The study’s methodology was unusually rigorous for survey research of this kind. The team designed a structured questionnaire with three sections: twenty-one questions probing environmental awareness, twenty-four questions examining environmental behavior, and a third section recording demographic and socioeconomic characteristics. All attitude questions used a five-point Likert scale running from strongly disagree to strongly agree, while behavior questions ran from never to very often. Before deployment, the instrument was refined through a pilot study with twenty randomly selected citizens, whose responses were excluded from the final analysis. Data collection took place in 2024 across 160 Greek cities, spanning metropolitan areas, medium-sized and small cities, and islands, with questionnaires gathered roughly half face-to-face and half by telephone.

The sampling strategy was equally deliberate. Using stratified and simple random sampling, the researchers collected questionnaires in different parts of each city at different times of day and on different days of the week, respecting national ratios of sex, age, and education drawn from the Hellenic Statistical Authority. Although 1,440 questionnaires were collected, only the 1,410 fully completed forms were retained, yielding a margin of error of just 2.61 percent. A formal representativeness test against the 2021 Census confirmed that the sample mirrored the adult Greek population in gender, age group, marital status, number of children, education, and professional status, though the authors caution that representativeness was assessed only for these specified characteristics.

The analytical framework distinguished between indicators calculated at the level of the whole sample and indicators calculated for each individual respondent. Four specific sample-level indicators captured awareness of water pollution, air pollution, climate change, and waste management, with four matching indicators for behavior, plus two aggregate indicators averaging across all four issues. At the individual level, the team computed ten specific and aggregate indicators plus a global individual indicator combining awareness and behavior across all domains. Statistical tests, including independent-sample t-tests and one-way ANOVA with Tukey and Scheffé post-hoc comparisons, then probed how demographic and socioeconomic characteristics shaped these scores.

The headline numbers are telling. Air pollution topped the awareness ranking with a mean score of 3.68, followed by waste management at 3.49, climate change at 3.30, and water pollution at 3.26. Yet the behavior ranking flipped almost entirely: water pollution led at 3.48, followed by climate change and waste management, both at 3.30, with air pollution collapsing to 2.53, the lowest score in the entire study. In other words, the issue Greeks worry about most, driven by vehicle emissions, industry, and biomass burning in their cities, is the one where they least often take protective action such as using public transport, carpooling, or avoiding products like tobacco, insecticides, and pesticides.

Plotted against each other, the four awareness and behavior scores reveal a negative trend: the higher a population’s awareness of an issue, the lower its corresponding behavior tends to fall. This pattern echoes a well-documented phenomenon in environmental psychology known as the value-action gap, the difference between what people say and what they do. The aggregate indicators crystallize the discrepancy. The aggregate awareness indicator stood at 3.43, while aggregate behavior lagged at 3.15. Citizens, the authors conclude, largely know what should be done for the environment, but knowledge alone does not translate into intention or action, with socio-psychological and practical factors intervening at every step.

Yet the individual-level analysis complicates the picture in an intriguing way. When awareness and behavior were regressed against each other for each respondent, the relationship turned strongly positive and linear for all four issues, with coefficients of determination all exceeding 0.90. For water pollution the slope was 1.0290, for climate change 0.9775, for waste management 0.9218, and for air pollution 0.6793. This means that at the individual level, awareness is a highly reliable predictor of behavior, even though the population-level pattern shows behavior consistently trailing awareness. The two findings are not contradictory: individuals who are more aware do act more, but nearly everyone acts less than their stated concern would predict.

The frequency distributions add texture. For air pollution, 51.13 percent of respondents agreed they were aware of the issue and 30.43 percent strongly agreed, yet 29.93 percent reported acting pro-environmentally only rarely and 43.62 percent only sometimes. For waste management, nearly half of respondents reported often acting pro-environmentally, but the authors note that Greek citizens remain insufficiently informed despite recent legislation, including Law 4819/2021 introducing Pay-As-You-Throw systems and European directives targeting a reduction of municipal landfilling to 10 percent by 2035. For climate change, awareness and behavior sat at identical levels of 3.30, which the researchers interpret as a shared failure to grasp both the causes of climate change and the urgency of responding to it, possibly because Greece’s warm Mediterranean climate makes some impacts feel distant.

