Predicting mathematical performance: The role of language proficiency,digital competencies,and demographic factors

Abstract

This study examines the key predictors of mathematics performance among Grade 10 (age 15–16) Italian secondary school students using data from the INVALSI 2025 national assessment. We investigate how language proficiency, digital competencies (measured via the DigComp 2.2 framework), and demographic factors (gender, socioeconomic status, school type, and region) influence mathematics achievement. Using multiple regression, machine learning, and network analysis, we identify the most significant predictors and their structural relationships. Results confirm Italian proficiency as the strongest predictor, followed by overall digital competence. Machine learning models highlight these factors’ dominance, while network analysis reveals digital competencies form a tightly interconnected cluster, distinct from demographic influences. Students in Northern Italy and academic-track schools (i.e., lyceums) outperformed peers from other geogprahical regions and school typologies. Gender differences persist but are modest compared to socioeconomic effects. Our findings suggest that while language and digital skills are central to mathematics performance, structural inequalities, linked to school type and geography, remain persistent barriers.

Keywords

competence digital factors Italy mathematics

1. Introduction

Mathematics performance is a critical indicator of academic success and future opportunities, yet it is shaped by a complex interplay of cognitive, linguistic, socio-demographic, and technological factors (Ayebale et al., 2020; Gamazo & Martínez-Abad, 2020; Mosia et al., 2025; Wang et al., 2023). The present study examines how language proficiency and digital competencies, alongside well-established demographic factors, jointly predict mathematics performance in a national standardized assessment. While mathematical ability is often viewed as a domain-specific skill, research increasingly highlights the role of broader competencies, such as language proficiency and digital literacy, in shaping students’ achievement (Ayebale et al., 2020; Wang et al., 2023). In Italy, the national assessment organized by the National Institute for the Evaluation of the Education and Training System (Istituto Nazionale per la Valutazione del Sistema Educativo di Istruzione e di Formazione – INVALSI) provides a valuable dataset for examining these relationships, offering insights into how different variables interact to influence mathematics outcomes. INVALSI tests are mandatory, standardized, and census-based, making them a central reference point for educational research and policy. A novel feature of the 2025 INVALSI cycle is the first-time inclusion of a standardized digital competencies test based on the European DigComp 2.2 framework.

The influence of language proficiency on mathematics performance is well-documented, particularly in assessments that rely on word problems and complex instructions (Ajello et al., 2018; Prediger et al., 2018). Mathematical learning and assessment are inherently language-mediated, as they require comprehension of problem statements, instructions, and subject-specific academic language (Schleppegrell, 2007). Empirical studies consistently show that reading and language skills are strong predictors of mathematics achievement, particularly for tasks involving word problems and conceptual understanding (Vilenius-Tuohimaa et al., 2008). Similarly, the economic, social, and cultural status (ESCS) remains one of the strongest predictors of academic achievement, reflecting systemic inequalities in educational access and resources (Eriksson et al., 2021; Lee & Stankov, 2018). In the Italian upper secondary system, these inequalities are further reflected in differences by school track (lyceum vs. vocational tracks) and geographic macroregion (North–South divide in Italy) (Argentin & Triventi, 2015; Giofrè et al., 2020).

A more recent area of exploration is the role of digital competencies in mathematics achievement. As education systems increasingly integrate technology into curricula and assessments, students’ ability to navigate digital tools may influence their problem-solving and analytical skills (Fraillon et al., 2020; Vuorikari et al., 2022). However, despite growing international interest in digital literacy, mathematics education research has not yet reached consensus on how digital competencies should be conceptualized or measured, nor on the extent to which they contribute to mathematics performance beyond established predictors such as language proficiency and ESCS. This uncertainty constitutes a key research gap. The INVALSI (2025) assessment operationalizes digital competence using the European DigComp 2.2 framework. DigComp provides a structured way to assess students’ digital skills across multiple domains, allowing systematic investigation of its association with mathematics outcomes.

This study leverages INVALSI data to investigate the relative contributions of language proficiency, digital competencies, and demographic factors in predicting mathematics performance. Mathematics performance was operationalized as students’ scores on the INVALSI mathematics assessment, with Italian language proficiency measured through the corresponding INVALSI Italian test. Using multiple analytical approaches, including regression, machine learning (Boosting and Random Forest), and network analysis, we aim to: (1) identify the strongest predictors of mathematics achievement, testing whether digital competencies hold independent predictive power alongside traditional factors like language and ESCS; and (2) examine structural interdependencies among variables, determining whether digital skills form a distinct cluster or interact diffusely with other influences. The present study focuses on Italian Grade 10 students (approximately 15–16 years old), a crucial transition stage in the Italian education system corresponding to the first two years of upper secondary school.

By addressing these questions, this research contributes to both theoretical and policy discussions on equity in mathematics education. Findings may inform targeted interventions, such as integrated language-mathematics instruction or digital skill development, to reduce achievement gaps and enhance learning outcomes. The use of advanced analytical techniques strengthens the evidence base by providing a multidimensional view of the factors shaping mathematics achievement.

2. Literature review

2.1 The INVALSI national assessment

The INVALSI national assessment is a large-scale standardized testing program implemented across Italian schools to evaluate students’ competencies in Italian language, mathematics, and English as a foreign language. These assessments are conducted annually at key stages of the education system, that is, grades 2, 5, 8, and 13 (last year of upper secondary school), and are designed to provide objective, comparable data on student learning outcomes across regions, school types, and demographic groups (INVALSI, 2022, 2025). The mathematics test evaluates numerical skills, geometry, data analysis, and problem-solving abilities, aligned with national curriculum standards.

INVALSI plays a pivotal role in monitoring system-level equity and effectiveness in education by producing disaggregated performance data based on student background variables such as gender, ESCS, and geographic area (INVALSI, 2022, 2025). Its longitudinal and comparative nature makes it particularly useful for identifying regional disparities (e.g., North–South divide) and differences linked to school typology (Agasisti & Longobardi, 2014).

