Scientific Competence in Developing Countries: Determinants and Relationship to the Environment

sustainability Article Scientific Competence in Developing Countries: Determinants and Relationship to the Environment JoséMauricio Chávez Charro 1, Isabel Neira 1and Maricruz Lacalle-Calderon 2,*   Citation: Chávez Charro, J.M.; Neira, I.; Lacalle-Calderon, M. Scientific Competence in Developing Countries: Determinants and Relationship to the Environment. Sustainability 2021,13, 12439. https://doi.org/10.3390/ su132212439 Academic Editor: Jordi Colomer Feliu Received: 12 October 2021 Accepted: 1 November 2021 Published: 11 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Quantitative Economics Department, Universidad de Santiago de Compostela, 15705 Santiago de Compostela, Spain; [email protected] (J.M.C.C.); [email protected] (I.N.) 2Economic Development Department, Universidad Autónoma de Madrid, 28049 Madrid, Spain *Correspondence: [email protected] Abstract: In 2015, the United Nations General Assembly adopted Agenda 2030 to guarantee sustainable, peaceful, prosperous, and just life, establishing 17 Sustainable Development Goals (SDGs). According to this declaration, pursuing the path of sustainable development requires a profound transformation in how we think and act. People must have scientific competences—not only knowledge of science, but also skills, values, and attitudes toward science that enable them to contribute to the goals proposed. This overall approach, known as Education for Sustainable Development (EDS), is crucial to achieving the SDGs. Scientific competences not only depend on what students learn in their countries’ formal education systems but also on other factors in the environment in which the students live. This study aims to identify the factors that determine scientific competence in students in developing countries, paying special attention to the social and cultural capital and the environmental conditions in the environment in which they live. To achieve this goal, we used data provided by PISA-D in the participating countries—Cambodia, Ecuador, Guatemala, Honduras, Paraguay, and Senegal—and multilevel linear modelling. The results enable us to conclude that achieving scientific competence also depends on the social and cultural capital of the student’s family and on the cultural and social capital of the schools. The higher the score in these forms of capital, the greater the achievement in sciences. Keywords: scientific competence; SDG; PISA-D; multilevel 1. Introduction In 2015, the United Nations General Assembly adopted Agenda 2030 to guarantee sustainable, peaceful, prosperous, and just life, establishing 17 Sustainable Development Goals (SDGs) [ 1 ]. According to this declaration, pursuing the path of sustainable development requires a profound transformation in how we think and act. People must have scientific competences—not only knowledge of science but also skills, values, and attitudes toward science that enable them to contribute to the goals proposed. This overall approach, known as Education for Sustainable Development (EDS), is crucial to achieving the SDGs. Scientific competences not only depend on what students learn in their countries’ formal education system but also on other factors in the environment in which the students live. These factors could thus ensure provision of the scientific competences necessary for preparing students to face the grave environmental problems that have especially strong negative effects on the poorest and developing countries. This study aims to identify the factors that determine the scientific competence of students in developing countries according to the results of PISA-D, paying special attention to social and cultural capital and to the environmental conditions of the area in which the students live. The study also seeks to fill the gap in studies on education about the environment and sustainability in Africa, south and central America, and southeast Asia. To achieve these goals, we analysed data from PISA-Development in the participating countries of Cambodia, Ecuador, Guatemala, Honduras, Paraguay, and Senegal [ 2 ]. To Sustainability 2021,13, 12439. https://doi.org/10.3390/su132212439 https://www.mdpi.com/journal/sustainability Sustainability 2021,13, 12439 2 of 16 identify the factors that determine the scientific competence of students in these countries, we performed a multilevel analysis, considering three levels of information: from the student (level 1), from the school (level 2), and from the community (level 3). The remainder of the paper is organised as follows: Section 2offers a brief theoretical framework on education, scientific competences, and sustainable development, focusing on the determinants of scientific competences; Section 3develops the empirical analysis, presenting the data, model, and method used; Section 4presents the results; finally, Section 5presents the conclusions. 