Search Results (712)

The rapid proliferation of generative artificial intelligence (GenAI) in educational settings has created unprecedented opportunities for language instruction, yet empirical evidence regarding its efficacy in primary-level English as a Foreign Language contexts remains scarce, particularly concerning pre-service teachers’ implementation experiences during formative practicum periods. This investigation, conducted in a public school in a non-Anglophone country during the Spring of 2025, examined the impact of GenAI-driven gamified activities on elementary pupils’ English language competencies while exploring novice educators’ professional development trajectories through a mixed-methods quasi-experimental approach with comparison groups. Four third-grade classes (n = 119 individuals aged 8–9) in a public school were assigned to either ChatGPT-mediated voice-interaction games (n = 58) or conventional non-digital activities (n = 61) across six 45 min lessons spanning three weeks, with four female student-teachers serving as instructors during their culminating practicum. Quantitative assessments of grammar, listening comprehension, and pronunciation occurred at baseline, post-intervention, and one-month follow-up intervals, while reflective journals captured instructors’ evolving perceptions. Linear mixed-effects modeling revealed differential outcomes across linguistic domains: pronunciation demonstrated substantial advantages for GenAI-assisted learners at both immediate and delayed assessments, listening comprehension showed moderate benefits with superior overall performance in the experimental condition, while grammar improvements remained statistically equivalent between groups. Thematic analysis uncovered pre-service teachers’ progression from technical preoccupations toward sophisticated pedagogical reconceptualization, identifying connectivity challenges and assessment complexities as primary barriers alongside reduced performance anxiety and individualized pacing as key facilitators. These findings suggest selective efficacy of GenAI across language skills while highlighting the transformative potential and implementation challenges inherent in technology-enhanced elementary language education. Full article

Conventional academic warning systems in higher education often rely on end-of-semester grades, which severely limits opportunities for timely intervention. To address this, our interdisciplinary study developed and validated a comprehensive socio-technical framework that integrates social-cognitive theory with learning analytics. The framework combines educational data mining with culturally responsive, personalized interventions tailored to a non-Western context. A two-phase mixed-methods design was employed: first, predictive models were built using Learning Management System (LMS) data from 2,856 students across 64 courses. Second, a quasi-experimental trial (n = 48) was conducted to evaluate intervention efficacy. Historical academic performance, attendance, and assignment submission patterns were the strongest predictors, achieving a Balanced Area Under the Curve (AUC) of 0.85. The intervention, specifically adapted to Confucian educational values, yielded remarkable results: 73% of at-risk students achieved passing grades, with a large effect size for academic improvement (Cohen’s d = 0.91). These findings empirically validate a complete prediction–intervention–evaluation cycle, demonstrating how algorithmic predictions can be effectively integrated with culturally informed human support networks. This study advances socio-technical systems theory in education by bridging computer science, psychology, and educational research. It offers an actionable model for designing ethical and effective early warning systems that balance technological innovation with human-centered pedagogical values. Full article

The integration of emerging technologies is reshaping the teaching skills required in the 21st century, yet little evidence exists on how contextualized training supports rural teachers in adopting active methodologies and critically incorporating AI into entrepreneurship education. This study evaluated the impact of a 40-h professional development program implemented in Educational District 15D01 in the Ecuadorian Amazon. Thirty-nine secondary school teachers participated (mean age = 43.1 years); 36% lacked prior entrepreneurship training, and 44% had not recently mentored student projects. A sequential explanatory mixed-methods design was employed. The quantitative phase employed a 22-item questionnaire that addressed four dimensions: entrepreneurial knowledge, competencies, methodological strategies, and AI integration. Significant pre–post improvements were found (p < 0.001), with large effects for knowledge (d = 1.43), methodologies (d = 1.39), and AI integration (d = 1.30), and a moderate effect for competences (d = 0.66). The qualitative phase analyzed 312 open-ended responses, highlighting greater openness to innovation, enhanced teacher agency, and favorable perceptions of AI as a resource for ideation, prototyping, and evaluation. Overall, the findings suggest that situated, contextually aligned training can strengthen digital equity policies, foster pedagogical innovation, and empower educators in underserved rural communities, contributing to sustainable pathways for teacher professional development. Full article

