Lichens are symbiotic partnerships between fungi and photosynthetic partners, usually algae, and they are famously sensitive to air quality, substrate chemistry and microclimate. Epiphytic lichens, the kinds that grow on tree bark, are therefore excellent biological indicators of environmental conditions, but they are also easily overlooked because many species are small, crust-like and inconspicuous. Although botanical gardens are celebrated reservoirs of cultivated plant diversity, the non-target organisms that colonize their trees spontaneously have received comparatively little attention. The new study set out to close that gap for Pisa, where no published survey of the lichen biota had ever been carried out, apart from a handful of nineteenth-century herbarium specimens.

The garden itself provides an unusually rich substrate. Founded in 1543, when Pisa pioneered the Italian tradition of physic gardens alongside Padua and Florence, the site now covers about 25,000 square metres in seven sectors and hosts more than 2,000 cultivated species in roughly 30 collections. Its Arboretum alone contains about 200 species, including monumental trees planted in 1787, such as a Ginkgo biloba and a Magnolia grandiflora, plus specimens of Myrtus communis and Platanus hispanica that are more than two centuries old. During 2023, the researchers surveyed more than 100 trees representing 61 different taxa, selected to capture the full range of bark variability, and identified the lichens using standard morphological, anatomical and chemical methods. Voucher samples were deposited in the herbarium of the University of Pisa and in the herbarium of the Plant Science and Biodiversity Centre of the Slovak Academy of Sciences.

The tally of 57 lichen taxa and one non-lichenized fungus, belonging to 53 species and subspecies within 40 genera, included several species of conservation concern. According to the Italian red list of epiphytic lichens, three recorded species are considered Vulnerable, one is Near Threatened, two are Least Concern and two are Data Deficient. The most striking discoveries were Arthopyrenia platypyrenia and Coenogonium tavaresianum, both new records for Tuscany, while Lecania cyrtellina is so far known in the region only from the Botanic Garden of Pisa. The record of Arthopyrenia platypyrenia, an inconspicuous fungus that lives inside the bark and shows itself only as a discoloration, is only the second for the whole of Italy; the previous record came from Calabria in 1996.

Each of the rare species tells its own ecological story. Coenogonium tavaresianum, listed as Vulnerable and thought to be declining, formed olive-green granulose thalli abundantly on the bark of the incense cedar Calocedrus decurrens, its distribution in Italy otherwise confined mainly to humid woodlands along the Tyrrhenian coast. Lecania cyrtellina, a crustose lichen with clustered tiny apothecia and narrow, mostly simple spores, was found on a Chilean wine palm, Jubaea chilensis, more than 130 years old; the species was long confused with its relative Lecania cyrtella and only recently distinguished consistently. Lecanographa amylacea, a rare Italian species tied to old trees in ancient woodlands, urban parks and old olive groves, was observed on nine different tree genera in the garden, including the 230-year-old Magnolia grandiflora, and represents only the second known Tuscan locality. Micarea misella, previously known in Tuscany from a single 2016 record in the mountains of Abetone, appeared on the same old Jubaea palm, and Porina borreri, a species of humid old woodlands, had only one earlier Tuscan record near Volterra.

The composition of the lichen community reflects both the garden’s assets and its urban setting. About 70 percent of the biota consists of acidophilous and oligotrophic species, mirroring the availability of acidic bark substrates among the diverse tree collection. At the same time, nitrogen-tolerant species such as Hyperphyscia adglutinata, Physcia adscendens, Candelaria concolor and the familiar yellow Xanthoria parietina were widely distributed, particularly in more open parts of the garden. These eutrophication-tolerant lichens thrive on dust and nitrogen compounds and are characteristic of urban atmospheres. The effect was especially evident on conifers, which in natural environments are typically colonized by acidophilous epiphytes but in the Pisa garden were more often covered by nitrophilous species, a clear signature of the surrounding city’s air chemistry.

Colonization intensity varied dramatically from tree to tree, and the reasons are a textbook illustration of how lichen ecology works. Lichen establishment depends on interacting factors including light availability, relative humidity, bark chemistry, water-holding capacity, the health of the substrate and pollution levels, many of them shaped by the individual tree and the surrounding vegetation. Some trees, such as Araucaria bidwillii, which has stood in the garden for 152 years, showed no lichen colonization at all, while sparse growth appeared on Camellia, Cedrus, Citrus, Sequoia, Viburnum and Wisteria. In contrast, Aesculus pavia, the hybrid Chitalpa tashkentensis, Melia azedarach, Phillyrea angustifolia, Pyrus communis and Vitex agnus-castus each hosted more than ten lichen species. Notably, no lichens were found on any of the trees grown in the greenhouses, where only green algae colonized some barks, confirming that outdoor conditions are essential for this biota.

The study’s broader message is that botanical gardens act as refugia for species with demanding ecological requirements, not merely as showcases for cultivated plants. Alongside the urban nitrophiles, the Pisa garden hosts species normally associated with undisturbed forests and ancient woodlands, including Coenogonium tavaresianum, Gyalecta liguriensis, Lecanographa amylacea and Porina borreri, several of which were found on the garden’s monumental trees. Six of these woodland-affinity species, including C. tavaresianum, G. liguriensis, L. amylacea, Micarea prasina, Opegrapha niveoatra and Waynea stoechadiana, have also been recorded in the Botanical Garden of Rome, reinforcing the idea that such gardens provide alternative habitats across cities. The pattern is not unique to Italy: the New Botanical Garden of the University of Zurich, despite covering only 53,000 square metres, hosts roughly ten percent of all lichen species recorded in Switzerland.

There is also a historical dimension to the discovery. Herbarium specimens collected in the Pisa garden in the nineteenth century, now preserved in Trieste and Pisa, document lichens gathered there as early as 1862 by P. Savi and O. Beccari, and in 1882 and 1883 by G. Arcangeli and A. Mori. One variety, Lecanora subfusca var. diffusa, was even described from the garden itself, having grown on a roughly hundred-year-old Ginkgo biloba. In Florence, the pioneering lichenologist Cengia-Sambo reported in 1928 an unidentified Lecanora growing on the leaves of a palm in a warm greenhouse. These fragments of history show that lichens have quietly inhabited Italy’s botanical gardens for at least 160 years, even when nobody was systematically counting them.

For conservation, the implications are concrete. The lichen biota of the Botanic Garden of Pisa includes rare species listed in the Italian red list, and for three taxa the garden is currently the only known locality in Tuscany, which gives a single urban green space an outsized role in regional biodiversity. The authors conclude that botanical gardens contribute to conservation not only through the taxa they cultivate but also through non-target, spontaneously growing organisms such as lichens, which benefit indirectly from the habitats and conditions the gardens provide. As cities expand and intensify, the diverse tree collections of botanical gardens, with their wide range of physical and chemical bark properties, create habitat mosaics that surrounding environments cannot match. The hidden richness clinging to the bark of a two-century-old magnolia or a Chilean wine palm is a reminder that biodiversity hotspots can hide in plain sight, on the oldest trees in the middle of town.

Subject of Research: Epiphytic lichen diversity and conservation value of the Botanic Garden of Pisa in an urban environment

Article Title: Islands of lichen diversity in urban environments: a hidden richness in botanical gardens

Article References: Fačkovcová, Z., Spagli, G., D’Antraccoli, M., Roma-Marzio, F., Peruzzi, L., Paoli, L., & Guttová, A. (2024). Islands of lichen diversity in urban environments: a hidden richness in botanical gardens. Italian Botanist, 18, 245-258. https://doi.org/10.3897/italianbotanist.18.144373

Image Credits: AI Generated

DOI: 10.3897/italianbotanist.18.144373

Keywords: lichens, botanical gardens, urban biodiversity, epiphytes, Pisa, Tuscany, biodiversity conservation, red list species, air quality indicators, monumental trees, nitrophilous species, refugia

The design of the test itself is inspired by the PISA 2022 mathematics framework, which defines mathematical literacy not as the reproduction of isolated procedures but as the capacity to use mathematical knowledge and reasoning to solve problems in meaningful personal, occupational, societal, and scientific contexts. The framework emphasizes mathematical reasoning alongside three problem-solving processes — formulating, employing, and interpreting mathematics in real-world situations. The researchers translated this competency-oriented view into an explicit test blueprint with four content domains: Number and Algebra, Geometry, Statistics and Probability, and Comprehensive Practice, each with a target share of the total score points. Importantly, the team did not attempt to reproduce PISA’s official psychometric machinery, which relies on Item Response Theory and large-scale booklet designs; instead, they kept the statistical constraints within Classical Test Theory, where item difficulty is simply the proportion of test-takers who answer an item correctly.

