Search Results (201)

An innovative, STEAM-based educational approach uses LEGO^® robots to improve the visualisation and understanding of trochoid curves in tertiary mathematics education. The method involves a two-step process: first, the curves are drawn based on the classical definition of trochoids using a custom-designed LEGO^® robot that employs LED light to trace the shapes. Then, the same process is replicated with a marker, with the robot controlling the movement of the drawing head to reproduce the curves accurately. To deepen students’ comprehension and visualisation, Desmos dynamic geometry software was used in parallel to draw all three types of trochoids (prolate, curtate, and cusped). This hands-on technique aims to make these motion curves more tangible and engaging within a classroom setting. A quantitative experiment involving 94 first-year IT BSc students was conducted during the spring semester of the 2024/2025 academic year using a quasi-experimental design. We had one control group and two experimental groups. One of the experimental groups did not use educational robotics; participants could only see how the robots worked via video. The other experimental group gained first-hand experience by building and testing LEGO^® drawing robots. The aim was to evaluate the effectiveness of an innovative teaching method that used educational robotics to improve understanding of the mathematical properties of trochoids, compared to traditional teaching methods and presentations containing short videos. The Mann–Whitney U test was used in all cases during hypothesis testing. Only watching videos of drawing robots does not have a statistically significant effect on learning outcomes. In this case, the effect size was only 0.12. However, the results of the group performing educational robotics activities showed a statistically significant difference compared to the other two groups, with large effect sizes (0.68 and 0.7). Our results suggest that visualisation using LEGO^® robots significantly improves students’ knowledge of parametric curves. Educational robotics offers promising opportunities because it is an attractive and interactive teaching tool. Its great advantage is that it combines abstract mathematical concepts with modern technology, thus creating an effective learning environment. 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

Recent policy initiatives, such as the Ministry of Education of China’s guidelines on student sleep management, together with international empirical studies on daytime rest, highlight that appropriate opportunities for midday rest can support students’ attention, memory, and overall learning. Motivated by these developments, this study introduces a conflict-sensitive, multidimensional framework for evaluating nap-compatible classroom chairs. The proposed PLEASURC framework integrates eight evaluation dimensions, organized into four domains—ergonomic, operational, perceptual, and affective—thereby combining both expert and student perspectives across three use scenarios (writing, adjusting, and napping). Perceptual divergence between groups is addressed through Jensen–Shannon divergence–based adaptive weighting, which adjusts expert–student influence according to their agreement level. Scenario-sensitive patterns are extracted via tensor decomposition (a statistical factor analysis technique), while SHAP-enhanced machine learning (an explainable model interpretation method) is employed to identify the most influential predictors of perceived comfort. Findings indicate that relational and emotional dimensions (e.g., Relatability, Learnability, Aesthetics) significantly influence perceived comfort, surpassing structural considerations alone. The study also demonstrates a closed feedback loop from evaluation to redesign, supporting the practical utility of the framework through the optimized chair prototype. Overall, this research offers a replicable and interpretable framework for ergonomic evaluation and data-driven redesign of multifunctional school furniture, contributing to both student-centered learning environments and sustainable educational infrastructure. Full article

The significance of children’s mathematical competence during the early years is well established; however, the methods for developing such competencies remain less understood. Specifically, there is a need to identify what constitutes high-quality educational environments and effective instruction. Both the study and promotion of high-quality educational environments and teaching, through coaching and other professional development initiatives, necessitate the use of observational instruments that are reliable, efficient, and valid, including content, internal, external, and consequential validity. Moreover, domain-specific measures are essential, as general quality measures often fail to adequately assess curriculum content, scope, or sequence, and they do not reliably predict improvements in children’s learning outcomes. This study employed innovative analytical techniques to evaluate the scoring and interpretation of an existing domain-specific observational measure: the Classroom Observation of Early Mathematics Environment and Teaching (COEMET). We applied non-linear modeling approaches (i.e., Random Forest [RF] and Generalized Additive Models [GAMs]) to investigate and provide a comprehensive overview of the relationships between COEMET’s measures—at both the scale and item levels—of teachers’ practices and children’s mathematical competencies. The study first employed the RF machine learning method to identify the most important COEMET items for prediction, followed by the use of GAMs to depict the non-linear relationships between COEMET predictors and the outcome variable. The analysis revealed that certain teaching practices, as indicated by the COEMET items, exhibited non-linear and even non-monotonic associations with children’s mathematical competencies. Full article

