Problem

In 2019, France's average score on the Trends in International Mathematics and Science Study (TIMSS) math assessment was 487, scoring below the Organisation for Economic Co-operation and Development’s (OECD) average of 502. Although this score increased from 482 in 2015, the trends shown are still a cause of concern. Martin’s (2023) article on the French Education Crisis illuminates the urgency to reform the system in order to alleviate declines in the knowledge of students. Zheng et. al’s (2016) meta-analysis of 1-to-1 laptop programs found positive effects on student achievement in math, with an average effect size of 0.35 standard deviations higher for students in programs with sustained access to individual laptops. This finding was mirrored in Shapley et. al’s (2011) study where they found a positive relationship between levels of technology implementation fidelity and student achievement gains in math in a high-needs middle school classroom in Texas, USA.

As such, with various studies pointing to increased math learning after technology integration into the classroom, we are interested in exploring in more nuance the usage categories that promote learning in French high schools. We are also interested in investigating the difference between public and private schools as a report on a comparative study by Capital (2022) shows that approximately 17% of students in France attend private schools. In mathematics, the score in the public sector is 67.2% accurate compared to 68.4% in the private sector, a difference of 1.2% (Fougère et. al 2017). We hypothesize that the difference in resources and funding may play a role in the extent to which technology supports or hinders math learning.

https://www.gisreportsonline.com/r/france-education-crisis/

Hypothesis

Do digital device usage in private schools lead to better math outcomes for French high school students compared to public schools?

Findings

We found that for 10th graders who never or hardly use a digital device at school, 83.3% of those in private schools score between 400-600 on their math assessments. In contrast, only 37.5% of those in public schools with the same characteristics score between 400-600 on their math assessments.

We also found that for 10th graders who use a digital device at school once or twice a week, 81.8% of those in private schools score between 400-600 on their math assessments. For those in public schools with the same characteristics, the percentage of those who score between 400-600 on their math assessments increases to 91.6%. For these findings, while we again cannot infer any causation, we see that there is a relationship.

From our confusion matrix, we saw random forest does a decent job of predicting math outcomes based on the type of school (public or private), the student’s grade (9th and 10th), and how digital devices are used at school. The random forest model is doing a good job of predicting those who are scoring between 400-600 on their math assessments (93.9% accuracy). While we cannot infer the causation of these independent variables with math outcomes, we see that there is a relationship hence the high accuracy in prediction.

Interpretation & Recommendation

The results from our analysis highlight current issues in the French educational system that need to be addressed by decision-makers. Our findings reveal a substantial opportunity gap between students in public and private high schools where digital devices were not utilized in classrooms in France. Equal access to digital devices has the potential to close the opportunity gap in high school students. High math performances seen in classrooms with high levels of technology usage in the sample population is supported by a meta-analysis in the effects on digital tools on Mathematics learning (Li & Ma, 2010). Hence, advocating for the integration of digital technology in the classroom may enhance students’ learning in the broader population in France, and in high-need and low-resources communities, the utilization of educational technology can serve as an intervention for learners who will benefit from more support (Shapley et al., 2011).

Compared to private institutions, which tend to have more monetary and parental support, many French public schools lack funds to broadly procure new digital tools or provide teacher training due to budget constraints. While teachers are familiar with technology, more emphasis and teacher training on technology-mediated classroom practices may further increase teachers’ digital literacy; therefore, they can make more informed decisions when integrating technology into their classrooms (Li & Ma, 2010; Shapley et al., 2011). It is important for decision-makers to not only acknowledge the existing barriers, but also consider the funding and resources needed for both teachers and students to exceed.

In our study, the sample size (n=252) might not be able to represent the broader population of high school students in France. Due to data collection constraints, our sample only consisted of 9th and 10th graders. There is a need to further conduct more research on how educational technology can enhance academic achievement in math for high school students in France. To further understand the difference in math scores between private and public school students, relevant factors, such as students’ socioeconomic status and former knowledge/experience with educational technology, should be controlled. There may be more confounding factors contributing to the relationship between the use of digital technology and mathematics outcomes between private and public high school students in France.

Overall, our recommendation for policymakers is supported by the results presented in the paper and grounded in research findings. It is crucial to recognize the complexity in different educational environments and systems, and the recommendations should be considered as informed suggestions. Additional research is necessary to adapt and iterate on existing programs and supports.

References

Fougère, D., Monso, O., Rain, A., & Tô, M. (2017). Qui choisit l'école privée, et pour quels résultats scolaires?. Éducation & formations, (95), 59-85.

Li, Q., & Ma, X. (2010). A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review, 22, 215-243.

Martin, E. (2023, May 30). France's education crisis is real. GIS Reports. https://www.gisreportsonline.com/r/france-education-crisis/

Organisation for Economic Co-operation and Development (OECD). (2019). PISA 2018 Results (Volume I): What Students Know and Can Do. https://doi.org/10.1787/5f07c754-en: https://www.oecd.org/pisa/publications/PISA2018_CN_FRA.pdf

Shapley, K. S., Sheehan, D., Maloney, C., & Caranikas-Walker, F. (2010). Evaluating the implementation fidelity of technology immersion and its relationship with student achievement. The journal of technology, learning and assessment, 9(4).

Education Data Mining: Math Learning & Digital Tool Use in France