The study, led by Ariel Torres of Universidad Tecnológica Indoamérica in Quito, together with colleagues at Universidad de Las Américas, is deliberately framed as a critical analytical reflection rather than a conventional systematic review. Instead of exhaustively cataloguing every primary study on university teaching, the team purposively selected the highest tier of evidence available: systematic reviews, meta-analyses, umbrella reviews, and scoping reviews. Searches of Scopus, Web of Science, PubMed, and Google Scholar were refined iteratively, with studies admitted only if they addressed higher education and touched at least one of five analytical domains the researchers had defined in advance. Opinion pieces, editorials, and narrative reviews without methodological grounding were excluded.

The analytical machinery behind the synthesis was equally deliberate. The researchers built an analytical matrix to organize each study’s characteristics, methodological design, key findings, and conceptual contributions, allowing systematic cross-study comparison. Iterative coding then grouped recurrent findings into thematic categories, which were refined until the team reached what they call conceptual stability: the point at which further analysis no longer produced substantive changes in a category’s content or boundaries. Interpretative validity was strengthened through conceptual triangulation across study designs and educational contexts, filtering out isolated or idiosyncratic findings in favor of patterns that held across multiple bodies of evidence.

What emerged from this process was striking convergence around five interrelated dimensions. The first is teacher–student interaction, which appeared throughout the evidence not as a standalone construct but as the connective tissue of pedagogical practice, embedded in simulation, team-based learning, clinical case discussion, collaborative work, and concept mapping. Meta-analytic evidence cited in the study indicates that teaching-related factors, including the classroom learning environment and the quality of teacher–student interaction, are significantly associated with student learning outcomes, giving this relational core an empirical anchor.

The second dimension encompasses higher-order cognitive processes: critical thinking, clinical reasoning, and decision-making. These were reported across simulation-based instruction, error-based learning, concept mapping, and self-regulated learning contexts, where students plan, monitor, and evaluate their own learning. In medical education specifically, clinical reasoning surfaced in diagnostic and decision-making tasks, while scoping reviews of artificial intelligence documented AI applications that engage learners in reasoning and judgment. The third dimension, pedagogical strategies, is notable for what it does not claim: no single method dominates. Rather, the evidence describes a constellation of active and adaptive approaches, from problem-based and game-based learning to experiential designs, whose effectiveness depends on how well they organize interaction and cognitive engagement rather than on any inherent superiority of one technique.

Assessment forms the fourth dimension, and the synthesis repositions it fundamentally. Formative and authentic assessment, particularly when built around feedback processes and real-world tasks, emerged not as a terminal stage of instruction but as an embedded component of learning itself. A systematic review of 94 studies on authentic assessment documented the use of professional-context tasks in which student performance is evaluated through work that mirrors practice. A separate review of 73 studies on online peer feedback found that cognitive, task-focused comments were the most frequent form of feedback, typically structured through rubrics and student preparation. Notably, the evidence also revealed gaps: relatively few studies combined peer, teacher, and automated feedback, and student-related variables within feedback processes were rarely examined.

Digital technologies constitute the fifth dimension, and here the study takes a deliberately contrarian stance against technological determinism. Across the evidence, technologies, from conventional online and blended environments to learning analytics and artificial intelligence, function as mediating tools embedded within pedagogy and assessment, not as independent drivers of quality. A scoping review synthesizing 278 publications documented AI applications across admissions, teaching, assessment, and clinical reasoning in medical education, while a systematic review of 83 studies identified instructional designs in AI-supported environments featuring automatic feedback, intelligent tutoring, and personalization. The message is that the impact of any tool depends entirely on how it is integrated into instructional design.

From this convergence, the researchers propose their central contribution: the Dynamic Pyramidal Model of Teaching Excellence. The pyramidal architecture, the authors stress, is not an empirically derived hierarchy or causal sequence but a theoretical structure for visualizing how the components relate. Social and contextual conditions, including family, institutional environments, and socio-economic factors, form the base within which educational processes operate. Teacher–student interaction sits at the relational core, interconnected with higher-order cognitive processes, pedagogical strategies, formative assessment, and technological mediation. Motivation and vocation, which did not emerge as primary categories from the analytical corpus but were incorporated from the broader theoretical and policy literature, appear as individual influences. Teaching excellence itself occupies the apex as an integrative conceptual construct rather than a proven endpoint, with bidirectional relationships representing continuous interaction and feedback among all components.

The model also surfaces an uncomfortable geographical truth about the evidence base itself. The reviewed studies reveal a pronounced concentration of high-level research in North America and Europe, particularly in areas related to artificial intelligence and innovative pedagogical practices. The authors argue that this imbalance highlights the need for substantially more high-level evidence from underrepresented regions, including South America, before any framework of teaching excellence can claim global applicability. The study is candid about its own limitations as well: the purposive, non-PRISMA search strategy may have introduced selection bias, and the interpretative nature of the analysis means that category identification remains influenced by the researchers’ analytical perspective.

Perhaps the most consequential implication is practical. If teaching excellence is a dynamic, systemic construct, then efforts to improve it cannot succeed through isolated interventions, such as a one-off training workshop on active learning or a standalone rollout of new educational software. The authors argue that quality enhancement requires the coherent integration of multiple dimensions within educational systems, aligning objectives, activities, assessment, and technology in a continuous feedback loop. The Dynamic Pyramidal Model has not yet undergone empirical validation, and the researchers explicitly call for future work to operationalize and test its proposed relationships across diverse disciplinary, institutional, and cultural settings. But as universities worldwide grapple with AI disruption, post-pandemic learning recovery, and intensifying scrutiny of teaching quality, the study offers a compelling reframing: excellence in higher education is not something a teacher possesses, but something a system produces.

Subject of Research: A conceptual model of teaching excellence in higher education integrating five interrelated dimensions derived from a synthesis of 35 evidence reviews

Article Title: A dynamic pyramidal model of teaching excellence in higher education: a critical analytical reflection

Article References: A dynamic pyramidal model of teaching excellence in higher education: a critical analytical reflection. (n.d.). https://doi.org/10.3389/feduc.2026.1960685

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1960685

Keywords: teaching excellence, higher education, dynamic pyramidal model, teacher-student interaction, formative assessment, digital technologies, critical thinking, pedagogical strategies, artificial intelligence in education, systematic review, educational research, constructive alignment

The study began with a broad sweep of the Web of Science Core Collection, specifically the Social Sciences Citation Index, using a Boolean search that combined student descriptors such as “college student” and “university student” with terms including “self-feedback,” “feedback literacy,” and “self-feedback ability.” The initial retrieval on June 15, 2025 identified 372 records. After a PRISMA-guided screening process — including the removal of 13 duplicate exports and 30 records with incomplete or inconsistent metadata — only 14 publications met the eligibility criteria and formed the final analytical corpus. Two researchers independently screened titles, abstracts, and full texts, achieving high inter-rater agreement with a Cohen’s kappa of 0.927.

That small corpus is both the study’s central finding and its central caveat. Cheng is explicit that the results describe exploratory patterns within the retrieved dataset rather than definitive representations of the wider research field. The reliance on a single database, the exclusion of descriptors such as “undergraduate” and “learner” from the search string, and the partial coverage of 2025 all constrain what can be concluded. Yet even a corpus of fourteen papers can reveal the skeleton of an emerging discipline: where it publishes, who drives it, which concepts anchor it, and how it is connecting across borders.

The publication trajectory tells a story of slow ignition followed by steady acceleration. Between 2012, when the first eligible paper appeared, and 2019, output was minimal — in some years, a single article across three. From 2020 onward, activity climbed visibly, rising from two articles in 2021 to three projected for 2024 and 2025, with an average annual growth rate of 8.82 percent. The average publication age of the literature was just 3.14 years, marking it as unusually fresh. Cheng interprets this as evidence of increasing scholarly attention rather than proof of theoretical maturity, noting that publication growth alone cannot establish methodological quality or empirical consensus.

