GPH-International Journal of Educational Research

Ethnocentrism and Social Cohesion in Multi-Ethnic Societies: A Systematic Review of Evidence from Africa

2026-08-07T11:17:43+00:00

Ethnic diversity is not, by itself, a threat to nationhood; what determines whether multi-ethnic states cohere or fracture is how citizens and institutions treat difference, a distinction that motivates this systematic review of Ethnocentrism and Social Cohesion in Multi-Ethnic Societies: Evidence from Africa. The paper achieved four objectives: examining the manifestations and drivers of ethnocentrism among ethnic nationalities within multi-ethnic African states; assessing its effects on social cohesion, governance, and development; evaluating xenophobia in South Africa as a form of intra-African ethnocentrism directed at foreign nationals; and identifying mechanisms capable of mitigating ethnocentric division. Social Identity Theory was adopted as the guiding framework, explaining ethnocentric behaviour as a product of in-group favouritism and out-group derogation pursued for positive distinctiveness. The review followed PRISMA 2020 protocol, screening records for scholarly traceability before synthesising twenty-three included studies. Findings of the paper showed that ethnocentrism, rooted in colonial classification and sustained by elite political competition and resource pressure, weakens governance and depresses development outcomes across Kenya, Nigeria, and Rwanda, while South African xenophobia reproduces this same logic across national rather than intra-state boundaries, driven more by institutional grievance than by migrants' actual conduct. The paper concluded that durable cohesion requires addressing both the psychological and structural roots of ethnocentrism, and recommended merit-based public appointment systems, factual correction paired with service delivery reform in South Africa, and structured intergroup contact combined with inclusive superordinate identity narratives.

Career Self-Orientation Competence Among High School Students in Hanoi

2026-08-16T09:52:03+00:00

This article focuses on understanding students' career self-orientation competence, based on quantitative and qualitative data collected from high school students at some schools in Hanoi. The quantitative database is taken from survey of high school students, while the qualitative database is drawn from in-depth interviews with high school students across different grades and school types.The article presents the main results of the research. First, it lays out the research basis, including the subject, object, and scope of the study, together with the theoretical basis of career self-orientation competence and an overview of related domestic and international research. Second, it presents the current status of students' career self-orientation competence, covering their knowledge of jobs, self-assessment ability, and confidence in career decision-making. Third, the research concludes by identifying career self-orientation competence and proposes some recommendations to increase the student’s competence.

ARGUMENTATION FROM EVIDENCE: A BIBLIOMETRIC ANALYSIS OF THE KNOWLEDGE STRUCTURE OF AN INTERDISCIPLINARY RESEARCH FIELD

2026-09-01T10:51:15+00:00

Argumentation from evidence is treated as a core scientific practice in contemporary science education, yet the term argumentation is used with different meanings across several research communities. This creates difficulty in delimiting the literature when a review is undertaken. This study maps the knowledge structure of the literature on argumentation associated with data and evidence, using metadata from Scopus and four science mapping techniques implemented in VOSviewer. A high-sensitivity query on the TITLE-ABS-KEY field returned 14,938 records, with no restriction on year, document type, subject area or language. The keyword co-occurrence network retained 1,000 keywords in five clusters; the co-citation network retained 251 cited references in seven clusters; the co-authorship network retained 13 authors in three clusters; and the document citation network retained 21 documents in six clusters. The four analyses converge on a single observation: the field is a set of communities that share terminology rather than an integrated research area. The science education cluster in the co-citation network has the highest total link strength among the seven clusters and contains a stable set of foundational works published mainly between 2000 and 2010. The argumentation theory cluster is the smallest in size but has the highest mean link strength, indicating that it is cited across clusters. Keywords emerging in the final two years of the dataset are associated with generative artificial intelligence. Two implications are drawn, one for the design of search strategies in review studies and one for research on argumentation in school science.

Artificial Intelligence Use in Learning Among Students at a High School in Quang Ninh Province, Vietnam

2026-09-01T11:15:34+00:00

Generative artificial intelligence tools have entered the everyday learning of high school students faster than schools have produced guidance on their use. This study describes the current state of that use at one public high school in Quang Ninh province, Vietnam. A cross-sectional survey was administered to 124 students in grades 10, 11 and 12 during the 2025-2026 school year. The instrument contained a demographic section and sixteen four-point Likert items organised into three groups covering the frequency of AI use in learning activities, students’ views on the effects of AI on their learning, and the difficulties they encountered. Data were processed in JASP. Internal consistency was .863, .831 and .846 (Cronbach’s alpha) for the three groups. All students reported owning a smartphone and 51.6% had been using AI tools for more than twelve months, yet only 24.2% had received subject-specific guidance from teachers. Item means ranged from 2.52 to 2.73 on the four-point scale, and every item showed a dispersed response pattern in which between 19.4% and 31.5% of students selected the lowest level while between 27.4% and 37.9% selected the highest. The item on checking AI-generated content against textbooks was among the least frequently endorsed practices, while the item on inaccurate or curriculum-misaligned content received the highest difficulty rating. The findings indicate a gap between the availability of AI tools and the presence of school-level guidance on their use, and point to verification practice as an area for instructional attention.