2.2 Factors predicting students’ mathematics performance

Student performance in mathematics is influenced by several interrelated factors that extend beyond cognitive ability, as demonstrated in both international and national standardized assessments (cf. Ayebale et al., 2020; Gamazo & Martínez-Abad, 2020; Mosia et al., 2025; Wang et al., 2023). Language proficiency has been repeatedly shown to be a strong predictor of mathematics achievement, particularly because word problems and test instructions often rely on reading comprehension skills (Ajello et al., 2018; Arikan et al., 2017; Prediger et al., 2018; Wang et al., 2023). For example, PISA 2018 results emphasized that students with higher reading literacy tend to perform better in mathematics, highlighting the interdependence of these domains (OECD, 2019). Similar associations have been found in INVALSI assessments in Italy, where students with stronger Italian language skills also demonstrated higher mathematics scores (Ajello et al., 2018; Azzolini et al., 2012; INVALSI, 2022).

Gender differences in mathematics performance continue to be debated (Else-Quest et al., 2010; Lu et al., 2023). While boys have traditionally outperformed girls in some countries, the gap has narrowed or reversed in others (Else-Quest et al., 2010). PISA 2018 showed minimal overall gender differences in OECD countries, but with variability by context and task type (OECD, 2019). In Italy, INVALSI data indicate a modest but persistent advantage for male students, which is therefore treated as a control factor in the present analyses (Contini et al., 2017; Giofrè et al., 2020; INVALSI, 2022).

ESCS is one of the most robust predictors of academic achievement across all subjects (Acıslı-Celik & Yesilkanat, 2023; Eriksson et al., 2021; Lee & Stankov, 2018; Wang et al., 2023). Students from higher socioeconomic backgrounds generally have access to more learning resources, both at home and in school, which contributes to better mathematics outcomes. According to PISA data, ESCS accounts for a significant portion of performance variance across countries (OECD, 2019). In Italy, the INVALSI results echo this pattern, demonstrating that students with higher ESCS tend to achieve better results, while those from disadvantaged backgrounds are at greater risk of underperformance (Agasisti & Longobardi, 2014; Cornoldi et al., 2013; Giofrè et al., 2020; INVALSI, 2022).

School typology, that is, the type of upper secondary education track (e.g., lyceum, technical, vocational), also plays a critical role in Italy (Argentin & Triventi, 2015). In the Italian system, lyceums (e.g., scientific, classical) are academically oriented upper secondary schools preparing students primarily for university education. Technical institutes offer applied training in fields like technology or business, while vocational schools focus on job-specific skills. Students in lyceum tracks, especially scientific and classical lyceums, typically score significantly higher in mathematics than their peers in technical or vocational schools (INVALSI, 2022). This gap reflects both selection effects and differences in curricular rigor and instructional quality. These differences reflect both prior selection and curricular differentiation, making school track an essential contextual variable rather than a purely instructional one.

Geographic macroregions (North East, North West, Center, South, South and Isles) are another significant factor in national assessments (Daniele, 2015; Giofrè et al., 2020). INVALSI data consistently show a persistent performance gap between Northern and Southern Italy, with students in the North achieving markedly higher mathematics scores. This geographic inequality reflects broader disparities in school infrastructure, teacher qualifications, and socioeconomic conditions (Agasisti & Vittadini, 2012; Costanzo & Desimoni, 2017; Giofrè et al., 2020).

2.3 Digital competencies and mathematics performance

Digital competencies are becoming increasingly relevant in contemporary educational assessments. In general terms, digital competence refers to students’ ability to use digital tools, process information, solve problems in digital environments, and engage critically with digital content (Vuorikari et al., 2022). These skills are increasingly recognized as transversal competencies that may support learning across subjects, including mathematics. Digital skills have been associated with problem-solving and data-handling abilities that are critical for mathematics. As digital technologies become more embedded in instructional practices and assessment formats, students’ ability to engage with digital tools appears to have a growing impact on their academic success (Fraillon et al., 2020). Most existing studies have focused on the use of digital technologies, such as specific mathematics software, reporting a positive relationship between technology use and mathematics achievement (cf. Engelbrecht & Borba, 2024; Viberg et al., 2023). However, far fewer studies have examined the direct relationship between measured digital competencies and mathematics performance. While technology use in mathematics classrooms has been widely studied, the role of students’ underlying digital skills, independent of specific software or instructional tools, remains underexplored in mathematics education research.

Digital skills are related to algorithmic thinking (Csernoch & Biró, 2015; Moylan & Code, 2024), problem-solving skills (Luengo-Aravena et al., 2024; Van Laar et al., 2017), critical thinking (Meirbekov et al., 2022; Van Laar et al., 2017), and reasoning (Gregersen & Baccaglini-Frank, 2023). All these skills are also connected to mathematics literacy (OECD, 2018). In the Italian context, the INVALSI (2025) assessment measured digital competence using the European DigComp 2.2 framework (Vuorikari et al., 2022). DigComp provides a structured way to assess students’ digital skills across multiple domains, enabling empirical investigation of how such skills relate to mathematics outcomes. Within DigComp 2.2, five competence areas are identified: Information and data literacy, Communication and collaboration, Digital content creation, Safety, and Problem solving. In this study, DigComp serves as an operational tool for measuring digital competence, rather than as a theoretical foundation for defining the research gap. Chaw and Tang (2024) found that all competence areas except safety significantly predicted students’ learning performance, including information and data literacy, communication and collaboration, digital content creation, and problem-solving. These findings suggest a plausible connection between digital competencies and mathematics performance.

2.3.1 Digital competencies and the INVALSI assessment

For the first time, in the 2024/25 school year, only grade 10 students from the sample classes of upper secondary schools took an INVALSI test on Digital competences (INVALSI, 2025). This test is based on the European DigComp 2.2 framework (Vuorikari et al., 2022), which conceptualizes digital competence as a key prerequisite for active citizenship and participation in social and economic life. The assessment is performance-based, placing students in authentic digital scenarios rather than relying on self-reports.