The geography of the field is striking. China led the corpus with a country frequency of 14, followed by Australia with 7 and the United Kingdom with 6, while the United States, Cambodia, Canada, and New Zealand appeared only marginally. International co-authorship accounted for 35.71 percent of documents, with the China–Australia connection emerging as the most frequent collaborative link. Institutionally, Deakin University and the Education University of Hong Kong tied for the lead with four articles each, followed by the University of Hong Kong and the University of Leeds with three apiece. Cheng cautions that these frequencies reflect the retrieved corpus rather than any ranking of national research capacity.

The journal landscape reinforces the field’s assessment-centred identity. Assessment & Evaluation in Higher Education dominated with five publications, followed by Assessing Writing and Innovations in Education and Teaching International with two each. Notably, the leading journal published nothing relevant between 2012 and 2019, then accelerated sharply from 2020 onward — a pattern that mirrors the field’s own inflection point. The presence of papers in venues as varied as Politics, Psychology in the Schools, and Sustainability hints at a topic spilling beyond its educational boundaries into disciplinary territory few would have predicted a decade ago.

The most cited works anchor the field’s intellectual core. A 2020 paper by Molloy E in Assessment & Evaluation in Higher Education led with 258 citations, followed closely by Sutton P’s 2012 paper in Innovations in Education and Teaching International with 210. Dawson P’s 2021 article had accumulated 54 citations, while newer entries from 2023 through 2025 were still in the early stages of building influence. The authorship structure is correspondingly distributed: 39 authors contributed across the corpus, averaging 2.86 co-authors per document, with no single dominant research group — a sign, Cheng suggests, of a field still open to newcomers.

Keyword analysis reveals the conceptual architecture. “Feedback literacy” appeared seven times and dominated the thematic map, with the highest betweenness centrality in the network, indicating its role in connecting otherwise separate concepts. “Engagement,” “cognition,” and “self-assessment” each appeared three times, while “formative assessment,” “writing assessment,” “exemplars,” “behaviour,” and “feedback seeking” rounded out the secondary layer. From these co-occurrence patterns, Cheng proposes an exploratory conceptual representation built on five interconnected dimensions: feedback literacy, engagement, self-assessment, perceptions, and feedback seeking. The framework is explicitly not a formal theory, but it offers a preliminary lens for organizing future empirical work on how students monitor and regulate their own learning.

The temporal keyword trends add texture to that architecture. Research mentioning “feedback literacy” remained at a single article for much of 2012–2019 before climbing to five and seven articles in 2024 and 2025 respectively. “Engagement” was absent until 2023 and then stabilized at three articles. Terms such as “sustainability” and “school psychology” surfaced only in the past few years, suggesting the field’s periphery is still forming. Cheng reads these patterns as evidence of a conceptual shift: feedback is increasingly understood not as information transmitted from teacher to student, but as a process in which students actively interpret, evaluate, and deploy information about their own performance.

The study’s limitations are candidly catalogued. Beyond the single-database strategy and the tiny final corpus, bibliometric analysis cannot establish causal relationships, collaboration motivations, or the theoretical meaning of co-occurring keywords, and no co-citation or co-author network analysis was performed. Cheng’s recommendations for the field follow directly: replicate the search with multi-database datasets such as Scopus, conduct qualitative content analysis to sharpen the distinction between self-feedback literacy and adjacent constructs like self-assessment, and pursue comparative research across higher education systems. As artificial intelligence tools begin to mediate how students evaluate their own work, the question of who teaches learners to judge themselves — and how well the research keeps pace — is only becoming more urgent.

Subject of Research: Bibliometric mapping of research on university students' self-feedback literacy in higher education

Article Title: Systematic bibliometric analysis of university students’ self-feedback literacy from 2010 to 2025

Article References: Systematic bibliometric analysis of university students’ self-feedback literacy from 2010 to 2025. (n.d.). https://doi.org/10.1007/s44217-026-02312-0

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02312-0

Keywords: feedback literacy, self-feedback, higher education, bibliometrics, self-assessment, student engagement, assessment, Web of Science, educational research, self-regulated learning, formative assessment, international collaboration

The study, published in the journal Discover Education, was led by Muhammad Takwin Machmud of the Department of Community Education, together with colleagues Nurhudayah Manjani and Natalia Silalahi. The team set out to answer a deceptively simple set of questions: how well do university students understand plagiarism, how often do they engage in plagiarism-related behaviors when completing assignments, how do they use plagiarism detection tools, and how do their similarity scores compare with the benchmarks their university imposes?

When asked to define plagiarism in their own words, 81 of the 105 surveyed students, or 78.6 percent, produced answers that matched the expected conceptual keywords, such as stealing or duplicating other people’s work, failing to credit the original author, or claiming someone else’s work as one’s own. On the surface, that sounds encouraging. But the researchers noted that most students framed plagiarism narrowly, as literal copy-pasting, while only a small minority described it in broader terms such as academic dishonesty or violations of intellectual ethics. The team also acknowledged a caveat: because the questionnaire was administered without supervision, some students may have looked up definitions online before answering, a limitation consistent with prior research showing that self-reported measures of academic integrity knowledge can be inflated by external assistance or socially desirable responses.

The behavioral data told a more complicated story. A majority of students, 61.9 percent, reported that they only occasionally paraphrase when adapting information from original sources, while 31.4 percent said they always paraphrase and just 6.7 percent said they never do. Direct copying was far more common than the researchers anticipated: 87.6 percent of students admitted to occasionally copying information directly from sources, and 5.7 percent said they always do. When students were interviewed about why, many explained that copying had become a routine time-management strategy, even though they still cited the original authors. The researchers interpreted this as evidence that many students simply struggle to paraphrase properly, hampered by heavy assignment loads, difficulty understanding dense reading materials, limited vocabulary, and, by their own admission, occasional laziness.

Citation practices, by contrast, appeared relatively healthy. More than half of the surveyed students, 58.1 percent, said they always credit the original author, 37.1 percent said they do so occasionally, and only 4.8 percent reported never citing sources. In other words, students are not wholesale thieves of ideas; they are writers who attribute their sources but often fail to transform them, reproducing sentence structures and phrasing so close to the original that detection software flags them anyway.

To move beyond self-reports, the researchers analyzed 30 scientific article assignments written for a Research Methodology course, running each document through university-approved plagiarism detection software under standard institutional settings that excluded bibliographies and quoted text and ignored small matches below one percent. The results were striking. Similarity index scores ranged from 5 percent to 85 percent, with an average of 41 percent and a median of 36.5 percent, and a standard deviation of roughly 22.5 percent indicating enormous variability in writing practices. Against the university’s benchmark, which caps similarity at 35 percent for undergraduate students in the social sciences and humanities, only 14 of the 30 assignments passed. Sixteen, or 53.3 percent, exceeded the threshold.

Perhaps the most technically revealing finding concerned where the matched text came from. Internet sources accounted for the largest share of similarity matches, averaging 39.7 percent, followed by previously submitted student papers at 18 percent and academic publications at just 12.4 percent. This pattern suggests that students lean heavily on open web resources when completing assignments, and that some flagged text may echo work already sitting in detection databases from earlier student submissions. The researchers connect this to broader concerns raised in the literature: the sheer abundance of online material and the ease of accessing it have been cited as key drivers of rising plagiarism, and reports from Indonesia indicate that student plagiarism cases climbed by nearly 70 percent in 2017 alone.

The study also examined how students actually use plagiarism detection tools, and here the findings were more optimistic. Turnitin dominated, used by 73.3 percent of surveyed students, well ahead of Grammarly at 36.2 percent, Duplichecker at 21 percent, Unichecker at 11.4 percent, and other tools at 8.8 percent. Students gravitated toward paid software, explaining that the university provided premium access and that paid platforms offer richer features: vast databases spanning journals, books, magazines, and webpages; detailed similarity reports identifying the original sources of matched text; highlighted passages of concern; and support for many document formats. Turnitin’s own database, according to figures cited in the study, contains 1.9 million student papers, 190 million subscription articles, content from 1,500 publishers worldwide, and coverage of 97 percent of the top 10,000 journals.

