Search Results (936)

This study examined rural pre-service teachers’ preferences for online learning platforms, Telegram, WhatsApp, and Moodle discussion forums in the Open Distance e-Learning environment. This group of students experiences digital illiteracy, limited access to assistive technologies, and network challenges, which may prevent them from optimally utilising formal learning platforms such as Moodle. They can, however, use Telegram and WhatsApp, as they regularly engage informally on these platforms. Against this backdrop, this study explored rural pre-service teachers’ experiences with Moodle and these social media platforms in an Open-Distance e-Learning space. This study employed a descriptive, qualitative case study with semi-structured interviews, guided by Siemens’ Connectivism theory. Fifteen student teachers from the College of Education in an ODeL institution were purposively sampled to provide in-depth insights into their lived experiences of platform use. The findings revealed that, although each platform served a unique instructional function, their perceived professionalism, safety, and interactivity differed substantially. Social media platforms such as Telegram and WhatsApp were lauded for their immediacy, accessibility, and low bandwidth usage, chiefly among rural pre-service teachers from economically disadvantaged communities. However, participants perceived these platforms as unprofessional, disruptive, and unsafe. Conversely, Moodle’s discussion forum was viewed as a credible, structured space that fostered academic discipline through the presence and guidance of lecturers. These contrasting perceptions highlight tensions between accessibility and academic regulation within ODeL environments. Although prior studies support incorporating social media platforms into LMSs, this research extends this discourse by emphasising the need to balance accessibility, interaction, and academic integrity within resource-constrained contexts. The study concludes that social media platforms and discussion forums can complement each other in ODeL, encouraging student interaction and inclusion, while discussion forums ensure educational rigour, safety, and institutional integrity. Full article

Digital literacy (DL) is a cornerstone of sustainable digital transformation in education, yet its systemic cultivation among pre-service teachers in resource-constrained regions remains critically under-theorized. This study develops and validates a contextualized DL measurement framework grounded in the “triple identity” of pre-service teachers as citizens, learners, and future educators. Employing a multiphase mixed-methods design, this systemic framework was refined via Delphi consultation and rigorously validated through exploratory (n = 287) and confirmatory factor analyses (n = 1462) across 24 universities in Southwest China. The newly validated framework, comprising five core dimensions and 41 specific indicators, demonstrates robust psychometric properties and systemic structural validity. Findings reveal a moderately high overall DL (M = 3.44) but highlight a significant “skills-awareness” gap, where operational proficiency exceeds conceptual cognition, particularly in comparison with findings reported in other Chinese regions. Hierarchical regression indicated that structural variables added substantial explanatory power beyond individual demographic characteristics, with institution type emerging as the strongest predictor and urban-rural background making an additional significant contribution. These results underscore the paradox between DL’s inherent malleability and the rigid constraints of structural inequality. The study offers a validated measurement tool and an evidence-informed roadmap for policy interventions aimed at bridging the digital divide in underdeveloped teacher education systems, advocating for a shift from technical training toward systemic equity. Full article

Social justice has become a cornerstone of contemporary educational systems, serving as both an ethical principle and a criterion for evaluating equity in learning opportunities. In the field of Physical Education (PE), its bodily and relational nature makes social hierarchies regarding ability, gender, and body image highly visible. This study adopted a qualitative descriptive design to explore how pre-service PE teachers conceptualize social justice and how they envision its didactic implementation within the Spanish curricular context. The findings provide a critical roadmap for teacher education programs, suggesting that fostering social justice requires moving beyond theoretical discourse toward specific pedagogical tools that address power dynamics and inclusion within Physical Education contexts. Full article