The INVALSI digital competencies test provides a standardized, performance-based assessment administered at the end of the first two years of upper secondary school, with the aim of informing both system-level monitoring and targeted educational interventions (INVALSI, 2025). Rather than relying on self-reports, the test places students in authentic digital scenarios requiring the demonstration of applied knowledge and skills. Given its transversal nature, the Problem solving area was not assessed separately but indirectly through items belonging to the other areas (INVALSI, 2025). Accordingly, the test assessed four domains:

Information and data literacy: identifying information needs, retrieving and evaluating digital information, and managing data;

Communication and collaboration: interacting and collaborating through digital technologies and managing digital identity;

Digital content creation: creating and editing digital content, integrating information, and understanding copyright;

Safety: protecting devices, personal data, well-being, and understanding the environmental impact of digital technologies.

Based on DigComp 2.2 proficiency descriptors, student performance in each area was classified into three levels: Basic, Intermediate, and Advanced. At the national level, over 80% of students reached at least the intermediate level across all areas (INVALSI, 2025). Geographic patterns mirror those observed in Italian and mathematics assessments, with higher performance in the North-West and North-East and lower proportions of students reaching intermediate levels in Southern regions, while the Centre closely approximates the national average.

INVALSI (2025) highlights that digital competence outcomes reflect the combined influence of individual characteristics (e.g., gender, school track, socio-economic background, migration status) and contextual factors (e.g., geographic area). Findings include:

girls score lower than boys in “safety,” but slightly higher in “communication and collaboration”;

no significant difference between lyceum and technical institutes; vocational schools show disadvantages in several areas;

small advantages for students from higher ESCS backgrounds in “communication and collaboration” and “safety”;

first- and second-generation immigrant students outperform native peers in some areas;

territorial differences remain significant even after controlling for background variables.

Performance in the digital competences test is also correlated with results from other INVALSI tests, particularly in Italian. For each additional point in Italian, digital competence increases by 0.3 points; for mathematics, by 0.2 points.

2.4 Aims of the research

Based on national and international literature, it might be stated that mathematics performance in standardized assessments is not solely a function of innate mathematical ability but is shaped by a network of linguistic, socio-demographic, and even technological factors. Understanding the interplay among these variables is essential for designing interventions and policies aimed at promoting equity and excellence in mathematics education.

The primary aim of this study was to identify and analyze the key demographic, linguistic, and digital competence factors that predict students’ mathematics performance as measured by the INVALSI assessment in Italy. Specifically, the study sought to:

examine the predictive power of language proficiency (Italian), digital competencies (DigComp), and demographic variables (e.g., ESCS, gender, school typology, and regional differences) on mathematics performance;

explore the structural relationships between mathematics performance and other variables using network analysis to identify central and clustered influences.

Based on the results, the study addressed the following research questions:

RQ1: Which factors (language proficiency, digital competencies, and demographic variables) are the strongest predictors of mathematics performance?

RQ2: What is the structural role of mathematics performance within a network of academic and demographic variables?

RQ3: Do digital competencies form a distinct cluster in the network, and how do they interact with other variables?

RQ4: How do school-level factors (typology, macroregion) and student background (ESCS, gender) influence mathematics performance when accounting for academic competencies?

3. Materials and methods

3.1 Methodology

The present study is a secondary data analysis employing a quantitative, non-experimental, descriptive research design for causal investigation.

3.2 Data preparation

The sample used in this study was retrieved from the INVALSI official webpage (https://serviziostatistico.invalsi.it/invalsi_ss_data/microdati-campione-g10-2024-25/). All measures of student performance in mathematics, Italian, and digital competencies are standardized INVALSI test scores. On the official webpage, microdata for grade 10 students who took the INVALSI national assessment in the school year 2024/25 is present. Information about their performance in mathematics, Italian, and digital competencies is presented as three different databases. There were 19,008 students included in the INVALSI mathematics database, 18,876 students in the Italian database, and 19,020 students in the Digital competencies database. There were 17,491 students in the intersection of all three databases. These students were included in the final database that was considered for future analysis. No data was missing.

3.3 Sample

The sample comprised 17,491 Grade-10 Italian students. Among them, 8,222 (47.0%) were male and 9,269 (53.0%) were female. Regarding birth year, 34 students (0.2%) were born in 2006 or earlier, 293 (1.7%) in 2007, 1,528 (8.7%) in 2008, 14,194 (81.2%) in 2009, and 1,442 (8.2%) in 2010. In terms of school type, 8,294 students (47.4%) attended a scientific, classical, or linguistic lyceum; 2,988 (17.1%) attended another type of lyceum; 4,139 (23.7%) attended a technical school; and 2,070 (11.8%) attended a vocational school. Geographically, 3,707 students (21.2%) were from North-Western Italy, 3,629 (20.8%) from North-Eastern Italy, 3,469 (19.8%) from Central Italy, 3,451 (19.7%) from Southern Italy, and 3,235 (18.5%) from Southern Italy and the Isles. In terms of citizenship, 15,226 students (87.0%) were native Italians, 471 (2.8%) were first-generation immigrants, and 1,411 (8.2%) were second-generation immigrants.