Crucially, the researchers found that detection tools function not merely as gatekeepers but as revision aids. Analyzing institutional records from 24 final-year social science students who defended their theses between January and June 2025, the team documented students running multiple checks to drive their scores down. One student cut a similarity index from 42 percent to 13 percent between the first and second checks; another dropped from 40 percent to 14 percent; a third ran three checks, gradually descending from 28 percent to 26 percent to 25 percent. Across all 24 theses, final similarity indices ranged from 5 percent to 31 percent, averaging 22.8 percent, a level most students achieved only after iterative revision. The software, in effect, had become part of the writing process itself, with 82.9 percent of surveyed students using it for scientific articles, 52.4 percent for academic reports, and 39 percent for daily assignments.

The authors caution that a similarity index is an indicator of textual overlap, not proof of misconduct, since properly cited quotations, references, and common academic phrases can inflate scores, and that thresholds vary widely across institutions and publishers, with no single universal standard. They also note challenges such as false positives and privacy concerns that universities must manage through clear policies and training. Still, their conclusion is pointed: plagiarism among university students should be treated not only as an ethical failing but as an academic literacy problem demanding pedagogical solutions, from explicit instruction in paraphrasing, quoting, and summarizing to formative checking systems that let students revise before final submission. At Universitas Negeri Medan, that philosophy is already institutionalized, with a dedicated Plagiarism Detection Team, tiered similarity thresholds by degree level and discipline, mandatory originality statements, and library-run programs such as Kelas Literasi, a class devoted to avoiding plagiarism through better citation and writing practice. Detection software, the study suggests, raises awareness and saves instructors time, but lasting change will come from teaching students how to write with sources, not merely how to avoid being caught.

Subject of Research: University student plagiarism behavior and the use of plagiarism detection tools in academic writing

Article Title: University student plagiarism behavior and the role of plagiarism detection tools

Article References: Machmud, M. T., Manjani, N., & Silalahi, N. (2026). University student plagiarism behavior and the role of plagiarism detection tools. Discover Education, 5(1), Article 1004. https://doi.org/10.1007/s44217-026-02180-8

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02180-8

Keywords: plagiarism, academic integrity, university students, plagiarism detection software, Turnitin, similarity index, paraphrasing, citation practices, higher education, academic writing, Indonesia, educational research

The rationale for the work rests on a well-documented paradox. Both individual educators and the institutions that govern them recognize the benefits of applying educational evidence to teaching, yet the gap between academic research and frontline practice has persisted since at least the early 1990s, when Charles Kaestle famously described the awful reputation of education research. Previous studies have traced the problem to multiple, overlapping causes. Research competence is poorly integrated into initial teacher education, so many graduates never learn to interpret studies or judge the quality of evidence. Few professionals are trained to distinguish reliable findings from unfounded claims. Time is another critical constraint: teachers are unwilling to invest scarce hours in engaging with research, and those in leadership positions are often unwilling to fund the resources that would make such engagement feasible.

The new study also highlights subtler obstacles that operate at the psychological and structural level. Teachers may resist change because new evidence conflicts with pre-existing beliefs and values, a phenomenon long noted in the literature. Research on teacher effectiveness suggests a kind of ossification: classroom effectiveness improves rapidly during the first five to seven years of teaching and then plateaus, a shift the authors of that earlier work attribute to the transition from deliberate, reflective practice to habitual routines at the neural level. Meanwhile, the way scientific articles are written poses an accessibility problem in itself, and the realities of a given classroom often differ so much from the conditions of the original research that teachers judge the findings irrelevant. Political pressures and the rush to publish significant results can further compromise research quality and ethics, leading society to confuse impact with quality and to adopt ineffective practices.

Against this backdrop, the researchers set out to build a measurement tool rather than another exhortation to collaborate. Their questionnaire follows a cross-sectional design and draws on Dillman’s Tailored Design Method, a framework for constructing surveys that produce valid and reliable results. Questions were drafted with clarity, impartiality, and conciseness in mind, each addressing a single element. Crucially, the instrument defines what it means by educational research and scientific evidence, ensuring that all respondents share a common conceptual framework, since definitions of research can vary widely across the teaching population. The questionnaire measures three main variables on five-point Likert scales: perception of barriers to accessing and transferring scientific evidence, perception of potential facilitators, and perception of the potential of new technologies to help teachers integrate evidence into the classroom.

Content validity was established through a formal expert judgment process. Eleven experts, nine from the university sector and two schoolteachers with university affiliations, evaluated every item for comprehension, relevance, and importance. Selection criteria required experience in education and educational technologies, training in scientific research, more than three years of professional experience, and membership of different educational bodies and institutions. The experts’ own competence was quantified using the K Coefficient, or Expert Competence Coefficient, which combines a knowledge coefficient with an argumentation coefficient. The results were reassuring: most experts scored above 0.8, indicating high competence, with only two in the medium range. Every item received an average score above 3.5 out of 4, and with the standard threshold of 80 percent expert agreement met, all items were retained for the pilot phase.

The pilot test involved 278 students enrolled in the Master’s Degree in Teacher Training at four Catalan universities: Universitat de Lleida, Universitat Ramon Llull, Universitat Rovira i Virgili, and Universitat de Vic. Drawn from a total population of 450 prospective secondary teachers through non-probabilistic convenience sampling, the sample exceeded the 259 participants calculated as necessary for a 99 percent confidence level with a 5 percent margin of error. The questionnaire was distributed by email and in person, with students able to respond via a QR code on their mobile devices. Notably, 37.05 percent of the sample combined their studies with active teaching work, a situation the authors describe as distinctive to the Catalan context.

The statistical analysis is where the study earns its technical credibility. Because Mardia’s multivariate kurtosis coefficient of 18.963 indicated non-normal data, the researchers selected the Weighted Least Squares estimation method, chosen for its robustness against departures from normality in exploratory factor analysis. Sampling adequacy was confirmed with a Kaiser-Meyer-Olkin value of 0.847, comfortably above the 0.80 threshold considered good, and Bartlett’s test of sphericity was highly significant. An oblique Promax rotation was applied to allow factors to correlate, and the resulting correlation matrix justified that choice. Applying the Kaiser criterion, a scree test, and a parallel analysis comparing observed eigenvalues against those from random data, the team retained seven factors, each represented by at least three items with loadings above the 0.32 significance threshold.

The seven factors tell a coherent story about what shapes evidence use. Factor 1 captures technology as a facilitator, addressing how digital tools can help integrate scientific evidence into classrooms. Factor 2 reflects institutional support, including time to implement evidence and the accessibility of research in terms of language and complexity; a negatively loading item within this factor signals that research lacking relevant conclusions for practice can undermine even improved access. Factor 3 concerns direct contact between teachers and research teams. Factor 4 covers educational resources, Factor 5 groups relevance, accessibility, and practicality, Factor 6 encompasses physical and cognitive barriers to accessing scientific articles, and Factor 7 measures the value of scientific literature itself as a professional resource. Reliability analysis showed Cronbach’s Alpha values above 0.7 for all factors except Factor 7, which scored 0.492; the authors retained it because its eigenvalue exceeded 1 and its low consistency likely reflects the small number of items, while also noting that merging it with Factor 4 would have produced an acceptable alpha of 0.764.

The practical implications extend well beyond psychometrics. By mapping the latent structure of teachers’ perceptions, the instrument gives policymakers, teacher educators, and researchers a diagnostic tool for identifying exactly where the pipeline from evidence to practice breaks down, whether at the point of access, interpretation, institutional backing, or technological support. The authors acknowledge limitations: convenience sampling limits representativeness, and the sample of Catalan master’s students may not generalize to the broader teaching population. They propose administering the questionnaire to more active teachers across experience levels and complementing future quantitative analyses with qualitative and mixed-methods designs to strengthen validity. Still, the study delivers what Kaestle argued was essential decades ago: a clear picture of the frontline of the system. With a validated instrument now available, the dialogue between educational research and classroom practice has, for the first time, a reliable way to measure itself, and a concrete pathway toward closing a gap that has persisted for more than thirty years.

