Although the extensive literature captures the experiences of established academics, far less attention has been paid to the transitional journeys of early-career academics entering the field of teacher education. This paper foregrounds the voices of novice lecturers of English, who have moved from teaching in Basic Education (BE) to lecturing in Higher Education (HE).

The study investigates how these emerging academics navigate the complexities of HE while drawing on their prior professional identities and competencies shaped in BE. Using a qualitative research design, this study incorporates semi-structured interviews, solo vlogs, and visual reflective artefacts to uncover the thematic challenges that surface during this transition. These include the need to recalibrate pedagogical approaches, respond to increasingly diverse and vulnerable student cohorts, and translate BE teaching expertise into the distinct demands of HE.

Early findings reveal that foundational skills developed in BE often serve as stabilising resources, supporting novice lecturers as they cultivate adaptive and resilient teaching practices within HE. Anchored in Transformative Learning Theory, this research underscores the significance of supportive institutional structures, evolving professional identities, and ongoing development opportunities in shaping resilient academic trajectories during periods of systemic disruption.

By offering a narrative inquiry into how early teaching experiences inform and transform tertiary teaching practice, this paper contributes valuable insights for educational leaders, policymakers, and teacher‑education scholars seeking to strengthen resilience and quality within HE settings, navigating persistent and emerging crises.

Innovative instructional strategies are increasingly needed to enhance student engagement in quantitative engineering courses. This study examines a collaborative-competitive learning design implemented in Industrial Statistics course for Undergraduate Student of Industrial Engineering, Telkom University. Students collaborate within groups at each meeting by working on questions in groups and in some sessions, students competing across groups. Students may respond differently to such instructional designs. To evaluate how students respond to this instructional innovation, a data-driven using cluster analysis was employed. Data were collected from 200 undergraduate students through a structured questionnaire after implementing the learning design innovation. Instruments measured collaboration quality, attitude towards work group, sense of belonging, perceived learning outcomes, and competitive orientation. Following data screening, descriptive analysis, reliability testing and analyzed using K-means clustering. Based on silhouette evaluation and interpretability considerations, a three-cluster solution was retained. The findings reveal three distinct engagement profiles: (1) highly engaged students demonstrating strong collaboration, belonging, and competitive motivation; (2) moderately engaged students showing balanced but less intense responses; and (3) a small segment of disengaged students with consistently lower scores across dimensions. These findings indicate that collaborative–competitive learning generates differentiated engagement patterns rather than uniform effects. The proposed data-driven framework offers practical implications for designing adaptive and inclusive learning environments in engineering education.

The growing prominence of data-driven decision-making across scientific, economic, and social domains has intensified the demand for graduates possessing strong competencies in statistics, data literacy, and data science. In response, higher education institutions are increasingly revising curricula and instructional practices to incorporate data science and statistical education across diverse academic disciplines. This study presents a systematic review of the scholarly literature examining pedagogical approaches for teaching data science and statistics in higher education. The review aims to synthesize existing research on instructional strategies, curriculum design, and the integration of computational tools that support the development of analytical and data-oriented competencies among university students. To ensure methodological rigor, a structured literature search was conducted using the Scopus database, focusing on peer-reviewed publications addressing statistics education, data science pedagogy, and data literacy within university-level contexts. The retrieved studies were screened and selected based on predefined inclusion and exclusion criteria, enabling the identification of relevant pedagogical patterns and emerging instructional practices. The analysis reveals several dominant approaches within the literature, including project-based learning, inquiry-oriented statistics education, the use of real-world datasets for applied data analysis, and the integration of programming environments that support computational thinking and data exploration. Furthermore, recent research highlights the growing role of digital tools and interactive technologies in facilitating data visualization, collaborative learning, and experiential engagement with complex datasets. Overall, the findings indicate a gradual pedagogical shift from traditional lecture-based instruction toward more applied, interdisciplinary, and technology-enhanced learning environments. This review contributes to a deeper understanding of evolving teaching practices in data science and statistics education and identifies key directions for future research in higher education pedagogy.

This abstract is framed within a doctoral dissertation that examines the challenges of education in the twenty-first century, particularly at the intersection of digital training and identity construction in sociotechnical environments. In a context where digital interaction dynamics (e.g., likes, shares, trends, and comments) shape processes of socialization and identity formation, digital (Redecker, 2020), democratic (Evans et al., 2018), and educational competences (RD 126/2022; RD 218/2022), together with the requirements established for pre-service teachers (LOSU, 2023), call for preparation grounded in an intersectional gender perspective within the digital sphere.

In response to this need, the study analyzed the critical capacity of pre-service teachers through the examination of social media excerpts widely consumed by Generation Alpha. The research was grounded in Grounded Theory and Feminist Critical Discourse Analysis, complemented by a mixed-methods approach to data analysis.