3.4 Variables

In all analyses, students’ mathematics proficiency (INVALSI mathematics score) was specified as the dependent variable, while Italian language proficiency, digital competencies, and demographic characteristics were treated as predictors. The variables that were available in the official microdata database and that were used in the study are (those indicated by * are continuous variables using the Rash model, where the mean of the population is set to M = 200, and the standard deviation to SD = 40):

Students’ biological sex (Male; Female);

Students’ school type (Scientific, classical, or linguistic lyceum; Other lyceum; Technical school; Vocational school);

Schools’ geographic macroregion (North-Eastern; North-Western; Central; Southern; Southern and Isles);

Students’ ESCS (continuous variable, where the mean M = 0 represents a middle ESCS; standard deviation is set to SD = 1);

Students’ proficiency in Italian language (*);

Students’ proficiency in Mathematics (*);

Students’ DigComp 2.2 competencies (*);

Students’ Information and data literacy (*);

Students’ Communication and collaboration competencies (*);

Students’ Digital content creation competencies (*);

Students’ Digital safety competencies (*).

3.5 Data analysis

Data was analyzed using descriptive statistics and multiple regression to estimate the unique contribution of each predictor to mathematics performance. To validate the robustness of the regression findings against potential non-linearities, machine learning techniques (Boosting and Random Forest regression) were also run. Network analysis was used to explore the structural relationships and interdependencies among all variables, providing a holistic view of how competencies and background factors interact. All analyses were performed with JASP v. 0.17.1.0.

3.5.1 Machine learning models

Machine-learning models (Random Forest and Boosting) were employed solely as robustness checks to verify whether non-linear or interaction effects altered the pattern of predictors identified in regression. For the Boosting regression, 20% of the sample was used for validation data. The shrinkage was set to 0.1, the interaction depth to 1, and the minimum observations in a node were set to 10, while 50% of the training data was used per tree. As a loss function, the Gaussian function was selected. The number of trees was optimized with a maximum of 1000 trees. For the Random Forest regression, 20% of the sample was used for validation data. The training data used per tree was set to 50%, while the features per split were automated. The number of trees optimized with a maximum of 1000 trees.

3.5.2 Network analysis

Network analysis was used to explore the structural relationships among variables, focusing on how linguistic, digital, and demographic factors cluster around mathematics performance. To interpret the network structure, we computed standard centrality indices: strength (overall connectivity), expected influence (connectivity accounting for edge signs), betweenness (role as a bridge), and closeness (proximity to all nodes). These measures identify which variables are most influential within the system of relationships. We also computed clustering coefficients (Barrat, Onnela, Watts–Strogatz, Zhang) to detect tightly interconnected groups of variables, indicating cohesive constructs. The network was estimated using the EBICglasso method, which applies graphical lasso regularization with an Extended Bayesian Information Criterion (γ = 0.5) to produce a sparse and reliable partial correlation network. This method controls for all other variables, filtering out weak associations. Pairwise Pearson correlations were used as input. Centrality measures were normalized to a 0–1 range for comparability, and the full sample was used to ensure stability.

4. Results

4.1 Preliminary analysis

Table 1 presents descriptive statistics of the continuous variables and the Pearson's correlation coefficients between the variables. Mathematics performance showed strong positive correlations with Italian proficiency (r = .635) and DigComp competencies (r = .531), and a weaker positive correlation with ESCS (r = .230). The DigComp subskills and overall score were highly intercorrelated (r > .65).

Table 1.
Descriptive statistics of the variables and correlational analysis.

Variable M SD Min Max 2. 3. 4. 5. 6. 7. 8.

1. Mathematics performance 198.65 35.41 81.56 296.41 .635 .531 .395 .336 .374 .366 .230

2. Italian proficiency 200.65 38.02 28.55 321.82 - .579 .436 .391 .402 .392 .267

3. DigComp competencies 202.72 39.22 −74.83 377.10 - .698 .660 .696 .683 .176

4. Information and data literacy 202.25 39.44 31.05 325.42 - .313 .343 .339 .128

5. Communication and Collaboration 201.93 39.53 36.67 309.65 - .300 .295 .125

6. Digital content creation 202.00 39.30 21.95 318.88 - .337 .117

7. Digital safety 201.49 39.46 14.59 303.59 - .117

8. Student's ESCS .158 .954 −3.75 2.59 -

Variable	M	SD	Min	Max	2.	3.	4.	5.	6.	7.	8.
1. Mathematics performance	198.65	35.41	81.56	296.41	.635	.531	.395	.336	.374	.366	.230
2. Italian proficiency	200.65	38.02	28.55	321.82	-	.579	.436	.391	.402	.392	.267
3. DigComp competencies	202.72	39.22	−74.83	377.10		-	.698	.660	.696	.683	.176
4. Information and data literacy	202.25	39.44	31.05	325.42			-	.313	.343	.339	.128
5. Communication and Collaboration	201.93	39.53	36.67	309.65				-	.300	.295	.125
6. Digital content creation	202.00	39.30	21.95	318.88					-	.337	.117
7. Digital safety	201.49	39.46	14.59	303.59						-	.117
8. Student's ESCS	.158	.954	−3.75	2.59							-

Note. All correlations are statistically significant with p < .001.

4.2 Factors predicting mathematics performance

To identify the strongest independent predictors, a multiple regression analysis was conducted (Table 2). The final, fully adjusted model (Model 4) explained 51.5% of the variance in mathematics scores. Italian language proficiency was the strongest predictor (β = .420, p < .001). Overall digital competence was the second strongest predictor (β = .181, p < .001). Among demographic and contextual factors, school typology showed substantial negative effects, with students in vocational (β = −.537) and other lyceums (β = −.435) scoring significantly lower than those in academic lyceums. Gender (β = −.339, p < .001), indicating lower scores for female students, and school macroregion, with students from Southern Italy and the Isles scoring lowest (β = −.360), were also significant predictors. The effect of ESCS was positive but very small (β = .022, p < .001). The specific DigComp subskills did not retain significant independent predictive power in the full model. This answers RQ1 by establishing a clear hierarchy: language proficiency is the dominant predictor, followed by general digital competence, with school type, gender, and region also exerting significant independent influence.

Table 2.
Coefficients of the multiple regression analysis.