Subject of Research: Design and validation of a self-perception questionnaire on teachers' use of scientific knowledge in teaching practice

Article Title: Design and validation of a self-perception instrument on the use of scientific knowledge in teaching practice

Article References: Guinovart-Pedescoll, M., & Palau, R. (2025). Design and validation of a self-perception instrument on the use of scientific knowledge in teaching practice. Journal of New Approaches in Educational Research, 14(1), Article 3. https://doi.org/10.1007/s44322-024-00026-5

Image Credits: AI Generated

DOI: 10.1007/s44322-024-00026-5

Keywords: educational research, scientific evidence, questionnaire validation, teacher training, exploratory factor analysis, evidence-based practice, teaching quality, research-practice gap, psychometrics, educational technology, Catalonia, teacher perceptions

The research team, led by Gül Yılmaz with Salih Çepni serving as supervisor, took a deliberately two-pronged approach. The study used what methodologists call an explanatory sequential mixed-methods design: first, quantitative measurements were taken to determine whether the intervention changed children’s problem-solving skills at all; then, semi-structured interviews with the children, their parents, and the classroom teacher were conducted to explain how and why those changes occurred. This combination matters because a test score alone cannot capture whether a child who solves a puzzle faster is also more persistent, more curious, or more willing to collaborate—qualities that parents and teachers observe daily and that standardized instruments often miss.

The intervention took place in a public preschool, where nineteen children formed the experimental group and sixteen children served as the control group. Over eight weeks, the experimental group engaged in play-based STEM activities designed to embed engineering-style design challenges and scientific exploration into ordinary play. The control group followed the standard preschool curriculum. To measure outcomes, the researchers used the Problem-Solving in Science Education Scale, a validated instrument that assesses how children approach and resolve problems across different domains, including problems involving the use of materials and problems related to science and nature. Comparing the two groups before and after the intervention allowed the team to isolate the effect of the play-based STEM program from normal developmental progress.

The quantitative results were striking. Children in the experimental group showed statistically significant improvements in problem-solving abilities compared with their peers. The effect sizes—a statistical measure of how large a difference is, not merely whether it exists—were large for material usage problems and for total scale scores, and medium-to-large for science and nature problems. In practical terms, effect sizes of this magnitude indicate that the changes were not marginal statistical artifacts but substantial shifts in how children approached challenges. For a relatively short intervention of eight weeks, delivered to children still learning to sit still for a story, the magnitude of the gains is notable and suggests that early childhood may be a particularly responsive window for cultivating problem-solving skills.

The qualitative strand of the study painted a picture that numbers alone could not. When researchers interviewed the children themselves, they found that the young participants genuinely enjoyed design-based activities—the open-ended challenges that ask a child to imagine a solution, build it, watch it fail or succeed, and try again. Perhaps more telling, the children demonstrated persistence when facing challenges, returning to difficult tasks rather than abandoning them. This willingness to struggle is a hallmark of what developmental psychologists call executive function and what educators often describe as grit, and its emergence in preschoolers through play is consistent with theoretical work by scholars such as Alison Gopnik, who has argued that young children’s play resembles the hypothesis-testing of working scientists.

One of the most intriguing findings was what happened at home. The interviews revealed that children transferred their learning beyond the preschool walls, applying problem-solving strategies and engineering-style thinking to situations in their home environments. Transfer of learning—taking a skill acquired in one context and deploying it in another—is notoriously difficult to achieve in education, and its appearance in four- and five-year-olds suggests that play-based STEM activities tap into something fundamental about how young children encode and generalize knowledge. Parents, for their part, reported enhancements across cognitive, social-emotional, and motor domains, indicating that the developmental ripple effects of the program extended well beyond the problem-solving skills the researchers set out to measure.

The classroom teacher’s observations reinforced this holistic picture. According to the study, the teacher highlighted increased curiosity, creativity, and teamwork among the children participating in the STEM play activities. These outcomes align with a long tradition in developmental theory. Jean Piaget’s constructivist account of cognitive development holds that children build knowledge through active manipulation of their environment, while Lev Vygotsky’s sociocultural theory emphasizes that learning is fundamentally social, scaffolded by interactions with more capable peers and adults. Play-based STEM education, in this light, is not a novelty but a natural synthesis: it places children in socially rich, hands-on environments where cognitive challenge and joyful exploration coincide.

The study was not without its difficulties, and the researchers were candid about them. Implementation challenges included time constraints—the perennial obstacle for preschool teachers juggling multiple curricular demands—initial difficulties with teamwork, as children unaccustomed to collaborative design had to learn to share materials and negotiate ideas, and the limited sustained attention that is simply developmentally normal for young children. These hurdles are important for curriculum designers to understand, because they suggest that successful play-based STEM programs require not just well-designed activities but also realistic scheduling, explicit support for collaboration skills, and activities calibrated to short attention spans. The findings echo earlier research noting that early childhood educators often report low confidence in teaching STEM subjects, making professional support a critical ingredient.

The broader context gives the findings added urgency. Around the world, policymakers have identified early STEM education as a strategic priority, and organizations such as the Joan Ganz Cooney Center have argued in influential reports that STEM learning must begin in early childhood rather than waiting for middle or high school. In Türkiye, where the study was conducted, the Ministry of National Education’s updated preschool curriculum has incorporated STEM content, and industry groups have called for expanded STEM capacity. Yet questions have persisted about whether formal STEM instruction is developmentally appropriate for preschoolers, or whether it risks imposing school-like structure on children who learn best through play. This study’s answer is that the dichotomy is false: when STEM is delivered through play, it amplifies rather than supplants the developmental work of early childhood.

The implications reach teachers, parents, and curriculum designers alike. For educators, the study offers evidence that guided play with clear STEM goals—what researchers have called guided play, where curricular objectives meet playful pedagogy—can yield measurable cognitive gains. For parents, the finding that children carried their problem-solving home suggests that everyday moments of building and tinkering with a caregiver may reinforce classroom learning. And for curriculum designers, the results argue for embedding engineering design challenges into early childhood programs while building in time for collaboration and attention-friendly pacing. As the authors conclude, play-based STEM education holds real potential to promote holistic development in the earliest years—a stage when, as decades of developmental science have shown, the brain is most malleable and the appetite for exploration is at its peak. The study received no external funding, and the authors declare no competing interests.

Subject of Research: Effects of play-based STEM education on problem-solving skills and development in preschool children

Article Title: Play-Based STEM Education in Early Childhood: A Mixed-Methods Study on Problem-Solving and Developmental Outcomes

Article References: Yılmaz, G., & Çepni, S. (2026). Play-Based STEM Education in Early Childhood: A Mixed-Methods Study on Problem-Solving and Developmental Outcomes. Early Childhood Education Journal. https://doi.org/10.1007/s10643-026-02362-x

Image Credits: AI Generated

DOI: 10.1007/s10643-026-02362-x

Keywords: STEM education, early childhood, play-based learning, problem-solving skills, preschool, mixed methods, child development, engineering design, guided play, curriculum design, social-emotional development, educational research

The research, published in the Journal of New Approaches in Educational Research by Francielle Marques, Davinia Hernández-Leo, and Carlos Castillo, analyzed official course satisfaction surveys from two academic years, 2021-2022 and 2022-2023, at the Spanish university. The dataset originally covered 3,066 bachelor-level course groups across 25 degree programs. To ensure statistical reliability, the team applied a filtering method based on the bound of the error of estimation in survey sampling, known as the Reliability Assessment Score, keeping only groups whose responses were numerous enough to yield dependable averages. That left 586 course groups distributed across nine schools, spanning communication, economics and business, engineering, health and life sciences, humanities, law, political and social sciences, and translation and language sciences.