This study presents a systematic review of the literature on teaching techniques that enhance student motivation and academic performance across basic, secondary, and higher education levels. The review is grounded in the distinction between intrinsic and extrinsic motivation, highlighting their decisive roles in engagement and achievement. The analysis focuses on active learning methodologies such as project-based learning, collaborative learning, gamification, and flipped classrooms. It identifies the mechanisms by which each approach fosters students’ interest, sense of competence, and persistence. Four international databases were consulted, and studies published between 2000 and 2024 reporting quantitative measures of motivation and/or performance were selected. Five investigations met all eligibility criteria and were assessed for methodological quality. The results indicate moderate motivational effects, especially when interventions last at least eight weeks, provide frequent feedback, and place students at the center of authentic problem-solving. Greater gains were also observed in STEM disciplines and in contexts that encourage peer collaboration. Based on these findings, practical recommendations are proposed for educators: structure interdisciplinary projects, incorporate playful elements in the initial stages of formal education, combine autonomous work with small-group discussions, and use data analysis tools to deliver personalized feedback. The study concludes that adopting diverse, student-centered pedagogical practices enhances motivation and academic achievement, leading to deeper and more lasting learning outcomes. Full article

Traditional assessment methods in physical education often emphasize final grades, lacking real-time monitoring and feedback during the learning process. To address this limitation and enhance the formative evaluation of student performance, this study proposes a real-time assessment system for Baduanjin instruction in physical education, utilizing a commercially available inertial measurement unit-based motion capture device. The system was developed in four stages. First, a dataset was created by recruiting 20 university students and one expert physical education instructor. Participants were asked to perform standardized Baduanjin routines while wearing wireless inertial measurement unit sensors on key body joints. The collected kinematic data, sampled at 100 Hz, included joint angles and movement trajectories. Second, preprocessing and feature extraction techniques were applied to the raw data to construct a labeled dataset for training. Third, supervised machine learning algorithms were used to build models for motion type recognition and motion accuracy evaluation. Model performance was assessed using cross-validation and compared with expert evaluations. Finally, a user-facing formative assessment system was developed and tested in a controlled classroom environment. The system demonstrated a high motion recognition accuracy of 99.77%, and the correlation coefficient between system-assessed motion accuracy and expert ratings exceeded 0.80, indicating strong validity. The results demonstrate that the formative assessment system built on inertial measurement unit is appropriate for the Baduanjin physical education. Full article

This study investigated the impact of preparing lesson plans and conducting microteaching activities—aligned with the learning outcomes of the mathematics curriculum—on the development of sustainability beliefs among teacher candidates. The rationale behind this research stems from the growing global emphasis on sustainability and the urgent need to embed sustainability literacy into teacher education programs, particularly in disciplines such as mathematics, which are often perceived as abstract and value-neutral. There is a recognized gap in equipping pre-service teachers with the pedagogical skills and conceptual awareness needed to integrate sustainability meaningfully into mathematics instruction. Employing a mixed-methods design, the Sustainability Belief Scale was administered to 45 teacher candidates (22 in the experimental group and 23 in the control group) as both a pre-test and post-test. During the intervention, participants in the experimental group collaboratively designed lesson plans and delivered them through microteaching sessions. Throughout the process, they maintained individual reflective journals. The lesson plans and microteaching performances were evaluated using instructor-developed rubrics. Data were analyzed using both quantitative statistical techniques and qualitative content analysis. The findings indicate that integrating sustainability themes into mathematics education significantly enhances teacher candidates’ sustainability beliefs and informs their pedagogical orientations. This study underscores the importance of structured, practice-based learning experiences—such as sustainability-focused microteaching—as a means to develop the competencies needed for education for sustainable development in mathematics classrooms. Full article

