\u0026rdquo; scale. Although students were classified in low achievement profiles, they liked learning science and reported a high cognitive activity. This can possibly be explained by the fact that students\u0026rsquo; characteristics (such as prior knowledge, cognitive abilities, motivation and social background) have been shown to predict their ratings of instructional quality (Igler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It would be conceivable that students with lower achievement levels rate their own cognitive activation higher, as they might have a lower prior knowledge and therefore tasks in lessons might be perceived as more demanding (Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe differences between students' mean scores on the sub-scales of content and cognitive domains and their achievement profiles emphasized the importance of the application of profile analysis based on the assumption of \u0026ldquo;individual heterogeneity\u0026rdquo;. It is noteworthy that the findings obtained across and between countries, especially in terms of subscale scores, vary in terms of achievement profiles. The achievement profiles of different student profiles in different domains and the associated background variables need to be considered in countries' educational policies and classroom activities. Overall, it should be emphasized that this study is a descriptive approach at the national level for Germany and that the reasons for the relationship between these variables and high or low academic achievement can be identified in different samples. With regard to our findings, the following limitations must be noted that the results are based on cross-sectional data and no causal inferences can be made. In addition, instructional quality was rated by students. Even though student ratings are commonly used and show positive correlations with students' achievement (Fauth et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Teig et al., 2019), when interpreting the results, the level on which the ratings provide information must be borne in mind (e.g., L\u0026uuml;dtke et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this study the student ratings provide information at individual level and not at class or even school level. Furthermore, it should be emphasized that the concept of classroom management includes a wide range of sub-dimensions and this study is limited to the construct measured by the TIMSS German national sub-scales.\u003c/p\u003e\u003cp\u003eHowever, it is conceivable that the present result could provide an important starting point for how the performance of low achievers can be increased. For instance, different aspects of teaching quality are discussed in research regarding their potential sufficiently in the development of motivational-affective competences. In addition, different forms of cognitive support, such as reduction of task complexity, individual support, and feedback might be important to develop competence-related beliefs (e.g. confidence) (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Eccles, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, no statement can be made on the basis of this study this assumption would need to be tested in further studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has both theoretical and practical implications. The study suggests that latent profile analysis is a proper and feasible method for identifying students\u0026rsquo; achievement (or other variables of interest), and for exploring the associations among variables, particularly when variable-centered approaches do not apply due to nonlinear relationships among variables.\u003c/p\u003e\u003cp\u003eIn terms of practical implications, the results demonstrate that teachers, researchers and policymakers should take students\u0026rsquo; different profiles into account, developing adaptive and individualized strategies to improve students\u0026rsquo; science achievement. For example, for students with low achievement in science, who are more likely to feel less confident, it could be a useful to use an adaptive teaching strategy and assign specific tasks at which they will excel, provide flexible formative science assessment, and/or give genuine and positive feedback on their efforts rather than outcome. Moreover, the results also show that it is important for teachers to reduce gender stereotype in science, and in particular take effective measures to improve the participation and attitudes toward science of girls.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGYT developed the study design, performed the data analyses and contributed to the writing and revision of the study, JB contributed to the development of the study design, writing and revision of the study, MS contributed to the development of the study design, writing and revision of the study. All authors made substantial contributions to the interpretation and discussion of the results. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSome part of the datasets analyzed for this study cannot be available because this is an official data set and must have permission from \u0026ldquo;Institut zur Qualit\u0026auml;tsentwicklung im Bildungswesen\u0026rdquo; (IQB): https://www.iqb.hu-berlin.de/institut/staff). Some of the datasets that support the conclusions of this study are open access and can be downloaded as public use files from the IEA website: https://www.iea.nl/data-tools/repository/timss\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBauer, J. (2022). A Primer to Latent Profile and Latent Class Analysis. In: Goller, M., Kyndt, E., Paloniemi, S., Damşa, C. (eds) Methods for Researching Professional Learning and Development. Professional and Practice-based Learning, vol 33. 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Scandinavian Journal of Educational Research, 67(1), 69\u0026ndash;82. https://doi.org/10.1080/00313831.2021.1983863\u003c/li\u003e\n\u003cli\u003eYamashita, T., Smith, T. J., \u0026amp; Cummins, P. A. (2021). A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data. Journal of Educational and Behavioral Statistics, 46(4), 501\u0026ndash;518. https://doi.org/10.3102/1076998620978554\u003c/li\u003e\n\u003cli\u003eYin, L., \u0026amp; Fishbein, B. (2019). Creating and interpreting the TIMSS 2019 context questionnaire scales. Methods and procedures: TIMSS, 16(1), 1-331.\u003c/li\u003e\n\u003cli\u003eYounes, R., Salloum, S. \u0026amp; Antoun, M. (2023). The effects of language and home factors on Lebanese students\u0026rsquo; mathematics performance in TIMSS. Large-scale Assessment in Education,11, 30. https://doi.org/10.1186/s40536-023-00180-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Profile Analysis, Achievement Profiles, Motivation, Home Resources for Learning, Instructional Quality","lastPublishedDoi":"10.21203/rs.3.rs-6984158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6984158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study was to analyze the achievement profiles of fourth-grade German students in science by using content and cognitive domains in TIMSS 2019. We investigated students' achievement patterns across science content and cognitive domain subscales using latent profile analysis. Based on the results of the analyses, the optimum number of profiles was determined as four. When the achievement profiles of the students were analyzed, it was determined that the mean scores of the life science and applying subscales of the students in the low achievement profiles were significantly higher than the other subscale scores. In high achievement profiles, it was observed that the life and physical science subscale scores were similar and higher than the Earth subscale. When the cognitive domains in high achievement profiles were investigated, the highest mean score was found for the knowing subscale, which was followed by reasoning and applying subscales.\u003c/p\u003e\u003cp\u003eThe study also conducted covariate analyses with demographical and teaching variables commonly assumed to be associated with achievement in the literature, such as gender, language, home resources for learning and instructional quality. We also investigated science motivation scales across the covariate analysis. According to the results of the study, students who sometimes or never speak German in their homes were classified in low achievement profiles compared to students who always speak German in their homes. Likewise, students who had few or some home resources for learning at home were also classified in lower achievement profiles. In addition, students who were very confident in science were categorized in high achievement profiles. Similarly, it was found that students with the high ratings of classroom management and constructive support were classified in higher achievement profiles. However, even though the students were classified in the low achievement profile, it was found that they rated the teaching highly cognitive activating and liked learning science.\u003c/p\u003e","manuscriptTitle":"TIMSS Science Achievement Profiles and Their Associations with Motivation, Instructional Quality Scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 15:50:11","doi":"10.21203/rs.3.rs-6984158/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46e8c0ac-767d-45d0-acb9-58391795a62d","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T15:50:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 15:50:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6984158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6984158","identity":"rs-6984158","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}