Mathematics anxiety in apulian students: an exploratory study and didactic proposals
Bibliographic record
Abstract
Anxiety towards mathematics is a widespread and pervasive phenomenon among high school students, with significant impacts not only on their academic learning but also on the development of a positive attitude towards the discipline and on their self-esteem. This abstract introduces an ongoing doctoral research project, which aims to investigate the levels and manifestations of mathematics-related anxiety. The study was conducted by administering a structured questionnaire, consisting of ten statements, to a sample of second-year high school students from three distinct Institutes located in the Apulia region, specifically in the metropolitan city of Bari. The results of this investigation strongly and significantly mirror the evidence emerged from the research conducted in 2023 by the Serafico Institute of Assisi and subsequently published in the prestigious journal "Frontiers in Psychology." (Frontiers in Psychology, 14, 1185677). This study had already highlighted a clear and concerning correlation between high levels of mathematics anxiety, an intrinsically negative perception of the discipline, and a direct and unfavorable impact on the academic achievement of the students involved. Furthermore, Canadian research conducted by Nathan T.T. Lau and other researchers in 2022 on a huge sample of students from different countries, published in "Proceedings of the National Academy of Sciences," showed that students from countries with more marked levels of mathematics anxiety tend to confirm lower grades in mathematics, meaning students with greater anxiety tend to perform worse in mathematics. In light of this converging evidence, we believe it is of fundamental importance for the teaching staff to adopt and implement innovative teaching strategies aimed at reducing anxiety in students. Among these, the necessity of actively promoting the creation of a positive and welcoming learning environment, the systematic integration of practical and contextualized activities that make mathematics more tangible and relevant, and the conscious use of techniques that favor the development of emotional intelligence, as theorized and promoted by Daniel Goleman (1998), are highlighted. In particular, it is crucial that teachers commit to encouraging a growth mindset, intrinsically valuing the learning process rather than just the final result, and providing constant and personalized emotional support to students. Adopting such a pedagogical approach, also geared towards curiosity and even the search for new mathematical relationships and concepts, can substantially contribute to improving students' general attitude towards mathematics and, consequently, make the entire teaching-learning process more effective, inclusive, and rewarding for everyone, both students and teachers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".