This study examines the impact of digital tools on fraction comprehension among 5th-grade students with learning difficulties in mathematics. It assesses the effectiveness of three teaching methods: educational software, video tutorials, and their combination. The research involved 252 students from 8 state-funded elementary schools, employing a quantitative experimental design with pre- and post-test assessments. Grounded in Constructivist Learning Theory and Technological Pedagogical Content Knowledge (TPACK), this research explored how technology can enhance mathematical understanding. Results indicated that the combined method achieved the highest improvement (58%, p < .001, Cohen’s d = 3.03), significantly outperforming educational software alone (33%, p = .015, Cohen’s d = 2.52) and video tutorials alone (7%, p = .987, Cohen’s d = 0.14). These findings highlight the substantial benefits of integrating diverse digital tools to effectively support mathematics learning among students facing additional educational challenges.

Dimensionalizing has been discussed in the Grounded Theory Methodology (GTM) literature primarily as a component that informs the later stages of GTM coding (Strauss) or as a property and variety of human thinking (Schatzman). In this context, dimensional analysis is understood as an approach to concept formation, focusing on identifying the properties and empirical instances of a concept. Through this process, the scope conditions of a theory are clarified and explicitly defined. However, this perspective often overlooks how dimensionalizing contributes not only to concept formation but also to theory building. Specifically, the transition from dimensional analysis to theory development is seldom articulated. This article addresses this gap by using interview data from the substantive field of disability studies — specifically, parents raising a person with a disability (PwD). We demonstrate how dimensional analysis can be effectively applied at the conclusion of open coding, rather than solely at the final stages of GTM analysis, and how it can serve as a methodological tool for connecting categories, rather than for identifying the core category.

In recent decades, the work of teachers has been carried out in classrooms characterised by a high degree of diversity, necessitating the provision of pedagogical responses that promote the inclusion of students. This article is derived from the research, development and innovation (R+D+I) project 'Intergenerational Professional Development in Education Implications for the Professional Initiation of Teachers (DePrInED) and aims to analyse how teachers at compulsory education recognise, characterise and conceptualise the diversity and needs of students. A mixed methodological approach was employed, with information collected through focus groups and interviews with a total sample of 147 teachers from different educational levels (young, senior and retired). Thematic analysis, combined with a comparative analysis, was employed to analyse the data using ATLAS.ti software. The main categories analysed dealt with students' needs and their own diversity. The study's primary findings suggest that, in comparison to their retired counterparts, both young and veteran teachers make greater reference to the diversity and needs of students, and emphasise the scarcity of resources and support to effectively address the diversity of students in the classroom. Experienced teachers are found to make more frequent references to diversity and students' needs, while retired teachers make these references less often. This finding assumes particular significance in light of the paucity of studies that adopt a generational perspective to analyse how teachers perceive diversity.

In primary and middle schools in China, banzhuren is the teacher responsible for managing and overseeing a specific class of students. The lower job satisfaction of banzhurens has been a longstanding issue. This study employs a quantitative method to investigate the impact of banzhurens' self-efficacy and burnout on their job satisfaction. A total of 624 primary school banzhurens from G City (in Henan province, China) participated in an online survey assessing their perceived job satisfaction, self-efficacy, and burnout. The data were analysed using structural equation modelling analysis. The results revealed that (a) banzhurens' burnout negatively influenced their self-efficacy and job satisfaction; (b) banzhurens' job satisfaction was positively influenced by self-efficacy; (c) banzhurens' self-efficacy could mediate the adverse effects of burnout on job satisfaction. Therefore, we suggest that banzhurens' job satisfaction can be enhanced by increasing their self-efficacy, particularly in terms of communication with leaders, and by reducing their burnout.

