Statistical literacy is essential for biomedical researchers, yet doctoral training in applied biostatistics and reproducible data analysis remains limited despite the growing accessibility of open-source tools such as R. This retrospective observational programme evaluation examined three consecutive editions (2021–2023) of an intensive three-day doctoral course in applied biostatistics with R, delivered at the Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC) and the Universidad de Córdoba (UCO). Each edition enrolled 15 doctoral students with a 5:1 student-to-instructor ratio and used a problem-based curriculum of nine modules supported by a purpose-written open-access textbook. End-of-course examination performance was described using a ten-item multiple-choice examination; because the examination was not psychometrically equated across editions, score distributions are reported descriptively by cohort rather than used to infer cross-edition differences in learning or competence. Satisfaction was evaluated across four ACSA-aligned domains and was descriptively high in all editions; in the combined within-participant analysis, the Teaching Team domain was rated higher than the other domains. Self-reported theoretical and practical knowledge ratings increased descriptively from pre- to post-course within each edition. The 2021 ratings were reconstructed retrospectively from qualitative responses and were therefore not pooled or directly compared with the directly scaled 2022–2023 ratings. Qualitative feedback highlighted the theory–practice balance and low student-to-instructor ratio, with longer duration as the principal suggestion. The findings support the feasibility and acceptability of this intensive R-based training model, but the uncontrolled design, non-equated examination, small samples, and self-reported outcomes preclude claims about learning improvement, causal effectiveness, or durable competence.
Background: Gamification is widely used in higher education, yet its consequences for the relational structure of a class are rarely measured, and sociometric data are often analyzed without attention to how the underlying matrix is built. Methods: We report an exploratory, single-case, pre–post study of a gamified master’s course in Didactics and Media Education (N = 19), with a dual aim: to demonstrate a transparent procedure for reconstructing directed, weighted sociograms from raw questionnaire responses, and to explore how the peer-preference network changed over one semester of cooperative group work; inference used permutation methods suited to a small single network. Results: A pre-existing matrix contained roughly one-third of ties with no basis in the data, doubling the apparent centralization and mischaracterizing the least-chosen, most-rejected student as the most popular hub; reconstruction reversed these conclusions. On the reconstructed network, cohesion increased and previously isolated students were integrated, while centralization stayed stable—integration without hierarchy—with a reciprocal, transitive, and highly stable structure and no demographic homophily. Conclusions: Social network analysis (SNA) is a sensitive instrument for the relational evaluation of gamified courses, but the construction of the sociomatrix is itself a substantive analytic decision that must be reported transparently.
This conceptual article responds to the growing need for universities to embed sustainability education within programs to develop graduates capable of addressing complex global challenges. Drawing on the literature relating to education for sustainable development, transdisciplinary work-integrated learning (TWIL), programmatic assessment, and the ethical use of generative artificial intelligence (GenAI), it explores how assessment can support the development and evaluation of capabilities required for sustainable development. TWIL is presented as a curriculum-based pedagogy in which students and academics from multiple disciplines collaborate with external partners to address authentic environmental, social, and economic sustainability challenges. The article argues for a programmatic approach to assessment that supports authentic, collaborative, and longitudinal capability development while addressing contemporary higher education challenges, including academic integrity and the ethical integration of GenAI as a learning partner. Its principal contribution is the integration of these complementary fields into a conceptual framework that guides the design of longitudinal capability development and assessment within TWIL. It further presents an illustrative programmatic assessment framework demonstrating how scaffolded learning experiences, authentic assessment, learner ownership, and multiple sources of evidence can be integrated across a program. It calls for further empirical research to support the programmatic design, implementation, and scalability of TWIL.