The genetic algorithm works by encoding each candidate test form as a binary chromosome — a string with one position per item in the bank, where a 1 means the item is included and a 0 means it is excluded. The researchers initialized a population of 500 candidate forms, each constrained to contain exactly 25 items, providing a diverse starting pool of item combinations. Every candidate was then scored by a fitness function combining two weighted deviations: the average absolute difference between target and achieved score-point percentages across the four content domains, weighted at alpha = 0.4, and the absolute difference between the target overall difficulty of 0.65 and the difficulty achieved by the candidate form, weighted at beta = 0.6. The weights were chosen from preliminary pilot runs, with greater emphasis placed on matching the statistical difficulty target. A transformation converts the combined deviation into a maximization-oriented fitness value, so candidates with smaller total deviations receive higher scores.

From there, the algorithm runs its evolutionary loop. Parent forms are sampled according to fitness, with selection probabilities derived from the cumulative fitness distribution and duplicate selections avoided when constructing parent pairs. Selected parents undergo crossover and mutation to generate offspring, which are evaluated with the same fitness function. An elitist strategy retains the highest-fitness forms between generations so that high-quality solutions are never lost. The search terminates when the predefined iteration limit is reached or when a feasible high-fitness form satisfies the principal test-length, content, and difficulty constraints. Minimum representation of the required cognitive-process categories is imposed as a hard feasibility constraint, meaning no candidate can survive if it fails to cover the intended problem-solving processes. The item bank itself drew on released PISA-related assessment materials and publicly available educational repositories, with roughly 40 percent easy items, 40 percent medium, and 20 percent difficult, each tagged with content domain, cognitive-process category, preset difficulty, and point value.

The assembled form was then administered to 42 high-school students, sampled through a stratified random procedure based on academic performance bands of high, medium, and low. Multiple-choice items were scored dichotomously, while constructed-response items could earn partial credit according to scoring rubrics. The empirical results were strikingly close to the algorithmic targets. The point-weighted empirical difficulty of the complete form came in at 0.639 against the target value of 0.650 — an absolute deviation of just 0.011. At the item level, 18 of the 25 items, or 72 percent, fell within the expected performance range for their preset difficulty categories, while seven items showed noticeable deviation, a pattern the authors attribute to sampling variability, imperfect CTT difficulty estimates, or interactions between item content and the particular validation group.

Correlation analysis provided convergent quantitative evidence that the intended difficulty structure was preserved. Empirical scoring rates were strongly and negatively associated with ordinal difficulty codes, where easy = 1, medium = 2, and difficult = 3, yielding r = −0.823 with p < 0.001 — the expected negative sign because larger codes denote more difficult items and therefore lower scoring rates. When the same scoring rates were correlated with the preset expected scoring-rate values used in the item bank, the association was equally strong and positive, r = 0.827 with p < 0.001. These two statistics describe the same monotonic relationship from opposite coding directions, and together they demonstrate that the algorithm’s difficulty assignments tracked the observed performance of real students remarkably well.

Content coverage was less exact than difficulty matching, which is consistent with the stated weighting priorities. The target content percentages for the four domains were 44.00 percent for Number and Algebra, 33.00 percent for Geometry, 8.00 percent for Statistics and Probability, and 15.00 percent for Comprehensive Practice. The assembled form achieved 40.00, 41.34, 9.33, and 9.33 percent respectively, giving a mean absolute content-domain deviation of 4.835 percentage points. The largest discrepancy occurred in Geometry, which exceeded its target by 8.34 percentage points, while Comprehensive Practice fell 5.67 points below target. Because beta = 0.6 assigned more weight to difficulty than alpha = 0.4 assigned to content balance, this pattern aligns with the objective priorities, though the authors caution that it should not be read as causal proof of the weighting effect without repeated sensitivity experiments.

The validation sample itself performed strongly on the assembled form. The mean score was 94.157 with a median of 94.987 and a standard deviation of 11.459, over a maximum total score of 150 points. Skewness was 0.464, indicating only modest positive asymmetry, while kurtosis of 0.052 was close to a mesokurtic distribution. Five students, or 11.9 percent, were classified at proficiency Level 3 and 37 students, or 88.1 percent, at Level 4, with no students at Levels 1, 2, or 5. This concentration at Level 4 indicates that the validation group performed relatively well on this particular form, but it also limits how representative the results are of a broader population and reduces precision at the lower end of the ability distribution.

The authors are careful to frame the contribution as an application-level implementation and validation of a constrained automated test assembly workflow, not a claim that genetic algorithms themselves are novel. Prior research has shown that mixed-integer programming can provide exact or near-exact optimization when constraints can be expressed in a tractable mathematical form, while genetic algorithms offer a flexible heuristic search that is relatively easy to adapt when constraints are heterogeneous. The relative advantage of a GA in this context is implementation flexibility; its limitation is that heuristic search does not guarantee the global optimum and may be sensitive to the fitness specification and stopping criteria. For classroom and institutional settings where stable IRT calibration is unavailable, a CTT-based GA provides a transparent and computationally accessible alternative.

Several limitations constrain generalization. Empirical validation rested on 42 valid response records from a limited regional sample, so both item difficulty estimates and proficiency distributions may be sample-dependent. The assembly procedure uses CTT difficulty information rather than IRT parameters, limiting invariance across populations and preventing direct comparison with official PISA scaling. The 120-item bank is moderate in size and may not provide enough alternative items to satisfy all constraints simultaneously, and the fitness weights were selected heuristically without a complete repeated-run sensitivity dataset. Future work should validate the method with larger and more diverse samples, expand and independently calibrate the item bank, incorporate IRT parameters and test information constraints, compare GA performance with mixed-integer programming under repeated runs, and examine multiple-form parallelism, item exposure, subgroup fairness, and external validity. Until then, the method should serve as a decision-support tool that reduces manual search effort — with educators still reviewing the resulting forms for curriculum alignment, accessibility, and fairness before administration — rather than a replacement for expert judgment or psychometric calibration. As evidence of feasibility, the study makes a compelling case that evolutionary search can bring PISA-style competency assessment within reach of ordinary classrooms.

Subject of Research: Genetic algorithm based automated test assembly for mathematical literacy assessment inspired by the PISA framework

Article Title: Genetic algorithm based automated test assembly for mathematical literacy assessment inspired by the PISA framework

Article References: Wang, F., Wu, S., & Zhang, Y. (2026). Genetic algorithm based automated test assembly for mathematical literacy assessment inspired by the PISA framework. Discover Artificial Intelligence, 6(1), Article 1529. https://doi.org/10.1007/s44163-026-02161-8

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02161-8

Keywords: Genetic, algorithm, based, automated, test, assembly, mathematical, literacy, assessment, inspired, PISA, framework

Researchers Zi Jun Leong and Norulhuda Ismail of Universiti Teknologi Malaysia have constructed what they call the Integrated Cognitive-Mathematical-Technological, or ICMT, Framework. It weaves together three strands of scholarship that are usually treated separately: Jean Piaget’s theory of cognitive development, John Mason’s model of mathematical thinking processes, and the literature on calculators as cognitive tools. The target population is Malaysian Form 4 students aged 15 to 16, and the immediate proving ground is the quadratic functions topic in the national KSSM curriculum.

The urgency behind the work is stark. In the PISA 2022 assessment, only 41 percent of Malaysian students demonstrated basic mathematical reasoning skills, well below the OECD average of 69 percent. Recent TIMSS results point in the same direction, showing performance in mathematical reasoning that remains under international benchmarks. Malaysia’s Education Blueprint 2013-2025 explicitly prioritizes higher-order thinking skills, defined as the analysis, evaluation, and creation of mathematical solutions to non-routine problems, yet classroom practice has not closed the gap between policy and performance.

The authors argue that the missing ingredient is integration. Developmental theories such as Piaget’s tend to remain abstract, mathematical thinking models often lack any account of technological mediation, and technology integration efforts tend to focus on technical proficiency rather than cognitive growth. Existing frameworks each capture only part of the picture. TPACK describes what teachers need to know but says little about student cognition, while problem-based learning models emphasize authentic tasks without developmental sequencing. The ICMT Framework is designed to bind all three dimensions into a single causal pathway.