Demographic patterns emerged with statistical significance. Citizens without children scored higher on awareness of water pollution (3.33 versus 3.20), climate change (3.55 versus 3.43), and waste management (3.55 versus 3.43), as well as on the aggregate awareness indicator and the global indicator. Unmarried respondents outperformed married ones on both climate change awareness (4.24 versus 3.92) and climate behavior (3.33 versus 2.64). Residents outside Athens showed greater awareness of waste management and a higher global indicator than those inside the capital, where roughly a third of Greeks live. Income, education, and employment status, by contrast, showed no significant correlations, a finding that challenges assumptions about wealth and schooling as drivers of environmental engagement.

The authors are careful to situate their results within the Greek and Mediterranean context. Environmental awareness and behavior vary considerably across countries, shaped by cultural values, institutional trust, environmental education, and the visibility of local problems. Northern Europeans, for instance, generally report higher concern and more frequent sustainable behavior, partly reflecting longer-established environmental policies. Still, the researchers argue that the indicator framework itself, integrating natural and anthropogenic dimensions across social, economic, and political parameters, is likely transferable to other Mediterranean countries facing similar pressures from climate change, water scarcity, urban air pollution, tourism, and waste. Future comparative studies, they suggest, should test the external validity of these indicators across Europe and track how awareness and behavior evolve over time.

For policymakers, the message is sobering but actionable. Awareness campaigns alone, the study implies, will not close the gap, because awareness is already high where behavior is weakest. Instead, interventions should target practical barriers, strengthen personal responsibility, and promote engagement through environmental education, community initiatives, recycling programs, energy-saving incentives, and sustainable transport options. The paradox at the heart of the data, that the best-informed citizens act least on the issue they know best, suggests that information is only the first step in a much longer chain linking knowledge to habit. Bridging that chain, the authors conclude, will require mobilizing citizens not merely to understand environmental problems but to change the daily routines that sustain them.

Subject of Research: Quantitative indicators of environmental awareness and behavior among Greek citizens

Article Title: Quantification of environmental awareness and behavior using indicators

Article References: Gareiou, Z., & Zervas, E. (2026). Quantification of environmental awareness and behavior using indicators. Environmental and Sustainability Indicators, 32, Article 101552. https://doi.org/10.1016/j.indic.2026.101552

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101552

Keywords: environmental awareness, pro-environmental behavior, value-action gap, Greece, survey indicators, air pollution, water pollution, climate change, waste management, Likert scale, demographic factors, environmental policy

The study, published in SN Social Sciences, surveyed 260 first-year students enrolled in Mathematics in the Modern World, a general education course at a Philippine public university. The course itself is a product of national higher education reform, mandated by the Commission on Higher Education’s 2013 memorandum to cultivate holistic understanding and intellectual competencies rather than narrow technical skill. That makes the student population unusually diverse: the course draws learners from many senior high school strands, not only those bound for STEM degrees. Travero chose this setting deliberately, since mathematics appreciation, unlike raw achievement, is an affective and motivational construct that shapes whether students remain willing to engage with quantitative reasoning long after their required coursework ends.

Random forest regression, the analytical engine behind the study, is an ensemble learning method introduced by statistician Leo Breiman in 2001. Instead of fitting a single equation to the data, the algorithm builds hundreds of decision trees, each trained on a random bootstrap sample of the observations and considering only a random subset of predictor variables at each split. The trees’ predictions are then averaged, a process that reduces variance and guards against overfitting in ways that classical linear regression cannot. Random forests have become a workhorse of educational data mining precisely because they capture nonlinear relationships and interactions between variables without the analyst having to specify them in advance. Previous work has used the method to predict college engineering major choice from demographic and high school factors, to evaluate pedagogy and inform personalized learning, and to forecast high school mathematics performance.