Despite growing curricular emphasis on computational thinking (CT) and engineering design practice (EDP), preparing elementary teacher candidates (ETCs) for integrated, practice-based instruction remains challenging. Grounded in Communities of Practice (CoP) and design-based implementation research (DBIR) frameworks, this study examines how ETCs (pre-survey: n = 23, post-survey: n = 13) develop learning-to-teach practices for integrating coding and robotics into PreK–5 STEAM learning (STEM with Arts integration) to foster these competencies. The study is situated within an interdisciplinary, community-partnered course, Toying with Technology (TwT), where ETCs collaborate in Critical Friends teams to design, facilitate, and reflect on community partners’ need-oriented, empathy-centered STEAM experiences. Utilizing PreK–5 learning technologies and field-based insights from STEM/STEAM professionals, ETCs developed 5E lesson plans integrating science, literacy, mathematics, and robotics, culminating in both an in-person and a virtual STEAM Family Night. Data sources include pre- and post-surveys measuring ETCs’ technology integration perceptions, post-field exit tickets, family feedback, community partner reflections, and course artifacts. Pre-survey results indicated limited prior experience and neutral confidence regarding technology integration. In contrary, post-survey findings demonstrated significant increases in self-reported confidence, conceptual understanding of CT and EDP, and readiness to facilitate empathy-centered, technology-rich learning. Qualitative analysis further revealed growth in collaboration, problem-solving, and pedagogical reasoning, while parent and partner feedback highlighted high levels of engagement and increased STEAM interest. Overall, this study provides initial empirical evidence, within a community-partnered, empathy-oriented course design, showing how community-engaged teacher education, informed by CoP and DBIR frameworks, prepares ETCs to teach CT, EDP, and critical problem-solving through integrated, authentic STEAM integration.
Despite the ongoing debate surrounding oral corrective feedback (CF), recent studies emphasize CF’s pedagogical value in foreign/second language teaching, as it informs learners about the accuracy of their utterances and helps increase their awareness of language forms. Viewing CF as a classroom-based instructional strategy for supporting language development, this study examined the effects of different types of oral CF (recasts, explicit correction, and metalinguistic feedback) on young learners’ grammar acquisition within Focus-on-Form (FonF) instruction in an EFL context. The study involved 88 fifth-grade students, aged 11–12, who were assigned to three experimental groups, each receiving one type of oral CF during FonF instruction, and a control group that received FonF instruction without feedback. Data were collected through oral production and scenario description tasks administered as pre-tests, immediate post-tests, and delayed post-tests. The instructional phase included approximately 10.7 h of treatment, and the assessment phase included approximately 15 h of testing. All classroom interactions and test recordings were transcribed and quantitatively analyzed. The results showed that oral CF statistically significantly enhanced young learners’ grammar acquisition compared to no oral feedback. Among the feedback types, explicit correction showed a significant advantage over the other feedback conditions in both immediate and delayed-test performance; however, this advantage was not consistent across all assessment tasks. These findings also highlight the pedagogical value of integrating oral CF into FonF instruction as an evidence-based classroom practice for promoting grammar acquisition and language development in young learner EFL classrooms.
Research on teacher autonomy in STEM has been a popular topic in the field. In order to explore the factors influencing STEM teachers’ attitudes toward teaching autonomy in vocational education and its structure, this study constructed a conceptual model of influencing teachers’ attitudes toward teaching autonomy. By analyzing 590 valid questionnaires, this study used SEM (structural equation modeling) to verify the validity of the model. It was found that the effects of perceived past behavior (PPB) and subjective norms (SN) on teaching autonomy attitudes (TA) were both significant and positively correlated, and perceived teaching autonomy support (PTS) can significantly and positively influence PPB and SN. Additionally, PPB significantly and positively influenced SN. This study fills the gap of vocational education teachers in the STEM teacher research population, and the findings are useful for teachers and school organizations to improve teacher autonomy attitudes.
Integrating data science into undergraduate teaching in the United States requires educators to navigate disciplinary boundaries, integrating knowledge in ways that reshape teaching practices and disciplines themselves. While existing models of teaching and learning effectively describe expertise within single disciplines, they do not account for interdisciplinary knowledge movement or the specific challenges of bringing data science into established curricula. The Apian Model addresses this gap using bee behavior as a metaphor: instructors (bees) gather insights (nectar) from diverse curricular domains (flowers), transform them into learning experiences (honey), and disseminate them within and beyond their disciplines (hives). Through incorporation, communication, pollination, and hive splitting dynamics, this model explains how teaching evolves from individual learning to broader disciplinary change, processes especially relevant for navigating the complexity and interdisciplinarity of data science education. The model was developed with data science education in higher education in mind, but future research may establish its broader utility for interdisciplinary education more generally, particularly as a complement to existing models designed for single-discipline instruction.