What makes the study distinctive is its decision to deconstruct satisfaction itself. Rather than treating the survey as a single number, the researchers separated the items that probe satisfaction with specific facets of the learning design: the teaching methodology, including the design of class sessions, student activities, and materials; the perceived workload, measured against the credits assigned to the subject; and compliance with the teaching plan and obligations. Students rated each dimension on a numerical scale from zero, complete dissatisfaction, to ten, complete satisfaction. Crucially, the surveys were administered toward the end of each quarter or semester but before final exams, meaning that professors received summaries only weeks after grades were submitted, ruling out any feedback loop in which evaluations could influence grading.

The first major finding concerns what actually drives overall course satisfaction. Satisfaction with teaching methodology correlated strongly with the holistic judgment of the subject, and satisfaction with the teaching received tracked it closely. By contrast, satisfaction with the workload and with compliance with the teaching plan showed comparatively weaker correlations with overall satisfaction. All correlations were statistically significant, with p-values below 0.001. In practical terms, the way a course is taught, its sessions, activities, and materials, appears to dominate how students remember and judge the entire experience, while the sheer volume of work plays a smaller, though still measurable, role.

Then comes the question at the heart of the grade-bias debate. Using aggregate performance statistics for each course group, the proportions of students failing, passing, earning a notable, or achieving excellence, the researchers estimated average performance and correlated it with every satisfaction measure. The result: correlations were weak, ranging from 0.16 to 0.25, though statistically significant with p-values below 0.0001. Satisfaction scores were spread across a broad range at every level of performance. Notably, the highest satisfaction ratings were almost never found in courses where performance was abnormally low, where fewer than one in five students passed. Instead, the densest cluster of satisfied students sat in courses where large majorities passed, often with high grades, a pattern that is more consistent with good teaching producing both learning and satisfaction than with grades purchasing goodwill.

The study also examined contextual factors. Class size emerged as a consistent negative influence: larger classes, particularly those with 100 or more students, tended to be penalized in satisfaction scores for methodology, workload, and overall subject satisfaction. Course level showed only a weak relationship with overall satisfaction. These contextual effects matter because they represent factors largely outside an individual instructor’s control, and the authors argue that institutional analytics should account for them before survey results are used to judge teaching quality or make promotion decisions.

Perhaps the most intriguing analysis concerns workload outliers. Focusing on courses where at least some students rated the workload poorly, the team split the data into cases where satisfaction with teaching was higher than expected given the workload ratings, and cases where it was lower. In the first group, satisfaction with the teacher’s fulfillment of obligations and adherence to the teaching plan was high and showed low variance, suggesting that students are more forgiving of a demanding, or even excessive, workload when the professor honors the plan and meets their obligations. In the second group, satisfaction dropped across multiple dimensions, with the largest gap appearing in ratings of methodology, the very dimension most strongly tied to overall course satisfaction. Workload, in other words, does not operate in isolation; its effect on evaluations depends heavily on the surrounding quality of the teaching.

The authors frame their analytical approach through the concept of generative uncertainty, an interpretive stance in which institutional data is used not to deliver final verdicts but to provoke productive questions. Under this view, a dashboard showing a course with low satisfaction and low grades should trigger inquiry rather than punishment: is the problem the design of the course, the size of the class, the composition of the cohort, or something else entirely? The researchers argue that universities should build this uncertainty-aware mindset into their institutional analytics, using patterns in the data to generate new hypotheses and continuous reflection rather than treating survey averages as self-evident truths about teaching quality.

The implications cut against a belief that is widespread among faculty and echoed in some earlier studies, which reported that grading leniency boosts evaluations. In this Spanish context, at least, the hypothesis that professors who give good grades secure better student satisfaction found only minimal support. Courses with strong methodological design achieved high satisfaction ratings that could not be explained by grades or by easy workloads. The authors are careful about scope: the study covers a single university and two academic years, and only aggregate grade distributions were available rather than individual scores, which limits how precisely average performance could be estimated. They call for future work incorporating more years of data, finer-grained performance measures, learning management system interactions, and qualitative analysis of the written comments students leave in evaluations.

Still, the core message is one that universities, instructors, and students can all use. Satisfaction surveys are not pure noise, and they are not simply mirrors of the gradebook. They respond most powerfully to the visible craft of teaching, the design of sessions, activities, and materials, and they punish impersonal, oversized classes. Grades leave only a faint statistical fingerprint on how students rate their courses, and even that fingerprint is inconsistent across contexts. For institutions that rely on these surveys to steer quality assurance and career advancement, the lesson is to read them holistically, alongside performance data and contextual factors, and to remain alert both to bias and to the possibility that, sometimes, the survey is telling the truth about teaching after all.

Subject of Research: Bias and learning design factors in higher education student satisfaction surveys

Article Title: Beyond bias in student satisfaction surveys: exploring the role of grades and satisfaction with the learning design

Article References: Marques, F., Hernández-Leo, D., & Castillo, C. (2025). Beyond bias in student satisfaction surveys: exploring the role of grades and satisfaction with the learning design. Journal of New Approaches in Educational Research, 14(1), Article 9. https://doi.org/10.1007/s44322-025-00030-3

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00030-3

Keywords: student satisfaction surveys, higher education, learning design, grade bias, teaching evaluations, institutional analytics, class size, workload, teaching methodology, course quality, educational research, Pompeu Fabra University

The scale of the problem the researchers set out to address is considerable. UNESCO estimates that one in three young people experiences some form of violence in or around school, ranging from physical and psychological aggression to bullying and cyberbullying. A study of fourteen to seventeen year olds across six European countries found that more than a fifth had experienced cyberbullying in the previous twelve months, with girls affected more often than boys. School-related gender-based violence, defined by the United Nations Girls’ Education Initiative as acts or threats of sexual, physical or psychological violence occurring in and around schools and enforced by unequal gender norms and power dynamics, affects more than 115 million girls and adolescents worldwide according to UNGEI and UNESCO. The consequences extend far beyond the classroom, with victims showing elevated risks of anxiety disorders, insomnia, fatigue, digestive problems, deficits in social and emotional skills, and, according to one European study, suicidal thoughts in up to 38 percent of affected children. PISA data indicate that 45 percent of frequently bullied students want to leave school after secondary education and perform worse academically.

What makes this study distinctive is its focus on a mechanism that most anti-bullying programs ignore. Isolating gender-based violence, also known as second-order sexual harassment, refers to attacks and reprisals directed not at victims themselves but at those who support them. The logic is brutal in its simplicity: if defenders are punished, isolated or ridiculed, no one will dare to stand beside a victim, and the victim remains alone and more vulnerable. Previous research quantified the effect of this dynamic, finding that 40 percent of people do not support victims of sexual violence precisely because they fear retaliation. Bystander studies have long shown that witnesses who do not intervene are perceived by aggressors as tacitly approving their behavior, and that violence is more likely to occur when witnesses stay silent. Yet witnesses consistently report barriers to intervention, including fear of becoming targets themselves.

The Zero Violence Brave Club emerged from the Dialogic Model for Violence Prevention, which was evaluated within the INCLUD-ED project, a large-scale European Commission study on inclusion and social cohesion running from 2006 to 2011. Since 2011, results of its implementation have been collected and the model has been included in the European Commission’s School Gateway database of successful projects. The program operates on two key components. First, it places at the center the brave attitudes and behaviors that should be promoted, aligned with the principles of dialogic learning, such as solidarity and the possibility of transformation for everyone. Second, it mobilizes the entire educational community to create environments where no aggression of any kind takes place. Students learn to take a stand against violence, denounce it whenever it occurs, value friendship, defend and support victims, and reject violent behavior regardless of who commits it.

To evaluate the program’s impact on gender-based violence specifically, the team used communicative research methodology, an approach recommended by the European Commission for studying vulnerable or discriminated groups because it generates knowledge collaboratively with participants rather than merely about them. The fieldwork involved six communicative focus groups with 21 teachers, six focus groups with 29 pupils aged between 5 and 21, and eight in-depth interviews with family members from four of the schools. An advisory board made up of family members, students and teachers validated the research question, the data collection instruments, the categorization scheme and the main findings. Data were analyzed using categories distinguished by transformative and exclusionary dimensions, with the study focusing on transformative elements, meaning situations in which unequal or discriminatory circumstances were actively overcome.

