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Article

Mathematical Competence Scale (MCS) for Primary School: The Psychometric Properties and the Validation of an Instrument to Enhance the Sustainability of Talents Development through the Numeracy Skills Assessment

by
Diego Bellini
1,*,
Alberto Crescentini
2,
Giovanna Zanolla
2,
Serena Cubico
1,*,
Giuseppe Favretto
1,
Lorenzo Faccincani
1,
Piermatteo Ardolino
1 and
Giovanna Gianesini
1
1
Department of Business Administration, University of Verona, I-37129 Verona, Italy
2
Department of Education and Learning, University of Applied Sciences and Arts of Southern Switzerland (SUPSI), 6928 Manno, Switzerland
*
Authors to whom correspondence should be addressed.
Sustainability 2019, 11(9), 2569; https://doi.org/10.3390/su11092569
Submission received: 28 February 2019 / Revised: 15 April 2019 / Accepted: 22 April 2019 / Published: 3 May 2019
(This article belongs to the Special Issue Psychology of Sustainability and Sustainable Development)
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Abstract

:
The adequate development of the numeracy skills is a target of the fourth of the Sustainable Development Goals and is considered the basis for a financial literacy: both are competences needed for successful social and professional inclusion. Building on these goals, we carried out a unidimensional Mathematical Competence Scale (MCS) for primary school. The aim of this study was to present the psychometric properties and the validation process of MCS, designed basing on Item Response Theory. The final version of the scale, which measures different domains of mathematical knowledge (Data Analysis and Relationships, Geometry, Dimensions and Measurements, Numbers and Calculations), was validated on the entire population of 2935 fourth graders in Ticino Canton, Switzerland. The results reveal the high level of correlation between the six mathematical dimensions and confirm the assumption of a latent “mathematical construct”. However, even the multidimensional model could be considered a good model because it fitted the data significantly better than the one-dimensional model. In particular, the differences of the deviance between the two models are significant (χ2 (20) = 642.66, p < 0.001). Moreover, findings show a significant gender effect and a positive correlation between students’ actual school performance during the same academic year and MCS scores. MCS allows a reading of the learning and teaching process in the perspective of the psychology of sustainability and sustainable development and helps a teacher to sustain student talent through the development of numeracy skills; in fact, the scale is intended both as an assessment tool and an innovative approach for shaping the development of curriculum, and therefore has potential to serve as a bridge between empirical research, classroom practice and a positive (school and professional) career development.

1. Introduction

The research area in the psychology of sustainability and sustainable development offers a useful framework to improve the quality of scholar life of teachers and their students [1,2,3,4,5]. Specifically, goal 4 (i.e., 4.4 and 4.6) of the 17th Sustainable Development Goals refers to quality education (Retrived on 28 April 2019, from https://www.un.org/sustainabledevelopment/education). Goal 4.4 targets to increase the number of people who have relevant skills, for employment, jobs and entrepreneurship. Goal 4.6 targets to ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracy.
One of the reasons for lack of quality education are due to inadequately trained teachers who do not have assessment tools to sustain relevant skills (e.g., numeracy) and students’ talents. Mathematics skills, found to be predictive of later mathematics achievement and relevant for the acquisition of financial literacy [6,7,8], are important for the design of interventions to teach those skills [9]. Research can support the development of math-related competences [10,11], and this journal has paid attention to the sustainable learning of mathematics [12]. However, the literature has to date partially considered the examination of how performance on various math tasks might be interrelated [13,14] to sustain quality education. Observing that there is little research on this topic, we add to this small body of literature by presenting the validation process of the unidimensional Mathematical Competence Scale (MCS) for primary school and its psychometrics properties. This study was carried out in Switzerland on the entire population of 2935 fourth graders in Ticino Canton.
This paper considers multiple domains of children’s mathematical knowledge and makes conclusions about how these competencies might influence long-run mathematical development and lifelong learning. Everyone has the right to acquire and understand which skills allow full participation in society and also help people choose appropriate fields of study [15]. Its aim is to provide a standard framework for both research and practice to support sustainable mathematical competences [16,17].