Teachers’ artificial intelligence (AI) competence is increasingly important in China’s educational modernization. Yet in contexts related to science, technology, engineering, and mathematics (STEM), how AI integration at the pedagogical level can be made observable in instructional design remains underexamined. This mixed-methods study developed an analytic developmental rubric that integrates the AI pedagogy aspect of UNESCO’s AI Competency Framework for Teachers with seven key characteristics of integrated STEM. The rubric uses gates to define the minimum evidence required for each STEM characteristic and uses verifiable descriptors and hallmarks to assign a competency level within each characteristic for preservice teachers’ use of AI to enable integrated STEM instructional design. Then, the rubric underwent a series of validation procedures. Eight experts from two universities conducted expert review and content validity assessment. A total of 108 preservice teachers from six teacher education degree programs spanning mathematics, science, and technology at a normal university in China completed integrated STEM instructional designs on the same topic. Three raters from the two universities independently scored the artifacts, and the resulting scores were used to estimate inter-rater reliability, examine intercorrelations and internal consistency, and assess known-group validity. The findings provided evidence supporting the rubric’s reliability and validity and indicated that it could consistently distinguish preservice teachers across training stages ordered a priori by expected competency. In this way, this rubric begins to fill the gap in the assessment of AI-enabled integrated STEM instructional design, and its scope of application is not confined to any particular STEM subject or instructional design framework.
Previous studies suggest that educator–infant interactions during mealtime have significant potential to promote early language learning. As most of this research has focused on Western contexts, little is known about whether and how educators’ interactions with infants during mealtime in early childhood education and care (ECEC) settings vary across cultures. To address this knowledge gap, using a multi-case study approach, we compared the natural mealtime interactions of 12 educators with infants in ECEC settings in Australia and China, with six educators participating in each country. Drawing on systemic functional linguistic (SFL) theory and multimodal discourse analysis (MDA), this study examined (1) the situational context of the mealtime practices (each meal’s duration, educators’ movement and whether they modelled eating behaviours, and infants’ eating behaviours); (2) educators’ infant-addressed language and gesture use; and (3) infants’ use of vocalisation and gesture. The analysis revealed several differences: (1) the Australian educators sat with the infants, while the Chinese educators moved around the tables and served the infants; (2) the Australian educators’ talk included a higher percentage of mental processes, statements, and gestures, while the Chinese educators used more material processes and commands in their speech and fewer gestures; and (3) the Australian infants vocalised more and used more gestures. Adopting a systemic functional and multimodal discourse analysis (SF MDA) approach enabled us to relate such differences to both the situational and broader cultural context of educator–infant interactions. This study thus contributes to a richer, more nuanced understanding of the factors that shape educator–infant interactions and the potential of mealtime in ECEC settings to provide infants with opportunities for language learning across cultures.
Writing is a key competence for academic and social success, and for future employability. From a sociocultural perspective, writing development is shaped by both school instruction and literacy-related experiences within the home. However, little is known about what specific writing practices and support families naturally provide, particularly for children at-risk of writing difficulties in early primary education. This study examines home writing practices and family support for 296 typically developing and at-risk children in Spain in grades 1 and 2. Students’ writing performance was monitored longitudinally using curriculum-based writing measures, identifying 48 at-risk writers. Parents completed the Home Writing Environment Questionnaire, assessing the frequency of formal and informal writing practices and types of family support, including procedural, motivational and material support. The results showed similar patterns of home writing environments between groups; however, at-risk children engaged in writing activities less frequently, especially those involving higher-level processes such as planning, composing and revising. Families of at-risk children reported slightly higher levels of contingent support, suggesting adaptive involvement. These findings highlight the importance of promoting systematic family engagement in early writing to support inclusion and prevent difficulties.
The rapid adoption of generative artificial intelligence tools, particularly ChatGPT, is transforming teaching and learning in higher education. This study proposes an explainable artificial intelligence (XAI) framework that integrates the Unified Theory of Acceptance and Use of Technology (UTAUT2), machine learning, and explainability techniques to examine students’ intentions to use ChatGPT in academic contexts. Survey data were analyzed using Ordinary Least Squares regression, Random Forest, SHAP, Necessary Condition Analysis (NCA), Importance–Performance Map Analysis (IPMA), and K-Means clustering. The results indicate that Habit, Performance Expectancy, Hedonic Motivation, Social Influence, and Facilitating Conditions significantly influence behavioral intention, explaining 67.6% of the variance. Habit emerged as the strongest predictor, whereas Price Value had negligible influence. XAI analyses revealed that Effort Expectancy acts as a necessary condition for high adoption levels despite its limited direct effect. Four distinct student profiles were identified, highlighting heterogeneous patterns of AI integration and informing strategies for responsible and effective educational adoption. These findings provide evidence-based guidance for integrating AI literacy, responsible AI practices, and pedagogically meaningful ChatGPT use in higher education curricula.