The developmental strand rests on Piaget’s formal operational stage, which typically begins around age 12 and equips adolescents with abstract thinking, hypothetical-deductive reasoning, propositional logic, and systematic problem-solving. Crucially, the framework adopts what the authors call a developmental potential model: rather than assuming students have already mastered formal reasoning, it designs tasks that actively cultivate it. This matters because cognitive readiness varies widely among individuals, and contemporary research confirms that adolescence is a critical period for the maturation of the executive functions and reasoning capacities that complex mathematics demands.

The mathematical strand comes from Mason’s four interlocking processes: specialising, examining specific cases to understand a general problem; generalising, identifying patterns across cases and formulating rules; conjecturing, making informed hypotheses from observed patterns; and convincing, justifying claims with argument and proof. These processes operate cyclically, and Malaysian research indicates that students struggle most with conjecturing and convincing, leaning on memorized procedures instead of constructing their own justifications. Mason’s framework offers observable, teachable behaviours that map cleanly onto Piagetian capacities, which is precisely why the authors chose it over alternatives such as Polya’s heuristics or Schoenfeld’s metacognition.

The technological strand reframes the calculator entirely. Drawing on cognitive load theory, the framework assigns the device specific cognitive functions at each phase of Mason’s cycle. In specialising, table mode rapidly generates multiple test cases, offloading arithmetic so students can hunt for patterns. In generalising, graphing features externalize patterns so an entire class can see and discuss them. In conjecturing, parameter manipulation turns hypothesis testing into quick what-if experiments. In convincing, the calculator supplies numerical and graphical evidence. In this role the calculator becomes what researcher Paul Drijvers terms a mediational tool, one that does not merely accelerate computation but actively shapes mathematical thinking.

Applied to quadratic functions, the instructional sequence unfolds in four phases. Students first use the calculator to solve equations with varying coefficients and record organized tables of results. They then graph families of quadratics and observe how parameter changes alter the curves, generating generalizations about coefficients. Next they conjecture conditions for real roots, such as discriminant requirements, and test their hypotheses against fresh equations. Finally, convincing splits into two sub-stages: empirical convincing, where calculator evidence builds inductive confidence, and deductive convincing, where students derive the quadratic formula or prove discriminant conditions by completing the square, using the calculator only to verify finished proofs. Assessment rubrics reinforce the distinction, awarding partial credit for calculator evidence but requiring logical derivation for full justification, so that students never mistake the observation that a pattern held in twenty examples for the algebraic necessity that it must always hold.

The authors are candid about the risks. Over-reliance on calculators can produce what Drijvers calls an instrumental genesis deficit, in which the tool’s efficiency undermines basic understanding. Graphical and numerical output can create an illusion of understanding, substituting empirical verification for genuine proof. Differential access to advanced models raises equity concerns, and success hinges on teachers who can mediate the boundary between thinking with a calculator and merely computing with one. The framework counters these dangers through design constraints: calculators are assigned specific cognitive functions, students must explain their reasoning before using the device for verification, and assessment evaluates both process and product. The framework also acknowledges its own limits, applying more naturally to algebra and functions than to geometric proof or statistics, and assuming reliable technology access that under-resourced and rural schools may lack.

Because the ICMT Framework is currently a propositional model, the authors propose a staged validation agenda: expert panels assessing content validity, pilot implementations in two to three Malaysian classrooms with teacher coaching, a quasi-experimental study comparing ICMT instruction with traditional teaching on TIMSS-like reasoning tasks, and a longitudinal study tracking national examination results, STEM enrolment, and the development of formal operational reasoning. They are now seeking funding and institutional partnerships to carry the work forward. If the validation succeeds, the payoff could be substantial, because scientific calculators are inexpensive, already permitted in Malaysian examinations, and already sitting in classrooms. A three-level technology spectrum, from standard scientific calculators to graphing calculators to computer algebra systems, would let schools upgrade hardware without changing the underlying pedagogy. In an era when education technology debates often centre on expensive tablets and artificial intelligence, the idea that a decades-old, pocket-sized device could be reprogrammed, in a pedagogical sense, into an engine of higher-order thinking is a quietly radical proposition, and one that could travel well beyond Malaysia to any education system wrestling with the gap between ambitious policy and everyday practice.

Subject of Research: A theoretical framework integrating Piaget's cognitive development theory, Mason's mathematical thinking processes, and calculator technology to foster higher-order thinking skills in secondary mathematics

Article Title: An integrated cognitive mathematical technological framework for calculator assisted higher order thinking skills instruction in secondary mathematics

Article References: Leong, Z. J., & Ismail, N. (2026). An integrated cognitive mathematical technological framework for calculator assisted higher order thinking skills instruction in secondary mathematics. Discover Education, 5(1), Article 1195. https://doi.org/10.1007/s44217-026-02304-0

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02304-0

Keywords: higher-order thinking skills, scientific calculator, mathematics education, Piaget, cognitive development, ICMT framework, secondary mathematics, quadratic functions, PISA, TIMSS, educational technology, Malaysia

The analysis, conducted by David Santarossa of the Education Department at Université TÉLUQ in Montréal, draws on two powerful concepts from cognitive science and social theory. The first is Campbell’s Law, formulated by psychologist Donald Campbell in 1979, which states that the more any quantitative social indicator is used for social decision-making, the more subject it becomes to corruption pressures and the more apt it is to distort the very social processes it was intended to monitor. The second is a pair of well-documented cognitive biases: the availability heuristic, which leads decision-makers to judge performance based on information that comes most readily to mind, and attribute substitution, which causes them to replace a difficult question with a simpler, more measurable proxy. Together, these mechanisms help explain why a rising graduation rate can coexist with flat or falling scores on an independent international assessment.

The story begins with the Politique de la réussite éducative, launched in 2017, which set an ambitious target of 90 percent graduation or qualification by 2030. From that moment, the graduation rate became the central Key Performance Indicator of Quebec’s education policy, dominating government communications, budget speeches, and media coverage. Financial investment served as the preferred lever: between 2018–2019 and 2024–2025, education spending rose by 55 percent, placing Quebec at the top of Canadian provinces for per-student expenditure in constant dollars. The results on the official indicator were impressive, with the rate climbing steadily from its 2008–2009 baseline of 71.9 percent. But when Santarossa turned to PISA, an externally administered assessment of 15-year-old students in more than 80 countries, the picture changed dramatically.

Quebec’s PISA scores tell a starkly different story from the graduation statistics. In mathematics, scores fell from 543 points in 2009 to 514 in 2022. Science showed a statistically significant decline, dropping from 524 to 512 over the same period. Reading followed a similar downward trajectory, from 522 in 2009 to 501 in 2022, despite a brief peak at 532 in 2015. Because Quebec still ranks well above the OECD average in all three domains, media outlets and government officials have generally celebrated the province’s strong relative position. But Santarossa argues that this favorable cross-national comparison masks a critical temporal signal: when compared against its own past performance, Quebec is regressing, not advancing. This contradictory indicator tempers the optimistic image conveyed by the rising graduation rate and improving pass rates on ministerial examinations.

The article identifies several rational institutional adaptations that are consistent with the incentive structure created by elevating the graduation rate to a dominant KPI. One of the most striking is the rapid expansion of students formally designated as having handicaps or learning difficulties, known in Quebec by the acronym EHDAA. At the turn of the 2000s, roughly one in ten students carried this designation; today the proportion has reached one in five province-wide and one in three at the secondary level. Each EHDAA student must have an individualized intervention plan that may include adaptations to assessment conditions, such as additional time or the use of assistive technologies. While the rise in designations may reflect legitimate factors including greater awareness of learning difficulties, expanded diagnostic practices, and inclusive policy orientations, its timing and direction align closely with the incentives created by the graduation-rate target.

The role of assistive technologies deserves particular scrutiny. Among the most common accommodation measures are spell and grammar-checkers and word predictors, which are increasingly permitted during assessments, including mandatory ministerial examinations required for diploma attainment. Longitudinal research shows that these tools significantly reduce spelling and grammar errors without improving overall writing competencies such as coherence, vocabulary, and narrative quality. A quasi-experimental Quebec study of 314 adolescents confirmed that gains are limited to lexical and grammatical spelling, with no effect on other dimensions of writing competency. In other words, these technologies function more as compensation than as a catalyst for deep learning, optimizing the surface indicator of passing an examination while potentially leaving intact the very cognitive gap the assessment was meant to measure. Critic Claude Boyer has characterized this shift in Quebec orthopedagogy as a transition from remedial intervention, which aims to close performance gaps, to what he calls relief intervention, which merely alleviates difficulties through accommodations without genuinely correcting underlying deficiencies.

