Academic performance in the context of bilingual education

Rendimiento académico en el contexto de la enseñanza bilingüe

https://doi.org/10.4438/1988-592X-RE-2025-409-701

José Roberto Arias García

Universidad de Valladolid

https://orcid.org/0000-0001-5344-7891

Mª Victoria Vega Agapito

Universidad de Valladolid

https://orcid.org/0000-0002-9435-9457

Abstract

Keywords:

Academic performance, Academic achievement, Performance factors, Socioeconomic factors, Primary education, Bilingual education, Student evaluation

Resumen

Palabras clave:

Introduction

Academic performance has been a perennial topic of interest in the field of education. Torres and Rodríguez (2006) define it as the level of knowledge relative to a given standard, while Vilches et al. (2018) interpret it as the effectiveness in achieving curricular objectives, reflecting the degree of learning acquired.

Martínez-Otero (2007), adopting a humanistic perspective, describes academic performance as “the output produced by students in educational institutions, typically expressed through school grades” (p. 34). Similarly, Caballero et al., Abello and Palacio (2007) emphasize that academic performance entails meeting goals, achievements and objectives of a program or subject, also expressed through grades.

As Grasso (2020) notes, academic performance is used to evaluate the effectiveness of the education system, encompassing both the level achieved by students and the level performed by teachers. Various tools have been used to measure academic performance, such as analyzing report card grades, which is considered a reliable and valid method (Edel, 2003).

To complement these tools, the Organic Law 8/2013 proposed a final evaluation for sixth-grade students (MECD, 2015). Article 21 establishes that an individualized assessment of Primary Education (PE) students must be conducted at the end of their stage to verify the degree of acquisition of linguistic communication competence, mathematical competence and basic competencies in science and technology (S&T), as well as the achievement of overall stage objectives (p. 16). This evaluation was conducted from the 2015/16 to the 2018/19 academic years on a sample basis for diagnostic purposes. The collected data generated several reports: one individualized report for each student regarding his/her acquisition of linguistic communication, mathematics and S&T competencies, issued also for each student, their family and tutor; one report for teaching and school management teams; and an aggregated report regarding the reference territory for education authorities. These efforts aimed to establish improvement measures across all levels of the educational administration and by all its members (MECD, 2015). Given that educational powers are transferred to the Autonomous Communities in Spain, regional administrations conducted these evaluations under the National Institute for Educational Evaluation guidelines (MEFP, 2019).

These evaluations provide a good context for analyzing PE student performance. However, it must be acknowledged that academic performance is a complex concept that often generates confusion (González et al., 2012) and its evaluation should consider students’ contexts and realities. Thus, beyond definitions, it is essential to explore factors influencing or conditioning students´ academic performance

Tejedor (2003), identifies multiple interrelated factors that condition academic performance, within a complex network where problems and causes are difficult to pinpoint. Torres and Rodríguez (2006) classify these factors into four categories: social and cultural, school-related, familial and personal. Several studies have investigated these factors. Regarding social and cultural aspects, research highlights families’ socioeconomic factors (Pozo-Burgos et al., 2022; Chaparro et al., 2016), and father and/or mother education levels (Zambrano-Mendoza, 2019; Glick et al., 2011). For schoolchildren, studies focus on the school environment (Barreto & Álvarez, 2017; Buckman et al., 2021), teacher influence (Ambussaidi & Yang, 2019; De la Torre & Ávila, 2002) and school quality (Willms, 2010). Familial factors address parents´ attitudes toward their children as perceived positively or negatively by them (Chaparro et al., 2016; Zambrano-Mendoza et al., 2019). Finally, personal factors include intellectual aptitudes (Castejón & Vera, 1996), self-concept (Chávez-Becerra et al., 2020; Redondo & Jiménez, 2020), self-esteem (García et al., 2006; Tejada, 2018) and social competence (Torres et al., 2020).

The context surrounding the students may include factors not encompassed within the previously mentioned categories, which may condition their academic performance. One such factor could be bilingual education. According to Vinuesa and Izquierdo (2019), bilingual education refers to an educational model in which exposure to a second language (a foreign language) serves as a medium for accessing non-linguistic knowledge. It involves the use of two or more languages as a medium of instruction for part or all of the school curriculum. Báez and González (2013) further define bilingual education as an approach where any subject content can be taught in both languages, with both serving instrumental purposes.