Findings reveal a significant lack of critical digital competence from a gender-informed perspective, regardless of participants’ gender identity. Notable resistance emerged regarding expressions related to new masculinities, transgender identities, and diverse sexual orientations. Low scores were also identified in areas concerning racialization, sexualization, gender roles and stereotypes, and affective-sexual relationships.

The study underscores the need to develop the SoGenDigCompEdu framework in order to foster comprehensive teacher education capable of promoting meaningful “onlife” social transformation.

The rapid advancement of artificial intelligence technologies has significantly influenced contemporary educational practices, particularly within higher education. Universities are increasingly exploring the pedagogical potential of AI–driven tools to enhance teaching, learning, and assessment processes across academic disciplines. Within this evolving educational landscape, understanding how artificial intelligence is being integrated into higher education pedagogy has become an important focus of educational research. This study presents a systematic review of the scholarly literature examining emerging educational practices associated with the use of artificial intelligence in university teaching and learning contexts. A structured search was conducted in the Scopus database to identify peer-reviewed publications addressing the pedagogical applications of artificial intelligence in higher education. The selected studies were screened and analyzed according to predefined inclusion criteria to synthesize current research trends, pedagogical models, and technological approaches discussed in the literature. The review identifies several key areas in which artificial intelligence is being integrated into higher education pedagogy, including intelligent tutoring systems, adaptive learning environments, automated feedback and assessment tools, and the use of generative artificial intelligence to support student learning and academic writing. Additionally, the literature highlights emerging discussions on ethical considerations, academic integrity, and the evolving roles of instructors and learners in educational environments mediated by artificial intelligence. Overall, the findings indicate that artificial intelligence is reshaping pedagogical practices by enabling more personalized, data-informed, and interactive learning experiences. This review contributes to a deeper understanding of how artificial intelligence technologies are transforming higher education pedagogy and outlines directions for future research on responsible and effective integration of artificial intelligence in university teaching and learning.

Introduction: In a social and educational context increasingly focused on the promotion of STEM competencies, mathematical skills represent key competencies of the 21st century and are essential for active and informed participation in contemporary society. Mathematics anxiety is a pervasive global issue that occurs across different cultural contexts and spans from childhood to adulthood.

It has significant repercussions on students’ performance and attitudes toward the subject. Despite this, most research has focused on secondary school and university, leaving the primary school context relatively underexplored. Understanding MA in the early years of schooling is crucial for developing preventive interventions and fostering a positive relationship with mathematics; therefore, this systematic review aimed to examine the relationship between MA and performance and identify intervention strategies.

Methods: The systematic review was conducted following the PRISMA 2020 guidelines. The databases Scopus, PubMed, and Google Scholar were consulted, considering publications from 2014 to 2024 in English, Italian, and Spanish. Meta-analyses, systematic reviews, and research articles on mathematics anxiety in primary school students were included. The search strategy used the keywords “math anxiety” AND “primary school.” Articles were selected through a screening process, and data were extracted and presented in tables reporting authors, study type, sample characteristics, methods, and main results.

Results: A total of 43 studies met the inclusion criteria. Evidence shows a negative relationship between mathematics anxiety and performance, even in the early years of schooling, suggesting a bidirectional relationship between anxiety and performance. The importance of teacher support also emerges, along with the need for interventions aimed at both enhancing mathematical competencies and managing students’ emotional experiences.

Conclusions: Early identification of mathematics anxiety and targeted interventions are essential to prevent avoidance behaviours and underachievement. The findings highlight educational implications useful for teachers and researchers, suggesting strategies to support emotional regulation and mathematical engagement in students.

Keywords: mathematics anxiety; primary school; STEM; 21st-century skills; academic performance; working memory.

In contemporary higher education, university professors’ emotional competencies-such as empathy, emotional regulation, relational sensitivity, and effective emotion management-are increasingly recognized as essential professional skills that extend beyond disciplinary expertise. These competencies influence students’ academic engagement, motivation, psychological well-being, and overall satisfaction with their learning experience.

Drawing on the ability model of Emotional Intelligence (Salovey & Mayer, 1990) and the situated concept of emotional competence (Saarni, 1999), this qualitative study builds on a recent systematic review (Brandao De Souza & Jacomuzzi, 2025) to explore how university students perceive and experience these competencies in everyday academic interactions. Data were collected through 21 in-depth semi-structured interviews with undergraduate and master’s students enrolled in humanities programs (philosophy, pedagogy, and related fields) at an Italian university in northern Italy. Interviews were conducted shortly after lectures or examinations to capture emotionally salient experiences. All interviews were audio-recorded, transcribed verbatim, anonymized, and analyzed through reflexive thematic analysis (Braun & Clarke, 2006).