Teaching to the test represents another mechanism consistent with Campbell’s Law. Students and teachers invest considerable effort in maximizing results on mandatory certification examinations, often through intensive preparation focused on memorization of standard formulas, learning of standardized writing structures, and repeated exam simulations. This phenomenon has been frequently documented in the Canadian and Quebec educational literature, although its precise prevalence and long-term effects remain subjects of ongoing empirical debate. From the perspective of policymakers, both the increase in EHDAA designations and the expanded use of assessment accommodations appear as inclusive solutions that simultaneously address equity and achievement objectives. But Santarossa argues that these developments may contribute, at the system level, to a loosening of the relationship between certification outcomes and the cognitive competencies measured by PISA, even if the available data do not allow a precise quantification of this mechanism relative to other factors.

The article is careful to acknowledge alternative explanations for the observed divergence. The COVID-19 pandemic caused major learning disruptions between 2020 and 2022, and PISA 2022 captured a cohort directly affected by school closures, whereas graduation rates reflect cohorts that experienced varying degrees of disruption and, in some cases, adjusted certification requirements. Demographic and socioeconomic changes, variations in PISA sampling frames, and student effort on a low-stakes assessment could also influence results. Curricular reforms, the expansion of vocational and alternative qualification pathways, and modifications to the rules governing access to the Quebec diploma may have facilitated higher certification rates without producing equivalent gains in applied cognitive skills. A comprehensive assessment of the relative contribution of these factors would require longitudinal microdata linking individual student trajectories, EHDAA status, accommodations received, and cognitive outcomes, data that are not currently publicly available in Quebec.

Beyond the cognitive biases that lead decision-makers to privilege easily measurable indicators, Santarossa raises a second and complementary problem: even if the graduation rate were a perfectly valid measure of system performance, the economics of education literature suggests it would still constitute a relatively weak proxy for the cognitive competencies that ultimately matter for individuals and society. Numerous studies conclude that there is no strong and consistent relationship between school resources and student performance once other factors are controlled for. Research by Eric Hanushek and Ludger Woessmann confirms that cognitive skills of the population, rather than mere school attainment, are powerfully related to individual earnings, income distribution, and economic growth. In short, a rising graduation rate and increased education budgets do not constitute reliable indicators of genuine cognitive development.

The Quebec case offers a cautionary tale for policymakers everywhere who rely on single headline metrics to navigate complex systems. The persistent divergence between the rising graduation rate and falling PISA scores has not yet triggered a formal governmental review, in part because Quebec continues to rank among the top performers in the OECD. Yet Santarossa argues that this situation highlights the urgent need for more robust decision-making protocols that integrate independent external benchmarks, aligning institutional incentives with genuine competency development rather than its statistical appearance. The recent creation of the Institut national d’excellence en éducation in 2025, whose mandate includes the promotion of evidence-based practices, may represent an important complementary mechanism. Effective governance in complex educational systems, the article concludes, is more likely to be achieved through balanced indicator frameworks that combine administrative outcome measures with independent external assessments, directing attention toward practices shown to enhance deep learning rather than solely toward easily measurable certification targets.

Subject of Research: How Campbell's Law and cognitive biases explain the divergence between Quebec's rising graduation rate and stagnant PISA scores

Article Title: Availability bias and Campbell's Law: why Quebec's rising graduation rate masks stagnant PISA scores

Article References: Santarossa, D. (2026). Availability bias and Campbell's Law: why Quebec's rising graduation rate masks stagnant PISA scores. Frontiers in Behavioral Economics, 5, Article 1903687. https://doi.org/10.3389/frbhe.2026.1903687

Image Credits: AI Generated

DOI: 10.3389/frbhe.2026.1903687

Keywords: Campbell's Law, availability bias, attribute substitution, PISA, graduation rate, Quebec education policy, performance indicators, assistive technologies, teaching to the test, behavioral economics, education spending, cognitive competencies

The stakes of the research are rooted in a persistent and well-documented problem. Mathematical literacy, the capacity to formulate, apply, and interpret mathematics in real-world contexts, is a central construct of the Programme for International Student Assessment, better known as PISA. Indonesian students have consistently ranked near the bottom of the PISA league tables, a result that researchers in mathematics education interpret as evidence of generally low mathematical literacy across the country’s school system. Students may learn to compute mechanically, the argument goes, but they struggle to translate a situation from daily life into mathematical terms, to select appropriate procedures, or to reason about what a result means once it has been obtained. This gap between procedural fluency and genuine mathematical understanding has motivated a decade of experimentation with context-based and culturally grounded teaching approaches.

One of the most influential of these approaches is ethnomathematics, a field pioneered by the Brazilian mathematician Ubiratan D’Ambrosio in the 1980s. Ethnomathematics rests on the observation that mathematical ideas are not confined to formal schooling; they are embedded in the practices, crafts, architecture, and rituals of every culture. By surfacing the mathematics hidden in familiar cultural objects, educators hope to make abstract concepts concrete and to signal to students that mathematics belongs to them, rather than being a foreign import. In Indonesia, a country with extraordinary cultural diversity, researchers have explored ethnomathematical modules, learning tools, and realistic mathematics education designs built on regional traditions. Meta-analyses of this work suggest that ethnomathematics-based learning can have positive effects on both problem-solving skills and mathematical literacy, which helps explain why the approach continues to attract attention.

The caping offers a particularly elegant case. The hat’s conical form is not merely decorative; it embodies the geometry of a right circular cone, a solid whose surface area depends on the radius of its circular base and the slant height running from the apex to the rim. Unroll the lateral surface of a cone and it flattens into a sector of a circle, whose area can be derived with basic formulas. For a hatmaker, the practical problem of how much woven bamboo is needed to cover the head is, in effect, a cone surface area problem solved with inherited craft knowledge. Previous literature reviews of caping ethnomathematics had already flagged its relevance to teaching cone material, but the Universitas Sebelas Maret team, led by Nuri Wahyu Widayanti with Rubono Setiawan and Laila Fitriana, wanted to go further: to observe how real students actually use the caping model to construct mathematical literacy, and whether that construction differs depending on how they learn.

The researchers adopted a qualitative phenomenological approach, a methodology designed to capture the lived experience of participants as they encounter a phenomenon. Rather than testing large samples with standardized instruments, phenomenological studies gather rich, detailed data from individual students, typically through written work, task-based problem solving, and observation, and then analyze that material thematically to reveal the structure of the experience. In this study, the data consisted primarily of students’ written work on problems involving the surface area of a cone, contextualized through the caping. The research was conducted at SMP N 1 Ngariboyo in Magetan, with formal permissions from both the university’s Faculty of Teacher Training and Education and the school’s principal, and with informed consent from all participants whose work was anonymized for analysis.

Central to the study’s design is the concept of learning styles, a framework popularized in educational practice by works such as DePorter and Hernacki’s quantum learning model. The framework classifies learners into three broad modalities. Visual learners prefer to process information through images, diagrams, and spatial representations. Auditory learners rely on verbal explanation, discussion, and hearing ideas articulated. Kinesthetic learners learn best through physical activity, manipulation of objects, and direct bodily engagement with the material. The hypothesis is straightforward: a culturally embedded model like the caping, which can be looked at, described, or handled, might interact differently with each modality, producing different levels of engagement with the three indicators of mathematical literacy that the study tracks.

Those three indicators, drawn from the international literature on mathematical literacy, involve the ability to formulate a real-world situation mathematically, to apply mathematical concepts and procedures to solve the resulting problem, and to interpret and evaluate the solution in terms of the original context. In a cone surface area problem framed by the caping, formulating means recognizing the hat as a cone and identifying the relevant dimensions; applying means correctly deploying the surface area formula and its derivation through the circular sector; interpreting means connecting the computed area back to the practical question of material or coverage. A student who is fully mathematically literate in this domain should demonstrate competence at all three stages, not merely at the computational middle.