The first transformative element the researchers identified was the alliance of upstanders. All teachers in the focus groups described how the program creates a sense of group identity among students committed to protecting victims, giving them the confidence and security to intervene without fear of retaliation. One teacher explained that supporting and defending victims is what defines the brave club, describing the group as a family whose members protect, love and care for one another, and noting that second-order harassment had not been observed as a result. A sixth-grade pupil offered a structural explanation for why aggression becomes difficult to sustain: there are always many people united to defend, so either the person who is harassed reports it or someone who sees it does, making an attack far harder to carry out. The mechanism resembles a shift in the social physics of the classroom, where the cost of aggression rises sharply because potential accomplices have been converted into potential defenders.

The second element involved breaking alliances with aggressors. Teachers unanimously agreed that in classrooms where the program is properly implemented, bullies struggle to find allies because there is a shared agreement rejecting violent behavior from anyone. One teacher contrasted this with classrooms without the club, where aggressors typically find support within their social group because classmates frame the situation as a personal conflict rather than a matter of justice. A mother volunteering at the special education center described watching children tell a would-be bully that his behavior would go to the brave club, depriving him of the applauding audience that sustains bullying. Students in one school described how, when an aggressor tells a defender to leave, the defender simply brings more people, and the aggressor ends up alone. This finding aligns with decades of research on the peer ecology of bullying, which shows that reinforcing bystanders sustain aggressive behavior and that removing that reinforcement undermines it.

The third element was the act of naming. Teachers who had received training on the preventive socialization of gender-based violence were familiar with the concept of isolating gender violence and could teach students to recognize attacks on defenders as a distinct form of aggression. Two of the schools adopted explicit norms, protect the one who protects and protect yourself, protect me, protect us. Pupils demonstrated sophisticated understanding, with one explaining that when witnessing injustice, the most effective strategy is to seek additional support so that second-order harassment does not occur, precisely because the more people who stand together, the less likely defenders are to be attacked. Families also received training through dialogic discussions of scientific articles at school, and one mother reported that being able to put names to the realities they encounter gave her confidence about what she could do. The stigmatizing label of snitch or whistle-blower, long used to silence reporting, has disappeared from the schools studied, replaced by the identity of the brave person who breaks the silence.

The study also documented how these changes translate into safe reporting environments with lasting effects. One mother recounted that her son, having learned in primary school how to protect girls from gender-based violence, intervened on his very first day of high school when he saw a boy touching a classmate without her consent, telling the girl to report it to the teacher. At the special education center, teachers described using playground assemblies and a rule called eyes wide open to transform the deeply rooted idea of the reporter into the idea of bravery. Perhaps most strikingly, a sixth-grade pupil reflected that he used to chase girls to hug and touch them against their will because he thought it was fun, but that the club helped him realize it was not fun and stop doing it, a direct illustration of how the program reshapes children’s understanding of consent.

When teachers, students and families were asked whether they feared supporting victims of violence at school, no one answered affirmatively, a result the researchers attribute to the chain of protection created when many people commit to standing together. The authors conclude that the Zero Violence Brave Club prevents gender-based violence through three interconnected mechanisms: creating alliances between upstanders who unite to protect, breaking alliances with aggressors by rejecting violence for the common good, and naming isolating gender violence so that it can be identified and countered. The findings echo a growing body of literature emphasizing bystander intervention, community engagement and victim protection as protective factors. The researchers suggest the approach could extend beyond formal schooling to summer camps, sports clubs and after-school activities, and they call for future research on its application in secondary schools, where approaches to violence differ considerably, as well as on the exclusionary barriers that can persist when the program is not fully implemented.

Subject of Research: Prevention of isolating gender-based violence among school students through the Zero Violence Brave Club educational program

Article Title: The Zero Violence Brave Club: a successful educational action to overcome isolating gender-based violence with young adolescents

Article References: Sevilla, S. C., Alcantud, P. M., Ríos González, O., de Gauna, I. T. R., & Barbany, J. M. C. (2025). The Zero Violence Brave Club: a successful educational action to overcome isolating gender-based violence with young adolescents. Journal of New Approaches in Educational Research, 14(1), Article 11. https://doi.org/10.1007/s44322-025-00031-2

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00031-2

Keywords: gender-based violence, school violence, bystander intervention, bullying prevention, isolating gender violence, Zero Violence Brave Club, dialogic learning, upstanders, second-order sexual harassment, educational research, child protection, Spain

The research, published in the Journal of New Approaches in Educational Research, was conducted by Pablo Palomero-Fernández and Eva Vicente-Sánchez, who set out to build and validate an instrument measuring the willingness of pre-service secondary teachers to use group dynamics techniques. These techniques, rooted in the classic work of Kurt Lewin and later codified by Cartwright and Zander, encompass methods such as cooperative learning, role-playing, case studies and teamwork, all of which harness group interaction to improve both learning and interpersonal relationships. Although such methods are widely promoted across European higher education, no standardised tool existed to assess whether trainee teachers genuinely intend to apply them once they enter the profession.

The theoretical foundation of the new instrument is the tripartite model of attitudes proposed by Rosenberg and Hovland in 1960, a framework that remains one of the most influential structures in social psychology. According to this model, any attitude can be decomposed into three components: cognitive beliefs, emotional responses and behavioural tendencies. The researchers translated this structure into three subscales. The first, Perceived Usefulness, captures what trainees believe about the value of group techniques for their students. The second, Emotional Experience, probes the feelings these techniques arouse during initial training. The third, Behavioural Engagement, measures the interest and intention to actually programme and deploy group methods in future professional practice.

Building a psychometrically sound instrument is a painstaking process, and the researchers followed the standardised ten-step procedure for test construction recommended by Muñiz and Fonseca. The first version of the questionnaire contained twelve items, four for each subscale, each rated on a five-point Likert scale from complete disagreement to complete agreement. To establish content validity, the draft was scrutinised by a panel of three expert judges from a Spanish university, selected against demanding criteria including at least ten years of experience in educational psychology or pedagogy, a decade of team leadership, and substantial experience teaching the master’s programme itself.

The expert judgement phase proved decisive. While the experts rated most items highly for clarity, coherence and relevance, statistical analysis using Aiken’s V content validity coefficient revealed unsatisfactory agreement regarding the adequacy of the three subscales as a whole. The qualitative feedback was rich and specific. One expert noted that the usefulness subscale focused too heavily on content and recommended adding items about participation, engagement and collaboration centred on students. Another highlighted the absence of variables such as perceived self-efficacy and the perceived control a future teacher has over the possibility of using these dynamics, a factor with direct influence on behaviour. Following this feedback, the researchers added new items, reworded others and reframed emotional experience items within the specific context of the group techniques trainees had encountered during their master’s coursework.

A second round of expert judgement confirmed the improvements. All items achieved Aiken’s V values above .71 in the lower bound of their confidence intervals, with sixteen items exceeding .80 and nine approaching the maximum. Overall agreement for the entire instrument reached an exceptional .93, and the adequacy of all three subscales was judged satisfactory. With content validity secured, the researchers turned to the field: 441 students, 161 men and 280 women, enrolled on the master’s programme at a Spanish university during the 2020/2021 academic year completed the questionnaire. This represented 61.33 percent of the total enrolment, with a mean age of 27.6 years and a strong representation of Arts and Humanities backgrounds at nearly half the sample.

The statistical analysis revealed both strengths and weaknesses in the draft instrument. Initial reliability coefficients were respectable, with Cronbach’s alpha at .853 and McDonald’s omega at .870, the latter being the preferred estimator for ordinal data. However, item-total correlation analysis flagged three items falling below the .300 threshold, indicating they contributed nothing to internal consistency. These items were removed, leaving a refined fifteen-item scale whose reliability improved to alpha of .871 and omega of .883, with all three subscales exceeding .700. The researchers then subjected the refined scale to confirmatory factor analysis, and the results were compelling: sampling adequacy was excellent with a Kaiser-Meyer-Olkin value of .915, and the three-factor model showed a good fit with a comparative fit index of .929, a Tucker-Lewis index of .914 and a root mean square error of approximation of .063, all within accepted benchmarks.