Models Model 1 Model 2 Model 3 Model 4

Variables β β β β

Italian proficiency .635* .492* .420*

DigComp competencies .385* .239* .181*

Information and data literacy .083*** .015 .016

Communication and collaboration .025* −.024* −.005

Digital content creation .053* .008 .021

Digital safety .050* .009 .002

School typology

OL – L −.435***

TS – L −.209***

VS – L −.537***

School macroregion

NE – NW .031

C – NW −.098***

S – NW −.225***

SI – NW −.360***

Student's gender

F – M −.339*

Student's ESCS .022*

Model parameters

Adjusted R² .403 .284 .444 .515

F 11800* 1386* 2325* 1098*

Models	Model 1	Model 2	Model 3	Model 4
Variables	β	β	β	β
Italian proficiency	.635***		.492***	.420***
DigComp competencies		.385***	.239***	.181***
Information and data literacy		.083***	.015	.016
Communication and collaboration		.025*	−.024*	−.005
Digital content creation		.053***	.008	.021
Digital safety		.050***	.009	.002
School typology
OL – L				−.435***
TS – L				−.209***
VS – L				−.537***
School macroregion
NE – NW				.031
C – NW				−.098***
S – NW				−.225***
SI – NW				−.360***
Student's gender
F – M				−.339***
Student's ESCS				.022***
Model parameters
Adjusted R²	.403	.284	.444	.515
F	11800***	1386***	2325***	1098***

Note. OL = other lyceums; L = Scientific, Classical, and Linguistic Lyceums; TS = Technical Schools; VS = Vocational Schools; NE = North-Eastern Italy; NW = North-Western Italy; C = Central Italy; S = Southern Italy; SI = Southern Italy and Isles; F = Females; M = Males.

* p < .05; *** p < .001.

To assess the robustness of the regression findings against non-linearities and complex interactions, we employed two ensemble machine learning methods: Boosting Regression and Random Forest regression. Critically, both models replicated the rank order of predictor importance identified by linear regression: Italian proficiency was the strongest predictor, followed by overall DigComp competence, and then school typology. The detailed specifications, performance metrics, and variable importance scores for both models are provided in the electronic Supplemental material.

4.3 Structural role of mathematics performance, digital competencies, and student demographic factors

Network analysis was used to examine the structural relationships among all variables. The resulting network (Figure 1) was relatively dense, with 44 of 55 possible connections present. To identify influential variables and potential clusters, we computed centrality and clustering measures (Tables 3 and 4). The results reveal a clear structure: digital competence (DigComp competencies) was the most central variable in the network, exhibiting the highest strength and betweenness. This indicates it acts as a key hub connecting other factors. In contrast, the four DigComp subskills (Information and data literacy, Communication and collaboration, Digital content creation, Digital safety) formed a tightly interconnected cluster (high positive clustering coefficients across all measures), confirming they represent a cohesive construct.

Figure 1.

The clustering created with the network analysis.

Table 3.

Centrality measures per variable.

Variable	Network
Variable	Betweenness	Closeness	Strength	Expected influence
Mathematics performance	1.827	1.806	−.396	−.125
Italian proficiency	−.537	1.323	−.556	.662
DigComp competencies	2.042	.633	1.777	2.786
Information and data literacy	−.537	−.229	.744	−.386
Communication and collaboration	−.537	−.217	.787	−.384
Digital content creation	−.537	−.213	.707	−.392
Digital safety	−.537	−.233	.705	−.420
School typology	.430	.234	−.385	−.963
School macroregion	−.537	−1.239	−1.201	−.381
Student's gender	−.537	−.205	−.750	−.262
Student's ESCS	−.537	−1.661	−1.433	−.134

Table 4.

Clustering measures per variable.

Variable	Network
Variable	Barrat	Onnela	WS	Zhang
Mathematics performance	−.033	−.666	−.440	−.960
Italian proficiency	−1.036	−.820	−1.374	−.884
DigComp competencies	.138	.471	−.271	.362
Information and data literacy	1.069	.768	1.079	1.117
Communication and collaboration	.444	.679	.066	1.077
Digital content creation	1.309	1.357	1.417	1.290
Digital safety	1.055	.669	1.079	1.194
School typology	.544	1.047	.674	−1.088
School macroregion	−1.562	−1.349	−1.374	−.697
Student's gender	−.694	−.881	−1.059	−.738
Student's ESCS	−1.234	−1.276	.202	−.674

Mathematics performance showed moderate centrality but negative strength, consistent with its role as a central outcome variable that is influenced by the network rather than a driver of it. Italian proficiency showed a positive expected influence, underscoring its supportive role. Demographic and contextual variables (ESCS, gender, macroregion, school type) generally occupied peripheral positions with low or negative centrality.

The partial correlation matrix (weights matrix, Table 5) clarified these relationships. The strongest edge was a positive link between Mathematics performance and Italian proficiency (.387), highlighting their unique association. Mathematics performance also had direct negative connections with School typology (−.278) and School macroregion (−.195), pointing to structural inequalities. Meanwhile, the DigComp subskills showed very strong mutual positive connections (>.72) but only weak direct links to mathematics performance, suggesting their influence may be indirect.

Table 5.

Weights matrix.

Variable	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.
1. Mathematics performance	.000	.387	.069	.010	.000	.010	.002	−.278	−.195	−.325	.000
2. Italian proficiency		.000	.069	.061	.034	.031	.036	−.248	−.031	.204	.042
3. DigComp competencies			.000	.723	.726	.735	.721	.000	−.001	−.012	.001
4. Information and Data literacy				.000	−.467	−.464	−.454	.000	−.021	−.010	.000
5. Communication and collaboration					.000	−.478	−.467	.000	−.019	.049	.004
6. Digital content creation						.000	−.456	.000	−.005	.000	.000
7. Digital safety							.000	.000	−.008	−.033	.000
8. School typology								.000	−.217	−.232	−.309
9. School macroregion									.000	−.084	−.034
10. Student's gender										.000	−.035
11. Student's ESCS											.000

Overall, the network analysis reveals: (1) language proficiency is the primary direct correlate of math scores; (2) digital competence is a central, cohesive cluster of skills that may support achievement indirectly; and (3) demographic and school-context factors maintain direct negative associations with outcomes, indicating persistent structural effects.