1.1. Mathematical Competence and Talents Sustainability Development

Education for 21st-century skills has stimulated researchers to suggest competencies that can promote talents development [18]. Among these competences, mathematical competence learning plays an important role in education. Mathematical Competence is defined in the European Recommendations for learning [19] as “the ability to develop and apply mathematical thinking to solve a range of problems in everyday situations” and is considered to be of key importance for lifelong learning within European countries. Numeracy, the basic mathematical skill [20], is the ability and disposition to use and apply mathematics in a range of context outside the mathematics classroom. In building a sound mastery of numeracy, process and activity are relevant as knowledge as well. In the European Recommendations for learning [19], mathematical competence involves the ability and willingness to use mathematical models of thought (logical and spatial thinking) and presentation (i.e., formulas, models, constructs, graphs, and charts). Counting competencies have been emphasised internationally as of primary importance for children’s development of mathematical proficiency. Clements and Sarama [21,22] shown that children’s mathematical skills are the strongholds of early numerical knowledge and are useful for all further work with number and operations. Counting competencies, particularly advanced counting skills, are also highly relevant for learning arithmetic. More advanced counting competencies, requiring complex thinking and procedures, are also necessary for later-grade mathematics achievement, such as algebraic reasoning. Moreover, at the elementary school level, geometry, measurement, data analysis, pattering, and spatial ability, as they support logical thinking and problem solving, have been shown to play an important role in children’s mathematical developments [9,23]. Geary et al. [24] found in an eight-year longitudinal study through eighth grade that the importance of prior mathematical competencies on subsequent mathematics achievements increase across grades, with number knowledge and arithmetic skills. Overall, they shown that domain-general abilities were more important than domain-specific knowledge for mathematics learning in early grades.
Nguyen et al. [9], in another longitudinal study found early numeracy abilities to be the strongest predictors of later mathematics achievement, with advanced counting competencies more predictive than basic counting competencies. They developed a theory of the developmental relationships between different sets of children’s mathematical competencies to help practitioners considerably improve mathematics education, especially among low socioeconomic status (SES) children. Research findings support the practice of evaluating children’s mathematical competencies during childhood as a mean of assessing any difficulties and forecasting academic achievement in future grades. A recent study on mathematics achievement in Turkish fifth graders [25] revealed that there is a significant difference between learning styles and students’ achievements in mathematics. Moreover, students with high metacognitive awareness, achieve better grades in mathematics than other students. A significant difference was also found between the metacognitive awareness scores of males and females. These differences maybe the results of biological, social and cultural factors [25]. Early predictors of mathematics achievement can be successfully targeted by educators in elementary school.
Assessing key mathematics competencies can support researchers and practitioners to identify children likely to struggle with math in later grades, and target more services toward these children able to support the sustainable development of their talents [4,5].

1.2. Assessing Mathematical Competence: The Use of Standardised Test

To assess mathematical competences, Curzon [26] suggested multiple-choice questions (MCQs). Ebel [27]. This methodological approach enables reliable and objective marking, quick feedbacks, and it allows for the standardisation and meaningful comparison of scores and students. Nevertheless, there are disadvantages to the use of MCQs, although Curzon [26] (p. 374) believed that “guessing in multiple-choice tests is not regarded as a major problem by examination boards, guessing multiple choice questions may be critical in certain circumstances, and additionally it may not allow students to attempt questions if those should be answered in a more discursive manner”. Further, the debate about the use of standardised tests in school underlines several potential problems, for instance they may evaluate only competence in short timeframe [28], tests results can be influenced by anxiety [29] and the use of these instruments may require preparatory work for teachers, thus take time away from teaching [30]. Conversely, standardised tests can provide useful and more objective information to teachers than the classical class evaluation. Standardised tests are usually administered, scored, and interpreted in standardised manner, which requires all test takers to answer the same questions, in a consistent way, with the same testing directions, time limits and scoring [31]. In school, tests are used as a fair and objective measure of student achievement [32]. They can be criterion-referenced (designed to measure student performance against a fixed set of predetermined criteria or learning standards) or standard-referenced [33], where students have to meet expected standards to be deemed “proficient“. In this second case, tests are designed to compare and rank test takers in relation to one another, and students’ results are compared against the performance results of a statistically selected group of similar test takers, typically of the same age or grade level, who took the same test [34]. The development of numerical abilities and math-related skills is a heavily researched topic. Despite the number of research studies, however, the current literature still lacks a thorough examination of how performance on various math tasks might be interrelated [13,14]. To combine these two types of tests and overcome issues and limitations in the assessment of students’ mathematical competence, the MCS was designed based on Item Response Theory (IRT) [35,36,37], as described in Section 2.
Furthermore, building on the most recent research literature on mathematics assessment and education and the work of vom Hofe and colleagues, the MCS was developed as a tool to analyse cognitive processing skills, different mathematical actions and the use of certain verbs to describe and differentiate the different levels of understanding.