One of the study’s most technically sophisticated contributions is the multigroup analysis examining whether the instrument functions equivalently for men and women. Previous research had suggested that female trainees show greater willingness to adopt active methodologies, raising the question of whether a single questionnaire could fairly measure the construct across both groups. The researchers tested four increasingly constrained structural equation models, from an unconstrained baseline through measurement weights, structural covariances and measurement residuals. The measurement weights model, which assumes that the weight of each item on its factor is identical across groups, provided the best fit. All regression weights were statistically significant for both sexes, and factor correlations exceeded .797 for women and .829 for men, supporting factorial invariance and confirming that the scale measures the same construct in men and women.

The practical implications extend well beyond psychometrics. The authors argue the instrument could support selection and guidance processes for admission to the master’s programme, complementing interviews and other tests in line with recommendations from the OECD and the European Commission about attracting better-qualified teaching candidates. Within the programme itself, the scale can identify different dispositional profiles, allowing educators to tailor support for trainees who are sceptical, anxious or merely unfamiliar with group methods, and to stimulate emotional self-awareness and reflection on the advantages and disadvantages of group techniques in secondary education. Perhaps most powerfully, the instrument enables pre-post research designs: administering it before and after the master’s course would allow programmes to demonstrate empirically whether they succeed in fostering the attitudes that drive methodological change.

The researchers are candid about the limitations of their work. The sample was drawn from a single university using convenience sampling, which may limit generalisability, and self-selection bias could have under-represented less engaged students. Arts and Humanities students were over-represented while Health Sciences accounted for barely seven percent of participants. As a newly developed instrument, the results remain provisional, and the authors call for replication with representative nationwide samples, application to in-service teachers, and further construct validity evidence using techniques such as exploratory structural equation modelling. Nevertheless, the study delivers something teacher education has long lacked: a validated, theoretically grounded and sex-invariant measure of whether future secondary teachers are genuinely prepared to abandon lecture-based transmission in favour of the interactive, group-centred classrooms that educational policy has demanded for decades. In an era when student motivation and participation are central concerns, knowing not just what trainees can do but what they are willing to do may prove the decisive metric.

Subject of Research: Design and validation of a questionnaire measuring pre-service secondary teachers' willingness to use group dynamics techniques

Article Title: Assessing the willingness of Spanish pre-service secondary school teachers to use group dynamics techniques. Design and validation of an instrument

Article References: Palomero-Fernández, P., & Vicente-Sánchez, E. (2025). Assessing the willingness of Spanish pre-service secondary school teachers to use group dynamics techniques. Design and validation of an instrument. Journal of New Approaches in Educational Research, 14(1), Article 16. https://doi.org/10.1007/s44322-025-00037-w

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00037-w

Keywords: teacher training, group dynamics, psychometrics, instrument validation, active learning, pre-service teachers, secondary education, tripartite model of attitudes, confirmatory factor analysis, factorial invariance, Spain, educational research

The study surveyed 649 undergraduate and postgraduate students across Indian higher education institutions, using a cross-sectional questionnaire design that captured how often and how thoughtfully students engaged with generative AI tools. Rather than treating AI use as a single behavior, the researchers decomposed it into distinct competencies. Prompt literacy — the craft of formulating effective instructions for AI systems — was measured as one construct. Critical AI literacy — the capacity to question, verify, and ethically evaluate what an AI produces — was measured as another. Assessment design, meaning the degree to which coursework is structured in ways that make genuine understanding visible, formed the third pillar of the model. The outcome variable, epistemic transparency, captured whether students disclose AI involvement, justify their sources of knowledge, and take responsibility for the claims they make in academic work.

To analyze the relationships among these constructs, the team employed partial least squares structural equation modeling, a statistical technique well suited to testing networks of direct, indirect, and interacting effects among latent variables that cannot be observed directly. The modeling rigor was considerable: the authors reported reliability checks using Cronbach’s alpha, discriminant validity assessment following the HTMT criterion established by Henseler and colleagues, and common method bias screening through Kock’s full collinearity approach. Missing data were handled with modern imputation strategies, and the measurement model passed the Fornell-Larcker benchmarks. In plain terms, the statistical machinery was strong enough that the findings deserve attention rather than dismissal.

The headline result is a chain of influence that runs from the humble prompt to the philosophy of knowledge. Prompt literacy, the study found, has a strong positive effect on critical AI literacy. Students who learn to write precise, contextualized, well-structured prompts do not merely get better outputs; they develop a more questioning stance toward the technology itself. The act of engineering a prompt forces a learner to articulate what they actually want to know, and that articulation appears to sharpen their skepticism about what comes back. This finding aligns with a growing body of literature, including work by Federiakin and colleagues framing prompt engineering as a genuine twenty-first-century skill, and research by Knoth and colleagues showing that AI literacy shapes prompting strategies in a reciprocal loop.

Intriguingly, prompt literacy’s direct effect on epistemic transparency was statistically significant but modest. Knowing how to talk to a chatbot does not, by itself, make a student candid about using one. The pathway runs instead through critical AI literacy, which partially mediated the relationship between prompting skill and transparent behavior. In other words, students become epistemically open when they can critically evaluate AI outputs — spotting hallucinations, recognizing training-data biases, and understanding that a fluent paragraph is not the same thing as a verified fact. The study’s authors suggest this is why prompt training alone, a popular quick fix in university workshops, is unlikely to change disclosure behavior on its own. It builds the tool skills but not the epistemic conscience.

The strongest practical lever, according to the data, was assessment design. When assignments are structured so that process matters — drafts, reflections, oral defenses, documented iterations, and evidence of reasoning — students report significantly higher epistemic transparency. Assessment design showed the largest direct contribution of any variable in the model, edging out both literacy constructs. This echoes a substantial international literature on evaluative judgment, including work by Bearman, Tai, Dawson, Boud, and Ajjawi arguing that assessment in the AI era should cultivate students’ capacity to judge quality rather than merely produce outputs, and studies by Bretag and colleagues linking poorly secured assessment designs to contract cheating. The new study extends that logic into the generative AI age: assessments that reward visible thinking make AI use legible, and legibility is the precondition for transparency.

One of the study’s more sobering findings is what did not work. The interaction between assessment design and critical AI literacy was not statistically significant, indicating that these two factors operate independently rather than synergistically. Transparent AI-supported learning, the data suggest, is not produced by combining clever assignments with critical students in some multiplicative fashion. Each factor contributes on its own track. For educators, this carries a practical implication: institutions cannot assume that strong assessment reform will automatically cultivate critical AI literacy, or that critical literacy curricula will compensate for opaque assessment practices. Both need deliberate, parallel investment.

The conceptual contribution of the paper may prove as influential as its statistical results. By defining epistemic transparency as a distinct, measurable learning outcome — grounded in the epistemology of social knowledge developed by Goldman and the ethics of knowing articulated by Fricker — the study gives universities something concrete to target. Transparency here is not a vague aspiration but an observable behavior: disclosing AI assistance, tracing claims to defensible sources, and exhibiting what the authors, drawing on Flavell’s metacognition tradition, describe as reflective monitoring of one’s own knowledge production. Related recent work, such as Lloyd’s proposal for community standards of epistemic responsibility in human-AI collaboration and Nieminen and Ketonen’s account of epistemic agency in assessment, suggests the field is converging on this framing from multiple directions.

For a sector gripped by anxiety over AI-fueled cheating, hallucinated citations, and unreliable detection tools — problems documented in studies ranging from Elsayed’s analysis of misinformation risks to Deep and colleagues’ evaluation of AI detectors — the study offers a refreshingly constructive agenda. Instead of policing students, it proposes equipping them: teach prompt literacy as a gateway skill, build critical AI literacy as the engine of honest evaluation, and redesign assessments so that the process of thinking is visible and valued. The authors are careful to note the limits of their design; a cross-sectional survey captures associations at one point in time and cannot establish causation, and the sample, drawn entirely from Indian institutions, may not generalize everywhere. Still, with 649 respondents and a rigorously validated model, the evidence is hard to ignore.