This study examines the transition challenges faced by students with learning disabilities (LD) from primary to secondary school, focusing on emotional, behavioral, and social aspects. Using a sample of 168 special education teachers, the study employs the Strengths and Difficulties Questionnaire (SDQ-Hel) to assess emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship difficulties, and prosocial behavior before and after the COVID-19 pandemic. Statistical analyses, including t-tests and repeated measures ANOVA, reveal significant increases in emotional and behavioral challenges post-pandemic. Effect sizes (Cohen’s d) indicate moderate to strong impacts in key areas, with emotional symptoms (η² = .06) and hyperactivity/inattention (η² = .05) exhibiting notable changes. The findings highlight the necessity for targeted interventions, such as teacher training on emotional regulation strategies and structured transition programs. Implications for educators and policymakers include the implementation of inclusive practices and specialized transition support structures to mitigate these challenges and enhance the overall well-being of students with LD.

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.

There are studies in the learning management literature examining the measure of system usage, but few explore how users apply the software tools to achieve specific work tasks, which in turn leads to perceived benefits. In the context of distance education, this study focuses on how Learning Management Systems (LMS) are fully used by faculty for their instructional needs. It extends existing research on LMS adoption by investigating how faculty members or instructors use the LMS tools for effective class teaching to achieve educational outcomes. Four usage patterns were identified: communication, content management, assessment, and class management. A model is presented to examine how these usage patterns interplay to achieve the perceived benefits. Data were collected from 544 instructors using LMS, such as Blackboard Learn, etc. Structural equation modeling using LISREL was employed to assess the research model. The results suggest that the usage for communication, content, and assessment activities positively impacts the usage for class management. In turn, the usage for class management influences the net benefits perceived by the instructors, and the usage for content also impacts perceived net benefits directly. These results provide practical guidelines for LMS developers’ design improvements and institutions’ policies, such as training instructors to fully utilize LMS features to achieve the maximum benefits of distance education.

Ensuring the trustworthiness of qualitative research remains a critical challenge in educational research. However, early career researchers often lack structured guidance on enhancing the credibility of qualitative data analysis. A key issue is the limited discussion on inductive approaches that support systematic theme generation and theory development. To address this gap, this study examines how two early-career researchers employed a three-level inductive methodology during their PhD studies to strengthen the trustworthiness of their findings. Using an autoethnographic approach, the study finds that this methodology deepened their understanding of participants’ experiences, facilitated the emergence of valid themes, and reinforced credibility, transferability, dependability, and confirmability. These findings offer concrete strategies for researchers undertaking similar approaches to ensure trustworthiness in their qualitative inquiry. This study also highlights the importance of equipping PhD researchers in education with strategies to navigate qualitative research rigorously, ultimately enhancing the quality of their studies.

Developing efficient and reliable tools for assessing early mathematical skills remains a critical priority in educational research. This study aimed to develop and validate a brief version of the Prueba Uruguaya de Matemática (Uruguayan Mathematics Test, PUMa), a digital tool to assess mathematical abilities in children aged 5 to 6. The original test included 144 items covering both symbolic (66%) and non-symbolic (34%) tasks, such as approximate number system, counting, numerical ordering (forward and backward), math fluency, composition and decomposition of numbers, and transcoding auditory-verbal stimuli into Arabic-visual symbols. Unlike most existing tools that require individual administration by trained professionals and lack cultural adaptation for Latin American contexts, PUMa is self-administered, culturally grounded, and suitable for large-scale assessments using tablets. Using a sample of 443 participants and applying parametric and non-parametric models within the framework of Item Response Theory (IRT), along with correlations with TEMA-3, preliminary evidence was generated showing that the brief version retained precision and validity. The resulting shortened tests included 69 and 73 items for the parametric and non-parametric versions, yielding a balanced representation of symbolic (56%) and non-symbolic (44%) tasks. Despite item reduction, ability scores remained highly correlated between original and brief versions (r > .90), and both brief versions demonstrated strong internal consistency (α = .94). PUMa improves upon existing assessments by combining cultural relevance, group-based digital administration, and real-time data collection, offering a scalable solution for early identification and intervention. These features support personalized educational strategies that foster cognitive and academic development from the earliest stages.