1.3. The MCS Theoretical Framework

The MCS theoretical framework is aligned with the conceptualisation of mathematical literacy [38] used in the Program for International Student Assessment (PISA) to monitor the outcomes of education systems. A draft of the Pisa [39] mathematics framework was circulated for feedback to over 170 mathematics experts from 40 countries. The overall intention of PISA is to give a forward-looking assessment of the outcome of primary schooling, by evaluating students’ learning in relation to the challenge of real life in different contexts in the three major areas of reading, mathematics and science [40]. PISA survey items have been prepared with the intention of assessing different parts of mathematical literacy independently of each other, and describe key conceptual ideas in mathematical modelling and problem solving [41,42] with emphasis on the acquisition of mathematical knowledge. In particular, Pisa’s second section “Organizing the Domain” describes the way mathematical knowledge is organised and the content knowledge that is relevant to an assessment of mathematical processes and the fundamental mathematical competencies underlying those processes. Specifically, the process of learning addressed in the OCDE [43] is characterised not only by the transmission of knowledge occurring during formal teaching, but also by individual and contextual variables affecting the children’s everyday learning process. Knowledge concerned on that student need to perform, while competences that on putting mathematical knowledge and skills to use (i.e., the process that students need to perform), rather than simply testing this knowledge as taught within the school curriculum [43,44]. In looking at the aspects of the content mathematical knowledge combined with mathematical competence, Swiss experts in didactic of mathematics have worked together since 2015 to produce a shared theoretical framework of the fundamental competences that each student needs to develop at the different stages of the compulsory education. Building on the mathematical knowledge and process, based on the PISA research theoretical background, the group developed the MCS. This instrument covers parts of the math curriculum in schools through the six dimensions reported in Section 2.2. The different domains are relevant for the fourth graders of primary school.

1.4. Objectives

This paper presents the psychometric properties and the results of the validation process of an innovative one-dimensional Mathematical Competence Scale (MCS) assessing six different domains of mathematical knowledge.

2. Materials and Methods

2.1. Context of the Study

The study was carried out in Switzerland, which is a federated nation with 26 different cantons and no common educational system. The Swiss school system, based on an inter-cantonal agreement [45], provides guidelines and a list of core educational competences, including mathematics, in an effort to standardise education across cantons. In 2011, the Plenary Assembly of the Swiss Conference of Cantonal Ministers of Education specifically asked the Centre for Innovation and Research on Education Systems (CIRSE) to develop standardised tests for assessing students’ mathematical competence in primary schools.

2.2. Scale Development

The content of the test has been decided by a group of school stakeholders in Switzerland (school directors, teachers, and disciplinary experts). The items have been produced by a group of teachers and disciplinary experts and evaluated by a teacher of didactic of mathematics to grant the adequacy to the content of the discipline and the pupils’ abilities. The scale comprises six different domains of mathematical knowledge: (1) data analysis and relationships (i.e., “knowing, recognizing and describing” (AR_SRD); (2) geometry 1 (i.e., “knowing, recognizing and describing” (GEO_SRD); (3) geometry 2 (i.e., “performing and applying” (GEO_EA); (4) dimensions and measurements (i.e., “performing and applying); (GM_EA); (5) numbers and calculations 1 (i.e., “arguing and justifying” (NC_AG); and (6) numbers and calculations 2 (i.e., “performing and applying”(NC_EA). The entire set of items are reported in [46]. The six dimensions of the scale and their characteristics are presented in Table 1.