In Castilla y León, two types of bilingual schools coexist: autonomous bilingual sections (SAB) and schools with the British Council Bilingual Education Program (PEB-BC).

The SABs were established under Order EDU/6/2006, which regulates the creation of SABs in publicly funded schools and defines bilingual sections as "schools that, once authorized, use a foreign language to teach content of certain areas or non-linguistic subjects” (p. 781). These schools are required to offer a minimum of two and a maximum of three non-linguistic subjects taught in a foreign language, with no more than 50% of total student instructional hours delivered in that language.

Neither the LOMCE (the educational law in effect at the time of this study; BOE, 2013) nor the current LOMLOE law (BOE, 2020) explicitly specifies which subjects must be taught in a foreign language, leading to variations among schools. Typically, subjects such as Natural Sciences, Social Sciences, (or their combination, Environmental Knowledge, that emerged after the LOMLOE), Art, Music or Physical Education are taught in English. Most schools primarily teach Natural Sciences in English (Vega-Agapito et al., 2021, Consejería de Educación, Ciencia y Universidades, 2023) and Mathematics is never taught in a language other than the native one.

The PEB-BC program was established through a collaboration agreement signed in 1996 between the Ministry of Education and the British Council Spain Foundation. Its main objective is to teach English language and culture through an integrated curriculum (Falcón-Diaz et al., 2019). In this program, non-linguistic subjects taught in English are specifically defined by legislation as Environmental Knowledge, Artistic Education, and Physical Education. During the PE stage, students receive between 10 and 12 hours of instruction in English per week (Falcón-Diaz et al., 2019).

Both SAB and PEB-BC fall under the “partial” bilingual programs, offering immersion close to 50% in the second language. These programs differ significantly from “transitional” programs (Baker and Wright, 2021), where students begin instruction in their native language, but eventually transition to using the second language as the medium of instruction. Schools with PEB-BC programs align more closely to bicultural bilingualism as defined by Molina (2003), whereas SABs represent monocultural bilingualism with predominance of the culture linked to the native language.

Implementing these programs should entail a measured, structured process based on previous research analyzing the potential effects of bilingual education on students’ academic performance. As Pavón (2018) notes, some question the benefits of bilingual education from a social perspective, while others point out the lack of sufficient scientific evidence to thoroughly assess the impact of these programs on academic content learning. Agraso-López et al. (2021) emphasize the lack of a generalized consensus needed to establish a clear relationship between bilingual education and academic performance. Nevertheless, they cite studies where the superiority of bilingual students over monolingual is evident.

According to Ardila (2012), individuals who speak two languages exhibit greater metalinguistic awareness, enhanced cognitive control, mental flexibility, better performance on verbal and nonverbal tasks, improved academic development, greater capacity for cognitive resources, better comprehension of the first language, stronger metalinguistic and metacognitive skills and better-controlled processing. Costa (2017) attributes these advantages to increased gray matter density in specific brain regions among bilingual individuals, which fosters the development of certain cognitive abilities.

In this regard, Mohr et al. (2018) discuss brain restructuring resulting from bilingual studies, while Chamorro and Janke (2022) have found that students engaged in bilingual education demonstrate significant improvements in cognitive and social skills, such as selective attention and cooperation. González and Duñabeitia (2024) argue that multilingualism in classrooms is culturally enriching and may enhance overall learning. Thus, there is evidence suggesting that bilingual contexts influence all subjects which makes it meaningful to study further. However, not everything is positive. Potential drawbacks are interlinguistic interference, reduced verbal fluency and delays in language acquisition. It is important to keep in mind that these potential disadvantages of bilingualism may depend on various factors and do not apply universally across all cases (Ardila, 2012; Costa, 2017). Moreover, the cognitive and academic benefits of bilingualism may outweigh these potential drawbacks by positively impacting school performance and cognitive skills development across different areas of knowledge.