Driven by Emerging Engineering Education and basic education reform, cultivating engineering literacy in pre-service teachers is vital for nurturing innovative talent. This qualitative multiple-case study examines current practices in nine leading Chinese normal universities, primarily through document analysis of institutional policies and curricula, supplemented by faculty interviews and a pre-service teacher survey in a subsample of institutions. Thematic analysis reveals prominent predicaments: a fragmented curriculum, monolithic training models, misaligned resources, and low student motivation. These issues stem from ambiguous conceptual positioning, weak institutional design, and a shortage of specialized faculty and platforms. To address these challenges, this paper proposes a systematic Four-in-One breakthrough framework encompassing Top-Level Design, Platform Foundation, Faculty Empowerment, and Project-Centric Cultivation. Central to this framework is a dual-track drive model, which integrates hands-on engineering practice with pedagogical application, enabling future teachers to develop engineering thinking and the competency to translate it into effective classroom teaching. While the proposed framework requires further empirical validation, this approach offers a theoretical and practical pathway for reconstructing teacher education and building a high-quality teaching workforce. Full article

As AI and digital tools become more widely adopted in school education, integrating them sustainably into teacher preparation has become a central concern for sustainable teacher education. This study examined how pre-service elementary teachers develop scientific concepts within AI-integrated lesson plans and how those patterns change within each case following teaching demonstrations and instructor feedback. Qualitative content analysis was conducted on twelve lesson plans—initial drafts and revised versions from six groups across two science units—produced within an elementary science methods course. Plans were analyzed along three dimensions of conceptual development (conceptual structuring, generalization, and conceptual explicitness) and three functional roles of AI and digital tools. In draft plans, tools were predominantly used for learner engagement and artifact production, with scientific concepts embedded in activity contexts. Following feedback, conceptual explicitness was the dimension most frequently revised, while changes in conceptual structuring and generalization appeared in fewer cases. Cases in which conceptual development reached higher levels in revised plans shared a common design feature: AI outputs were repositioned within the consolidation stage in connection with explicit concept statements, rather than serving as content presentation. These findings suggest that pedagogical judgment about positioning AI outputs within lesson stages, reflected across design–demonstration–feedback–revision cycles, is central to the quality of AI-integrated science lesson design and offers implications for sustaining teacher preparation in the era of AI. Full article

Computational thinking (CT) is increasingly recognized as essential in education, yet teacher preparation programs struggle to develop both computational proficiency and pedagogical readiness in pre-service teachers (PSTs). This study examines an AI-mediated, game-making course grounded in the emerging “vibe coding” paradigm, where 24 novice PSTs iteratively constructed programs through natural language prompting. Adopting a mixed-methods design, the study drew on pre- and post-course attitude questionnaires, reflective accounts of prompting strategies, and open-ended responses. Results indicate that participants substantively engaged with core CT practices, particularly debugging, iterative refinement, and problem decomposition. Nonetheless, this downward recalibration in self-reported coding and teaching confidence represents a productive adjustment rather than a failure. Conversely, attitudes toward game-making improved significantly, with a statistically significant medium effect size for perceived instructional value (d = 0.51), the largest practical effect observed across dimensions. Most participants intended to integrate CT into future teaching. These findings suggest that prompt-driven learning environments support meaningful engagement with computational processes when carefully scaffolded, but do not inherently ensure pedagogical readiness, particularly for higher-order CT practices such as abstraction and pattern recognition. Unlike prior research that has examined game-making processes or PST attitudes toward CT in isolation, this study empirically integrates all three within a single scaffolded instructional design using vibe coding. This integration enables a process-level account of how CT is enacted—and how it develops—when code generation is partially delegated to AI systems. Beyond documenting attitude shifts, the study introduces an analytical rubric for identifying CT engagement in AI-mediated prompting and derives evidence-based design principles that specify the pedagogical conditions under which vibe coding supports, rather than bypasses, computational reasoning. Full article