2. Theoretical Framework 2.1. Education, Scientific Competence, and Sustainable Development Not all kinds of education foster sustainable development. Therefore, we talk about Education for Sustainable Development (EDS), which is oriented to empowering learners with knowledge, skills, values, and attitudes to take informed decisions and make responsible actions for environmental integrity, economic viability, and a just society [ 3 ]. According to Tapio and Willamo [ 4 ], the factors that affect human action to face environmental problems can be divided into: (i) individual factors, (ii) social factors, and (iii) ecological factors. Individual factors include knowledge, or the rational logical part of human thinking and, more concretely, knowledge of specific information about the environment and environmental measures [ 5 ]. This knowledge is also known as scientific competence. The Programme for International Student Assessment (PISA) defines scientific competence as the ability to use scientific knowledge to understand and make decisions about the natural environment and the changes it undergoes in relation to human action [ 6 ]. Social factors include science, as the result of research and in turn as social organization [ 7 ]. Science is closely linked to education, which is an important means of protecting the environment [ 8 ]. Most environmental problems are due to a lack of environmental knowledge, a term used to mean knowledge and awareness of environmental problems and possible solutions to them [ 9 ]. Increasing knowledge of environmental problems can increase people’s concern and awareness [ 10 ]. Some authors argue that a common premise for promoting sustainability is to increase people’s awareness and education [11]. In line with the foregoing, the framework of action to achieve SDG 4 (ensure inclusive and equitable quality education and promote lifelong learning opportunities for all), establishes that all students should acquire the theoretical and practical knowledge essential for promoting sustainable development by 2030, establishing as goals: (i) “Percentage of students by age group (or education level) showing adequate understanding of issues relating to global citizenship and sustainability; (ii) Percentage of 15-year-old students showing proficiency in knowledge of environmental science and geoscience” [12] (p. 79). For years, the OECD’s Programme for International Student Assessment (PISA) has been evaluating the degree to which 15-year-old students nearing the end of compulsory education have acquired key knowledge and skills essential for full participation in modern societies. The competences PISA usually evaluates are reading competence, mathematical competence, and scientific competence [ 6 ]. To include developing countries, the OECD also established PISA-D, so that these countries could determine their students’ levels of competence (reading, mathematics, and scientific) and thus improve their public policy on educational issues. Thus, the PISA-D science framework considers scientific competence as key at both the local (intra-country) and international level to enable countries (individually and together) to face the tremendous challenges in water and food supply, disease control, energy production, and adaptation to climate change [ 6 ]. Facing all these challenges requires a significant contribution from science and technology. However, “this does not mean turning everyone into a scientific expert but enabling them to fulfil an enlightened role in making choices which affect their environment and to understand in broad terms the social implications of debates between experts” [ 6 ] (p. 28). Teaching and learning about science related directly to everyday life make knowledge useful for understanding how the natural world functions, while also teaching students to be informed citizens Sustainability 2021,13, 12439 3 of 16 who are prepared to tackle social issues related to science intelligently [ 13 ]. Scientific competence, understood as “the ability to use knowledge and information interactively— that is ‘an understanding of how it [a knowledge of science] changes the way one can interact with the world and how it can be used to accomplish broader goals’” [ 6 ] (p. 93), is perceived as a key competence all students must have [ 14 , 15 ]. With this competence, young people can respond to the current environmental and climate crisis by making informed critical decisions that influence their environment. This is the purpose of scientific literacy or competence [6]. 