Therefore, mathematics performance had moderate betweenness and closeness but negative strength, confirming its structural role as a central outcome variable that is influenced by the network rather than a driver of it (RQ2). Mathematics performance is, hence, a central, dependent node in the network. Additionally, DigComp subskills formed a tightly interconnected cluster, as evidenced by consistently high positive clustering coefficients across all measures. The overall DigComp competencies variable acted as a central hub for this cluster, exhibiting the highest strength and betweenness in the entire network. It answers RQ3 by demonstrating that digital competencies form a highly cohesive, distinct cluster, with the overall score serving as its central hub.

Furthermore, mathematics performance retained a strong direct positive link with Italian proficiency and weak direct links to digital skills. Crucially, it also maintained direct negative connections with School typology and School macroregion. The centrality measures showed that these demographic and contextual variables (ESCS, gender, macroregion, school type) occupied peripheral positions with low or negative centrality, indicating their influence is not mediated through the central competency variables. This answers RQ4 by showing that even after accounting for academic competencies (language and digital skills), school-level factors (type, region) and student gender maintain significant direct negative associations with mathematics performance, highlighting persistent structural inequalities.

5. Discussion

The present research aimed at unveiling some skills and demographic factors that are related to students’ mathematics achievements on the Italian national assessment INVALSI. In particular, we aimed at investigating the relationship between these newly assessed digital competencies and students’ mathematical competencies. While the study is situated in the Italian context, the patterns observed also connect to broader debates on how transversal competencies (e.g., language and digital skills) shape mathematics learning in large-scale assessments.

This study contributes novel insights to the international literature on student achievement by combining predictive modeling with network analysis to investigate the interrelations among cognitive, digital, and demographic variables in a large-scale standardized mathematics assessment. While prior research has widely recognized the importance of language proficiency (Ajello et al., 2018; Arikan et al., 2017; Prediger et al., 2018; Wang et al., 2023) and ESCS (Acıslı-Celik & Yesilkanat, 2023; Eriksson et al., 2021; Lee & Stankov, 2018; Wang et al., 2023) in shaping academic outcomes, few studies have examined digital competence within a DigComp-aligned framework in relation to mathematics achievement. This approach is particularly relevant given the growing international policy emphasis on digital skills (European Commission, 2020), and offers a transferable analytical model for other education systems adopting similar frameworks.

Descriptive analyses showed moderate to strong correlations among key variables. Mathematics performance was positively correlated with Italian proficiency and DigComp competencies, indicating a substantial association between these competencies and standardized mathematics test scores. Italian proficiency was also moderately correlated with DigComp competencies, suggesting a relationship between students’ language and digital skill levels. Among the DigComp subcomponents, all showed positive but weaker correlations with mathematics performance. This result partly supports previous findings, which highlighted that students’ digital competencies are correlated with students’ learning performance (Chaw & Tang, 2024). These correlations reinforce international evidence that mathematics achievement is embedded within a broader constellation of literacy and digital skills, rather than being an isolated cognitive domain.

ESCS was also positively correlated with mathematics achievement, though the correlation was smaller than that observed with Italian and digital proficiency variables. This result is consistent with previous findings (Acıslı-Celik & Yesilkanat, 2023; Eriksson et al., 2021; Lee & Stankov, 2018; Wang et al., 2023), which found that students with lower ESCS tend to have lower performance on standardized mathematics tests (cf. Agasisti & Longobardi, 2014; Cornoldi et al., 2013; Giofrè et al., 2020; INVALSI, 2022; OECD, 2019). The observed ESCS gradient aligns with global patterns documented in international assessments such as PISA, underscoring the cross-national relevance of socioeconomic disparities in mathematics education.

5.1 Language proficiency and mathematics performance

In the multiple linear regression models, Italian proficiency emerged as the most consistent and strongest predictor of mathematics achievement across all model specifications. This result supports previous findings (Ding & Homer, 2020; Thien et al., 2015; Wang et al., 2023). In particular, language skills and proficiency are key elements to understand mathematics problems and effectively solve them (cf. Mercer & Sams, 2006; Ufer & Bochnik, 2020). This finding was further confirmed by the machine learning analyses. Both Boosting and Random Forest regression models identified Italian proficiency as the dominant predictor. This consistency across modeling techniques underscores the critical role of language proficiency in mathematics achievement. These results reinforce the international consensus that mathematics performance in large-scale assessments is inherently language-mediated, regardless of national curriculum differences.

5.2 Digital competencies and mathematics performance

DigComp competencies were also statistically significant predictors in the first two models, whereas the specific DigComp subcomponents did not retain statistical significance in the fully adjusted model. Therefore, these results are partly consistent with findings on general performance (Chaw & Tang, 2024). Since digital competencies are related to several skills that are central to mathematical literacy, such as algorithmic thinking, problem-solving, critical thinking, and reasoning (cf. Csernoch & Biró, 2015; Moylan & Code, 2024), and students’ learning (Chaw & Tang, 2024), it is plausible that reinforcing these cognitive skills may also support mathematics proficiency. In particular, strengthening algorithmic thinking may have a beneficial effect on mathematics proficiency, especially with regard to procedural knowledge. Similarly, digital problem-solving skills may contribute to more effective mathematics problem-solving strategies, thereby potentially enhancing students’ mathematics performance (cf. Jonsson et al., 2014).

As reported by Chaw and Tang (2024), students’ safety competencies are not associated with learning performance, a finding consistent with our results. This may be because protecting data and navigating the web safely does not require higher-order mathematical competencies or reasoning. However, a notable divergence from Chaw and Tang's (2024) findings emerges when considering the other variables, that is, information and data literacy, communication and collaboration, and digital content creation, which did not have statistically significant predictive power for students’ mathematics achievement in our models. One plausible explanation is that the overall DigComp index subsumes these competencies, reducing their individual statistical contribution when modeled simultaneously.