Augmented Reality (AR) continues to generate interest as a pedagogical tool in contexts where English is a Foreign Language (EFL). However, its role in developing higher-order cognitive skills, such as inferencing, remains underexplored. This exploratory, mixed-methods study investigates whether AR can scaffold inference-making in advanced EFL learners. Forty-seven university students in XX were assigned to either a control group (CG) or an experimental group (EG). Both groups read Edgar Allan Poe’s “The Tell-Tale Heart” in digital format. The CG received a conventional inference-based comprehension lesson, while the EG engaged with two interactive AR scenes developed using the Onirix Studio platform. Pre- and post-tests assessed inferential comprehension, and qualitative data were gathered through open-ended responses. While the CG demonstrated modest post-test gains and the EG showed a slight decline, neither change reached statistical significance. Notably, qualitative findings revealed that a salient AR element—a ticking clock—likely prompted misinterpretation in the EG, disrupting symbolic reasoning and contributing to schema misalignment. However, some learners exhibited metacognitive insight suggestive of productive struggle. These results suggest that AR may hold untapped potential for developing metacognitive reflection and critical literacy. Directions for future research are outlined. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article

Learning Natural Sciences represents a key opportunity to spark scientific interest and foster fundamental skills across different educational stages. This study aimed to analyze the influence of motivation on academic performance in the learning of Natural Sciences at various educational levels. To this end, a systematic review method was employed following PRISMA guidelines, consulting the Web of Science and Scopus databases, identifying four relevant studies. The results showed that high levels of motivation were associated with a more positive classroom attitude and better conceptual understanding, which enhanced academic performance. The use of innovative methodological strategies, such as implementing immersive virtual reality in the classroom, PhET simulations (Physics Educational Technology), and the use of hypertext, significantly increased both student motivation and academic performance. The meta-analysis revealed a favorable effect in experimental groups, showing moderate heterogeneity (I² = 49) and significance of p = 0.0001. The concurrence analysis reported that current pedagogical practices should focus on strengthening student autonomy and active engagement, integrating critical reflection, the use of innovative methodological strategies, and technological resources that enhance meaningful learning in scientific literacy. Among the instruments used to measure motivation, the Motivation to Learn Science Questionnaire was identified, and for academic performance, the Motivated Strategies for Learning Questionnaire. In conclusion, the importance of implementing the identified methodological strategies across different educational stages is emphasized, in order to promote competency-based learning through meaningful and innovative acquisition of content in Natural Sciences. Full article

Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and manual discourse analysis are often limited by subjectivity, high labor costs, and poor scalability. These challenges underscore the need for automated, objective tools to support pedagogical assessment. This study explores and compares the use of Transformer-based language models for the automatic classification of teaching behaviors from real classroom transcriptions. A dataset of over 1300 utterances was compiled and annotated according to the teaching styles proposed in the circumplex approach (Autonomy Support, Structure, Control, and Chaos), along with an additional category for messages in which no style could be identified (Unidentified Style). To address class imbalance and enhance linguistic variability, data augmentation techniques were applied. Eight pretrained BERT-based Transformer architectures were evaluated, including several pretraining strategies and architectural structures. BETO achieved the highest performance, with an accuracy of 0.78, a macro-averaged F1-score of 0.72, and a weighted F1-score of 0.77. It showed strength in identifying challenging utterances labeled as Chaos and Autonomy Support. Furthermore, other BERT-based models purely trained with a Spanish text corpus like DistilBERT also present competitive performance, achieving accuracy metrics over 0.73 and and F1-score of 0.68. These results demonstrate the potential of leveraging Transformer-based models for objective and scalable teacher behavior classification. The findings support the feasibility of leveraging pretrained language models to develop scalable, AI-driven systems for classroom behavior classification and pedagogical feedback. Full article

Integrating information and communication technology (ICT) into STEM education enhances instructional quality and cultivates students’ interdisciplinary problem-solving. STEM teachers’ attitudes—driven by perceived ease of use (PEOU) and perceived usefulness (PU)—are pivotal in ICT adoption, and pre-service training offers a vital opportunity to shape these attitudes. Yet, empirical studies investigating how specific training strategies influence ICT attitudes via PEOU and PU remain scarce. Using a mixed-methods approach combining questionnaires and interviews, the results indicate that pre-service training significantly improved STEM teachers’ attitudes toward ICT-enhanced teaching. Socially interactive strategies (role models and collaboration) enhanced attitudes via PEOU by boosting confidence and reducing technology-related anxiety, cognitive design strategies (reflection and instructional design) operated through PU by emphasizing ICT’s pedagogical value, and experiential feedback strategies (authentic experience and feedback) influenced attitudes through both PEOU and PU, fostering integrated technical and pedagogical development. These findings support an integrated SQD–TAM framework and provide practical guidance for designing pre-service STEM teacher programs to promote sustained ICT adoption in China, and meanwhile highlights the importance of strategically sequencing training to cultivate both technological competence and pedagogical insight among future STEM educators. Full article