Scientific writing remains a persistent difficulty for Indonesian undergraduates, and the Indonesian language teaching materials currently in use neither embed project-based learning (PjBL) nor exploit interactive digital formats. This study examined whether students receiving a combined PjBL–interactive e-book intervention demonstrated greater gains in research proposal writing than students receiving conventional instruction. The e-book, developed through the ADDIE model and organized as five weekly project cycles culminating in a mini research proposal, was first evaluated by content, media, and language experts and then by lecturers and students in a limited pilot. Learning outcomes were compared using a quasi-experimental pretest–posttest control-group design with 138 students taking the compulsory Indonesian language course in two study programmes at two universities. Writing performance was scored with an analytic rubric comprising five aspects and twelve weighted sub-indicators derived from an established framework for proposal construction. Students who learned with the e-book outperformed their peers in conventional instruction on the posttest and on normalized gain. The between-group difference was large, remained similar after adjustment for pretest scores and in sensitivity analyses accounting for class clustering, and was of comparable magnitude in both disciplinary contexts. Because the intervention combines a project syntax with an interactive digital format, the design evaluates the combined instructional package; the independent contribution of the pedagogy and the medium cannot be separated and is not claimed here.
The increasing integration of AI in education imposes the need to examine the competencies of teachers for its pedagogically meaningful, safe, and responsible application. The goal of this research was to examine the self-assessments of teachers’ AI-related competencies, as well as to determine whether there were statistically significant differences regarding the subject area and years of teaching experience. The research was conducted on a sample of 165 primary and secondary school teachers in the Republic of Serbia. Data were collected using the Serbian-language version of the original Teacher Artificial Intelligence Competence Self-Efficacy (TAICS) Scale. The reliability and factor structure of the instrument were checked using Cronbach’s alpha coefficient, Kaiser–Meyer–Olkin (KMO) indicator, Bartlett’s test of sphericity, and exploratory factor analysis using principal axis factoring with Varimax rotation, while one-way analysis of variance (ANOVA) was used to examine differences. The results showed high internal consistency of the scale (Cronbach’s α = 0.972) and an interpretable three-factor solution explaining 71.202% of the total variance. The following factors were extracted: pedagogical–evaluative AI competencies, ethical–safety AI competencies, and operational–technical AI competencies. No statistically significant differences in pedagogical–evaluative or ethical–safety AI competencies were found according to subject area or years of teaching experience. For operational–technical AI competencies, an unadjusted difference was observed according to teaching experience; however, this finding did not remain statistically significant after the Bonferroni correction for multiple testing. These findings should be interpreted as preliminary and require replication in larger and more representative samples. The findings indicate that the competencies of teachers for artificial intelligence should be viewed as a multidimensional basis for further research, improvement of teaching practice, and planning of professional development in the field of application of AI in education.