It may also be that information and data literacy competencies are more closely linked to how digital information is obtained and how data are presented, skills that align more with statistical literacy than with mathematical literacy per se. Communication and collaboration skills, while important in a socially connected world, might exert limited influence on mathematics performance due to the relatively marginal role of collaboration in mathematics instruction (although communication is recognized as an important element of mathematics education; Morgan et al., 2014). Furthermore, digital content creation may involve creativity and digital skills that, despite their importance in mathematics (particularly creativity; Leikin & Pitta-Pantazi, 2013), manifest in forms that differ from domain-general creativity (although the two are positively correlated; Schoevers et al., 2020). These findings contribute to the international debate on digital competence by showing that digital skills matter for mathematics performance, but their influence may be indirect, mediated by broader cognitive and linguistic abilities. This nuance is relevant for countries adopting DigComp-aligned frameworks or similar digital literacy standards.

Complementing these findings, network analysis revealed that DigComp competencies hold the highest betweenness and strength centrality within the network, indicating that digital competence acts as a critical structural bridge connecting other variables, including mathematics performance and Italian proficiency. The strong clustering of digital subskills further highlights that these competencies form a cohesive cluster, suggesting that improvements in one digital skill are likely to positively influence others.

Interestingly, the network also shows relatively weak direct partial correlations between DigComp subskills and mathematics performance, suggesting their influence may be indirect or mediated via other variables such as language proficiency. This nuance may explain why individual DigComp subcomponents did not independently predict math scores in fully adjusted regression models, even though the overall digital competence score was important. This structural insight is valuable beyond the Italian context, as it suggests that digital competence may function as a transversal mediator in educational systems where digital assessments and digital learning environments are increasingly prevalent.

5.3 Demographic variables and mathematics performance

Among the demographic variables, school typology was significantly associated with mathematics performance, indicating differences in achievement across school types. These results align with previous research (Argentin & Triventi, 2015), which found that students from lyceums tend to achieve higher scores than those from technical and vocational schools. This finding should also be interpreted from a curricular perspective. Since these three school typologies have different focuses: lyceums providing a broad general education across all subjects, while technical and vocational schools emphasize professional development and practical knowledge, the mathematics curriculum reflects these distinctions. Lyceum students receive more general, theoretical mathematical instruction, whereas students in technical and vocational schools experience a more specialized, practice-oriented curriculum. Additionally, students’ motivation to learn mathematics may differ across these school types, which could partly explain lower INVALSI test scores among technical and vocational school students due to reduced motivation and effort.

Gender showed a statistically significant negative coefficient, suggesting that, on average, females had lower predicted scores in the sample. This result aligns with a substantial body of national (Contini et al., 2017; Giofrè et al., 2020; INVALSI, 2022, 2025) and international research (Else-Quest et al., 2010; Lu et al., 2023) on gender differences in mathematics. One theoretical explanation for these differences is the gender stereotype model (Cvencek et al., 2011), which posits that mathematics (and science) are perceived as “male domains,” whereas reading (and the humanities) are viewed as “female domains.” This stereotype has been repeatedly documented in Italian schools, where evidence suggests that the gender gap in mathematics tends to widen in higher grades (Muzzatti & Agnoli, 2007; Passolunghi et al., 2014).

ESCS was also a statistically significant, though relatively small, positive predictor. On one hand, this result corroborates findings from national and international literature (cf. Agasisti & Longobardi, 2014; Cornoldi et al., 2013; Giofrè et al., 2020; INVALSI, 2022, 2025; OECD, 2019); however, the effect size is smaller than expected. This may be because other factors, such as students’ macroregion, capture some of the variance that ESCS would otherwise explain. In particular, literature consistently shows that students from southern Italian regions generally have lower ESCS compared to those from northern regions (Daniele, 2015; Giofrè et al., 2020). Therefore, while international studies emphasize ESCS as a strong predictor of mathematics performance on standardized tests, in the Italian context, macroregion may serve as a proxy for ESCS to some extent. It is important to interpret this finding cautiously, as generalization is limited; hence, future research should aim to better understand the complex relationship between these two variables.

Machine learning results reinforced the importance of school typology. School macroregion, although less influential, emerged as a modest but consistent predictor, reflecting regional disparities highlighted in the literature. Gender and ESCS had relatively minor but statistically significant effects, with gender slightly more influential than ESCS in the models. Network analysis further illustrated that demographic variables such as ESCS, gender, and macroregion generally occupy peripheral positions with low or negative centrality measures, indicating they are less central in the network of predictors compared to cognitive and digital competencies. The negative partial correlations between these demographic factors and mathematics performance highlight potential structural inequalities influencing achievement.

5.4 Recommendations

Based on the findings, some practical recommendations emerge for educators and policymakers. Firstly, given the robust association between Italian proficiency and mathematics performance, integrating language development into mathematics teaching could improve comprehension and problem-solving. Strategies may include explicit vocabulary instruction and reading comprehension exercises contextualized in mathematical content. While this study draws on Italian data, the recommendation aligns with international research emphasizing the importance of mathematical language proficiency across diverse educational systems. Thus, rather than proposing system-specific reforms, we highlight the broader implication that mathematics instruction benefits from explicit attention to linguistic demands. We therefore suggest that educators develop specific laboratories or interdisciplinary classes to reinforce students’ language skills and interconnect them with mathematics problems.