2.3. Participants

The MCS was initially administered to a random sub-sample of 1683 fourth graders, of primary school, with the purpose of items selection. Successively, the scale was validated on the entire population of 2935 fourth graders of primary schools, aged 10–14 years, 1517 males (51.7%) and 1418 females (48.3%), from all 196 elementary schools in Canton Ticino, Switzerland. The majority of students (n = 2539) were 10 years old at the time of testing, 4 were 9 years old, 364 were 11 years old, 27 were 12 years old and just 1 was 13 years old.

2.4. Data Analysis: Item Response Theory

The MCS was designed based on Item Response Theory (IRT) [35,36,37]. IRT, by estimating each individual item’s discrimination on the latent trait and difficulty within a population, provides information on the validity of the items and identifies low performance indicators. Through the specification of a mathematical statistical model, IRT allows the evaluation of the performance of a given subject as a function of his/her latent ability. As a result, IRT assesses both respondents’ performance and the characteristics of each item offering the possibility to evaluate individual characteristics and to compare performance across subjects [37,47]. In other words, IRT is appropriate for developing instruments aimed at accurately measuring a specific level of the ability assessed. One example of the IRT is the Mathematical Achievement Test (MAT) [48,49], used across all grades of secondary school, which measures students’ modelling competencies and algorithmic competencies in arithmetic, algebra, and geometry. In psychometric field, the true unknown measure is latent and unobservable so the classical calibration models cannot be applied. In some statistical models, the answers to the items (Y1, Y2, …, Yn) are functions of some psychological characteristic (X) that is called latent. As suggested by De Battisti, Salini and Crescentini [49]: the most important model to define the measure of a latent variables is the Rasch model (Rasch, Copenhagen, Denmark, 1960) [50]”. The Rasch model allows measuring both item difficulty and subject ability. The following relation expresses the probability of a right answer in the dichotomous case: xij is the answer of subject i (i = 1, …, n) to item j (j = 1, …, k), i depicting the skill of the subject i and j is the difficulty of the item j.
According to IRT, to measure the discrimination capacity of an item, as well as its consistency within the six identified dimensions of mathematical knowledge, each item needs to be relevant to only one dimension. For this reason, it is recommended to administer an item to a least 200–300 students to get reliable results on the quality of the item. From the original pool of 300 items, previously identified [51], we selected the more suitable based on their measurement and discrimination quality [52], that is, the number of correct answers for each item. Items receiving a high number of correct answers only by students with the lowest overall score, and items with no or all correct answers were eliminated.
Analyses were performed using SPSS for descriptive analyses and ConQuest software (Version 3, Acer, Camberwell, Australian) for model fit and item analysis based on Item Response Theory (IRT) [53,54].

2.5. Procedure

This validation of the scale has structured into two phases, “test administration” and “model fit and item selection”
Phase 1: Test Administration
As shown in Figure 1, the initial pool of 300 items was administered over 10 test booklets (A–K) to a sample of 1683 fourth grader students. Every booklet consisted of about 30 items ranked by increasing difficulty. Consequently, we expected a very large number of students to initially respond correctly and this number to decline with the increasing difficulty, to reach the smallest number for the most difficult items, which only the most competent students could answer correctly (from three out of the six identified dimensions of mathematical knowledge). As each booklet covered only three of the six mathematical dimensions, they were combined and administered in pairs, to guarantee items from all six dimensions. Furthermore, every booklet was included in two different pairs, in different order.
Each student was administered two booklets covering items from all six mathematical dimensions. The second booklet was administered one week after the first administration, to reduce the effect of learning. However, in the second administration the number of correct answers slightly increased compared to the first. All students had enough time to confront all items. The Rasch model requires that each item be approached by all subjects and that the items be proposed in a growing difficulty order. For this reason, the test was split into two session, each 50 min.
Phase 2: Model Fit and Items Selection
Through IRT [55,56] analysis and subsequent trials, 120 items, divided into six dimensions, were selected from the original 300. We then analyzed whether the model fit could be improved by choosing a multidimensional Rash model instead of a one-dimensional model, following a three-step procedure.
First, we analyzed the fit of the data to different item response models (i.e., with one dimension and multidimension). Secondly, we matched the two models and the one-dimensional model with the subdomains. Finally, analyzed both items quality and item fit for the two models.