However, perceptions regarding bilingual programs remain diverse. Ruiz (2023) highlights that some families believe less content is learned in subjects like History or Natural Sciences compared to non-bilingual schools. Martínez-Garrido et al. (2022) add that some parents perceive these programs as overly focused on vocabulary memorization rather than deep learning. Esparza and Belmonte (2020) note that teachers question the effectiveness of current programs. Martínez and Felices (2022) emphasize students´ difficulties in understanding social content taught in English and in incorporating approaches such as addressing controversial topics.

This study aims to test Ardila’s (2012) and González and Duñabeitia’s (2024) claims regarding improved performance, enhanced academic development, or greater metalinguistic and metacognitive skills among bilingual individuals. Using data collected from various schools in the Autonomous Community of Castilla y León—both non-bilingual schools and those implementing SAB or PEB-BC programs—the study seeks to analyze whether bilingual education contexts significantly influence student performance in both Mathematics and S&T. Additional factors identified by existing literature as influencing academic performance will also be considered for analysis.

Method

This research employs a descriptive, cross-sectional and non-experimental quantitative methodology. It draws upon data collected by the Junta de Castilla y León about the final assessment of sixth grade in EP during 2018/2019 academic year. These data were obtained from the Directorate General of Innovation and Teacher Training of the Junta de Castilla y León upon request. The questionnaires employed (MEFP, 2019) encompassed assessments of linguistic competence in Spanish, Mathematics and S&T.

A two-stage, stratified, cluster sampling was implemented. Initially, schools were selected with a probability proportional to the number of sixth-grade students enrolled. Subsequently, two groups were chosen from each school. Stratification was based on school type (public, private, rural, urban, public and private-subsidized) and the total number of sixth-grade students per institution. These sampling characteristics, coupled with the extensive information gathered from students, teachers and families, ensure the robustness and reliability of the findings.

The sample consisted of 1,652 sixth-grade students, including 817 boys and 835 girls from 49 schools—representing 8% of the total sixth-grade student population in Castilla y León for the 2018/19 academic year. Among these students, 548 attended non-bilingual schools, 1,001 were enrolled in SAB and 103 studied in PEB-BC schools. Cognitive tests were administered to these students to assess their levels of linguistic competence (evaluating written expression and oral and written comprehension) mathematical competence, and basic competencies in S&T. This paper focuses exclusively on analyzing mathematical and S&T competencies within a bilingual education context; linguistic competence analysis is reserved for future publications.

The data provided a significant number of independent variables, many of which align with those identified in the existing literature, while others, gain relevance in light of the specific objectives established for this research (Table I).

TABLE I. Contrast variables of the study.

Source: Compiled by the author

The dependent variables are as follows (Table II):

TABLE II. Dependent variables of the study.

Source: Compiled by the author

Results

Initially, the Kolmogorov-Smirnov statistical analysis with a Lilliefors significance correction (Martínez et al., 2020) was conducted to evaluate whether the samples follow a normal distribution (Table III), which is essential for determining the type of statistical tests to be performed subsequently.

TABLE III. Kolmogorov-Smirnov analysis statistics.

Source: Compiled by the author

Based on these data, we cannot affirm that these variables follow a normal distribution. Consequently, the statistical tests used to evaluate significant differences between variables will be non-parametric (Martínez et al., 2020). To select the specific test, we will consider whether or not there is a relationship between the samples. Thus, for independent samples, following Martínez et al. (2020) we will apply the Kruskal-Wallis test, which serves as a non-parametric alternative to one-way ANOVA when comparing three or more categories. Meanwhile, the Mann-Whitney U test will be appropriate for comparing two categories. These non-parametric tests imply that hypotheses (the null hypothesis, H₀, assumes that the distribution is identical across the categories of the variable) are based on ranges, medians or data distribution rather than means. However, they exhibit statistical effectiveness equivalent to parametric tests. After hypothesis testing, post hoc tests with Bonferroni correction will be conducted when the Ho is rejected to determine which specific categories exhibit differences.

Considering each variable separately, we test whether performance distribution in each subject is consistent across the categories of independent variables (Table IV and Table V).

TABLE IV. Hypothesis testing of performance distribution across the bilingual education categories, gender, geographic area and ownership.

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

^b Mann-Whitney U test for independent samples

TABLE V. Hypothesis testing of performance distribution across ISEC categories, average absences, repetition and homework.