2.2. Determinants of Scientific Competence In scientific education, developing a student’s interest in science probably results in higher levels in understanding of science and environmental awareness [ 16 ]. Determining how students can achieve good performance in such scientific competence is a challenge for all countries, especially the least developed. Recent studies argue that achieving scientific competence for 15-year-old youths in the PISA test depends on socioeconomic, family, environmental, and attitudinal factors [ 17 – 21 ] (see Figure A1 in Appendix A). Among these factors, we highlight the following: The environment. The environment refers specifically to characteristics of the environment of both the school and the students’ households that support success and education through disciplinary and academic climate in the school, as well as through cultural norms and values that motivate students to achieve higher goals [ 22 ]. The environment in which the children’s families, schools, and community live their lives affects educational performance and thus students’ behavior and development [23–28]. Family factors or cultural capital. Coleman [ 29 ] and Bourdieu [ 30 ] have studied the relationship between academic achievement and family. Bourdieu has stressed the crucial role of family resources (relational, material, and cultural) in shaping children’s unequal education results. The family thus plays a fundamental role in students’ learning and performance [ 31 ]; this influence has been called cultural capital. Citing Bourdieu, Cervini [ 32 ] (p. 454) stresses that “cultural capital, then, plays a role of intermediary factor between the student’s social origin (family background) and their learning”. In other words, children of higher social class will possess inherited cultural capital that is valued more highly by the school and will thus have greater success than students without such capital [32]. Social capital. Social capital is defined as a set of relational resources that groups and individuals can access based on their interests [ 30 , 33 ]. Social capital has come to be viewed as a flexible conceptual instrument that can be used to explain a wide range of social problems, including education [ 22 ]. Studies of the effect of social capital on academic performance have found a positive correlation between the two [34–36]. It is very important to know how these capitals influence students’ achievement in countries with low and medium economic conditions and large cultural differences. It is also important to know how these capitals influence student achievement and how students’ achievement is related to their scientific literacy and thus indirectly to their “climate or ecological consciousness”, starting from the important natural wealth of developing countries . 3. Empirical Analysis 3.1. Data To empirically analyze the factors determining scientific competence in developing countries, we used data reported by PISA-Development in the participating countries of Cambodia, Ecuador, Guatemala, Honduras, Paraguay, and Senegal [ 2 ]. The data come from two questionnaires. The first, completed by students, is the questionnaire on antecedents. It includes information on students’ wellbeing, achievements, and attitudes towards school; learning in their households; and relationship with their parents, classmates, and/or professors; as well as parents’ education and occupation. The second questionnaire, completed by the director of the school, includes information on the school: where it is located, how Sustainability 2021,13, 12439 4 of 16 it is structured and organized, and what the learning environment is like. The PISA-D data represent around one million students 15 years old, 34,604 of whom from a total of 1299 schools completed the assessment (see Table 1). The sampling technique used for PISA-D for each participating country is a stratified sampling design in two stages. The sampling units at the first stage consisted of schools with eligible students (or with the possibility of having such students at the time of the evaluation). Schools were systematically sampled from a comprehensive national list with all PISA-D-eligible schools. The strata were defined for each of the countries according to their characteristics. The sampling units at the second stage consisted of students from the sampled schools. These students were chosen from a complete list of 15-year-old students from each of the sampled schools. A target cluster size (TCS) was set for each country; this value was usually 42 students. Table 1. Countries participating in PISA-D and their achievement in sciences. COUNTRY Country Sample Avg. in Sciences Cambodia 5162 330 Ecuador 5664 399 Guatemala 5100 365 Honduras 4773 370 Paraguay 4510 358 Senegal 5182 309 Zambia 4213 309 OECD avg. 493 Total 34,604 Source: [2]. We highlight that over half of the items were identical to those evaluated in PISA-2015, which enabled us to derive further information on the PISA results by connecting them to the scale. The other items were adapted to the PISA framework [ 6 ]. All this information considers the students’ personal and socioeconomic characteristics, their social and