The analysis identified five interconnected themes. First, empathy and the humanization of the professor–student relationship emerged as central elements, with attentive listening, emotional availability, and reduced power distance fostering trust and a stronger sense of belonging. Second, relational and communicative styles were found to shape classroom climate: passionate, dialogical, and respectful communication enhanced motivation and engagement, whereas rigid or impersonal styles generated emotional distance and stress. Third, emotional regulation during high-stress contexts, particularly examinations, played a crucial role in mitigating anxiety and supporting students’ confidence and performance. Fourth, students perceived disciplinary and contextual differences, with humanities and in-person teaching generally facilitating more relational dynamics than scientific fields or online formats. Finally, participants emphasized the need for balanced emotional openness, constructive approaches to error, and institutional initiatives supporting the development of these competencies as learnable professional skills.

The evolving demands of the global workforce have intensified the need for flexible, competency-based models within higher education. Micro-credentials have emerged as a promising mechanism to bridge the gap between academic qualifications and industry requirements by offering clearly defined competencies. This study examines the integration of micro-credentials into undergraduate programmes at a higher education institution in the United Arab Emirates (UAE), focusing on their role in enhancing graduate employment readiness. Adopting a pragmatic research paradigm and an explanatory sequential mixed-methods design, the study first analyses quantitative data and subsequently interprets these findings through qualitative inquiry. It explores the perceptions of students, faculty, and employers regarding the acceptance, perceived benefits, and implementation challenges of micro-credentials, while identifying priority skills considered critical for workforce alignment. The findings highlight broad stakeholder support for micro-credentials as instruments for enhancing employability, alongside concerns related to quality assurance, standardisation, recognition, and institutional integration. Situated within the UAE’s national strategies for economic diversification and human capital development, the study provides context-sensitive insights into the governance and sustainability of alternative credential models. By offering an empirically grounded, multi-stakeholder analysis, this research contributes to the growing scholarship on micro-credentials in higher education and presents actionable recommendations for policymakers and institutional leaders seeking to strengthen workforce readiness through modular credentialing frameworks.

The COVID-19 pandemic substantially disrupted teachers’ work environments, requiring educators to rapidly adapt to distance and hybrid instruction while navigating new digital tools and instructional practices. Prior research indicates that these disruptions increased teachers’ workload, stress, and professional demands (Mankki & Räihä, 2022; Gunnþórsdóttir et al., 2021). At the same time, some studies suggest that teachers expanded collaboration with colleagues to cope with these challenges and to exchange instructional resources (Anis, 2024). These developments raise an important question for the post-pandemic era: did teachers become more isolated in their work, or did collaborative professional practices strengthen?

Using data from the OECD Teaching and Learning International Survey (TALIS) 2018 and TALIS 2024, this study examined changes in teachers’ professional learning, professional collaboration, and perceived workload before and after the COVID-19 pandemic. First, we investigated whether teachers’ reported participation in professional learning, collaboration with colleagues, and perceived workload differ across the two survey cycles. Second, we examined whether the relationships among collaboration, workload, and teacher well-being have changed over time.

Prior to comparing structural relationships, measurement invariance across TALIS 2018 and TALIS 2024 was tested to ensure the comparability of constructs across the survey cycle. A mediation model was then estimated in which professional collaboration was associated with teachers’ perceived workload, which in turn predicted teacher well-being, operationalized as job satisfaction or intention to leave the profession. Multi-group structural equation modeling was used to compare these relationships between pre-pandemic (TALIS 2018) and post-pandemic (TALIS 2024) contexts.

The findings contribute to understanding how collaboration functions as an organizational resource supporting teacher well-being in the post-pandemic educational environment.

Background: Teaching models, philosophies and techniques continuously evolve and get adopted. One of these models is the role of patients or people with lived experience of a condition as teachers in clinical education. The main import of the model is to utilize the patients’ or individual’s experiential knowledge to enhance the learning journey of the students. Aim: The aim of this article is to report a final year class of physiotherapy students’ experiences of being taught by a person with a lived experience of spinal cord injury (SCI). Method: A middle-aged individual with about a 20-year experience of living with SCI was requested to teach the students about spinal cord injury, including its causes, care and any other thing on the condition the individual deemed fit. During a period of 2 hours, the individual taught the students the about the causes of SCI, its symptoms and complications, burden (psychological and social including relationships, and family), resilience, coping strategies, support groups, expectations, job opportunities, return to work, and treatments. The students were then asked to share their experience of the teaching, and the data obtained was analyzed using thematic analysis. Result: The class expressed that the teaching provided them with experiential knowledge, enlightenment and awareness, and emotional connectedness with patients, and that it influenced their perspectives on patients and the need for their care to be holistic. Conclusion: The significance of individuals with lived experience of a condition serving as teachers in clinical education cannot be emphasized. This is because, among other things, they can provide students with experiential knowledge that can enhance their ability to learn how to make effective clinical decisions.