The use of fuzzy logic to assess students’ knowledge is not a completely new concept. However, despite dealing with a large quantity of data, traditional statistical methods have typically been the preferred approach. Many studies have argued that machine learning methods could offer a viable alternative for analyzing big data. Therefore, this study presents findings from a Random Forest (RF) regression analysis to understand the influence of demographic factors on students’ achievements, i.e., teacher-given grades, students’ outcomes on the national assessment, and fuzzy grades, which were obtained as a combination of the two. RF analysis showed that demographic factors have limited predictive power for teacher-assigned grades, unlike INVALSI scores and fuzzy grades. School type, macroregion, and ESCS are influential predictors, whereas gender and origin have a lesser impact. The study highlights regional and socio-economic disparities, influencing both student outcomes and fuzzy grades, underscoring the need for equitable education. Unexpectedly, gender’s impact on achievements is minor, possibly due to gender-focused policies. Although the study acknowledges limitations, its integration of fuzzy logic and machine learning sets the foundation for future research and policy recommendations, advocating for diversified assessment approaches and data-driven policymaking.

The evolution of mathematics coincided with advancements in its teaching. The 19th and 20th centuries marked a pedagogical revolution in mathematics education. This paper argues that Bruner’s (1966) model, Gagné’s (1985) taxonomy, innovative teaching methods emphasizing research and problem-solving, and the inclusion of data analysis topics have shaped modern mathematics education. Additionally, the paper explores transformative trends, emphasizing mathematics literacy and the integration of virtual reality (VR) and artificial intelligence (AI) in education. This evolution emphasizes practical, contextually relevant approaches. VR enhances engagement and comprehension of abstract concepts, while AI offers personalized learning experiences, fostering deeper understanding and skill development.

Although many studies have investigated the possible use of virtual reality (VR), extended reality (XR), and augmented reality (AR), there is a lack of research that specifically focuses on educators’ opinions and awareness about the usage of these technologies in educational settings. Therefore, the present research aimed to investigate educators’ opinions about XR, VR, and AR technologies in education, exploring their awareness, experiences, and interest in these technologies. Findings revealed limited knowledge about VR/AR/XR technologies among educators. The awareness did not vary based on teaching experience or professional training duration. While the majority of educators expressed curiosity about VR/AR/XR technologies, most had not used them in their teaching practice. The research identified that more experienced educators and those with longer professional training had tried these technologies more often.

Assessing student mathematical knowledge is an important factor in the mathematics learning process because students obtain important feedback to improve their knowledge and learning. Despite the importance of student assessment, several researchers have shown that student grades comprise noncognitive and metacognitive factors and teachers’ prejudices and beliefs. One method to obtain a more objective view of student mathematical knowledge is through standardized assessments. In this paper, we analyze two methods of assessing student mathematical knowledge by considering their written and oral grades and achievements on the Italian National Assessment of Knowledge (INVALSI). The final grade was produced using the fuzzy logic inference system. It was tested on a sample of 2279 Grade 13 Italian high school students, who had both an oral and written grade in mathematics and who took the INVALSI assessment in the school year 2020–2021. Both tested fuzzy-logic-based assessment methods lowered the mean grades.

The COVID-19 pandemic represented a shift from face-to-face to online and distance learning. Teaching methods and assessing strategies changed, as well as grading standards. The focus of this paper is to address the differences between pre- and post-COVID-19 pandemic mathematics grades and, in particular, to investigate the possible differences between mid-term and end-term grades in Italy. To this end, 231 Italian middle and high school students’ grades were analysed. Using the Wilcoxon rank test (a non-parametric statistical test) the results showed a statistically significant difference in pre- and post-COVID-19 quarantine grades. End-of-year grades were higher than those before the COVID-19 confinement. Furthermore, the results indicated that more than half of the students in the sample achieved a higher grade at the end of the school year. Gender differences in mathematics grades were examined, since the literature about gender gap in mathematics achievement is not coherent about whether boys outperform girls or vice versa. Statistically significant differences at the end of the first semester were reported, in favour of female students although gender differences were not detected at the end of the school year. The findings suggest that greater caution should be paid in interpreting students’ grades pre- and post-COVID-19 confinement, since it cannot be excluded that such students’ achievements are inflated. Excessively high students’ grades that do not represent their actual knowledge and competencies could give educators and legislators misleading and even false information about the quality of distance learning and students’ knowledge.

In spring 2020, the COVID-19 pandemic forced a rapid shift to distance learning worldwide. Although recent research has focused on the impact that this transition had on students’ education and well-being, little has been done in particular on math education and on math anxiety (MA). Since MA is believed to be linked to the teaching methods, it could be hypothesized that the new learning environment affected MA levels. Thus, this study investigated whether students’ levels of MA evaluated before and during the first wave of the pandemic changed as a consequence of the distance learning implementation. Moreover, we were interested in investigating whether students’ satisfaction with the teaching methods, their effort in math, and their academic achievement were correlated to MA before and during the COVID-19 distance learning. Participants were 117 Italian middle and high school students. No significant differences between pre- and mid-pandemic MA were found when considering the entire sample. Analyzing separately, results indicated that high-MA students reported significantly lower MA levels during distance learning, however no difference was observed for moderate- and low-MA individuals. Furthermore, satisfaction with the teaching methods, effort in math, and math achievement were negatively correlated with MA, both before and during distance education.