cultural capital, and the characteristics of the environment that can influence the students’ scientific competence or academic achievement in the sciences. All these characteristics condition these students’ behavior in their relationship to the environment [ 6 ]. The data provided by PISA-D were used to evaluate these variables, while considering three levels of information: from the student (level 1), from the school (level 2), and from the community (level 3). Table A1 in Appendix Adescribes all variables used in this study. The data from the dependent variable were scaled using the Rasch model and expressed by assigning ten plausible values [ 37 ], presented on a continuous scale in which 500 points is equivalent to the average of the OECD countries, where the standard deviation is standardized at 100 points [38]. The independent variables associated with cultural capital were selected following Tramonte and Willms, who propose that there are two types of cultural capital, one static and the other relational [ 39 ]. The first is associated with possession of cultural goods and intellectual activities, and the second with discussions on cultural and political issues. Static cultural capital can only reflect the decisions and lifestyle of one’s parents, whereas relationship cultural capital reflects how capital is used and transmitted [40]. The variables related to social capital were chosen according to the approaches in Coleman [ 29 ], for whom social capital can be presented in three forms: expectations and obligations, information channels, and social norms. In PISA-D [ 41 ], the variables that can be included in level (I) are communication within the family, attitude towards school, and relationships between students and teachers. Level (II) includes climate of discipline in the classroom, teacher’s expectations, class size, and whether the school is in a high-crime area. For a more detailed description of each of the variables, see Table 2. Sustainability 2021,13, 12439 5 of 16 Table 2. Descriptive statistics of the variables. Characteristics of the Environment Area Variable Observations Mean Standard Deviation Min Max School environment (LEVEL 2) Type of community in which the school is located 33,734 2.5948 1.288359 1 5 Near highway or freeway 28,803 0.4222477 0.4939262 0 1 Near busy roads or intersections 31,153 0.713286 0.452234 0 1 Near dump or waste land 27,244 0.0934518 0.2910698 0 1 Near geologically unstable area 27,148 0.0754015 0.264043 0 1 Near industrial district 27,188 0.0798514 0.271068 0 1 Number of days school is closed due to weather or illness 32,244 0.4918124 1.593598 0 20 Time from home to school 32,898 1.916074 1.114331 1 5 School characteristics (LEVEL 2) Public or private school 34,396 0.9540063 1.216799 0 9 School resources 34,200 3.080643 1.399285 1 5 Characteristics of the Student and Their Socioeconomic Environment Area Variable Observations Mean Standard Deviation Min Max Student’s characteristics (LEVEL 1) Grade compared 34,604 −0.2975668 1.039366 −3 3 Repeater 33,692 0.305859 0.4607771 0 1 Gender 34,604 0.4853774 0.4997934 0 1 Family’s cultural capital (LEVEL 1) Number of books at home 32,189 2.38218 0.9530487 1 4 Mother’s education level 30,481 2.527771 2.020261 0 6 Household poverty index 33,501 3.253037 0.854383 1 4 Household properties 32,501 −1.790417 1.4060211 − 8.1792 3.9708 School’s cultural capital (LEVEL 2) Percentage of very poor students 33,418 4.428901 1.673123 1 6 Level of instructional resources 32,664 3.00 1.414 1 5 Family’s social capital (LEVEL 1) Communication within the family 29,615 1.60e-07 1.004 − 2.752 1.095 School’s social capital (LEVEL 2) Attitude towards school 32,413 7.536331 2.065439 0 10 Discipline in the classroom 32,593 0.1640453 1.045713 − 2.9394 2.2536 Student–teacher relationships 32,475 6.541837 2.002628 0 10 Teacher’s expectations 32,664 7.130877 2.38518 0 10 Class size 32,288 773.00 826.511 6 5111 Community’s social capital (LEVEL 2) Located in a high-crime neighborhood 27,403 0.1215195 0.326736 0 1 Source: Developed by authors. 3.2. Specification of Model and Estimation Procedure To consider the hierarchical structure of the data from PISA-D 2017 and to study the conditioners of students’ scientific competence in developing countries ( Yijk ), we applied multilevel linear models [ 42 , 43 ]. The econometric model for the estimation is given by Equation (1): Yijk =β0+β1X1ijk +β2X2ijk +γ1Z1jk +γ2Z2jk +γ3Z3jk +β3DK+µ0j+µ1jX2ijk +ε1ijk (1) Sustainability 2021,13, 12439 6 of 16 where, (i) indicates the student, who belongs to school (j) from country (k). The variable X1ijk is a series of variables related to the student’s personal and socioeconomic characteristics; X2ijk is a set of variables associated with the family’s cultural and social capital; Z1jk is a group of variables that characterize and approximate the school’s cultural and social capital; Z2jk is the set of