There is limited understanding regarding whether and how teachers’ talent beliefs evolve across career stages. While most prior research conceptualizes talent beliefs across domains, emerging frameworks emphasize field-specific talent beliefs. An established multidimensional model of talent beliefs provides a theoretically grounded structure for capturing these domain-specific perceptions. Yet comparative evidence across teacher career stages remains limited. Our study examines if verbal and mathematical talent beliefs among in-service teachers and pre-service teachers differ in terms of sources, structure and levels. A total of 307 in-service teachers and 215 pre-service teachers completed validated six-dimensional talent beliefs instruments for both domains and reported sources of their beliefs. Participants—especially pre-service teachers—most strongly attributed their talent beliefs to personal school experiences, while educational science and subject-didactic coursework played a marginal role. Both the mathematical and verbal talent belief scales demonstrated configural and metric invariance, supporting equivalent factor structures and factor loadings across pre-service teachers and in-service teachers. Latent mean comparisons showed that pre-service teachers hold systematically different talent beliefs in comparison to in-service teachers. In-service teachers emphasize talent beliefs concerning domain-specific skills and, for verbal talent, passion—consistent with contemporary talent development frameworks—whereas pre-service teachers focus on external teacher influence and, for mathematical talent, on internal factors. These findings reinforce theoretical claims that talent beliefs are experience-sensitive, multidimensional constructs shaped through socialization in educational contexts. Teacher (further) education should deliberately address the dominance of personal schooling experiences by fostering structured reflection, explicitly targeting belief formation in practice-based courses, and ensuring coherence between higher-education instruction and school-based experiences. Teachers’ impact on their students’ talent development should especially be reflected in further education, since in-service teachers assess their own influence as lower than pre-service teachers do; additionally, passion as a key driver of talent development and the relevance of talent domains should already be highlighted in initial teacher education. Full article

Recent studies indicate that Chinese K-12 teachers possess insufficient knowledge regarding learning disability (LD), hindering their ability to provide effective instruction. Given the foundational role of the pre-service phase in cultivating a scientific approach to teaching, this study aimed to boost pre-service teachers’ LD knowledge and explore its subsequent impact on teaching efficacy and attitudes. Fifty-one pre-service teachers with low levels of baseline LD knowledge were randomly assigned to either a training group or a control group. Utilizing a pretest–intervention–posttest design, the study measured changes in LD knowledge, teaching efficacy, and attitudes toward students with LD. Crucially, attitudes were assessed via a vignette paradigm that differentiated between two components of cognitive evaluations (expectations of future student failure) and emotional experiences (anger arousal towards academic failure). The results showed that pre-service teachers in the training group exhibited substantial gains in LD knowledge. These knowledge gains significantly predicted enhanced teaching efficacy, but this relationship was indirect. Mediation analysis revealed that improved knowledge reduced anger arousal, which in turn boosted efficacy. These findings suggest that fostering teaching confidence requires more than mere knowledge accumulation; it also entails using LD-related knowledge to mitigate negative emotions toward struggling learners. This underscores that teacher education programs must incorporate explicit cultivation of emotional and attitudinal competencies alongside conventional cognitive training. Full article

The importance of Education for Sustainability (EfS) in shaping values and fostering responsible sustainable behaviour has been acknowledged from the early years of education. Our research explores Preservice Preschool Teachers’ (PPTs) perceptions of using game-based learning (GBL) activities to teach EfS, and the impact of designing and delivering such activities on their confidence in teaching EfS. The research used qualitative methods. Data were collected from 12 university students, who developed and implemented GBL activities for preschool children on Monachus monachus. Preservice teachers participated in a group interview and filled reflective reports to describe their overall experience. A reflexive thematic analysis was performed using NVivo software. Outcomes indicate that participants appreciate GBL as an effective pedagogical approach in preschool education, given that it is appropriately and pedagogically designed. Participation, emotional engagement and understanding of complex sustainability concepts emerged as benefits from the involvement in GBL activities, both for the children and for the preservice teachers. Other benefits for the research participants concerned the development of their classroom adaptability, collaboration skills, reflection and confidence. Authentic, collaborative experiential approaches in teacher education and actual teaching opportunities can effectively empower preservice teachers’ confidence to teach about sustainability. Full article