Classroom attention is a fundamental cognitive function that is crucial to effective learning and significantly influences academic performance. Recent advances in investigating neural correlates of attention in classroom environments provide insights into underlying neural mechanisms and potentially enhance educational outcomes. This paper presents a scoping review of empirical studies investigating neural activities associated with students’ attention in classroom environments. Based on the 16 studies that we included after systematically searching, five main objectives were identified: (i) examination of neural markers of student attention in classroom environments, (ii) comparison of different learning environments, (iii) comparison of different classroom activities, (iv) data quality examination, and (v) student attention improvement. All selected studies used electroencephalogram (EEG) recording to measure neural activities, primarily using NeuroSky and Emotiv EPOC devices. Researchers measured classroom attention through brain-to-brain synchrony or frequency power. While differences in neural activity across classroom activities were noted, further investigation is needed for consistent results. Most studies focused on university students and had limited sample sizes, though they covered diverse study domains. Overall, while some preliminary results have been identified, there are several concerns regarding the neural measurements of attention used, contradictory findings, lack of verification, and limited sample sizes and techniques. Further studies are recommended to extend our understanding of neural evidence of attention in classroom environments. Full article

Background/Objectives: The transformation of nursing and midwifery education through digital technologies has gained momentum worldwide, with algorithm-based video instruction and virtual reality (VR) emerging as promising tools for improving clinical learning. This quasi-experimental study explores the impact of an enhanced flipped classroom model on Bulgarian nursing and midwifery students’ self-perceived competence. Methods: A total of 228 participants were divided into a control group receiving traditional instruction (lectures and simulations with manikins) and an experimental group engaged in a digitally enhanced preparatory phase. The latter included pre-class video algorithms, VR, and clinical problem-solving tasks for learning and improving nursing skills. A 25-item self-report questionnaire was administered before and after the intervention to measure perceived competence in injection techniques, hygiene care, midwifery skills, and digital readiness. Results: Statistical analysis using Welch’s t-test revealed significant improvements in the experimental group in all domains (p < 0.001). Qualitative data from focus group interviews further confirmed increased student engagement, motivation, and receptiveness to digital learning tools. Conclusions: The findings highlight the pedagogical value of integrating structured video learning, VR components, and case-based learning within flipped classrooms. The study advocates for the wider adoption of blended learning models to foster clinical confidence and digital competence in healthcare education. The results of the study may be useful for curriculum developers aiming to improve clinical readiness through technology-enhanced learning. Full article

This study presents the outcomes of the Erasmus+ European project Higher Education Classroom of the Future (HECOF), with a particular focus on chemical engineering education. In the digital era, the integration and advancement of artificial intelligence (AI) in higher education, especially in engineering, are increasingly important. The main goal of the HECOF project is to establish a system of new higher education teaching practices and national reforms in education. This system has been developed and tested through an innovative personalized and adaptive method of teaching that exploited digital data from students’ learning activity in immersive environments, with the aid of computational analysis techniques from data science. The unit operations—extraction process course—a fundamental component of the chemical engineering curriculum, was selected as the case study for the development of the HECOF learning system. A group of undergraduate students evaluated the system’s usability and educational efficiency. The findings showed that the HECOF system contributed positively to students’ learning—although the extent of improvement varied among individuals—and was associated with a high level of satisfaction, suggesting that HECOF was effective in delivering a positive and engaging learning experience. Full article

Providing inclusive education and engaging all students in reading and writing activities presents an ongoing challenge for teachers, not necessarily resolved by implementing digital technology. This study addresses the need to better understand teacher competencies within the digitally infused classroom, specifically in relation to inclusive education and reading and writing practices. The study investigates the competencies and supportive strategies of middle school teachers who perceive themselves as successful in this area. The study employs the Delphi technique, using iterative surveys through which these teachers describe and rate aspects of their competencies and strategies. The results are analyzed through a modified version of the Technological, Pedagogical, and Content Knowledge (TPACK) framework, with particular attention to how teachers support students using their content knowledge and digital competency. Findings reveal a range of strategies and competency aspects related to both proactive accessibility and reactive individualization, using a variety of digital tools and text modalities. The teachers describe a dual orientation in their ability to curiously explore digital tools while simultaneously being able to critically appraise their usefulness. The findings contribute insights on what can support teachers when collaboratively developing knowledge of local practices and their agency in relation to available digital tools. Full article

