With recent large-scale migration making societies increasingly diverse, there is a need for education systems and schools to integrate students from all backgrounds within their local contexts. However, educational research and practice fall behind, as gaps in learning outcomes associated with students’ immigrant background remain evident. To this end, an intervention was proposed to support local schools in promoting diversity and inclusion. Lower secondary schools with a relatively high proportion of students with immigrant background were recruited and randomly assigned to the experimental and control groups. The intervention was implemented as a whole-school improvement programme using the Dynamic Approach to School Improvement. Its effectiveness was evaluated in relation to two student learning outcomes relevant to key barriers faced by students with immigrant background: language achievement and multicultural attitudes. Data were collected from 1507 students across 70 classrooms in 15 schools. Analyses of student gains showed relatively low progress in student outcomes across participating schools, which highlights the structural challenges schools face in their efforts to integrate students from all backgrounds. Multilevel regression analyses indicated no overall effect of the improvement programme on either student outcome. Nevertheless, subgroup analyses showed a positive intervention effect on reading achievement for students with immigrant background, whereas the corresponding effect was not statistically significant for students without immigrant background. Implications and suggestions for further research are discussed in light of two important questions in school improvement research: under which conditions and for whom can schools effectively promote student learning outcomes?
Generative artificial intelligence (GenAI) is acknowledged to serve as a key feature of automated essay scoring and AI-powered feedback on academic writing. It has sparked concerns about its validity and equity in assessing second or foreign language (L2) writing. The present study examines Vietnamese English as a foreign language (EFL) teachers’ professional agency in using GenAI to assess undergraduate students’ academic essays. The researchers used an ecological approach to teacher agency, together with a unified perspective of validity, to conduct a multiple-case study with eight teachers from four universities. Semi-structured interviews, grading observations, focus group discussions, and assessment artefacts were used to collect data. Data were then analysed thematically. The findings indicate that the teachers were not merely passive adopters but were selective about GenAI tools for several reasons. They chose GenAI tools based on perceived availability and reliability, used institutional rubrics and task requirements as prompts, and considered AI-generated scores a reference to benchmark with human grading. In addition, they adapted scores, reworded and/or expanded feedback from GenAI, and identified the role of the teachers and GenAI tools in the writing assessment process, prioritising human professional judgment in grading decision-making. Such practices of teacher agency were affected by grading loads, facility constraints, inequitable access to professional learning, and a shortage of institutional guidance. The current study highlights the need for institutional guidelines and regular teacher training on the use of GenAI-mediated writing assessment.
Research on effective mathematics instruction informs teacher preparation, professional learning, student assessment, instructional materials, and classroom observation instruments. Observation protocols are designed to capture instructional quality and the extent to which research-based practices are embedded in classroom instruction. However, although such instruments are typically developed and validated within specific educational systems, they are increasingly used across national and cultural contexts, on the assumption that they will function similarly across settings. Relatively little research has examined how raters interpret and negotiate instructional quality when applying shared observation frameworks across contexts. This study investigates classroom observation as an interpretive process shaped by the interaction among protocol design, instructional context, and rater reasoning. Specifically, the study examines how researchers with different professional backgrounds used three validated observation instruments developed in the United States to analyze one mathematics lesson from the United States, Norway, and Finland. Using a mixed-methods design, the study combined observation scores with recorded collaborative scoring discussions to examine how raters justified and negotiated evaluative judgments. Through discourse-oriented qualitative analysis, the study identified recurring forms of interpretive variation related to operational ambiguity, contextual interpretation, construct overlap, and differing evidentiary thresholds across raters and instruments. The findings contribute to methodological discussions concerning interpretive validity, observer reasoning, and the transportability of classroom observation instruments across educational contexts. The study highlights the importance of examining not only observation scores, but also the interpretive processes through which judgments of instructional quality are produced.
This paper presents a case study of how an undetected wording change in the OECD’s Programme for International Student Assessment (PISA) may have led to erroneous conclusions. We focus on a survey question intended to measure student truancy across countries and over time. While the international documentation suggests that the same question was used across PISA cycles, national documentation reveals that the wording changed in some countries in 2015. We show how this change could easily be missed and how it may affect inferences about trends in truancy, particularly when comparing rates before and after the COVID-19 pandemic. We also examine whether artificial intelligence and large language models could help identify the problem. Instead, these tools provided overconfident but incorrect advice.
Providing effective feedback for student essays has been a longstanding challenge for EFL teachers. Although research has discussed the potentials of generative AI tools in rejuvenating automated writing feedback, few empirical studies have been conducted in authentic educational settings to demonstrate how teachers may collaborate with them in essay evaluation. The present study, adopting the approach of practical action research, examines a teaching assistant’s (TA) experience of employing ChatGPT to enhance teacher feedback in a mainstream college English course in China. Working with an instructor in two action-reflection cycles over a semester, the TA planned, implemented, observed and reflected on AI-assisted teacher feedback for two intermediate-proficiency classes. Data were collected from ChatGPT records, reflective notes, written feedback, student survey and semi-structured interviews to reveal the process and effect of AI-assisted teacher feedback. The first cycle showed that ChatGPT facilitated the TA’s evaluation while introducing new problems and leaving some student needs unaddressed. In the second cycle, a modified use of ChatGPT led to more efficient and satisfactory teacher feedback, yet individualized feedback still remained a longer-term goal. The study proposes a practical framework for integrating generative AI into teacher feedback and underscores the importance of iterative teacher-AI collaboration in advancing sustainable AI-empowered feedback practices.