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

^b Mann-Whitney U test for independent samples.

For the variables bilingual education and geographic area, there is no evidence of performance differences across the respective categories: the three types of bilingual education (Non-bilingual, SAB and PEB-BC) and the two settings (rural or urban). In contrast, for the variables Gender and Type of school by ownership differences in Mathematics performance were found. Finally, the variables ISEC, average absences, grade repetition and homework show significant differences across their categories in both performances studied.

Although the test conducted to assess the individual effect of bilingual education on performance in different subjects indicated no influence, since other variables do show an impact, we consider conducting tests on smaller samples derived from the groups of values of those variables.

The hypothesis test using the Mann-Whitney U test for the gender variable showed significant differences in Mathematics performance. Figure I shows a better average rank in Mathematics for boys.

FIGURE I. Mathematics performance by gender categories.

Source: Compiled by the author

Examining performance distribution results as a function of the variable bilingual education, the data (Table VI) show differences. For girls, the type of bilingual education does not significantly influence performance in S&T or Mathematics, whereas for boys significant differences are observed in Mathematics performance.

TABLE VI. Hypothesis testing of performance distribution across the gender and bilingual education categories.

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

Post hoc tests show that significant differences are found between boys from non-bilingual schools and boys from SABs (Table VII), with performance being higher in SABs (mean values 513.703 (SD 101.515) and 493.625 (SD 107.926), respectively).

TABLE VII. Post hoc tests for Mathematics performance in boys across bilingual education categories.

Source: Compiled by the author

The geographic area variable showed differences only in S&T performance (Table IV) across the different bilingual education categories for rural schools (Table VIII).

TABLE VIII. Hypothesis testing of performance distribution across geographic area and bilingual education categories.

Source: Compiled by the author

^a Kruskal-Wallis test conducted for independent samples

Among rural schools, non-bilingual schools show better performance in S&T while PEB-BC schools exhibit worse performance (Table IX).

TABLE IX. Average C&T performance in rural schools.

Source: Compiled by the author

The ownership variable presents significant differences in Mathematics performance between public and private-subsidized schools (Table IV), with private-subsidized schools showing a better average rank than public schools (Figure II).

FIGURE II. Mathematics performance by ownership categories

Source: Compiled by the author

Since the sample only provides data from public bilingual schools, we can only test the potential influences of the bilingual education variable on performance in each subject for public schools, and comparisons with private-subsidized schools cannot be made. We observe that there is no influence on either of the two studied subjects’ performances (Table X).

TABLE X. Hypothesis testing of performance distribution in public schools across bilingual education categories.

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

The next variable studied was ISEC. According to Villar (2018), 20.4% of students in Castilla y León belong to less advantaged families, 20.1% belong to families with greater resources, and 59.5% fall within the remaining group. Using these percentages, we divided ISEC data into three intervals. The tests conducted (Table V) indicated significant differences in all performance measures relative to this variable, with interval 3 (students from families with greater resources) showing the highest performance and interval 1 (students from less advantaged families) showing the lowest performance. Thus, we can conclude that this index influences performance such that the higher the ISEC, the better the student´s performance (Figure III).

FIGURE III. Performance by ISEC intervals

Source: Compiled by the author

Observing differences between the groups with different ISEC, we sought to determine if significant differences exist within each group based on whether they attend non-bilingual schools, SAB schools, or PEB-BC schools.

TABLE XI. Hypothesis testing of performance distribution across ISEC and bilingual education categories.

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

Significant differences based on the type of bilingual education were found in the group of students with greater resources only. Students from PEB-BC schools achieved lower performance scores in both subjects (Figure IV), while no significant differences were found between SAB schools and PEB-BC schools (Table XII). Non-bilingual schools achieved the best results, with significant differences compared to PEB-BC centers. Meanwhile, for students with fewer resources and those in the "remaining students" interval, no significant differences were found between the different types of schools.

FIGURE IV. Performance among students with greater resources by bilingual education modality.

Source: Compiled by the author

Source: Compiled by the author

Asymptotic significances are displayed (2-tailed tests). The significance level is .050.

a. Significance values have been adjusted by the Bonferroni correction for multiple tests.