The study’s central findings reveal a clear asymmetry. Visual and auditory learners demonstrated proficiency across all three indicators of mathematical literacy when solving the cone surface area problems built on the caping model. For visual learners, the hat’s distinctive silhouette and its unrolled sector diagram provided the spatial scaffold needed to formulate the problem and verify the sensibility of results. Auditory learners, meanwhile, could talk themselves through the formulation and interpretation stages, using verbal reasoning to connect the cultural context to the formal mathematics. Kinesthetic learners, by contrast, struggled to fully achieve all three indicators. Although the caping is a physical object that might seem ideally suited to hands-on learners, these students did not consistently reach the formulation and interpretation stages through the model as presented, suggesting that merely introducing a cultural artifact does not automatically guarantee that every learner will translate it into formal mathematics.

The practical implication the researchers draw is that culturally integrated mathematics instruction must be designed with learning styles explicitly in mind. A single ethnomathematical model, however vivid, will land differently across a classroom of students, and teachers who want to raise mathematical literacy effectively should offer multiple pathways into the same cultural-mathematical context: visual representations for those who think in pictures, structured verbal discussion for those who think in words, and carefully designed hands-on activities, possibly involving actual manipulation and measurement of the object itself, for kinesthetic learners. Without this differentiation, the benefits of ethnomathematics may flow disproportionately to students whose styles happen to match the presentation format, leaving the very students who most need concrete, embodied learning experiences behind. The finding is especially relevant to Indonesia’s broader reform efforts, where ethnomathematics is increasingly promoted as a national strategy for improving PISA-related competencies.

The study also contributes methodologically, showing how a phenomenological approach can illuminate the fine-grained texture of mathematical thinking that standardized tests flatten out. By examining students’ written work closely, the researchers could trace where in the formulation-application-interpretation chain each type of learner stumbled, offering a diagnostic precision that a test score alone cannot provide. The authors note limitations inherent to the design: the data derive from written work rather than publicly archived datasets, in order to protect participant confidentiality, and the qualitative scope means the findings speak to the structure of the experience rather than to population-level effect sizes. Even so, the message is compelling. A humble farmer’s hat from Magetan, when treated as a mathematical model, can open a window onto the geometry of curved surfaces and, simultaneously, onto the diverse ways students make sense of mathematics. Bridging Indonesia’s literacy gap, the study suggests, will require both the wisdom of cultural heritage and a sharper attention to the individual minds sitting in the classroom.

Subject of Research: Ethnomathematics-based mathematical literacy in cone geometry analyzed through students' learning styles

Article Title: Mathematical literacy analysis based on ethnomathematics modelling of caping and learning styles: study in Magetan, Indonesia

Article References: Widayanti, N. W., Setiawan, R., & Fitriana, L. (2026). Mathematical literacy analysis based on ethnomathematics modelling of caping and learning styles: study in Magetan, Indonesia. SN Social Sciences, 6(9), Article 373. https://doi.org/10.1007/s43545-026-01665-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01665-5

Keywords: mathematical literacy, ethnomathematics, caping, learning styles, cone surface area, PISA, Indonesia, phenomenology, mathematics education, Magetan, visual learners, kinesthetic learners

The study, published in PNAS Nexus and authored by Clotilde Napp, draws on two waves of the Programme for International Student Assessment, the triennial survey of fifteen-year-olds conducted by the Organisation for Economic Co-operation and Development. Using data from the 2012 and 2018 assessment rounds, the analysis covers roughly 750,000 students across 61 countries. PISA is unusually valuable for this kind of work because, alongside its tests in mathematics, reading, and science, it asks students an extensive battery of questions about their attitudes, motivations, and home lives. That combination allows researchers to connect what teenagers believe with how they actually perform.

At the heart of the study is a single, deceptively simple survey statement: “Trying hard at school is important.” Students indicated how much they agreed with it, and their answers became a measurable index of attitudes toward academic effort. The finding that emerged was striking in its consistency. In nearly every one of the 61 countries examined, boys were less likely than girls to agree that effort at school matters. This was not a quirk of one education system or one culture; it appeared across a wide range of economic and social settings, suggesting a pattern that transcends national borders.

Crucially, the attitude gap was not merely a survey artifact. Napp found that students’ responses to the effort question were correlated with their individual academic performance, and the link was especially pronounced in reading. Reading is the domain in which boys’ underachievement is most visible worldwide, and the study’s results suggest that a weaker belief in the value of effort may be part of the mechanism behind it. In other words, the students who doubted that hard work mattered tended, on average, to score lower, and boys were disproportionately represented among those doubters.

The geographic texture of the data adds another layer of complexity. The gender gap in attitudes toward effort was larger in more developed countries. That counterintuitive result complicates the assumption that gender inequality in education is primarily a problem of poorer nations. In wealthier economies, where girls already outpace boys in most measures of school success, boys appear particularly disengaged from the idea that diligence matters, raising questions about how modern, affluent societies socialize their sons into particular ideas about schoolwork.

The cross-country comparison also revealed a sobering ecological pattern: in nations where the attitude gap was larger, boys’ relative academic performance was weaker, and their participation in higher education was lower. The alignment held across most of the sample, with one conspicuous exception. The relationship did not extend to STEM fields, where boys’ enrollment and performance patterns follow different dynamics. This exception matters for policy discussions, because it suggests that the effort-belief gap operates most strongly in areas such as reading and general academic engagement rather than uniformly across the curriculum.

Behind the attitude gap may lie a deeper cultural story. According to the study’s author, the pattern is consistent with a persistent stereotype that associates talent and brilliance more with boys, while linking effort, diligence, and conscientiousness more with girls. If brilliance is coded as masculine, then a boy who struggles at school can rationalize his performance as a matter of innate ability rather than insufficient work. If diligence is coded as feminine, then trying visibly hard can feel at odds with a particular version of masculinity. Either way, the stereotype undermines the belief that sustained effort is the path to academic success, and the PISA data suggest that boys around the world have absorbed that message.

The study also examined the role of parents and found a further gendered divide. Boys were less likely than girls to report that their parents strongly supported their academic efforts. This difference in perceived parental support may both reflect and reinforce the attitude gap. Adolescents are sensitive to the expectations and encouragement they perceive at home, and if boys sense less active backing for their schoolwork, the cultural message that effort matters less for them is amplified. Parental behavior, in this framing, is not a separate factor but part of the same web of gendered expectations that shapes how teenagers approach learning.

The scale of the dataset lends the findings considerable weight. Three-quarters of a million students across six decades of countries, surveyed in two separate assessment waves, produce a picture that individual national studies could only sketch in fragments. Earlier research in specific countries had already hinted that boys held more negative attitudes about academic effort; what this analysis adds is the demonstration that the pattern is global, systematic, and quantitatively linked to real differences in achievement and educational trajectories.

For educators and policymakers, the implications are significant. Boys’ underachievement has often been treated as a structural problem solvable through curricular reform or teaching interventions, yet this study suggests that cultural beliefs about gender and effort deserve a central place in the conversation. If boys in developed countries are the most likely to reject the importance of trying hard, and if those beliefs track with poorer reading performance and lower university participation, then interventions aimed squarely at dismantling stereotypes about brilliance and diligence may be among the most effective tools available. The gender gap in education, the study reminds us, is not only about what happens in classrooms; it is about what boys come to believe before they ever sit down to study.

Subject of Research: Gender differences in attitudes toward academic effort and boys' educational underachievement

Article Title: Boys' academic underachievement and attitudes about effort

Article References: Boys' academic underachievement and attitudes about effort. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: boys' underachievement, academic effort, PISA, gender gap, education, PNAS Nexus, OECD, reading performance, higher education, gender stereotypes, parental support, international study

The classic model, proposed by Herbert Marsh in 1986, makes two elegant predictions. First, students who perform well in math tend to develop strong math self-concepts, because they compare their grades or test scores with those of peers—a so-called external, or social, comparison. This produces a positive within-domain effect. Second, a student who excels in math often feels comparatively worse at English, even with decent English grades, because the two abilities are contrasted internally. This dimensional comparison produces a negative between-domain effect: high math achievement actually suppresses verbal self-concept, and vice versa. Meta-analytic results have confirmed both patterns with striking consistency across countries, age groups, and school types, making the model one of the most robust findings in the discipline.

The trouble, as Wolff first argued in 2021, lies in how the model’s coefficients are interpreted. In the standard specification, a student’s math self-concept is regressed on both math achievement and verbal achievement. Researchers have traditionally labeled the math-achievement coefficient a pure social comparison effect. But dimensional comparison theory defines dimensional comparisons as contrasts between one’s own achievements in two domains. If that is what students actually do, then the verbal achievement coefficient in the classic model does not capture the contrast itself—it captures only the part of the contrast that remains after the shared variance between the two achievement measures is partitioned. The contrast, in other words, leaks into the within-domain coefficient, inflating it.

