inclusive-education
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- PaperOpenAlex — TESOL research22 Jul 2026
Inclusive English Language Teaching: Bibliometric Analysis of Multilingualism and Inclusive Classroom Practices
Cristine Ombao
A bibliometric analysis of 56 articles from Scopus (2017-2025) mapped publication trends, leading contributors, and thematic clusters in multilingualism and inclusive classroom practices in English Language Teaching. The United States and Indonesia led research output, with inclusive education and ELT as dominant theoretical anchors. Underdeveloped areas include decolonization and disability-inclusive pedagogy.
Original abstract
This bibliometric analysis examined published research on multilingualism and inclusive classroom practices in English Language Teaching retrieved from the Scopus database from 2017 to 2025. Guided by three research questions, the study mapped publication trends, leading journals, authors, institutions, and dominant thematic clusters across 56 articles using VOSviewer for keyword co-occurrence visualization. The findings revealed a steady acceleration in publication output over the analyzed window, with inclusive education and English language teaching emerging as the dominant theoretical anchors of the field. The United States and Indonesia led the field in research output, followed by a globally distributed tier including Poland, Iran, Spain, and South Africa. The University of Warsaw (3 publications) and Isfahan University of Technology (2 publications) ranked as the most productive institutions, while Chan and Lo, Lau and Shea, and Nijakowska et al. registered as the most cited authors. The keyword analysis clustered scholarship around inclusive, multilingual, EFL/ESL, and higher education strands, with peripheral domains such as decolonization and disability-inclusive pedagogy remaining underdeveloped. The findings provide a foundation for advancing evidence-based, multilingual, and equitable ELT research through the next decade.
- PaperTESOL Journal7 Jul 2026
Developing Critical Consciousness: Pre‐Service Teachers' Understanding of Racialization and Commitment to Critical Race Praxis
Christian Fallas‐Escobar, Huseyin Uysal
Pre-service teachers who recognize the racialization of English learners begin to envision themselves as agents of critical praxis, committed to fostering inclusive learning environments. This study, involving seven pre-service teachers through drawings, reflections, and focus groups, highlights the potential of praxis to challenge oppressive structures in education.
Original abstract
This study explores how pre‐service teachers conceptualize their teaching practices in response to their growing awareness of the racialization of English learners in US schools. The study involved seven pre‐service teachers (PSTs) and included drawings, reflection essays, discussion posts from 11 meetings in informal settings, and two focus group interviews. Findings show that by recognizing English learners' racialization, pre‐service teachers started to envision themselves as agents of critical praxis, committed to fostering inclusive learning environments. This study highlights the potential of praxis to helping pre‐service teachers commit to challenging oppressive structures in education.
- PaperJournal of Learning Analytics25 Feb 2026
Learning Analytics to Uncover Ethnic Bias in Educational Texts
Josmario Albuquerque, Bart Rienties, Martin Hlosta, Wayne Holmes
This study uses learning analytics and machine learning to detect ethnic bias in text-based educational materials. Support vector machines and random forest classifiers performed consistently (F1-scores ~0.70), while naïve Bayes achieved highest precision (0.75). The findings highlight correlations between perceived bias and social sciences content, offering a scalable foundation for automated bias detection.
Original abstract
Online learning platforms have expanded access to education but also raise concerns about biased content, particularly in text-based learning materials such as textbooks, lesson plans, and course excerpts. Such biases can perpetuate discrimination, can harm student outcomes, and can often be difficult to detect, as identification typically relies on time-consuming human review. Learning analytics (LA) can enhance this process by supporting human reviewers through automated detection, offering a scalable solution while retaining human judgment for nuanced evaluations. Accordingly, this LA study explores two research questions: RQ1: Which features might support the identification of ethnic bias in text-based online learning materials? and RQ2: Which classification approaches might be suitable for identifying ethnic bias in text-based online learning materials? First, we identified features signalling potential ethnic bias (presence or absence) in textual content using a dataset (N = 345) labelled by 193 students from diverse ethnic backgrounds. Then, we evaluated multiple machine learning (ML) models for their effectiveness in bias classification. The results suggest significant correlations between perceived bias and content from social sciences. Additionally, through bootstrap analysis, support vector machines and random forest classifiers showed consistent performance in bias identification (with F1-scores of 0.71 and 0.70 on the test set, respectively). In contrast, the naive Bayes (NB) model demonstrated the highest precision (0.75 on the test set). We discuss these findings and their implications for LA, emphasizing the importance of quality and inclusive educational tools. As an initial step toward automated bias classification in education, this study provides a foundation for spotting ethnic bias in learning content, supporting fairer technologies for more inclusive learning environments.