The average absences variable shows significant differences in performance in the studied subjects depending on the frequency of unjustified absences from class. Figure V shows that students who "Never or almost never” miss class achieve better performance in both subjects, while those who miss class most frequently achieve the worst performance.

FIGURE V. Performance by average absences categories.

Source: Compiled by the author

The hypothesis test based on the bilingual education variable for each group, both for those who rarely or never miss class and for those who frequently miss class, leads to conclusions similar to those reached for this variable without considering this differentiation (Table XIII).

Table XIII. Hypothesis testing of performance distribution across class absences and bilingual education categories.

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

When studying the repetition variable, it is observed that repeating a grade influences performance in all subjects. Students who repeat a grade achieve lower performance than those who do not repeat (Figure VI).

FIGURE VI. Performance by repetition categories

Source: Compiled by the author

The data also provide more specific insights regarding repetition, such as which grade repetition has an influence on performance. Post hoc tests were conducted to analyze this (Table XIV). It was found that repeating first grade does not influence performance in any subject, although it is true that very few students repeat this grade. For S&T performance, grade repetition in second and fourth grades was found to have an influence on performance.

TABLE XIV. Post hoc analysis of different performances for the repetition variable

Source: Compiled by the author

Further analysis in the context of bilingual education for the repetition variable reveals that performance in the studied subjects is not influenced by whether or not they receive bilingual education (Table XV).

TABLE XV. Hypothesis testing of performance distribution across repetition and bilingual education categories

Source: Compiled by the author

^a Kruskal-Wallis test for independent samples

Finally, the homework variable indicates that dedicating more or less time to homework influences performance in the studied subjects (Table V). Students achieve better performance as they dedicate more days to completing homework (Figure VII).

FIGURE VII. Performance by homework categories

Source: Compiled by the author

For groups of students dedicating different numbers of days to homework, the bilingual education variable influences S&T performance among those who dedicate 2 or 3 days to homework (Table XVI).

TABLE XVI. Hypotheses testing of performance distribution across homework and bilingual education categories.

Source: Compiled by the author

^a Kruskal-Wallis test performed for independent samples

After conducting post hoc tests (Table XVII), significant differences were only observed in the following cases: for S&T, the highest performance was observed in PEB-BC schools (Mean = 534.468, SD = 64.989) and the lowest performance in SABs (Mean = 461.523, SD = 99.578).

TABLE XVII. Post hoc tests for homework by bilingual education categories.

Source: Compiled by the author

Discussion

The results of this study initially suggest that the context of bilingual education does not significantly influence academic performance in non-linguistic subjects always taught in Spanish, such as Mathematics, or in non-linguistic subjects like S&T, which are taught in English or Spanish, depending on the type of school. No differences were found between non-bilingual, SAB schools or PEB-BC schools.

A more detailed analysis, resulting from the consideration of other factors, reveals differences in Mathematics performance related to gender across schools with different bilingual education modalities. This research confirms that boys from SAB schools excel in Mathematics compared to boys from non-bilingual schools and girls. This finding aligns with the studies of Echavarri et al. (2007) and Pozo-Burgos et al. (2022) which indicate that men and women employ different strategies to solve complex problems. Men tend to use spatial and abstract reasoning, while women rely on verbal strategies.

Better performance in Mathematics in bilingual schools, where it is never taught in English, may be justified by the reasons argued by Ardila (2012): greater cognitive control, mental plasticity, better results in non-verbal tasks, greater academic achievement or a superior development of mental resources.

Regarding S&T performance which is not taught in English in non-bilingual schools but is predominantly taught in English in SABs (Vega-Agapito et al., 2021) and entirely taught in English in PEB-BC schools, (Falcón-Díaz et al., 2019), the results did not show significant differences between the types of bilingual education (SAB, PEB-BC, non-bilingual), This suggests that bilingual education, at least in the context of this study, does not significantly affect performance in this area. The lack of differences in S&T performance could be related to the fact that this area typically involves both logical reasoning and the use of technical vocabulary, which may not be significantly affected by the linguistic context, especially in partial bilingual programs where the language of instruction for science may vary. This is particularly true for SABs, were science may be taught in either English or Spanish (Vega-Agapito et al., 2021).