3. Results

The results of the first step analysis confirm that the multidimensional model fitted the data significantly better than the one-dimensional model. In particular, the differences of the deviance between the two models are significant (χ2 (20) = 642.66, p < 0.001). However, the correlations coefficient between the six dimensions are high varying, from 0.67 to 0.84 [57], as shown in Table 2.
In looking at the high correlation between the six dimensions (from 0.67 to 0.84), we decided for a one-dimensional model. Those high correlation signal the existence of a “Mathematics” latent construct. The deviances of the one-dimensional model and the one-dimensional model with subdomains were compared. The results indicate the model with subdomains fitted the data significantly better than the one-dimensional model (χ2 (274) = 558.17, p < 0.001). The items with the highest quality and fit for testing the ability of the students were identified and selected by considering five indicators: percentage of correct answer for item and per dimension; item ability to discriminate; fit of the item to Rash Model (Weighted Mean Square; MNSQ); T statistics; and Items Characteristics Curve (ICC).
The difficulty of an item was represented by the percentage of students that solved it correctly. It can be expected that most of students have an average mathematical ability, consequently items should have average difficulty (i.e., a percentage of correct answer between 25% and 75%). Figure 2 shows the percentage of correct responses per item for each of the six dimensions. While the dimension “AR_SRD” presented a few items difficult to very difficult, all other dimensions were distributed in a more balanced way.
The discrimination of an item refers to its correlation with the number of items solved correctly. Therefore, if an item received a high number of correct answers, but only by students with low overall scores in the test, it was eliminated. Moreover, items with all or no correct answers were also eliminated, due to their poor ability to discriminate. A discrimination coefficient between 0.3 and 1 reflects differences in student achievement, high and low score in different dimensions so that distinctions may be made among the performances of students [58]. A coefficient near 0 indicates that an item does not differentiate between students and a negative coefficient indicates that expert students solve the item correctly less often than non-expert students. Items with a negative discrimination coefficient were therefore excluded as well. The presence of items covering all levels of difficulty in a mathematical test makes it possible to identify students at all levels of expertise.
The model in fit (Weighted Mean Square; MSNQ) indicates the fit of an item to the Rasch model by analyzing the number of unexpected answers that differ from the prediction of the Rasch model. A value close to 1 indicates that the item fits the model well [59]. However, values smaller than 1 indicate that the item discriminates stronger than predicted by the model (over-fit), while values greater than 1 indicate that the item discriminates less than predicted by the model (under-fit). Therefore, only items with MSNQ scores between 0.7 and 1.3 were selected for the final version of the MCS. Moreover, for every fit value, showing whether the in fit differs statistically and significantly from expected value 1, a t-test was calculated (MSNQ shows the difference between expected and observed value). This difference is statistically significant if t value is less than −1.96 or greater than 1.96 (p < 0.05) [58]. However, it is important to note that t statistics depends on the sample size [56].
Furthermore, the item characteristic curve (ICC) of each item was considered. ICC is a function of the ability of the students and the probability for solving an item correctly. It gives further information about the fit of item to the Rash model. The continuous line represents the expected curve of the item according to the model. The dotted line represents the observed curve. Figure 3 shows an ICC of an item that fit very well to the Rasch model. The two lines are close together, which indicates a good model fit.
At the end of the item selection, only 120 items, from the initial 300, fitted well the five indicators described above and were included in the final version of the MCS scale. Once aggregated in one mathematical factor, it showed a good reliability with a Cronbach’s alpha of 0.91. Cronbach’s alpha was also consistent across gender (male = 0.90 and female = 0.91; see Table 3).
As a second analysis method, to describe more in depth the Psychometric Properties of the MCS, we decided to run Exploratory Factor Analysis (EFA) [59]. EFA gave us relevant information about the Psychometric Properties of the MCS. Specifically, EFA, as expected, confirmed the six dimensions of mathematical knowledge: extraction with principal axis factoring identified the factors that explain most of the variance observed in all six dimensions. Bartlett’s Test of Sphericity, χ2 (15) = 1059 (p < 0.05), evidenced that the correlation matrix is not an identity matrix, and that the factor model is appropriate. The Kaiser–Meyer–Olkin showed a very good sampling adequacy (KMO = 0.902). A criterion generally used to decide how many factors must be extracted is to observe eigenvalues. The eigenvalue estimates the variance of the study variables take into account for a specific dimension or factor. If a dimension has a high eigenvalue, then it is contributing much to the explanation of variances in the variables. Thus, we selected only one factor (named “Mathematic”), which presented an eigenvalue higher than 1 and explained 68.6% of the variance.
Moreover, extraction communalities of the variance were estimated in each variable, accounting for the factor in the factor solution. Small values indicate the items that do not fit well with the factor solution and should be dropped from the analysis, because low communalities can be interpreted as evidence that the items analyzed have little in common with one another. Values of communalities for the items of the factor selected were all higher than 0.50, where coefficients equal or lower than |0.35| suggest a weak relation between item and factor, and thus were acceptable.