variables of the school’s surroundings and natural environment; Z3jk is the set of variables of the school’s characteristics; and DK represents the dichotomous variables that include the student’s country of residence. Furthermore, µ0j is the error of the random effects of the schools’ level, µ1j is the random slope for each school relative to the family’s cultural or social capital cultural, and ε1ijk is the error term in the students’ level. We start from the assumption that µ0j , µ1j , and ε1ijk follow a normal distribution with mean zero and variances σ2 0µ,σ2 1µ, and σ2 ε. The multilevel model used is explained by analyzing its fixed and random parts. The fixed component of the model, expressed by ( β0+β1X1ijk +β2X2ijk +γ1Z1jk +γ2Z2jk + γ3Z3jk +β3DK ), defines the relationship between the student’s academic performance and a set of co-variables of the student or the school, whose estimated slopes are assigned by the parameters β1 , β2 , γ1 , γ2 , γ3 , and another fixed effect defined by the slope of the country effect β3 . The random part of the model composed of ( µ0j , µ1jX2ijk , ε1ijk ) enables us to estimate the variances σ2 0µ , σ2 1µ , and σ2 ε , and includes the remainder h(ε]ijk . This method gives each school the possibility of maintaining its own error component [β]0+µ0j and its own random slope ( β2+µ1j ) for any explanatory variable of the student’s cultural or social capital. The model can thus indicate whether the effect of any variable of social or cultural capital on scientific competence changes among schools once we control for other characteristics of the educational institution itself considered in the fixed part of the model ( γZijk ). Given the conditions explained, we can analyze possible heterogeneity among schools and these conditions, while at the same time measuring the “average” effect of each variable [40]. Given the hierarchical structure of the data, we specified four multilevel models. Model I considers only the variables of cultural and social capital belonging to the student. Model II incorporates the variables of the family’s cultural and social capital. The goal of Model II is to show whether the cultural and social capital of the student’s family has a different effect, independent of the school the student attends. For this effect, we then incorporate the random slopes of the variables that were statistically significant in Model I. Model III incorporates the variables of social and cultural capital concerning the school. Model IV adds the variables related to the natural environment of the area in which the school is located, as well as the characteristics of the school itself. Model V, like Model II, incorporates the variables that approach cultural and social capital as random slopes of the schools. All the models include the gender, grade compared, and whether the student has repeated one or more grades in school as control variables. Since the PISA-D test included only seven countries, the multilevel characteristics of our data mean that the country effect is considered as a fixed effect, since capturing the variability between countries requires at least 30 countries [42,44,45]. 4. Results Table 3shows the results of our estimations. Of all the variables defining the school’s environment, only “size of the community where the school is located”, “number of days the school is closed”, and whether “the school is near a geologically unstable area” are statistically significant. According to the results of Model IV (see column 5 in Table 3), the larger the community where the school is located, the higher the results for scientific competences. This means that students in large cities have a comparative advantage over students in schools located in less populated areas, which are usually rural. This result is similar to that obtained by Miller and VotrubaDrzal [ 46 ], who argue that the urbanization level influences academic achievement based on the differences they find between urban and rural population settlements. The second Sustainability 2021,13, 12439 7 of 16 significant variable—with negative effects on scientific competences—is the number of days the school is closed due to weather or illness, a characteristic typical of developing countries. Finally, schools located in geologically unstable areas show higher student science competences than those in stable areas. This result must be related to the special awareness people develop when they live in such places. Table 3. Multilevel estimations of PISA-D scientific competences. Variables Model I Model II Model III Model IV Model V School environment Community size where the school is located 5.172 *** 4.764 *** (0.768) (0.717) Near highway or freeway 2.890 2.284 (1.745) (1.630) Near busy roads or intersections 0.423 1.646 (1.923) (1.785) Near a dump or wasteland −2.562 −3.923 (3.244) (2.958) Near geologically unstable area 8.211 ** 6.631 * (3.178) (2.934) Near industrial district −1.744 −1.290 (3.313) (3.104) No. of days school is closed due to the weather or illness −1.714 *** −1.632 *** (0.464) (0.430) Time from home to school −0.504 −0.537 (0.310) (0.307) School characteristics Public or private −6.376 *** −4.633 *** (1.314) (1.237) School resources 0.170 0.0601 (0.816) (0.750) Student characteristics Grade compared 16.32 *** 15.83 *** 15.07 *** 15.05 *** 14.73 *** (0.352) (0.348) (0.409) (0.436) (0.433) Repeater −10.39 *** −10.10 *** −10.30 *** −9.807 *** −9.616 *** (0.651) (0.645) (0.762) (0.820) (0.814) Gender/Male 10.31 *** 9.727 *** 10.44 *** 10.23 *** 9.641 *** (0.514) (0.613) (0.600) (0.646) (0.779) Family’s cultural capital Number of books at home 4.115 *** 3.906 *** 4.188 *** 4.306 *** 4.071 *** (0.308) (0.305) (0.360) (0.390) (0.386) Mother’s education 0.755 *** 0.387 * 0.776 *** 0.721 *** 0.477 * (0.145) (0.173) (0.169) (0.182) (0.216) Household poverty index 0.874 3.207 *** 0.960 1.212 2.595 ** (0.622) (0.635) (0.722) (0.778) (0.800) Household properties 1.914 *** 0.894 * 1.444 ** 1.062 * 0.477 (0.386) (0.421) (0.447) (0.488) (0.533) School’s cultural capital Percentage of very poor students −4.654 *** −3.007 *** −2.370 *** (0.527) (0.563) (0.530) Level of instructional resources 9.166 *** 5.074 *** 4.461 *** (0.714) (0.903) (0.835) Family’s social capital Communication within the family 4.387 *** 4.258 *** 4.443 *** 4.578 *** 4.448 *** (0.312) (0.329) (0.371) (0.400) (0.421) School’s social capital Attitude towards school −0.725 *** −0.645 *** −0.0333 −0.00760 0.0511 (0.131) (0.130) (0.179) (0.192) (0.209) Discipline in the classroom 1.496 *** 1.605 *** 1.576 *** (0.319) (0.341) (0.339) Sustainability 2021,13, 12439 8 of 16 Table 3. Cont. Variables Model I Model II Model III Model IV Model V Student–teacher relationships −2.758 *** −2.940 *** −2.818 *** (0.219) (0.238) (0.236) Teacher’s expectations 1.110 *** 1.122 *** 1.002 *** (0.186) (0.201) (0.200) Class size 0.0985 0.0443 0.00854 (0.0631) (0.0654) (0.0605) Community’s social capital Located in a high-crime neighborhood 2.165 −0.303 0.184 (2.526) (2.809) (2.591) Countries, reference Cambodia Ecuador 50.06 *** 34.61 *** 46.88 *** 42.66 *** 34.38 *** (2.984) (2.615) (3.179) (3.238) (3.011) Guatemala 30.55 *** 20.63 *** 31.42 *** 26.65 *** 21.48 *** (2.923) (2.515) (3.114) (3.310) (3.044) Honduras 45.05 *** 39.46 *** 53.40 *** 46.88 *** 43.79 *** (2.899) (2.512) (3.192) (3.395) (3.161) Paraguay 30.01 *** 21.02 *** 32.44 *** 33.60 *** 27.88 *** (2.954) (2.602) (3.326) (3.483) (3.267) Senegal −27.55 *** −29.86 *** −13.80 *** −23.54 *** −25.28 *** (3.059) (2.578) (3.268) (3.503) (3.182) Zambia −5.307 −6.003 * 5.924 4.622 3.154 (3.011) (2.627) (3.268) (3.261) (3.031) Constant 313.3 *** 311.0 *** 297.8 *** 311.7 *** 307.0 *** (3.825) (3.773) (5.937) (7.527) (7.288) Variance of random effects Student ch2 €654.4 *** 1607.53 *** 482.7 *** 407.6 *** 1030.99 *** School ch2 u0)1519.3 *** 1426.85 *** 1508.0 *** 1505.3 *** 1415.47 *** Slopes of Xijch2 u1) No. of books at home 116.324 *** 125.858 *** Mother’s education 8.983 *** 8.259 *** Household properties 18.514 *** 18.274 *** Communication within the family 10.97 *** 11.27 *** Attitude towards school 5.637 *** 4.546 *** Number of students 25,604 25,604 25,604 25,604 25,604 Number of schools 1282 1282 1282 1282 1282 2LR 100,184.84 393.96 71,813.85 26939.99 179.3 Standard errors in parentheses: * p< 0.05, ** p< 0.01, *** p< 0.001. As to the schools’ characteristics, it is very interesting that the school’s infrastructure and resources have no significant effect on scientific competences but that the public or private character of the school does. The results show that public school students achieve a lower level of competence than those educated in private schools. According to the OECD [ 47 ], private school students score higher in science than public school students. If the socioeconomic profiles of students and schools are considered, however, public school students score higher than private school students on average in all OECD countries. Similarly, Castro Aristizabal et al. [ 48 ] find that 87.2% of school performance differences in science are explained by whether students attend public or private schools. In addition to the school’s physical environment, its cultural and social capital are very important in explaining the student’s scientific competences. These capitals are conditioned by the socioeconomic characteristics of the community in which the school is located. The results of Model III (column 4 in Table 3) show that the school’s cultural capital, proxied by the “percentage of extremely poor students” and “school learning resource level”, is statistically significant in explaining the level of science competences students achieve. A high percentage of very poor students in the school lowers the science competence Sustainability 2021,13, 12439 9 of 16 