In Italy, all grade 10 students are required to take the national assessment of knowledge of mathematics, which is prepared by the INVALSI Institute. No official data about any taxonomic level of the ...questions in these assessments has been published on the Institute’s website. In the present work, we analyzed seven INVALSI examinations for school with Slovene as language of instruction and we focused on the Gagne’s taxonomic level of each individual question in the assessments. The most frequent category in national assessments is “Routine procedural knowledge”, followed by “Basic and conceptual knowledge”. We found that, even though the interest in problem-solving activities has increased in the past years, the taxonomic level “Problem-solving knowledge” is the less frequent. Moreover, we wanted to analyze the distribution of the different taxonomic levels among the question typologies (open- and closed-type questions) and we found that questions from lower taxonomic levels are more likely to be closed-type, while “Problem-solving questions” are more likely to be open-type. Furthermore, we were interested in analyzing the distribution of taxonomic levels among the four topic dimensions “Geometry”, “Data and prevision”, “Numbers and quantities” and “Relations and functions”. We found that the taxonomic level “Problem-solving knowledge” are more present in the categories “Number and quantities” and “Relations and functions”.
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.
National assessments can be used to explore the strictness of teachers in grading students by comparing student grades to their scores on standardised tests. Several factors influence teacher-given ...grades, including student gender, school type, geographical regions, and socioeconomic status. In this paper, we used data from the Italian institute INVALSI, responsible for the organisation of national mathematics assessments, to investigate how these factors influence teachers' grading standards. We considered a sample of 36,589 Grade 13 Italian students from 2,062 classes at 990 high schools. The relationships between the variables were analysed using hierarchical linear modelling. The findings reveal that teacher-given grades are related to student-level variables (e.g. gender, socioeconomic status, and score on the INVALSI test) and school-level variables (e.g. school type and location). When the difference between teacher-assigned grades and scores on the INVALSI test was considered, only student gender, school type, and location accounted for the gap in student achievements. Therefore, student socioeconomic status has a lower influence on their performance on the INVALSI test, suggesting that using standardised assessments might improve equity in assessment.
Several factors affect students’ mathematics grades and standardized test results. These include the gender of the students, their socio-economic status, the type of school they attend, and their ...geographic region. In this work, we analyze which of these factors affect assessments of students based on fuzzy logic, using a sample of 29,371 Italian high school students from the 2018/19 academic year. To combine grades assigned by teachers and the students’ results in the INVALSI standardized tests, a hybrid grade was created using fuzzy logic, since it is the most suitable method for analyzing qualitative data, such as teacher-given grades. These grades are analyzed with a hierarchical linear regression. The results show that (1) boys have higher hybrid grades than girls; (2) students with higher socio-economic status achieve higher grades; (3) students from scientific lyceums have the highest grades, whereas students from vocational schools have the lowest; and (4) students from Northern Italy have higher grades than students from Southern Italy. The findings suggest that legislators should investigate appropriate ways to reach equity in assessment and sustainable learning. Without proper interventions, disparities between students might lead to unfairness in students’ future career and study opportunities.
Although the idea of evaluating students’ mathematical knowledge with fuzzy logic is not new in the literature, few studies have explored the possibility of assessing students’ mathematical knowledge ...by combining teacher-assigned grades (i.e., school grades) with students’ achievements on standardized tests (e.g., national assessments). Thus, the present study aims to investigate the use of fuzzy logic to generate a novel assessment model, which combines teacher-assigned mathematics grades with students’ results on the Italian National Assessment of Mathematical Knowledge (INVALSI). We expanded the findings from previous works by considering a larger sample, which included more than 90,000 students attending grades 8, 10, and 13. The results showed that the tested model led to a lower assessment score compared to the traditional grading method based on teacher’s evaluation. Additionally, the use of fuzzy logic across the examined school levels yielded similar results, suggesting that the model is adequate among different educational levels.
Much research has been done on the first quarantine period in 2020, however little is known for what it concerns distance learning in Italian schools with Slovene as language of instruction. No ...extensive research explored teachers’ and students’ perceptions of this distance learning period, nor analyzed their opinion about positive and negative aspects of online learning, especially those related to the teaching material in Slovene language, which should address the Italian program. The present article presents the analysis of online semi-structured interviews that involved 15 high school teachers and 15 students who teach or attend Italian high schools with Slovene as teaching language, and it aims to answer these questions. We found that teachers and students preferred face-to-face classes, since they faced several issues connected with distance learning, such as a lack of interaction during distance learning, technology and connection problem, health issues and psychological distress. High school teachers and students faced less problems than those reported by primary schools’ pupils and teachers, since they are older and more independent than primary school pupils. High school students did also communicate through several social applications and peers might have helped them to overcome the language obstacles.
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