Gender Effect and Correlation with Students Performance

A one-way ANOVA with two levels, with “gender” as a factor and the selected “Mathematical Factor” mean scores as the dependent variable, was run to verify the ability of the MCS Scale to discriminate between genders. Results show a significant gender effect F(1.2901) = 6.235, p < 0.05 indicating higher mean scores for men (M = 49.69; SD = 0.46 ) compared to women (M = 48.06; SD = 0.46). Research literature on mathematics has given great relevance to gender differences. A Recent studies shown that gender gaps favoring males continues [60]:proportionally more male than females thought they were good in mathematics. However, both males and females assumed that gender is not a factor affecting mathematics performance. About the teacher role in the class, the correlation between scores in “Mathematical Factor” and teachers’ evaluation during the school year was positive (r = 0.63, p < 0.01).
In summary the descriptive measures of the MCS six mathematical dimensions are reported in Table 4.

4. Discussion

4.1. Developing Quality Education with the Mathematical Competence Scale

The aim of this research was to develop an innovative Mathematical Competence Scales, to assess the level of mathematical competence in fourth graders in six domains of mathematical knowledge and support teachers’ task in developing quality education and talents sustainable development. The 120-item scale, available for the teachers [46], was validated on a sample of 2935 boys and girls, aged 10–14 year in Switzerland. Statistical analysis supported the six identified dimensions of the scale as well as a single factorial structure of the scale, which best described the conceptual relationship among the 120 items and the six dimensions of mathematical knowledge. Results show a good reliability for the overall Scale (0.91) and differential item functioning across gender. This study provides details on the psychometric properties and the underlying factorial structure of the MCS, for replication. The MCS results provide a meaningful feedback on teaching quality and the educational system functioning and represents a standardized measure adaptable to other countries and contexts outside Switzerland and in Europe. The quality of assessment is highly relevant in the context of each national curriculum, national testing programs and educational reforms. It aims at improving outcomes, as distinct from simply reporting them. Moreover, the MCS moves beyond the assessment of a single indicator for mathematical success and recognizes the multifaceted nature of learning and achievement over time.