As generative AI becomes as unremarkable in seminar rooms as the calculator once was, the question facing higher education is no longer whether students will use these systems but whether their use will be honest, critical, and educationally meaningful. This study suggests the answer can be engineered — not through code, but through curriculum. If universities take its findings seriously, the future of AI in education may look less like an arms race between detectors and deceivers, and more like a classroom where the prompt, the critique, and the assignment all work together to make knowing itself transparent.

Subject of Research: The roles of prompt literacy, critical AI literacy, and assessment design in fostering epistemic transparency among university students using generative AI.

Article Title: Developing Epistemic Transparency in AI-Supported Higher Education: Roles of Prompt Literacy, Critical AI Literacy, and Assessment Design

Article References: N., A., Nayak, K. M., Divyashree, M. S., & Jain, U. (2026). Developing Epistemic Transparency in AI-Supported Higher Education: Roles of Prompt Literacy, Critical AI Literacy, and Assessment Design. Frontiers of Digital Education, 3(4), Article 28. https://doi.org/10.1007/s44366-026-0102-2

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0102-2

Keywords: generative artificial intelligence, higher education, prompt literacy, critical AI literacy, epistemic transparency, assessment design, PLS-SEM, academic integrity, AI literacy, educational research, Developing, Epistemic

The motivation for the work stems from a persistent gap in education systems worldwide. Governments and educational administrations increasingly call for the identification and dissemination of good practices with digital technologies in order to improve teaching quality, and international repositories such as the European School Education Platform and the UNESCO ICT in Education Repository collect and share such experiences. Yet defining what actually counts as a good practice has remained surprisingly elusive. Previous literature reviews have highlighted criteria such as effectiveness, transformative effect, sustainability, legitimacy and replicability, while regional agencies in Spain have emphasized features including curriculum integration, institutional support, improved outcomes, interdisciplinarity, inclusion and the involvement of the educational community. Without clear, observable criteria, judgments about quality risk being subjective and inconsistent.

To anchor the instrument in a coherent theoretical framework, the research team, based at the Department of Pedagogy of Universitat Rovira i Virgili, turned to Activity Theory, a cultural-historical framework originally developed by Vygotsky, Leontiev and Engestrom. Activity Theory conceives educational practice as an intentional, object-oriented action involving students and teachers working toward a common goal, mediated by physical and sociocultural tools. The theory models an activity system through its core components, including the subject, tools, community, rules, division of labor and object, and it emphasizes the dynamic, dialectical interactions among these elements. This holistic view aligns closely with the structure of a learning situation and allows observers to detect tensions, contradictions and disruptions within the classroom activity system, which the researchers see as essential to understanding how digital technologies are actually integrated into teaching and learning.

The study adopted a Design-Based Research approach, a methodology structured around iterative cycles of analysis, design, development and evaluation. The first cycle began with a systematic literature review following the PRISMA 2020 model, searching databases including Web of Science, Scopus, ERIC, SciELO and Dialnet for publications from 2013 to 2023 describing educational practices with digital technologies at any educational level. From an initial pool of 718 publications, 27 studies were selected for full content analysis using ATLAS.ti software. This analysis identified 26 variables linked to the seven components of Activity Theory, each operationally defined with its theoretical meaning, the type of evidence required for observation and its intended use within the guideline, forming the initial prototype of the instrument.

Validation proceeded through the Delphi method, a structured technique for aggregating expert judgment over successive rounds. The team formed a coordinating group and recruited a panel of experts across four professional profiles: teachers from early childhood, primary and secondary education; university professors; researchers; and professionals involved in educational management. Thirty-two experts were invited and 26 completed the competence questionnaire, which calculated an Expert Competence Index combining self-assessed knowledge and the quality of argumentation sources. Twenty-three experts scored at or above the 0.8 threshold denoting high competence and continued into the Delphi rounds, a panel size consistent with established guidance that Delphi studies typically require between 15 and 35 participants.

The three Delphi rounds progressively sharpened the instrument. In the first round, 23 experts ranked the variables derived from each Activity Theory component, and the researchers computed a Relative Importance Index to convert the rankings into interpretable percentages, selecting the two highest-priority variables per component to yield 14 variables and 14 indicators. In the second round, 20 experts rated the clarity and relevance of the proposed indicators, with most indicators exceeding 85 percent positive validation and five achieving unanimous acceptance; all indicators met the Content Validity Index threshold of at least 78 percent, the criterion the team used to classify indicators as excellent. In the third round, nine experts assessed their satisfaction with the revised indicators, and mean scores rose while standard deviations fell, with two indicators reaching a perfect average of 4.00 with zero variability, confirming that the iterative refinements had addressed the panel’s concerns.

With the validated prototype in hand, the team moved to pilot testing in authentic educational settings, an essential step for establishing external validity and feasibility. The pilot involved eight educational practices distributed across four stages, early childhood, primary, secondary and higher education, with two practices per stage, all hosted by different public institutions that had previously collaborated with the university. Sessions lasted one hour in the school stages and two hours at university, with class sizes ranging from 28 students in early childhood settings to as many as 80 university students. Researchers observed each practice simultaneously using individual copies of the guideline and then jointly interviewed the observed teachers with a complementary 13-question semi-structured interview guide, allowing triangulation of the observational data.

Reliability analyses during the pilot demonstrated that different observers could apply the instrument consistently. Weighted Cohen’s Kappa values ranged from 0.64 to 1.00 across the 14 indicators, with 10 of the 14 indicators exceeding 0.80, indicating substantial to almost perfect agreement according to commonly accepted interpretive benchmarks. Intraclass Correlation Coefficients, calculated under the two-way random-effects absolute-agreement model, ranged from 0.67 to 1.00 for single measures, while the global ICC reached 0.89 for single measures and 0.94 for average measures. These figures confirm that the guideline is robust, internally coherent and replicable across observers and contexts, a critical property for any instrument intended to support fair evaluation of teaching quality.

The pilot also generated concrete improvements to the instrument’s design and usability. The researchers added a new section capturing contextual information such as the observed teacher’s name, type of session, school type and dimensions, and technological devices used, reoriented the layout from horizontal to vertical for easier field use, reorganized indicators so that all indicators for each component appeared on the same page, expanded the open-observation field from three to seven lines, and moved illustrative examples directly into the guideline itself. One indicator was reworded to replace a conjunction with a disjunctive form, broadening its scope to capture situations in which community resources or external agents are identified but not necessarily used. Post-pilot analysis showed that 58 percent of the modifications involved new sections, 32 percent design adjustments and 10 percent content changes.

Beyond its immediate technical achievement, the instrument carries significant practical potential. The authors note that teacher training has traditionally emphasized technical mastery of digital tools rather than their pedagogical integration, and the observation guideline is well positioned to serve as a formative assessment tool, a self-reflection resource in mentoring programs, or an evidence-based component in departmental evaluations of technology-enhanced teaching. Its flexible design allows adaptation to different disciplines, educational levels and modalities, including hybrid and virtual environments. The authors caution that further research is needed, including criterial and convergent validation, sensitivity testing to detect changes in teaching practice over time, assessment of transferability across educational systems, and development of an English-language version, but the validated guideline already represents a substantial step toward making the evaluation of good digital teaching practices systematic, transparent and grounded in direct evidence from real classrooms.

Subject of Research: Design and validation of an Activity Theory-based observation instrument for identifying good educational practices with digital technologies through direct classroom observation

Article Title: Good educational practices with digital technologies: validation of an instrument for direct classroom observations

Article References: Good educational practices with digital technologies: validation of an instrument for direct classroom observations. (n.d.). https://doi.org/10.1007/s44322-026-00065-0

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00065-0

Keywords: digital technologies, educational practices, classroom observation, Activity Theory, Delphi method, instrument validation, teacher digital competence, Design-Based Research, educational research, inter-rater reliability, content validity, teaching and learning