Travero’s implementation examined eight demographic predictors in detail. Age and sex are the classic background variables. Academic strand refers to the specialized track a Filipino student follows in senior high school, such as science, technology, engineering and mathematics, accountancy and business, or humanities. The senior high school mathematics grade captures prior achievement. School type distinguishes public from private secondary education, a meaningful divide in the Philippine system. Socioeconomic status, learning modality preference, which became salient after the pandemic-driven shift to online and hybrid instruction, and even marital status round out the list. The appreciation outcome itself was measured as a composite of students’ enjoyment of mathematics, their perception of its value, and their willingness to engage with it.

The headline result is the near-total absence of explanatory power. With all eight demographic variables included, the random forest model achieved an R-squared of 0.000, a figure that would be unremarkable if the model were predicting something like lottery numbers but is genuinely surprising for educational attitudes that are so often assumed to track background characteristics. Travero then refined the model, retaining only the five most influential variables in an attempt to strip away noise. The refined model improved only marginally, reaching an R-squared of 0.011. In practical terms, even the best demographic model could account for barely one percent of the differences in mathematics appreciation among these students. The model’s mean absolute error stood at 2.80 points, with a mean absolute percentage error of 8.9 percent, indicating predictions that were systematically no better than simple averages.

Feature importance analysis, a standard output of random forest methods that quantifies how much each predictor contributes to the model’s splits, did reveal a hierarchy among the variables. Academic strand emerged as the most influential, followed by the senior high school mathematics grade and then age. This ordering is intuitively plausible: a student who spent senior high school immersed in the STEM strand and earned strong mathematics grades might reasonably be expected to view the subject more favorably than a humanities-track student who struggled through it. Yet the study’s central finding is that even these top-ranked contributors remained minimal in absolute terms, given the model’s weak overall performance. A variable can rank first in importance within a model that explains almost nothing; the ranking describes relative signal, not predictive strength.

The null result carries real theoretical weight. Travero situates the findings within Expectancy-Value Theory, the influential motivational framework developed by Jacquelynne Eccles and Allan Wigfield, which holds that students’ engagement with a domain depends on their expectancies for success and the value they attach to the task. Those values, in turn, are shaped by perceptions of usefulness, intrinsic interest, and the accumulated weight of learning experiences, not by demographic categories in themselves. The study’s results are broadly consistent with that account: if appreciation flowed directly from demographic background, a flexible nonlinear learner like a random forest should have detected it. Instead, the data suggest that the drivers of mathematical appreciation lie elsewhere, in affective responses, motivational beliefs, instructional quality, and the specific contexts in which students encounter mathematics.

The findings also push back against what the study calls demographic determinism, the tendency in both research and popular discourse to treat background categories as destiny. A substantial body of prior research has linked socioeconomic status to academic achievement, examined gender gaps in STEM participation, and explored how demographic features relate to mathematics performance and beliefs. Those literatures are not invalidated by a single null result, and achievement is a different outcome from appreciation. But the study adds an important caution: patterns observed for performance do not automatically transfer to attitudes, and correlations reported in aggregate can be far too weak to support prediction for individuals. Machine learning, which is often deployed to squeeze predictive signal out of whatever variables are available, here serves as an honest auditor, reporting that the available demographic variables simply do not contain the signal educators might have hoped for.

For mathematics educators, the practical implication is a shift of focus. If demographic profiles cannot identify which first-year students will appreciate mathematics, then interventions cannot be targeted by background alone. Instead, the study argues for context-sensitive, student-centered approaches that address the variables the demographic model left out: students’ perceptions of the value and relevance of mathematics, their motivational beliefs, their instructional experiences, and the affective climate of their classrooms. Future predictive models, Travero suggests, should incorporate these affective, motivational, instructional, and contextual variables if they are to explain meaningful variation in appreciation. In an era when institutions increasingly reach for algorithmic tools to personalize education, the study offers a humbling demonstration that the most important predictors of a student’s relationship with mathematics may not be found in any enrollment record, but in the day-to-day experience of learning itself.