4.2. Strengths, Limitations and Future Trends

This contribution has several strengths. We viewed mathematics achievement as a multidimensional construct that includes different competencies requiring different cognitive abilities and presented a framework outlining children’s core competencies in mathematics into meaningful and distinct categories of mathematical competencies. We used item response theory [35,36,37], which is considered an appropriate method for developing instruments aimed at accurately measuring a specific level of the ability assessed. Finally, our sample comprised the entire population of 2935 fourth graders in Ticino Canton, Switzerland. Generally, the MCS gives some useful elements toward which to target the lesson and to reduce negative consequences of different levels of mathematical learning.
The study also has several limitations. First, to promote the mathematic knowledge, child development in the early years varies depending on numerous variables, which warrant further research [9]. The relationship between metacognitive awareness, learning style, gender and mathematics grades need to be further examined. In the process of learning, each student has a different method to retrieve and process information [54]. It is important to take into account the differences in those abilities as well as attitude toward mathematics, self-efficiency, academic self-conception, thinking processes, and problem posing and solving.
Second, future research may use this innovative scale to further investigate gender gap in mathematics achievement and gender differences in assessment skills, as well as to explore the complexity of everyday class evaluation to provide teachers and the educational system an instrument that promotes an even more sustainable development of learning.
Third, this kind of test may be perceived by the teacher as a push towards the teaching to evaluate the habits or teachers’ competences.
Finally, the validation of the scale should be tested in other countries.
Future research may use the MCS for collecting quantitative data at school in different samples. The results may be helpful to plan intervention and to promote a deeper equity in school. One of the aims of compulsory education is to overcome social differences and give all children a fairer condition. Further, comparable data on children are also useful in teacher education course. Although the teacher’s role remains central in mathematics education, other standardized testing led to more reliable judgment and student’s real achievement measures and can be used to target interventions and tracking leaning growth over time. The data collected are actually part of a wider research project that have the ambition to develop a predictive model of school performances to sustain development of young talent.

5. Conclusions

Our study, focused on mathematical competencies that are needed for a growing number of educational and professional tasks, provides an instrument to enhance the sustainability of talents. The development of the MCS could help students to understand their level of competence in math and then to improve their abilities to solve mathematical problems that are considered to be a core goal of mathematics education [61]. The scale may have a great relevant also in jobs that currently require higher mathematical proficiency than ever before [9]. Our findings suggest the importance to consider the sub-dimensions of mathematical competence to fill the gap among students in specific domains of mathematical competence. This result holds promise for future research; for instance, it gives teachers some useful elements for targeting interventions to enhance children’s learning speed. Primary schools, in particular, have a central role to grow children’s mathematical competence [62]. Generally, an understanding of mathematics plays a central role to young person’s preparedness for life in modern society. The MCS could promote a positive experience in mathematics early in school that is a critical moment to facilitate future engagement in the subject. Furthermore, the teaching of Science, Technology, Engineering, and Mathematics (STEM) [63] has taken on new importance as economic competition has become truly global. Engaging teacher and students in high quality STEM education requires tests to assess mathematics curriculum, and also promote scientific inquiry. To address the ecological and social problems of sustainability in our modern times, students need to be supported by the teacher in understanding of STEM concepts and practices [64].