This article explores Truman Capote’s parodic/reconstructive exploitation of decadent aesthetics in his “Southern Gothic” novel, Other Voices, Other Rooms (1948), as dissident self-distancing from postwar conservatism. Modernist Southern Gothic writers owe European decadent culture for their thematization of the sociocultural decay of the antebellum South and characterization of dandiacal dissidents, while often reiterating the claustrophobic mood of the patriarchal and racist society and excluding/villainizing those dandies. Critically analogizing the nationalist heteronormativity of the early-Cold War American society with the oppressive patriarchy of the South, OVOR playfully deconstructs the tragic narrative of Modernist Southern Gothic by foregrounding the reparative aspect of decadent aesthetics mainly through the pedagogic relationship between a Wildean dilettante, Randolph, and a young protagonist, Joel. Simultaneously, with the ironical self-satire against the potential authoritarianism of white bourgeois decadence, Capote democratizes decadent aesthetics as a non-normative survival method through the exposure of Randolph’s vulnerability and the parodic adaptation of his dilettantism by the non-white characters. I argue that OVOR marks the vacillating but inevitable transition from decadence to camp as a seemingly non-political but necessary survivalist strategy in the Cold War/Pre-Stonewall American society that conducts surveillance of, persecutes, and stigmatizes as “decadence” non-normative genders and sexualities. Full article

Rapid urbanization and climate change demand innovative green solutions in city planning. Tiny forests—small artificial wooded areas in urban or peri-urban settings—are gaining attention. This paper explores the use of the Miyawaki method to establish such forests in Italy, highlighting their environmental and educational benefits. The study defines micro-forests (100–200 m²) and mini-forests (200–2000 m²) per legislative standards and describes the qualitative features needed for self-sustaining ecosystems. Mimicking natural succession, these forests support biodiversity, reduce urban heat, improve air quality, and act as carbon sinks. Beyond ecological functions, they offer strong pedagogical value, fostering naturalistic intelligence and reconnecting people with natural rhythms and ecosystems. Case studies from Vigevano and Rome show practical applications, demonstrating how tiny forests can enhance sustainability, community well-being, and environmental awareness in cities. Full article

This study interrogates whether Augmented Reality (AR) enhances vocabulary and content acquisition within Content and Language Integrated Learning (CLIL), situating the question in the broader debate on how immersive, multimodal technologies shape achievement and engagement. This study’s novelty lies in its direct AR-versus-print comparison in a real CLIL classroom using markerless, smartphone-based technology. Using a mixed-methods, classroom-based experiment, we drew on a convenience sample of 129 secondary students (ages 16–18), assigning them to an AR intervention (n = 64) or a print-based control (n = 65). Both cohorts received parallel instruction covering identical objectives and materials; vocabulary attainment was gauged using matched pretest and post-test measures, while engagement, attitudes, and perceived usefulness were captured through paired pre- and post-surveys and open-ended prompts. Quantitative analyses compared change scores across conditions and were complemented by qualitative summaries of learner comments. Results indicate that exposure to AR exerted a positive influence on learners’ engagement and supported learning processes, with perceptible shifts in students’ views of AR between baseline and post-intervention; nevertheless, effects were heterogeneous across instruments, items, and subgroups, suggesting that benefits accrued in a targeted rather than uniform fashion. Compared to the print-based group, students using AR demonstrated greater gains on visually supported vocabulary and content items, while other items showed no significant differences between groups. We conclude that AR constitutes a promising pedagogical resource for CLIL, capable of scaffolding vocabulary/content development and motivating participation, while the observed variability underscores the need for principled, context-sensitive integration. Future work should specify boundary conditions—such as task type, prior proficiency, cognitive load, and technology familiarity—and employ robust mixed-methods designs to determine for whom, and under which instructional circumstances, AR yields the greatest and most sustainable gains. Full article

Although Artificial Intelligence (AI) is transforming teachers’ knowledge and professional practice, its full potential has yet to be fully realized. To incorporate AI effectively into pedagogical contexts, it is essential that teachers possess the knowledge necessary to guide its responsible use. However, in Latin America, there remains limited empirical evidence to support this process. To address this gap, this empirical study analyzes teachers’ knowledge of AI using the Intelligent-TPACK framework, which includes an ethical dimension. A validated and adapted questionnaire was administered to 709 primary and secondary school teachers from the Metropolitan Region of Chile, using a non-probability sampling method. The sample is compositional–descriptive in nature for the study variables and is not statistically representative of the broader population. Data were analyzed through descriptive and inferential statistical methods. The results reveal mixed levels of knowledge—slightly higher in technological knowledge yet lower in terms of integration and ethical awareness. Significant differences were found by gender, age, teaching level, and subject area. Regression models identified teaching experience, gender, and educational level as the most consistent predictors. Additionally, cluster analysis revealed four exploratory professional profiles characterized by varying degrees of knowledge. These findings are discussed in light of teacher training needs and aim to inform the development of professional learning programs better aligned with the actual demands of the teaching profession. Full article

