A network psychometric analysis of maths anxiety factors in Italian psychology students
- URL: http://arxiv.org/abs/2503.01568v1
- Date: Mon, 03 Mar 2025 14:11:16 GMT
- Title: A network psychometric analysis of maths anxiety factors in Italian psychology students
- Abstract summary: This study translated the 3-factor MAS-UK scale in Italian to produce a new tool, MAS-IT.<n>A sample of 324 Italian undergraduates completed the MAS-IT.<n>CFA results revealed that the original MAS-UK 3-factor model did not fit the Italian data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Dealing with mathematics can induce significant anxiety, strongly affecting psychology students' academic performance and career prospects. This phenomenon is known as maths anxiety and several scales can measure it. Most scales were created in English and abbreviated versions were translated and validated among Italian populations (e.g. Abbreviated Maths Anxiety Scale). This study translated the 3-factor MAS-UK scale in Italian to produce a new tool, MAS-IT, validated specifically in a sample of Italian undergraduates enrolled in psychology or related BSc programmes. A sample of 324 Italian undergraduates completed the MAS-IT. The data were analysed using confirmatory Factor Analysis (CFA), testing the original MAS-UK 3-factor model. CFA results revealed that the original MAS-UK 3-factor model did not fit the Italian data. A subsequent Exploratory Graph Analysis (EGA) identified 4 distinct components/factors of maths anxiety detected by MAS-IT. The items relative to "Passive Observation maths anxiety" factor remained stable across the analyses, whereas "Evaluation maths anxiety" and "Everyday/Social maths anxiety" items showed a reduced or poor item stability. Quantitative findings indicated potential cultural or contextual differences in the expression of maths anxiety in today's psychology undergraduates, underlining the need for more appropriate tools to be used among psychology students.
Related papers
- Global Patterns in Student Stress and Academic Performance: A Machine Learning Study Using PISA 2022 [4.417564179511245]
The main goal of this project is to confirm or reject the existence of a similar global pattern.<n>We focused on various psychological indicators such as anxiety score, test anxiety, math anxiety, math confidence, wellbeing, and sense of belonging.<n>The analysis was conducted across six continents by splitting the dataset into six separate case studies.
arXiv Detail & Related papers (2026-05-30T16:12:39Z) - Math Education Digital Shadows for facilitating learning with LLMs: Math performance, anxiety and confidence in simulated students and AIs [0.0]
MEDS (Math Education Digital Shadows) is a dataset mapping how large language models reason about and report mathematics across human- and AI-like conditions.<n> MEDS involves 28,000 personas from 14 LLMs (from families like Mistral, Qwen, DeepSeek, Granite, Phi and Grok) shadowing either humans or AI assistants.<n>Each record/shadow includes a set of prompts along with psychological/sociodemographic persona metadata and four types of math tasks.<n> MEDS will benefit learning analytics experts, cognitive scientists, and developers of safer AI tutors in mathematics.
arXiv Detail & Related papers (2026-04-30T09:08:58Z) - PsihoRo: Depression and Anxiety Romanian Text Corpus [46.53794328211006]
Psychological corpora in NLP are collections of texts used to analyze human psychology, emotions, and mental health.<n>We have created the first corpus for depression and anxiety in Romanian, by utilizing a form with 6 open-ended questions.<n>PsihoRo is a first step towards understanding and analyzing texts regarding the mental health of the Romanian population.
arXiv Detail & Related papers (2026-02-20T16:24:23Z) - Math anxiety and associative knowledge structure are entwined in psychology students but not in Large Language Models like GPT-3.5 and GPT-4o [10.71149623650681]
This study employs a framework based on behavioural forma mentis networks to explore individual and group differences in the perception and association of concepts related to math and anxiety.<n>Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety.<n>Experiment 4 focuses on group-level perceptions extracted from human students, GPT-3.5 and GPT-4o's networks.