To fix this, Wolff proposed the difference I/E model, a reparameterization in which self-concept in one domain is predicted by achievement in that same domain plus the difference between achievement in that domain and achievement in the other. Algebraically, the two models contain exactly the same information: they fit the data identically and explain the same variance. The between-domain effects are equal in magnitude and differ only in sign. But the within-domain effect in the classic model equals the sum of the within- and between-domain effects in the difference model. Using meta-analytic estimates, the authors calculate that the genuine within-domain effect on math self-concept might be around β = .37 rather than .57, and on verbal self-concept around β = .29 rather than .46—shifting the ratio of within- to between-domain effects from roughly 2.8 to about 1.8.

What was missing until now was a direct empirical test. The new research delivers one across six pre-registered studies. In the first, the team reanalyzed PISA 2000 data from 55,582 students in 26 countries—the same dataset Marsh and K.T. Hau used in their landmark 2004 cross-cultural validation. The logic was subtle. If the classic model’s within-domain effect already contains a between-domain component, then explicitly adding the other domain’s achievement as a predictor should increase the within-domain effect, because the shared contribution is now being counted twice. Conversely, in the difference model, adding the achievement difference should decrease the within-domain effect, because the dimensional component is being removed from it. Both patterns appeared exactly as predicted, and the changes scaled with the strength of the between-domain effects: in the Netherlands, where the negative between-domain effect on math self-concept was strong, the within-domain effect jumped by more than a quarter of a standard unit when the classic model was fully specified.

Study 2 brought in something the models alone cannot provide: students’ own reports of the comparisons they make. Nearly 950 German ninth- and tenth-graders rated how they judged their math and German achievement relative to classmates and relative to each other. When these explicit ratings were added to the models, the results were decisive. Controlling for social comparison ratings shrank the within-domain effects in both model versions while leaving the between-domain effects untouched. Controlling for dimensional comparison ratings reduced all four effects in the classic model, but only the between-domain effects in the difference model. Strikingly, the explicit comparison ratings were stronger predictors than the objective grades themselves, and the within-domain effects dropped by more than 60 percent—up to 85 percent in the difference model—once the ratings were accounted for.

Study 3 attacked the question from a third angle using moderator variables. Two beliefs—the conviction that math and verbal abilities are negatively related, and the perceived dissimilarity of the two subjects—should, on theoretical grounds, strengthen dimensional comparisons but have no reason to affect social comparisons. In the classic model, both beliefs significantly amplified all four effects, including the supposedly social within-domain effects. In the difference model, they amplified only the between-domain effects. This is precisely the signature one would expect if the classic model’s within-domain coefficients are contaminated by dimensional comparison processes, and it echoes earlier findings that had puzzled researchers who lacked the difference-score framework to interpret them.

The remaining three studies asked whether relative achievement information—the difference between grades in two subjects—is actually salient to students when they evaluate themselves. University students studying to become teachers reported relying on the difference between their math and German grades more than twice as strongly as on their grade in the other subject alone when reflecting on their school-day self-concepts. A replication with 229 secondary school students assessing their current self-concepts showed the same pattern, albeit with smaller effect sizes. In a deliberately exploratory sixth study, the authors posed the same forced choice to ChatGPT-5 across 145 prompts per domain; the language model chose the achievement difference in every single case, reasoning that the contrast between grades offers a more direct index of relative strength. The authors are careful to note that LLM responses reflect patterns in training data rather than human cognition, but the unanimous preference is at least consistent with the conceptual plausibility of difference-based self-evaluation.

The implications ripple outward. Prior studies that labeled the classic model’s within-domain effects as social comparison effects now require qualification, since those coefficients bundle together social and dimensional processes. Group comparisons—across ages, countries, ability levels, or teacher knowledge—may have drawn premature conclusions when between-domain effects differed alongside within-domain effects. Intervention research faces a parallel challenge: programs designed to boost self-concept in one domain can inadvertently depress it in another, as one study found when a math-values intervention lowered students’ German values. Promisingly, an intervention that explicitly taught students about dimensional comparisons raised math self-concept without harming verbal self-concept, with effects lasting at least six months.

The authors stop short of declaring the difference model superior. Both specifications encode identical information, and neither directly isolates psychological mechanisms; the difference model’s real contribution is interpretive transparency, making a theoretically grounded contrast explicit rather than hidden in algebraic cross-talk. Their practical recommendation is pragmatic: researchers can keep estimating the classic model within a single statistical framework, then use modern software to decompose the within-domain effects along difference-score lines. What the six studies establish is that the field’s most trusted coefficients deserve a more cautious reading—and that a 40-year-old model still has the capacity to surprise the scientists who know it best.

Subject of Research: The role of social and dimensional comparisons in academic self-concept formation within the internal/external frame of reference model

Article Title: Revisiting the Role of Social and Dimensional Comparisons in the Internal/External Frame of Reference Model: A Systematic Comparison of the Classic and the Difference I/E Model

Article References: Wolff, F., Schumacher, J., & Möller, J. (2026). Revisiting the Role of Social and Dimensional Comparisons in the Internal/External Frame of Reference Model: A Systematic Comparison of the Classic and the Difference I/E Model. Educational Psychology Review, 38(1), Article 132. https://doi.org/10.1007/s10648-026-10212-w

Image Credits: AI Generated

DOI: 10.1007/s10648-026-10212-w

Keywords: academic self-concept, internal/external frame of reference model, dimensional comparisons, social comparisons, educational psychology, difference I/E model, achievement, self-concept formation, PISA, meta-analysis, regression models, student motivation

The review, published online on August 1, 2026, in ECNU Review of Education, was conducted by Luís Miguel Carvalho and Catarina Gonçalves of the Institute of Education, University of Lisbon. The authors examined 42 peer-reviewed articles published between 2005 and 2023, mapping a body of work known as the sociology of policy instruments in education. Their goal was twofold: to take stock of what this research tradition has revealed about contemporary education policy, and to identify where the field should go next. The result is both a synthesis of nearly two decades of scholarship and a framework for understanding how apparently mundane administrative devices carry profound political weight.

The intellectual core of the sociology of policy instruments is the claim that instruments are simultaneously technical and social devices. Legislation, contracts, assessments, benchmarks, indicators, funding mechanisms, and communication platforms all embody specific values and assumptions about the relationship between government, education professionals, learners, and society. A national testing regime, for instance, encodes beliefs about what knowledge matters, how learning should be demonstrated, and who is entitled to judge school quality. A funding contract encodes assumptions about incentives and accountability. Studying these embedded assumptions, the review argues, makes it possible to see how power operates through arrangements that are usually presented as merely practical.

The literature reviewed shows a clear center of gravity. Researchers have concentrated especially on performance-based instruments: the OECD’s PISA assessments, national large-scale assessments, school evaluation and self-evaluation schemes, indicators, standards, and accreditation procedures. This concentration, the authors suggest, reflects a broader transformation in how education systems are governed—a shift toward governance through comparison, data, and performance-oriented accountability. In such systems, the instrument is not an afterthought to policy; it is the medium through which policy is imagined, debated, and enforced.

The review also documents a second trend in the research: growing attention to European education governance, to global intergovernmental organizations, to private companies, and to intermediary actors who work between national policy and local practice. This shift mirrors changes in the policy landscape itself. As international assessments and transnational benchmarks gain influence, the actors who design, interpret, and circulate these tools—organizations, consultants, data providers, and broker institutions—acquire authority that traditional accounts of education policy rarely captured. Instrument-based research, the review shows, is particularly well suited to illuminating this redistribution of influence.

To organize the existing knowledge, Carvalho and Gonçalves distinguish three dimensions. The first concerns the institutional ideas embedded in instruments—the values, theories of schooling, and assumptions about governance that tools carry within them. The second concerns the trajectories instruments follow as they are designed, adopted, and used in practice. The third concerns the effects instruments generate once they are in circulation. Crucially, the review emphasizes, instruments do not simply travel unchanged from policymaking centers to schools. National traditions, political interests, professional identities, and local conditions can lead to adoption, opposition, hybridization, or the creation of entirely new tools. A similar assessment or indicator scheme may therefore take quite different forms in different systems, depending on the institutional soil in which it is planted.