Author Contributions

Conceptualization: D.B., A.C., S.C., G.F., L.F. and P.A.; Methodology: D.B.; Formal analysis: D.B and G.Z.; Investigation: D.B., A.C., G.Z., S.C., G.F., L.F. and P.A.; Resources: A.C. and G.Z.; Data curation: D.B.; Writing—original draft preparation: D.B. and G.G; Writing—review and editing: A.C., S.C., G.F., L.F., P.A. and G.G; Visualization: D.B.; Supervision: S.C.; Project administration: D.B.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Multi Matrix Test Design.
Figure 1. Multi Matrix Test Design.
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Figure 2. Percentage of correct responses per item (20) for each dimension.
Figure 2. Percentage of correct responses per item (20) for each dimension.
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Figure 3. Item characteristic curve of an item the fit very well to the Rasch model.
Figure 3. Item characteristic curve of an item the fit very well to the Rasch model.
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Table
Table 1. MCS six dimensions of mathematical knowledge.
Table 1. MCS six dimensions of mathematical knowledge.
CompetenceDimensionsActivitiesDescription
AR_SDRData Analysis and RelationshipsKnowing, recognising, describingDisplay and analyse data; use various measures associated with data to draw conclusions, identify trends and describe relationships.
GEO_SRDGeometryKnowing, recognising and describingIdentify, compare, sort, and classify two dimensional shapes
GEO_EAGeometryPerforming and applyingUnderstand concepts of two-dimensions and applying this technique to solve real world problems.
GM_EADimensions and measurementsPerforming and applyingCompare and transform unit of measurement (money, lengths, widths, mass, time, capacity)
Calculate
NC_AGNumbers and calculationsArguing and justifyingMotivate statements concerning mathematical rules
NC_EANumbers and calculationsPerforming and applyingPerform with mental and written calculation techniques the 4 operations with numbers up to 5 digits and decimals up to hudredths.
Apply the properties of operations to simplify the calculation.
Compare two calculations and decide equivalence.
Table
Table 2. Correlation between the MCS subdomains of mathematical knowledge.
Table 2. Correlation between the MCS subdomains of mathematical knowledge.
CompetencesAR_SRDGEO_EAGEO_SRDGM_EANC_AGNC_EA
GEO_EA0.761.00----
GEO_SRD0.690.841.00 --
GM_EA0.780.840.771.00--
NC_AG0.720.800.780.831.00-
NC_EA0.670.680.740.790.811.00
Table
Table 3. Gender difference and Cronbach’s alpha for the MCS.
Table 3. Gender difference and Cronbach’s alpha for the MCS.
GendernMeanSDF-Valueα
M151751.378106.61700.179 *0.91
F141849.732103.54697.260 *0.90
n = 2935; * p < 0.05.
Table
Table 4. Descriptive measures of the MCS six mathematical dimensions: means (M), standard deviations (SD), kurtosis (K) and skewness (S).
Table 4. Descriptive measures of the MCS six mathematical dimensions: means (M), standard deviations (SD), kurtosis (K) and skewness (S).
DimensionsMeanSDKS
Data analysis and relationships—to know, to recognise and describing67.2422.5084217−0.427−0.588
Numbers and calculations—to perform and applying50.2521.5575608−0.6180.268
Geometry—to know, to recognise and describing51.3919.4771146−0.5580.197
Geometry—to perform and applying48.5721.0432216−0.6160.305
Dimensions and measurements—to perform and applying44.6222.9892453−0.6950.405
Numbers and calculations—to argue and justifying41.1619.6416040−0.1660.543
N = 2935.

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Bellini, D.; Crescentini, A.; Zanolla, G.; Cubico, S.; Favretto, G.; Faccincani, L.; Ardolino, P.; Gianesini, G. Mathematical Competence Scale (MCS) for Primary School: The Psychometric Properties and the Validation of an Instrument to Enhance the Sustainability of Talents Development through the Numeracy Skills Assessment. Sustainability 2019, 11, 2569. https://doi.org/10.3390/su11092569

AMA Style

Bellini D, Crescentini A, Zanolla G, Cubico S, Favretto G, Faccincani L, Ardolino P, Gianesini G. Mathematical Competence Scale (MCS) for Primary School: The Psychometric Properties and the Validation of an Instrument to Enhance the Sustainability of Talents Development through the Numeracy Skills Assessment. Sustainability. 2019; 11(9):2569. https://doi.org/10.3390/su11092569

Chicago/Turabian Style

Bellini, Diego, Alberto Crescentini, Giovanna Zanolla, Serena Cubico, Giuseppe Favretto, Lorenzo Faccincani, Piermatteo Ardolino, and Giovanna Gianesini. 2019. "Mathematical Competence Scale (MCS) for Primary School: The Psychometric Properties and the Validation of an Instrument to Enhance the Sustainability of Talents Development through the Numeracy Skills Assessment" Sustainability 11, no. 9: 2569. https://doi.org/10.3390/su11092569

APA Style

Bellini, D., Crescentini, A., Zanolla, G., Cubico, S., Favretto, G., Faccincani, L., Ardolino, P., & Gianesini, G. (2019). Mathematical Competence Scale (MCS) for Primary School: The Psychometric Properties and the Validation of an Instrument to Enhance the Sustainability of Talents Development through the Numeracy Skills Assessment. Sustainability, 11(9), 2569. https://doi.org/10.3390/su11092569

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