This study examines the effectiveness of the strategic session format in teaching sustainable development within a university ecology course, with a particular focus on fostering student engagement. A pedagogical experiment was conducted with first-year undergraduate students, who were divided into four stakeholder groups—Ecologists, Developers, Residents, and Authorities—to work on the following question: “What should a sustainable city of the future be like?” Team roles were assigned based on a diagnostic survey assessing individual collaboration styles. The online session was structured in two stages, combining small-group discussions and plenary meetings, and was moderated by third-year students. The collaboration was supported by digital tools, including online boards and structured templates. Data collection involved student surveys, discussion transcripts, and moderator observations. The results indicate that students preferred the interactive strategic session format over conventional instruction methods. Participants demonstrated high levels of engagement, an ability to analyze complex sustainability issues, and a willingness to reconcile differing stakeholder perspectives. The findings also revealed areas for improvement, which informed further adjustments to the format. This paper offers a documented example of using the strategic session as an educational tool for sustainable development, aligning with active learning principles. It highlights the format’s potential for interdisciplinary learning and its adaptability through accessible digital platforms. Full article

Non-traditional learners comprise approximately 73% of undergraduate enrollment, representing diverse populations including first-generation college students, adult learners, veterans, multilingual learners, and students with family responsibilities. Despite their numerical dominance, these students face systemic barriers that traditional pedagogical approaches often fail to address. This mixed-methods study examined how Universal Design for Learning (UDL) principles impact non-traditional learners’ educational experiences in higher education. Using a convergent parallel design with 154 participants from a Hispanic-serving institution, the study collected quantitative data through the validated Personalized Learning Supporting Instrument (PLSI) and qualitative data from open-ended questions. The refined 12-item PLSI demonstrated strong psychometric properties. While UDL factors showed limited direct association with GPA overall, Flexible Instructional Methods and Materials significantly predicted academic performance. Qualitative analysis identified six barrier themes (online learning difficulties, course content issues, financial constraints, balancing responsibilities, accessibility challenges, and health interruptions) and five positive impact themes (interactive learning, supportive environments, skill development, goal clarification, and effective assignments). Demographic analysis revealed counterintuitive patterns—students with traditional “barriers” achieved high GPAs at rates of 73–76%, while first-generation students showed the lowest high GPA rate (53.2%). These findings challenge deficit-based assumptions about non-traditional learners while revealing important equity gaps. This study demonstrates both the promise and limitations of UDL for diverse populations, suggesting institutions need comprehensive approaches with differentiated support strategies. Full article

The global push toward digital transformation in education presents both opportunities and challenges for achieving sustainability goals. While digital technologies promise enhanced learning experiences and reduced environmental impacts, their implementation often overlooks complex trade-offs between pedagogical effectiveness, resource efficiency, and social equity. This study examines these critical intersections through a comprehensive investigation of geography education in Chinese secondary schools, comparing traditional, fully digital, and hybrid models across diverse urban, suburban, and rural contexts. Through a mixed-methods comparative design involving 262 participants (pilot) and 810 students (main study) and analysis of 17 geography textbooks, we assessed the environmental impacts, learning outcomes, economic viability, and social equity dimensions of each approach. Our findings reveal that thoughtfully designed hybrid models—which strategically combine digital tools for high-impact activities with traditional methods for local engagement—achieve optimal sustainability performance. These hybrid approaches reduced carbon emissions by 72.7% compared to traditional methods while improving learning outcomes and maintaining cost parity over five-year periods. Importantly, hybrid models demonstrated superior adaptability across different socioeconomic contexts, addressing equity concerns that purely digital approaches often exacerbate in resource-limited settings. This research challenges the prevailing technology-first narratives in educational reform, demonstrating that sustainable education transformation requires nuanced, context-sensitive integration strategies rather than wholesale digital transformation. The empirical evidence from this research provides robust support for achieving the United Nations Sustainable Development Goals through educational innovation. The hybrid model’s 73% carbon reduction and simultaneous improvement of learning outcomes by 35% directly support SDG 4 (Quality Education) and SDG 13 (Climate Action). The 78% reduction in paper consumption advances SDG 12 (Responsible Consumption and Production), while successful implementation across urban, suburban, and rural contexts addresses SDG 10 (Reduced Inequalities). These findings demonstrate that sustainable educational transformation can effectively balance technological innovation with environmental stewardship and social equity. Full article