From this synthesis, the authors distill six recurring effects of policy instruments. Instruments can legitimize certain actors and coalitions while marginalizing others. They can reorganize political spaces and timelines, changing who participates in decision-making and on what schedule. They can privilege certain forms of knowledge and expertise over others, elevating statistical and managerial competence in the process. They can gather actors around common issues, creating new arenas of cooperation and contestation. They can produce hybrid policies when several tools and governing logics interact within the same system. And they can generate unintended consequences that no designer anticipated.

The review offers concrete illustrations of these dynamics. Comparative assessments such as PISA may strengthen the authority of international organizations and structure national debate around rankings and league tables. Indicators designed to inform policy deliberation can harden into simplified rankings that obscure the complexity they were meant to capture. Performance tools introduced to encourage innovation may instead intensify supervision or standardize classroom practice. In each case, the effect flows not from malice or error but from the way the instrument itself organizes attention, incentives, and authority—a reminder that design choices are consequential even when they appear innocuous.

“Studies on policy instruments are well suited to address the complexity of education policy: the multiple policy scales as interdependent contexts, the variety of individual and collective actors involved, and the various governance patterns, ranging from state-oriented to market-oriented regimes, and from norm-based governance patterns to performance-oriented ones,” said Carvalho and Gonçalves. The methodological strength of this approach lies precisely in its capacity to connect levels: a single testing regime can be analyzed simultaneously as a national political project, a transnational data infrastructure, a professional constraint on teachers, and a market opportunity for private providers. Few other lenses in education research hold these scales together so naturally.

The review is candid about its limits and the gaps it exposes. The evidence base remains strongly centered on Europe, leaving large regions of the world underrepresented in the literature. Researchers have also paid less attention to combinations of multiple instruments, even though real education systems almost always deploy tools in bundles rather than in isolation. The authors call for a broader geo-cultural range of studies, clearer theoretical foundations, and more systematic explanations of why the same instrument follows different trajectories in different settings. Answering these calls, they suggest, would turn a promising research perspective into a genuinely comparative science of educational governance.

For policymakers and education leaders, the findings carry a practical caution: choosing a policy instrument is itself a political decision. A test, an indicator, a contract, or a digital platform may affect not only whether a policy achieves its stated goals, but also what goals appear legitimate in the first place, who gains authority over schools, and which realities remain visible or become obscured by the data collected. A ministry that selects a new evaluation framework is, in effect, redrawing the map of who may speak credibly about education. Recognizing this does not mean abandoning instruments—governance without tools is impossible—but it does mean treating their selection and design as matters requiring the same scrutiny as policy content itself.

For researchers, the review provides a map of a developing field and a framework for examining the complex links between technical devices, institutional change, and power in education. The sociology of policy instruments, as synthesized by Carvalho and Gonçalves, invites scholars to ask deceptively simple questions about the tools that surround schooling: What ideas do they carry? Whose interests do they serve? How do they travel, mutate, and combine? And what worlds do they make possible—or foreclose? As education systems worldwide continue to embrace data, comparison, and performance, those questions grow only more urgent, and the instruments themselves grow only more consequential.

Subject of Research: The role of policy instruments in shaping ideas, actors, and governance in education policy

Article Title: Policy instruments are never neutral — they shape ideas, actors, and change

Article References: Policy instruments are never neutral — they shape ideas, actors, and change. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: education policy, policy instruments, scoping review, PISA, accountability, governance, indicators, school evaluation, Europeanization, international organizations, performance governance, sociology of education

The research, conducted by Claudia Finger, Sandra Hupka-Brunner and Andrés Gomensoro of the University of Bern, draws on the Swiss TREE study, short for Transitions from Education to Employment. TREE is one of the rare panel surveys in the world that tracks PISA participants over the long term. Its first cohort was launched in 2000, built on a nationally representative sample of Swiss ninth graders tested when the country joined PISA for the first time. The PISA survey achieved a remarkable 95 percent response rate, and roughly half of the initial TREE sample consented to follow-up contact, yielding a baseline of 6,343 respondents. Ten panel waves later, the most recent data cover almost twenty years and follow respondents from early adolescence to an average age of about 35.

This long observation window matters enormously, and it is precisely what most other PISA follow-up studies lack. Previous analyses in Luxembourg, Israel, Australia, Canada and Denmark have generally stopped observing participants in their early or mid-twenties. That cutoff creates two problems. First, more than a fifth of participants are still enrolled in tertiary education at that age, so the labor market outcomes of those already working are biased toward the less educated. Second, economists have shown that early-career earnings estimates suffer from a downward lifecycle bias, because people with higher lifetime earnings experience steeper wage growth. Returns to skills rise sharply until around age 35 and only then stabilize. By following respondents to 35, the Swiss team could capture outcomes far closer to their mature form.

The study’s central finding concerns educational attainment. Among the 2,887 respondents analyzed, 43 percent had obtained a tertiary degree by age 35, whether at a university, a university of applied sciences, or through a professional tertiary qualification. The researchers found that a 100-point increase in PISA reading scores, equivalent to one standard deviation on the international scale, raised the probability of earning a tertiary degree by almost 23 percentage points. Given that the sample average was already 43 percent, that is a substantial effect. Reading literacy, measured with Warm’s Weighted Likelihood Estimator and checked against plausible-value methods, proved to be the single strongest incremental predictor of degree attainment in the models, adding significant explanatory power beyond gender, migration background, parental occupational status and school track.

Perhaps the most technically interesting result involves the interaction between reading skills and Switzerland’s famously early tracking system. Swiss students are streamed into two to four lower-secondary tracks after grade six, at around age twelve, and although policy formally postulates permeability, initial placement is largely irreversible. Test-score distributions overlap substantially across tracks, yet the track itself acts as a powerful signal. The study shows that for students scoring below 500 points, the probability of later obtaining a tertiary degree differs little between tracks. But among higher-scoring students, track-specific differences widen dramatically: being in the lowest rather than the highest track significantly weakens the effect of strong reading skills. In other words, high literacy is especially helpful for students in the academic track, while it matters far less for those placed lower, because lower tracks do not commonly qualify for tertiary education despite formal permeability. Early tracking, the authors conclude, makes educational pathways path-dependent in ways that can neutralize individual skill advantages.

On the labor market, the picture is more nuanced. Neither PISA scores nor tertiary degrees were related to whether respondents were employed at all, which is unsurprising in a country where unemployment stood at just 4.4 percent in 2024 and the vocational education and training system integrates most school leavers into work. But among the 93.5 percent who were employed or self-employed, a 100-point increase in reading scores was associated with 8.5 percent higher gross hourly income, and with higher occupational prestige as measured by the International Socioeconomic Index. Crucially, the income association was fully mediated by tertiary degree attainment, while the occupational status association was reduced but remained visible after controlling for degrees. The direct effect on occupational status, however, was modest: three ISEI points amount to only a fifth of a standard deviation in prestige, corresponding to minor shifts between occupations. In a system with strong vocational specificity and powerful signaling value of certificates, educational credentials appear to be the main channel through which adolescent skills translate into labor market rewards.

The study also ventured beyond education and work into domains that PISA follow-ups rarely touch: health, wellbeing and political interest. Here the results were strikingly mixed. Self-assessed health, life satisfaction on a ten-point scale, and an additive index of positive life attitudes all showed no relationship to reading scores, and none to educational attainment or employment either. The authors offer several possible explanations. Self-rated health is a subjective measure that may capture different aspects than the objective health indicators used in earlier research linking skills to longevity and health service use. Health inequalities by education often widen over adulthood and may simply not have materialized by age 35. The TREE sample also excludes people who migrated to Switzerland after compulsory schooling, potentially underrepresenting more disadvantaged groups, and baseline health and life satisfaction in Switzerland are among the highest in the OECD, compressing the room for skill-based differences to appear.

One unexpected positive finding concerns democracy. Reading literacy at fifteen showed a modest but significant relationship with political interest at thirty-five, partly mediated by tertiary education. Intriguingly, the same analyses run with mathematics and science scores found no such link, suggesting that it is reading literacy specifically, rather than numeracy or scientific understanding, that feeds civic curiosity. In a country whose semi-direct democracy asks citizens to vote several times a year on federal, cantonal and local questions, that distinction carries real weight. It hints that the ability to comprehend complex texts may be a foundational civic skill, one that shapes engagement with political information long after the classroom test is forgotten.

