English Language Performance Assessment in Education: A Systematic Review of Methodological Approaches and Geographical Trends
Ebitiminipre Mercy Ogbise
*
School of General Studies, Nigeria Maritime University, Okerenkoko, Nigeria.
Nwogu Hope Ozioma
Department of English and Communication Arts, Faculty of Humanities, Ignatius Ajuru University of Education, Port Harcourt, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This systematic review examined 50 studies on English language performance to synthesise evidence on population distribution, geographical coverage, publication trends, research instruments, and factors affecting learner outcomes. Findings indicate that secondary school populations dominated the literature (48%), followed by post-secondary settings (40%), while primary school learners were substantially underrepresented (12%). Geographically, the Philippines contributed the most studies (n=18), followed by China and Nigeria (4 and 3 respectively). When classified by language status, ESL contexts accounted for 52% of studies, non-native English settings comprised 46%, and native English-speaking contexts were minimally represented (2.0%). Publication output surged from minimal activity between 2020 and 2023 to a peak of 28 studies in 2025, indicating accelerating research engagement. Methodologically, SPSS, questionnaires, and proficiency tests were most frequently employed, with AI and digital tools increasingly integrated. The synthesis revealed that appropriate teaching styles, learner engagement strategies (digital platforms, mobile apps), and technological tools (AI, CALL, gamification) positively influence English performance. Evidence on language anxiety was mixed: some studies reported negative correlations with achievement, while others found no significant relationship. Identified anxiety factors included grammatical issues, vocabulary gaps, fear of mistakes, and low confidence. The review concludes that while research has grown rapidly, significant gaps persist in primary education and native English-speaking contexts. Limitations include geographical imbalance, predominance of cross-sectional designs, and partial data for 2026.
Keywords: English language performance, systematic review, learning analytics, artificial intelligence, statistical methods, higher education