Other factors do emerge as influential on S&T performance. As noted by Pozo-Burgos et al. (2022), differences were found related to socioeconomic environments. According to these authors, a high socioeconomic environment supports superior performance in areas like science. Regular attendance at school is also positively related to performance in S&T since, as Ndjangala et al. (2021) highlight, missing class prevents students from keeping up with content and benefiting from practical explanations often required for these subjects. Additionally, students who repeat grades tend to show lower performance in S&T, possibly due to difficulties in acquiring basic skills at earlier stages, which impacts their ability to follow advanced scientific content .

Another factor—the time dedicated to completing homework—shows a direct relationship with S&T performance: students who practice and review concepts at home consolidate their learning more effectively. This relationship between homework dedication and performance is consistent with the theory that constant reinforcement of concepts allows students to integrate scientific knowledge more effectively (Willms, 2010).

Overall, we found that absenteeism has a negative impact on academic performance, especially when students frequently miss classes. Similar to Custodio et al. (2022) frequent absences is a significant negative influence on academic performance for those with high levels of absenteeism, disconnecting them from the school environment and the role of teachers. This finding underscores the need for interventions aimed at reducing absenteeism to improve educational outcomes.

Finally, the study shows that students who dedicate more time to homework tend to achieve better results across all subjects. This finding emphasizes the importance of study habits and time management for academic success and suggests that both parents and teachers should encourage consistent study routines. Murillo and Martínez-Garrido (2013) found that among third-grade primary students from nine Latin American countries, their academic performance improved when homework was assigned for completion at home, and subsequently reviewed in class. However, neither the time spent on homework nor its frequency or type were as relevant as its review process.

These results contrast with findings for China by Zhibin (2024) who showed that students in bilingual programs significantly outperformed their peers in monolingual programs with substantial academic gains. This aligns with the studies by Chamorro and Janke (2022) conducted in Spain. Similarly, studies by Tennakoon (2024) for Sri Lanka report cognitive benefits and enriched learning for bilingual programs, but note dependencies on factors such as teacher quality, curriculum design and socio-economic conditions.

Conclusions

While it initially appears that a bilingual education context does not significantly influence performance in S&T, even though it is predominantly taught in English, it may have an impact on areas that are not taught in English, such as Mathematics. Instruction in a second language could add a cognitive load that affects performance in analytical areas, suggesting the need for differentiated approaches based on gender. This includes encouraging the development of analytical skills among girls and the strengthening linguistic skills among boys.

This study is based on a sample of schools in Castilla y León, which limits the generalization of the findings to other educational contexts with different policies and resources. In Spain, there are several bilingual autonomous communities with two co-official languages, so in them, the educational context would be trilingual rather than bilingual. This scenario was not considered in our research, conducted in a monolingual community; therefore, we cannot extend the validity of our results to those regions. Future studies should expand the sample to include data from other autonomous communities, both monolingual and those with a second co-official language. Additionally, the cross-sectional nature of the study does not allow for an analysis of changes over time. Longitudinal studies in the future would help to better understand the long-term impact of bilingual education on academic performance by evaluating students over several years.

Another limitation was the reliance on performance data based on standardized tests, which may not fully capture all dimensions of learning. Further work could provide interesting insights by using an ad hoc, non-standardized questionnaire that evaluates all dimensions.

It would be also valuable to incorporate classroom observation methods to analyze how bilingual interactions affect learning in real time. Furthermore, studying the role of additional variables, such as students’ self-concept and motivation, could offer a more comprehensive view of the factors influencing performance in schools with bilingual education programs.

Finally, expanding the study to include teachers by conducting a competency evaluation of teachers in bilingual schools and recording their perception of bilingual programs would provide further insights.

Acknowledgments

We would like to express our gratitude to the Consejería de Educación de la Junta de Castilla y León through the Dirección General de Innovación y Formación del profesorado, for granting access to the data from the 2018/19 End-of-Stage Evaluation for Primary Education, which served as the basis of the present research.

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Información de contacto / Contact info: José Roberto Arias García. Universidad de Valladolid. E-mail: joseroberto.arias@uva.es