This qualitative review of the literature explores current conversations about the impact of Artificial Intelligence (AI) on teacher education in general and pre-service teachers in particular. Recent advances in AI are beginning to influence teacher education, where curricula, practicum, and school field experience now incorporate AI in curriculum-based instruction and as a context for teaching digital literacy, not as an isolated tool. Researchers regularly situate these shifts alongside broader educational practices and policy. There is also substantial literature dealing with pressing ethical and practical questions such as data privacy and algorithmic bias, equitable access to technology, and the challenges experienced by under-resourced schools. Together, these studies indicate that teachers are redefining and reconfiguring both their own teaching and teacher education, enabled by AI in new, more flexible and responsive ways. Within this shifting paradigm, pre-service and in-service teachers are not conceived as mere end-users but as reflective practitioners who take up such tools, critically question their ramifications, and, sometimes, lead the way in utilizing AI in educational practice, including mainly pedagogical practices. To explain the shared components identified in the present review, this paper offers a post hoc conceptual synthesis of eight recurring dimensions of sustainable AI integration in teacher education. Full article

Lesson planning is a core professional practice for pre-service teachers, yet opportunities for timely, individualized feedback are frequently constrained by educator workload. While generative AI has the potential to enhance planning processes and expand opportunities for individualized feedback, the provision of comprehensive lesson plans may lead to excessive reliance. This conceptual design paper details the development and theoretical underpinnings of an artificial intelligence-assisted feedback tool that provides self-efficacy-strengthening feedback on lesson plans for pre-service teachers. To promote constructive feedback, the AI-assisted feedback tool integrates principles from educational feedback research and structures feedback to foster teachers’ lesson-planning self-efficacy through mastery-oriented affirmations, vicarious examples, social persuasions, and emotional reassurance. Curriculum alignment is incorporated to support content validity and contextual appropriateness. While the initial implementation of the feedback tool focuses on Western Australian teacher education, an explicit transfer perspective is considered for the German vocational education context. The paper describes the iterative development process that follows a design-based research approach including platform evaluation, internal refinement, and expert review by teacher educators in Western Australia. The resulting system prompt architecture comprises 11 dimensions including general baselines, the interaction between the Lesson Planning Coach and PSTs and the theoretical foundations mentioned above. The tools’ environment, including examples for provided feedback on lesson plans, is presented and discussed. Finally, an outlook is given on the planned empirical research to evaluate the effectiveness of the tool. Full article

Learning management systems (LMS) are essential for sustainable teaching and learning procedures due to the growing integration of digital technologies in higher education. Despite the widespread adoption of platforms such as Moodle, limited research has examined the students’ behavioral engagement and their subjective learning experiences. Addressing this gap, this study investigates the relationship between learning analytics indicators and academic performance, and how students’ experiences influence their participation in online learning environments. It adopted a convergent parallel design. Quantitative data were collected from the Moodle-based BirDeHa platform, drawing on learning analytics logs of 137 pre-service teachers enrolled in various programs within a faculty of education. Key indicators included frequency of material downloads, system usage, video engagement, and quiz performance. Qualitative data were collected via focus group interviews with nine participants. The results revealed a clear relationship between students’ interaction patterns within the LMS and their academic performance. Indicators of active engagement, particularly time spent on the platform and frequency of interaction with course materials, emerged as strong predictors of academic success. Qualitative findings further indicate that students perceive the LMS as flexible, inclusive, and supportive of their learning needs. Overall, this study underlines the importance of integrating data-driven insights with student-centered perspectives to achieve a comprehensive understanding of online learning environments and to inform effective design. The findings contribute to the sustainability of digital learning environments by providing behavioral indicators that can inform data-driven instructional design. Full article

