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Attention to diversity is one of the great challenges facing education and, in particular, mathematics education. That is why Innovamat has carried out a pilot test of the implementation of one of the most widespread frameworks, the Response To Intervention (RTI).

RTI is a framework for optimizing educational resources. In other words, it is a theory that defines how and when to detect learning difficulties and intervene so that we use educational resources in the best possible way and, therefore, can reach the maximum number of children. RTI has proven to be better than other models of attention to diversity, and is commonly used in schools in countries such as the United States, Finland and the Netherlands.

How does RTI work?

The first main idea of RTI is that the earlier educational interventions take place, the better the results they have. This means that we don’t have to wait for children to show symptoms of having difficulty learning, but rather we move the process forward and look for these possible difficulties before they become too obvious. In other words, we have to overcome the current model of ‘waiting for something to fail’.

The second main idea behind RTI is defined by DSM V (Diagnostic and Statistical Manual of Mental Disorders), which states that we cannot formally detect learning difficulties without first having carried out an intervention of at least 4 to 6 months in duration. The reason is that there may be children with poor performance due to causes other than a diagnosable cognitive difficulty. The reasoning is, let’s carry out an intervention to determine which students respond positively and get up to speed (this would mean that they don’t have any cognitive difficulties) and which students, even after the intervention, continue to show difficulties (which would mean that they need more specific attention).

Thus, the most common implementation of the RTI model involves a universal performance screening (in our case, mathematical) through an exam at the beginning of 1st grade, followed by an intervention of between 12 and 16 weeks that ends with another test to measure whether students have responded to the intervention, or not. From here on, the corresponding decisions are made about students who have not responded to the intervention and who, therefore, are candidates for a formal detection of a specific learning difficulty (for example, in the case of mathematics, the most common is dyscalculia, the difficulty of understanding the concept of the number and manipulating it in basic arithmetic operations).

ARTIST results

Following the model, last year we developed the tools that allow a universal screening of mathematical performance, as well as the tools for carrying out an intervention. In addition, we tested its effectiveness by comparing 1st grade students from 5 different schools (149 students) who received the intervention (henceforth, schools with intervention) with students from 8 different schools (269 students) that did not receive it (henceforth, control group). Students from all 13 schools underwent a math performance test in January. Then, we started the intervention with the 30% of the lowest performing students from the 5 schools with intervention. This consisted of working with the Innovamat App on activities specially created for this type of low-performing student, for 15 minutes a day, 4 days a week, for 15 weeks, and always outside of usual scheduled math time. Afterwards, students from the 13 schools took the test again.

The results show that this extra practice was carried out satisfactorily and that the students in the group with intervention improved significantly, more than their peers in the control group in terms of performance in mathematics (Figure 1), and also that they leave the underperforming zone (below the 30th percentile) and the risk zone for mathematics learning difficulties (below the 15th percentile) significantly more than students in the control group (Figure 2).

With these results, we conclude that we have been able to successfully develop and implement an RTI framework with a scalable tool that is relatively easy to implement in all schools that use Innovamat and that want to implement it. In this way, we continue to work to help teachers.

Figure 1. Comparison of the differences between additive arithmetic fluency before and after the intervention period, given in corrected total operations, by the three study groups: the ‘No’ group corresponds to students from the 13 schools that have scored equal to or higher than the 30th percentile. The ‘Needed’ and ‘Yes’ groups are those of the 8 control schools and 5 intervention schools, respectively, which scored lower than the percentile.
Figure 2. Percentage of students who remain A) below the 30th percentile (low performance zone); B) below the 15th percentile (risk zone for dyscalculia), depending on whether they had intervention.
  • Eudald Correig

    Eudald is a physics graduate from the University of Barcelona, he has a master’s degree in theoretic physics from the University of Utrecht and a postgraduate degree in string theory from the University of California in Santa Cruz. He has worked in artificial intelligence applied to cognition and learning difficulties. Currently, he is a professor of medicine teaching degree level at the Universidad Rovira i Virgili, and he is head of investigation at Innovamat.

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