arXiv Detail & Related papers (2025-11-03T13:25:11Z) - CogMath: Assessing LLMs' Authentic Mathematical Ability from a Human Cognitive Perspective [68.94793547575343]
CogMath formalizes human reasoning process into 3 stages: emphproblem comprehension, emphproblem solving, and emphsolution summarization.<n>In each dimension, we develop an emphInquiry-emphJudge-emphReference'' multi-agent system to generate inquiries that assess LLMs' mastery from this dimension.<n>An LLM is considered to truly master a problem only when excelling in all inquiries from the 9 dimensions.
arXiv Detail & Related papers (2025-06-04T22:00:52Z) - You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools [74.98850427240464]
We show that sentiment analysis tools disagree on the same dataset.
We show that the sentiment tool used for sentiment annotation can even be predicted from its outcome.
arXiv Detail & Related papers (2024-10-18T17:27:38Z) - Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population.
Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app.
We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
arXiv Detail & Related papers (2024-09-24T16:05:17Z) - Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models [57.518784855080334]
Large Language Models (LLMs) have demonstrated exceptional task-solving capabilities, increasingly adopting roles akin to human-like assistants.
This paper presents a framework for investigating psychology dimension in LLMs, including psychological identification, assessment dataset curation, and assessment with results validation.
We introduce a comprehensive psychometrics benchmark for LLMs that covers six psychological dimensions: personality, values, emotion, theory of mind, motivation, and intelligence.
arXiv Detail & Related papers (2024-06-25T16:09:08Z) - MathScale: Scaling Instruction Tuning for Mathematical Reasoning [70.89605383298331]
Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving.
However, their proficiency in solving mathematical problems remains inadequate.
We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data.
arXiv Detail & Related papers (2024-03-05T11:42:59Z) - Who is ChatGPT? Benchmarking LLMs' Psychological Portrayal Using
PsychoBench [83.41621219298489]
We propose a framework, PsychoBench, for evaluating diverse psychological aspects of Large Language Models (LLMs)
PsychoBench classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities.
We employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs.
arXiv Detail & Related papers (2023-10-02T17:46:09Z) - Inducing anxiety in large language models can induce bias [47.85323153767388]
We focus on twelve established large language models (LLMs) and subject them to a questionnaire commonly used in psychiatry.
Our results show that six of the latest LLMs respond robustly to the anxiety questionnaire, producing comparable anxiety scores to humans.
Anxiety-induction not only influences LLMs' scores on an anxiety questionnaire but also influences their behavior in a previously-established benchmark measuring biases such as racism and ageism.
arXiv Detail & Related papers (2023-04-21T16:29:43Z) - Personalized State Anxiety Detection: An Empirical Study with Linguistic
Biomarkers and A Machine Learning Pipeline [7.512067061195175]
Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations.
Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques.
arXiv Detail & Related papers (2023-04-19T19:06:42Z) - A Causal Framework to Quantify the Robustness of Mathematical Reasoning
with Language Models [81.15974174627785]
We study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.
Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants.
arXiv Detail & Related papers (2022-10-21T15:12:37Z) - DASentimental: Detecting depression, anxiety and stress in texts via
emotional recall, cognitive networks and machine learning [0.0]
This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text.
We train the model to spot how sequences of recalled emotion words by $N=200$ individuals correlated with responses to the Depression Anxiety Stress Scale (DASS-21)
We find that semantic distances between recalled emotions and the dyad "sad-happy" are crucial features for estimating depression levels but are less important for anxiety and stress.
arXiv Detail & Related papers (2021-10-26T13:58:46Z) - Network psychometrics and cognitive network science open new ways for
detecting, understanding and tackling the complexity of math anxiety: A
review [0.0]
Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts.
It affects roughly 20% of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance.
It poses a concrete threat to students' well-being, computational literacy and career prospects in science.
arXiv Detail & Related papers (2021-08-31T12:43:43Z) - Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units
and a Unified Framework [83.21732533130846]
The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2.
It presents the design of two classes of deep neural networks trained with these databases.
A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
arXiv Detail & Related papers (2021-03-29T17:36:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.