Education for Sustainable Development (ESD) has emerged as a key priority in contemporary education systems, emphasizing the need to equip learners with the knowledge and competencies required to address complex environmental and societal challenges. Mathematics education can play an important role in achieving these goals by enabling students to analyse data, interpret real-world problems, and develop critical thinking skills related to sustainability issues. However, despite the growing interest in sustainability-oriented mathematics education, limited empirical evidence exists on how structured mathematical modelling interventions influence pre-service primary teachers’ perceptions, modelling orientation, and confidence in designing sustainability-based mathematics lessons. This study investigates the impact of sustainability-oriented mathematical modelling activities on pre-service primary teachers’ perceptions of integrating sustainability into mathematics education. The study employed a quasi-experimental design involving 68 pre-service primary teachers enrolled in a mathematics education course at a university. Participants engaged in a six-week intervention consisting of modelling activities based on real-world sustainability contexts, including water consumption, energy use, waste management, and sustainable transportation. Data were collected using a pre- and post-intervention questionnaire examining participants’ perceptions of sustainability integration, mathematical modelling, and teaching confidence. Statistical analyses, including reliability analysis, descriptive statistics, paired-sample t-tests, effect size estimates, and correlation analysis, as well as multiple regression analysis, were conducted to examine the impact of the intervention. The results indicate significant improvements in participants’ perceptions of sustainability-oriented mathematics teaching and their confidence in designing modelling-based sustainability activities. The largest improvement was observed in teaching confidence, while mathematical modelling perception emerged as a significant predictor of teaching confidence. The findings suggest that mathematical modelling can serve as an effective pedagogical approach for integrating sustainability topics into mathematics education and preparing future teachers to connect mathematical reasoning with real-world environmental challenges. The study contributes to the growing body of research at the intersection of mathematics education, teacher education, and sustainability education by providing empirical evidence on the potential of modelling-based learning for supporting sustainability-oriented teaching practices. More specifically, it shows how mathematical modelling can function as a concrete pedagogical mechanism for translating Education for Sustainable Development into primary mathematics teacher education. Full article

This study examines how pre-service English teachers in Turkey experience the dimensions of proactive personality, job crafting, flow, and work engagement while making sense of their professional lives. In the phenomenological design research, semi-structured interviews were conducted with 13 teacher candidates selected through purposive sampling. Qualitative data were transcribed and subsequently analyzed using MAXQDA 2022 qualitative data analysis software and high inter-coder reliability was found. The findings were grouped under four themes. Under the heading of proactive personality, solution-focus, patience and communication, seeking support, and continuous development came to the fore. In the flow experience, open feedback from students, activities that made learning enjoyable, changes in the perception of time, and full participation in the natural flow of the lesson stood out. Professional craftsmanship manifested itself through strategies such as establishing trust-based relationships with students, colleagues, and parents, adapting methods, self-care, and time management. Dedication to work was defined through passion for the profession and the vitality provided by working with different student profiles. The results showed that teacher candidates demonstrated resilience-oriented professional strategies by combining individual initiative, social support, and continuous development tendencies. Furthermore, student-centered feedback and community-based relationships are understood to strengthen flow experiences and nurture dedication to work. The study points to the importance of supporting proactive tendencies in teacher training and designing learning environments conducive to flow. Full article

Artificial intelligence is reshaping teacher education, yet many existing approaches still rely on static assessment and provide limited support for interpretable and actionable diagnosis. This limitation is especially important for pre-service kindergarten teachers, whose TPACK development is dynamic, multidimensional, and context-sensitive. To address this gap, this study proposes a multimodal temporal diagnostic and personalized intervention framework for sustainable teacher preparation. Using longitudinal data from 186 pre-service kindergarten teachers across four developmental stages over three academic years, the framework integrates self-report, behavioral, performance, and reflective evidence to model developmental trajectories, identify latent states, and generate explainable support recommendations. The results show that overall TPACK increased from 3.21 to 3.73, with the largest gains in technology-related integration dimensions. Compared with representative baselines, the proposed framework achieved the best diagnostic performance (MAE = 0.171, RMSE = 0.238, Accuracy = 0.821, Macro-F1 = 0.806). These findings suggest that AI can support teacher education not only through more accurate diagnosis, but also through interpretable and development-sensitive intervention. Full article