Digital access occupies a prominent place in educational policy debates, yet its predictive contribution to mathematics achievement, relative to psychological and socioeconomic factors, remains insufficiently quantified. Existing evidence often relies on linear models, examines predictor blocks separately, or considers technological resources without placing them alongside psychological constructs within a common framework. This study compares the predictive importance of psychological, socioeconomic, demographic and ICT-access indicators for mathematics achievement in PISA 2022. The analysis used data from 141,563 students across 19 education systems and included seven predictors: mathematics self-efficacy, mathematics anxiety, sense of school belonging, economic, social and cultural status (ESCS), gender, ICT resources at home and ICT resources at school. Weighted gradient boosting models were fitted separately to each of the ten plausible mathematics values and interpreted using TreeSHAP; a weighted random forest with permutation importance was used as a robustness check. The full model explained 39.2% of the weighted test-set variance in the plausible-value outcomes (R2 = 0.3922, SEtotal=0.0066, and RMSE = 77.95 score points). Mathematics self-efficacy ranked first under both criteria (42.3% of SHAP importance; 57.8% of permutation importance), ahead of socioeconomic status (30.9%; 32.0%), while the ICT-access block contributed 6.9% and 2.1%, respectively and added 0.0122 to test-set R2. The two importance rankings were identical (Spearman’s ρ = 1.000). The SHAP ranking was unchanged across all 800 plausible-value × replicate-weight runs, matched XGBoost permutation importance, and was reproduced under a school-grouped train–test split. The model also detected a non-monotonic association between school belonging and predicted achievement, together with a MATHEFF × ESCS interaction pattern, supported by a direct-outcome interaction model, in which the modelled association between self-efficacy and achievement was stronger at higher ESCS levels. Because PISA 2022 is cross-sectional and plausible values are designed for population-level inference, these findings should be interpreted as predictive and associational rather than causal. The results suggest that, in systems where ICT access is already widespread, reported access to technological resources contributes comparatively little to prediction once psychological and socioeconomic indicators are considered, although sensitivity analyses indicate that home ICT access is partly affected by missingness patterns.
This study analyses changes in university students’ self-perceived eco-social competences through active methodologies linked to holistic sustainability and human rights. A multidimensional rubric based on the KNOW–CARE–DO model was applied to assess four eco-social competences: (1) critical contextualisation of knowledge in relation to social, economic, and environmental issues; (2) respect for human rights, diversity, equity, a culture of peace and participation; (3) undertaking actions for the common good and sustainable development; and (4) the application of ethical principles linked to sustainability and human rights. A pre-experimental design incorporating pre-test and post-test measurements was used. The study involved 61 students from universities in Spain, Colombia, and Italy. An open-ended question was included to complement the quantitative data. The results indicate significantly higher self-perceived competence scores at post-test than at pre-test, particularly in the critical contextualisation of knowledge. The rubric demonstrates acceptable-to-high internal consistency, although further psychometric validation is required.
Quantum physics presents distinctive conceptual and epistemic challenges for K-12 learners, who must make sense of probabilistic models that depart from classical intuitions. This manuscript introduces epistemic guideposts, concise instructional cues designed to orient students toward productive ways of knowing during quantum instruction. Drawing on research in epistemic cognition, scaffolding, framing, and scientific practices, we articulate a design-anchored construct that supports learners’ engagement with epistemic aims, ideals, and reliable processes. We elaborate a three-layer framework for guideposts: framing guideposts that establish global expectations for modeling and interpretation, sensemaking guideposts that reorient reasoning as new representational tools are introduced, and boundary guideposts that redirect unproductive reasoning patterns. Using secondary-level quantum topics, we illustrate how guideposts can help students interpret wavefunctions, compare predicted and observed distributions, and evaluate competing models. We argue that repeated use of guideposts can accumulate into a classroom metaframe that stabilizes expectations for evidence-based reasoning and supports conceptual integration over time. The manuscript concludes with implications for curriculum design, teacher professional learning, and classroom discourse analysis, proposing epistemic guideposts as a tractable, observable, and theoretically grounded lever for strengthening students’ epistemic performance in counterintuitive STEM domains.
Background: The growing inclusion of students with developmental disabilities (SWDs) in mainstream physical education (PE) highlights the need for teaching approaches that support participation while maintaining educational effectiveness. This study examined the effects of a 12-week para-volleyball program on motor abilities in students without developmental disabilities (SWDDs). Methods: A total of 217 fifth- and sixth-grade primary school students were assigned to an experimental group (n = 110), in which students participated in a 12-week para-volleyball program, and a control group (n = 107), in which students followed the regular PE curriculum. Anthropometric characteristics and motor abilities, such as explosive strength, agility, and aerobic capacity, were assessed before and after the intervention using standardized field tests. Results: The experimental group showed significantly greater improvements in agility and explosive strength than the control group. Specifically, agility test performance improved by 0.92 s (p < 0.001, ηp2 = 0.16), and standing long jump distance increased by 10.60 cm (p < 0.001, ηp2 = 0.17). No significant effects were found for aerobic capacity (p > 0.05) or body composition, while flexibility improved only in the control group (+4.41 cm, p < 0.001). Conclusions: A 12-week para-volleyball program significantly improved agility and explosive strength in primary school students compared to standard PE. These findings suggest that para-volleyball is an effective tool for developing motor abilities and promoting inclusive PE.