Secondly, the central role of DigComp competencies highlights the importance of digital skills in supporting mathematical learning. Schools should prioritize fostering algorithmic thinking, digital problem-solving, and critical reasoning skills alongside traditional mathematics content. Although recent Italian initiatives (e.g., coding programs supported by PNRR) illustrate one possible approach, the broader implication is that educational systems worldwide may benefit from integrating digital competence development into mathematics curricula in ways that reflect their own policy contexts and technological infrastructures. This might help students to gain digital competencies, develop their critical thinking, algorithmic thinking, reasoning, and mathematical modeling, which would, in turn, result in better achievement in mathematics as well.

Thirdly, differences across lyceums, technical, and vocational schools reflect varied curricular emphases. Educational strategies should consider these differences, providing support that addresses both curriculum content and student engagement, particularly in technical and vocational settings. Tracked or stratified educational systems often produce differentiated mathematics outcomes, and targeted support for students in less academically oriented tracks may be necessary across many countries. Being mathematical literacy of great importance in today's society and labor market, specific interventions are needed to guarantee students from each school type comparable mathematical skills.

5.5 Limitations and future directions

Despite its contributions, this study has some limitations that should be considered when interpreting the findings. First, the study relies on observational data from the 2024/25 INVALSI national assessment, which limits the ability to establish causal relationships among language proficiency, digital competencies, demographic variables, and mathematics performance. Second, while the dataset is large and nationally representative, some variables, such as ESCS and macroregion, may share overlapping variance, potentially complicating the interpretation of their unique effects. The finding that ESCS had a relatively smaller effect size than expected may reflect this issue, as macroregion might act as a proxy for socioeconomic status in the Italian context. Third, the study focuses on broad measures of digital competence using the DigComp framework; however, some subdomains showed limited independent predictive power, suggesting that further refinement or alternative conceptualizations of digital skills might be needed. This raises questions that extend beyond Italy, particularly for countries adopting or adapting DigComp-aligned frameworks, where the granularity and operationalization of digital competence remain active areas of debate. Additionally, motivation, attitudes, and other affective factors influencing mathematics performance and digital competence were not included, representing an important gap. Finally, the cross-sectional nature of the data precludes analysis of developmental trajectories or changes over time, such as the evolution of the gender gap in mathematics achievement.

Future research should address these limitations to deepen understanding of the complex factors influencing mathematics achievement. Longitudinal studies could clarify causal pathways and examine how language proficiency, digital competencies, and demographic factors interact over time, particularly across critical educational transitions. Further investigation into the role of affective and motivational variables, including students’ attitudes toward mathematics and digital learning, may offer additional explanatory power. Given the limited predictive power of certain DigComp subcomponents, qualitative or mixed-methods studies might explore how specific digital skills relate to mathematical problem-solving in classroom contexts. Moreover, research should unpack the intertwined roles of ESCS and regional factors within Italy, investigating how socioeconomic and geographic inequalities shape learning opportunities and outcomes. Finally, targeted research is needed to explore mechanisms underlying gender disparities in mathematics, particularly interventions that might reduce stereotype effects and promote equitable engagement in STEM subjects.

6. Conclusions

The results of the Boosting and Random Forest regression models supported the findings from multiple regression. In all these models, Italian proficiency was the most influential predictor of mathematics achievement. DigComp competencies ranked second in influence, followed by school typology. Other variables, including ESCS, gender, and DigComp subskills, had lower variable importance scores.

These results suggest that language and digital competencies, along with certain demographic variables such as school type and gender, are associated with variation in student performance on the INVALSI mathematics test. However, the direction and magnitude of relationships were not uniform across all predictors. While language and digital skills were positively associated with outcomes, demographic variables had smaller or negative associations, and some subskills within DigComp did not show independent predictive power when controlling for higher-level constructs and other covariates.

Beyond the Italian context, these findings contribute to the broader international discourse on mathematics education by demonstrating that mathematics performance in large-scale assessments is strongly shaped by transversal competencies, particularly linguistic and digital skills. This reinforces global evidence that mathematical achievement cannot be understood solely as a domain-specific construct but must be interpreted within a wider ecosystem of literacy, digital competence, and structural factors.

The findings underscore the centrality of language and digital competencies in educational performance, suggesting that interventions targeting these skills may have widespread benefits. Additionally, the peripheral roles of demographic factors in the network highlight the importance of focusing on cognitive and skill-based variables, rather than solely relying on background characteristics, when designing educational support strategies. As education systems globally strive to integrate digital competence frameworks and address post-pandemic learning gaps, this study provides timely evidence for informing equitable and skill-oriented education policies.

Supplemental Material

sj-docx-1-mea-10.1177_27527263261426412 - Supplemental material for Predicting mathematical performance: The role of language proficiency, digital competencies, and demographic factors

Supplemental material, sj-docx-1-mea-10.1177_27527263261426412 for Predicting mathematical performance: The role of language proficiency, digital competencies, and demographic factors by Daniel Doz in Asian Journal for Mathematics Education

Footnotes

ORCID iD

Daniel Doz

Ethical Approval

This study involved a secondary analysis of anonymized data collected and made publicly available by the INVALSI Institute. Since no new data were gathered directly from participants and all identifying information had been removed prior to access, no additional ethical approval was required. The analysis complied with relevant data protection regulations and adhered to ethical standards for research using publicly available datasets.

Contributorship

The author conceptualized the study, designed the methodology, conducted the data analysis, interpreted the results, and wrote and revised the manuscript.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

Author Biography

Daniel Doz, PhD, is an assistant professor in Mathematics Education at the Faculty of Education, University of Primorska, Slovenia. His work focuses on students’ conceptual and procedural understanding in mathematics, with particular attention to common misconceptions, mathematical reasoning, and the role of affective factors such as anxiety and enjoyment. A significant part of his research investigates large-scale assessment data (e.g., INVALSI), using advanced statistical and machine-learning techniques to explore the predictive role of demographic and non-cognitive variables in students’ achievement. He has also contributed to research on innovative assessment approaches, including the use of digital systems (such as STACK) and fuzzy logic to enhance fairness, validity, and formative feedback in mathematics evaluation.

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