This study explores how artificial intelligence (AI) technologies can be theoretically integrated into literature curriculum development, using the works of Anton Chekhov and Lu Xun as illustrative case texts. The aim is to reduce barriers to language and cultural understanding in literature education and increase the efficiency and accessibility of cross-cultural teaching. We used natural language processing (NLP) techniques to analyze textual features, such as readability index, lexical density, and syntactic complexity, of AI-generated and human-translated “The Chameleon” and “A Madman’s Diary”. Teaching cases from universities in China, Russia, and Kazakhstan are reviewed to assess the emerging practice of AI-supported literature teaching. The proposed theoretical framework draws on hermeneutics, posthumanism, and cognitive load theories. The results of the data-driven analysis suggest that AI-assisted translation tends to simplify sentence structure and improve surface readability. While anecdotal classroom observations highlight the role of AI in initial comprehension, deeper literary interpretation still relies on teacher guidance and critical human engagement. This study introduces a conceptual “AI Literature Teaching Model” that positions AI as a cognitive and cultural mediator and outlines directions for future empirical validation. Full article

The growing implementation of digital platforms and mobile devices in educational environments has generated the need to explore new approaches for evaluating the learning experience beyond traditional self-reports or instructor presence. In this context, the NPFC-Test dataset was created from an experimental protocol conducted at the Experiential Classroom of the Institute for the Future of Education. The dataset was built by collecting multimodal indicators such as neuronal, physiological, and facial data using a portable EEG headband, a medical-grade biometric bracelet, a high-resolution depth camera, and self-report questionnaires. The participants were exposed to a digital test lasting 20 min, composed of audiovisual stimuli and cognitive challenges, during which synchronized data from all devices were gathered. The dataset includes timestamped records related to emotional valence, arousal, and concentration, offering a valuable resource for multimodal learning analytics (MMLA). The recorded data were processed through calibration procedures, temporal alignment techniques, and emotion recognition models. It is expected that the NPFC-Test dataset will support future studies in human–computer interaction and educational data science by providing structured evidence to analyze cognitive and emotional states in learning processes. In addition, it offers a replicable framework for capturing synchronized biometric and behavioral data in controlled academic settings. Full article

This theoretical paper expands upon previous research and proposes a guide for promoting mathematical talk and play through shared book reading (SBR), with a focus on the home environment. Building on a previously developed classroom-based model, this article describes a design-based research approach to extend the guide to including diverse literary genres—such as narrative, informational, multicultural, and math-specific books in a home setting. Parent–child shared book-reading in authentic contexts can provide a rich platform for “math talk”, where references are made to mathematical words, concepts, and content, and may support children’s mathematical skills. SBR with quality children’s literature can play a promising role in motivating and engaging children’s interest and pleasure in both reading and mathematics. However, few studies have explored this with diverse literary genres in the home setting, as the main focus has been in the classroom and using books specifically written to teach math content such as counting or sorting books. The proposed guide provides direction and practical examples for fostering parent–child math talk and play activities that can be used to extend concepts covered during the SBR. The potential application of the SBR guide, and how it can encourage parent–child talk to support a full range of mathematical concepts, encourage home-preschool collaboration, promote effective SBR techniques, and facilitate parent–child conversations about math in new and confident ways is discussed. Full article

In order to improve education for sustainability (EfS) in English-speaking schools in North Cyprus, this study investigates the use of playful learning and video feedback as cutting-edge pedagogical techniques. Engaging students in transformative learning experiences is crucial in an era characterized by environmental issues and the pressing need for sustainable development. Students better understand their learning processes when paired with video feedback, which facilitates reflective practice. This study uses a qualitative case study methodology to investigate how instructors employ video feedback and playful learning activities to help students grasp sustainability ideas. Data were gathered through teacher interviews, classroom observations, and evaluations of student performance and feedback. The results demonstrate how these approaches can promote active learning, boost student enthusiasm, and enhance comprehension of sustainability-related topics. This study’s conclusion includes recommendations for educators and legislators looking to integrate creative, student-centered approaches into EfS curricula. Full article