levels. According to Alivernini and Manganelli [ 19 ], the factor that seems to be most closely associated with the difference between average school outcomes is the school’s average socioeconomic level. Cohen-Vogel et al. [ 24 ] term the concentration of students with high poverty levels “ghettoization” of urban centers. Schools in these urban centers achieve poor academic results because most students—poor children and young people without access to adequate housing, health care, and nutrition—find it very difficult to concentrate and learn well. Many factors explain the interaction between social stratification and cultural production in schools and communities where racial, ethnic, and socioeconomic groups show persistent differences in academic performance [49,50]. Our results show that the “school learning resource level” variable is also very important in explaining the scientific competences students achieve. This result is similar to those of Murillo and Román [ 51 ], who suggest that schools’ learning resources have significant effects on scientific competences in middleand low-income countries, even when controlling for students’ socioeconomic characteristics. Furthermore, our results contrast with those obtained for developed countries, such as Spain [ 6 ], where schools’ technological resources showed no significant values [ 20 ]. These results probably reflect the fact that schools in developed countries have all the elements needed for learning, whereas educational institutions in developing countries do not. If we now examine the effect of the school’s social capital on the scientific competences students achieve, our estimations of Model III show positive results (column 4 in Table 3 ). Specifically, a good climate of classroom discipline and good teacher’s expectations yield better student performance in science. These results are in line with Acar [ 22 ], who indicates that social capital, in the form of the school’s disciplinary and academic climate, supports success and education. The results also show that cultural norms and values motivate students to achieve higher goals. This criterion reinforces Putnam’s argument that the development of children and youth is strongly determined by the school’s social capital [ 33 ]. These results contrast, however, with those of Glewwe and Kremer [ 52 ], who cannot draw any general conclusions about which teaching, and school variables increase learning in developing countries. The values of the variable “student–teacher relationships” are noteworthy, as the relationship is significant but has a negative sign. If we examine the results of the variance among the schools, we see a decrease when comparing Model II (value 1426.85) to the variance of Model I. This finding indicates that including the variables of families’ social and cultural capital as random slopes (Model II) as well as the variables of the schools’ social and cultural capital (Model V) reveals differences in the results between schools due to the influence of social and cultural capital associated with the student [34]. In addition to all previously discussed variables related to the school (e.g., its physical environment or cultural and social capital), other student-level variables are very important in explaining the results of students’ scientific competence, and thus climate awareness. At this level, we considered not only the individual students’ characteristics but also their cultural and social capital, variables again conditioned largely by the socio-economic environment of the student’s family. As we can see in Table 3, the results of our estimations show that the student’s cultural capital, proxied by the “number of books at home”, is not only statistically significant but is also the major factor explaining students’ scientific competences. Other authors obtain similar results for mathematics in Latin-American countries [ 20 , 53 ], and for reading competence in OECD countries [39]. As to the student’s social capital, we follow Coleman’s definition as “relationships between children and parents” [ 29 ] and proxy this variable with “communication within the family”. The results in Table 3indicate that this variable is statistically significant in all models and is positively related to competence in science. This result is in line with Caro [ 54 ] and Dufur and Parcel et al. [ 34 ], whose studies find a positive interaction of communication Sustainability 2021,13, 12439 16 of 16 49. Carter, P.L. “Black” Cultural Capital, Status Positioning, and Schooling Conflicts for Low-Income African American Youth. Soc. Probl. 2003,50, 136–155. [CrossRef] 50. Le Mener, M.; Meuret, D.; Morlaix, S. L’accroissement de l’effet de l’origine sociale sur la performance scolaire: Par oùest-il passé?Rev. Française Sociol. 2017,58, 207–231. [CrossRef] 51. Murillo, F.J.; Román, M. 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