The digital transformation of higher education requires assessment approaches capable of capturing complex learning processes. In teacher education, immersive virtual reality (VR) simulations generate multimodal records, yet transforming them into pedagogically meaningful evidence for assessing classroom-management competence remains challenging. This methodological pilot study examines Mission Control–Didascalias VC, an analytics infrastructure for classroom-management simulations. Of 626 sessions, 595 were retained in the cleaned analytical corpus. Data sources comprised session metadata, verbal interactions, programmed challenging events, vocal and movement signals, dashboard exports and, for 17 focal cases, video recordings and transcripts. The analysis examined data quality, traceability and interpretability, developed and refined a multimodal coding framework, and included a worked example of sequence-based coding. Results show that simulation records do not constitute assessment evidence in themselves. Their formative value emerges through cleaning, temporal alignment, multimodal triangulation and contextual interpretation, which transform isolated technical traces into pedagogically meaningful interactional sequences. Critical verbal interactions, participant responses, emergent classroom situations, and vocal and movement patterns provided complementary evidence, including in sessions without programmed challenging events. The study concludes that VR analytics can support sustainable formative assessment when technical records are transformed into pedagogically interpretable interactional evidence for debriefing, feedback and evidence reuse over time.
Homeownership has become increasingly difficult for many college graduates in the United States. This quantitative study examines the conditional associations among undergraduate major, post-college income, student loan debt, and homeownership using restricted data from the 2021 College and Beyond II Alumni Survey. Grounded in Human Capital Theory and informed by Status Attainment Theory as a secondary interpretive perspective, the study analyzes a sample of 2513 college graduates approximately a decade after degree completion. Logistic regression models examine differences in the conditional odds of homeownership across undergraduate major categories while accounting for post-college economics, student loans, and demographic characteristics. The findings indicate that arts and humanities graduates had lower conditional odds of homeownership than graduates did in the STEM-health, business, education, and social science fields. Higher salary and other household income were associated with higher conditional odds of homeownership, whereas student loan default and higher monthly loan payments were associated with lower conditional odds. Ever borrowing was also positively associated with homeownership, although this relationship should not be interpreted as evidence that student borrowing causes or facilitates homeownership. The findings highlight the complex relationships among educational pathways, post-college economic circumstances, student loan experiences, and homeownership and underscore the need for further research on the long-term financial outcomes associated with higher education.
Artificial intelligence (AI) is expanding opportunities for adaptive and personalised learning that responds to learners’ abilities, interests, and educational needs. However, formal gifted education programmes may still offer limited opportunities for individualised learning pathways. In this study, gifted education programmes refer to formal provision for identified gifted students that supports differentiated depth, complexity, pace, challenge, enrichment, and talent development. This study proposes an AI-Supported Double-Helix Framework for Personalised Learning in Gifted Education and examines preliminary empirical support for its hypothesised relationships. The framework integrates the Unified Theory of Acceptance and Use of Technology (UTAUT), AI-Supported Adaptive Learning (ASAL), Personalised Learning Experience (PLE), and curriculum integration. Using a quantitative cross-sectional design, PLS-SEM was applied to data from 437 gifted secondary school students. The measurement model demonstrated satisfactory reliability, convergent validity, discriminant validity, and measurement invariance. Performance Expectancy showed the strongest positive association with ASAL, which was positively associated with PLE. PLE was positively associated with perceived Double-Helix Framework Implementation (DHFI), which was subsequently associated with perceived Learning Effectiveness, Talent Development, and Future Readiness. A secondary multi-group analysis indicated that most structural relationships were comparable across gender groups. The study extends UTAUT by linking technology adoption factors with AI-supported adaptation, personalised learning, curriculum integration, and perceived developmental outcomes. It also situates these processes within the context of Education 4.0 through adaptive, data-informed, and future-oriented learning. However, the cross-sectional self-report design provides preliminary empirical support only for statistical relationships among the measured constructs. It does not establish the framework’s educational effectiveness or causal effects on learning. Longitudinal, experimental, behavioural, and performance-based studies are needed to evaluate its educational effectiveness and practical implementation.