The authors are careful about the limits of their evidence. Panel attrition is a constant concern in a twenty-year study, and the team used logistic-regression-based weights to correct for non-response, finding that participation in the final wave was predicted by prior participation patterns, education, nationality and, notably, PISA reading scores themselves. Between 15 and 17 percent of wave-ten respondents skipped the complementary questionnaire containing the health, wellbeing and political items, so the researchers tested whether adjusting for variables related to that non-response, such as life satisfaction, marriage and children, changed their conclusions; it did not. They also caution that respondents have completed less than half of their life course, that returns to skills may still be underestimated for those still establishing careers, and that health trajectories have decades left to unfold. An eleventh panel wave, fielded when respondents reached an average age of 40, will be released in 2027, with further waves planned at five-year intervals.

Beyond the Swiss case, the study speaks to a broader frustration in assessment research. PISA claims to measure the knowledge and skills needed in adult life, yet the evidence base for that claim has long been thin, and the handful of national follow-ups that exist are barely comparable, each built with different instruments, timelines and frameworks. An OECD report as far back as 2010 called for better coordination of longitudinal follow-ups, arguing that it would for the first time allow cross-national analysis of young people’s pathways from education into work. The Swiss findings, showing that adolescent reading literacy echoes through two decades of educational, economic and even civic life while leaving subjective wellbeing untouched, add a compelling piece to that cumulative puzzle, and a reminder that answering PISA’s central promise will require long-term commitment from funders, sustained panel data, and far more international coordination than currently exists.

Subject of Research: Long-term predictive validity of PISA reading literacy measured at age 15 for educational, labor market, health, wellbeing and political outcomes at age 35 in Switzerland

Article Title: Cognitive skills and long-term life outcomes: evidence from 19 years of PISA 2000 follow-up surveys in Switzerland

Article References: Finger, C., Hupka-Brunner, S., & Gomensoro, A. (2026). Cognitive skills and long-term life outcomes: evidence from 19 years of PISA 2000 follow-up surveys in Switzerland. Large-scale Assessments in Education, 14(1), Article 49. https://doi.org/10.1186/s40536-026-00322-w

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00322-w

Keywords: PISA, reading literacy, longitudinal study, TREE study, educational attainment, labor market outcomes, income, political interest, health, wellbeing, school tracking, Switzerland

The study exploited a rare opportunity. When Sweden administered PISA 2022, the national agency for education recorded students’ personal identification numbers, allowing the researchers to link the test scores of 6,072 fifteen-year-olds with official register data containing their Grade 9 national test results and final subject grades in Swedish, mathematics, and science. Because PISA samples by age rather than grade, nearly 97 percent of the participants were ninth-graders, making the comparison across the three assessment types unusually clean. Linkage was achieved for 99.5 percent of the sample, and the researchers applied survey weights and adjusted for the clustering of students within 262 schools to ensure that their statistical models reflected the true population structure.

The conceptual stakes are high because the three measures were built for different purposes. PISA, run by the OECD, deliberately avoids any national curriculum. It assesses what the OECD calls literacy: the capacity of students to analyse, reason, communicate, and apply knowledge in unfamiliar, real-world situations, whether or not those situations resemble classroom content. National tests and school grades, by contrast, are explicitly curriculum-based, evaluating mastery of subject syllabuses. The stakes also differ sharply. PISA is low-stakes for individual students, who receive no feedback and face no consequences, whereas Swedish grades determine admission to upper secondary and tertiary education, and national test results feed into grading decisions. Motivation research suggests these differences can affect how hard students try, potentially depressing PISA performance relative to what students know.

The first striking finding concerned the correlations among the PISA domains themselves. PISA reading, mathematics, and science scores were intercorrelated at values between 0.81 and 0.88, an extraordinarily high degree of overlap for tests that are supposedly distinct subject assessments. By comparison, the correlations between PISA scores and the school achievement measures ranged from 0.58 to 0.74, moderately strong but clearly not indicating interchangeability. Meanwhile, the school-based measures correlated with each other at 0.62 to 0.92, with the strongest link, 0.92, between mathematics grades and mathematics national test results. Notably, PISA reading correlated almost equally with grades and tests in Swedish, mathematics, and science, around 0.61 to 0.63, suggesting that reading literacy acts as a broad competence that underpins performance across the entire curriculum.

To disentangle what drives these patterns, the researchers turned to confirmatory factor analysis, specifically a bifactor model with a multitrait-multimethod structure. In this framework, the three subjects, Swedish or reading, mathematics, and science, serve as traits, while the three assessment types, PISA, national tests, and grades, serve as methods. A general achievement factor captures variance common to all nine measures, while orthogonal specific factors capture variance unique to particular subjects or particular assessment formats. The researchers built up their models step by step: a single general factor fit poorly, adding method factors improved things only marginally, but a model combining the general factor with both method and subject factors achieved acceptable fit across all standard indices, including CFI, TLI, RMSEA, and SRMR.

The factor loadings told a clear and, for PISA, sobering story. All nine measures loaded strongly on the general achievement factor, with loadings between 0.69 and 0.96, confirming that a broad academic ability underlies performance regardless of how it is measured. But the method factors revealed an asymmetry. The PISA method factor showed substantial loadings of 0.54 to 0.62, meaning a large share of PISA variance is specific to the PISA assessment format itself. The national test method factor, in contrast, loaded below 0.20, indicating that national tests share nearly all their variance with grades and add little method-specific noise. In other words, grades and national tests behave as close relatives, while PISA stands apart.

The subject factors delivered the study’s most consequential result. Grades and national tests showed strong loadings of roughly 0.50 on the Swedish and mathematics specific factors, demonstrating that these school-based measures genuinely differentiate between what a student knows in one subject versus another. The corresponding PISA loadings were tiny, between 0.12 and 0.16. PISA, in short, barely distinguishes reading from mathematics from science. Rather than mapping subject-specific curricular learning, it appears to capture a general, literacy-oriented competence shaped by its own assessment framework. This finding echoes earlier item-level studies, including work by Pokropek and colleagues across 33 countries, which found that a general factor accounted for about 84 percent of the common variance in PISA scores, with subject-specific factors too weak to interpret reliably.

Why does PISA blur the boundaries between subjects? The authors point to several plausible mechanisms. PISA’s literacy framework emphasizes applied, real-world contexts, and its items are notoriously text-heavy: research on Swedish PISA mathematics tasks found they demand far more reading than the national mathematics tests, so reading comprehension skill can drive performance even in the mathematics domain. PISA’s plausible-value imputation procedure may also inflate inter-domain correlations, because each student’s scores are conditioned on performance in other domains and on background variables. Additionally, PISA administers items from all domains in a single session, so fatigue, motivation, and other situational factors may influence performance across domains simultaneously. Low stakes may further dampen effort; as the OECD itself notes, PISA captures what students actually do under low-consequence conditions, not their maximum potential.

The policy implications are significant. Because PISA results often trigger national reforms, the authors warn against interpreting PISA scores as direct indicators of subject-level proficiency or using them to justify curriculum-specific interventions. Scholars have cautioned that over-reliance on international assessments can narrow curricula and misdirect priorities. At the same time, the researchers are careful not to declare PISA useless: it remains a valuable, curriculum-independent benchmark for cross-national comparison and trend monitoring, immune to national grade inflation. The honest conclusion is that PISA, national tests, and grades measure overlapping but non-interchangeable constructs. Whether PISA’s broader literacy focus or the schools’ subject-specific measures better capture socially relevant learning is, the authors stress, a normative question that statistics alone cannot settle. What statistics can settle, and this study does, is that treating these metrics as equivalent would be a measurement mistake with real consequences for education policy.

Subject of Research: Comparing the measurement structure of PISA scores, national tests, and teacher-assigned grades in reading, mathematics, and science

Article Title: Crossing metrics: are PISA, national tests, and grades measuring the same?

Article References: Borger, L., Strietholt, R., Johansson, S., & Greiff, S. (2026). Crossing metrics: are PISA, national tests, and grades measuring the same?. Large-scale Assessments in Education, 14(1), Article 47. https://doi.org/10.1186/s40536-026-00321-x

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00321-x

Keywords: PISA, educational assessment, psychometrics, school grades, national tests, large-scale assessment, bifactor model, multitrait-multimethod, test validity, education policy, Sweden, student achievement