Subject of Research: Predicting first-year college students' mathematics appreciation from demographic factors using random forest regression

Article Title: Demographic factors and mathematics appreciation among first-year college students: a random forest approach

Article References: Travero, A. S. (2026). Demographic factors and mathematics appreciation among first-year college students: a random forest approach. SN Social Sciences, 6(10), Article 476. https://doi.org/10.1007/s43545-026-01730-z

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01730-z

Keywords: mathematics appreciation, random forest regression, demographic factors, Expectancy-Value Theory, mathematics education, machine learning, first-year college students, educational data mining, STEM attitudes, Philippines, socioeconomic status, academic strand

As governments worldwide implemented safety measures, such as social distancing and mask mandates, a pertinent question emerged: how does a person’s experience with COVID-19—whether mild or severe—affect their mental state and overall fear of the virus? This study specifically focused on individuals’ encounters with infection, both personally and among family members, using data compiled from a comprehensive longitudinal Internet survey spanning three years, from 2020 to 2022. This innovative approach provided a thorough examination of fear of infection over time, allowing researchers to develop a nuanced understanding of psychological responses to COVID-19.

The researchers found that not just the occurrence of infection but also the severity of symptoms significantly influenced fear levels. Surprisingly, individuals with mild COVID-19 symptoms reported decreased levels of fear, suggesting that some degree of familiarity might mitigate anxiety. Conversely, those who experienced severe symptoms or had close family members endure severe illness reported heightened fear. This dichotomy highlights the complex interplay between personal experience and psychological response, suggesting that severity—rather than mere infection—plays a crucial role in shaping one’s mental health landscape.

Additionally, the study indicated demographic factors such as sex, age, and previous respiratory health conditions also contributed to variations in fear levels. For instance, younger individuals and females displayed higher levels of fear compared to older adults and males. This disparity underscores the importance of considering demographic variables when assessing psychological impacts during a health crisis. Understanding these differences can help inform targeted mental health strategies during future pandemics.

Moreover, the researchers tracked trends in fear over the study period. Their findings revealed a notable decrease in fear across the population as time progressed, indicating that anxiety around COVID-19 may lessen with familiarity and adaptation to the ongoing crisis. However, this trend was not uniform; those with severe experiences of the virus continued to exhibit elevated fear levels. Thus, while some may acclimate, others may require additional support to navigate their fears, particularly those who have been heavily impacted by COVID-19.

The implications of the study stress the necessity of tailored mental health support strategies. For individuals experiencing severe symptoms, mental health professionals should focus on providing reassurance and coping mechanisms to alleviate undue fear. In contrast, those recovering from mild cases may benefit from educational initiatives to reinforce infection prevention behaviors, promoting a proactive approach towards maintaining health and safety.

Furthermore, this research resonates with other global studies investigating the psychosocial ramifications of the pandemic. Various studies continually emphasize the need for integrated health responses that encompass both physical and mental health considerations. Policymakers and healthcare providers must work collaboratively to develop comprehensive frameworks that address these challenges, ensuring that mental health support is an integral component of pandemic response strategies.

In conclusion, as we grapple with the ongoing realities of COVID-19, this study sheds light on the profound relationship between personal experiences of infection and fear responses. It emphasizes the need for an empathetic, informed approach to mental health during health crises, ensuring that treatment and support structures are responsive to the diverse experiences of individuals.

Moving forward, continued research will be vital in understanding the evolving psychological landscape as the pandemic continues to unfold. By focusing on these nuanced factors, future interventions can be designed to diminish fear associated with infectious diseases and enhance the resilience of communities worldwide.

The authors of this study, supported by reputable grants, invite further exploration into the intricate dynamics of disease experience, mental health, and social behavior in assisting individuals during similar health crises.

Subject of Research: The psychological effects of COVID-19 infection experiences on fear levels.
Article Title: Exploring the relationship between personal and cohabiting family members’ COVID-19 infection experiences and fear of COVID-19: A longitudinal study based on the Japan COVID-19 and Society Internet Survey (JACSIS).
News Publication Date: 20-Dec-2024.
Web References: Institute of Medicine, BMJ Open DOI.
References: Study supported by the Japan Society for the Promotion of Science (JSPS) grants.
Image Credits: Not specified.

Keywords: COVID-19, fear, mental health, infection experience, psychological response, public health, Japan.