Britain International of Linguistics Arts and Education (BIoLAE) Journal

Factors Influencing General Science Performance of Female Students in Grade 7 at Rimeti Primary School, West Harerge, Ethiopia

2026-02-12T08:16:10+00:00

This study explores the impact of teacher practices and classroom dynamics on the science performance of 75 Grade 7 female students at Rimeti Primary School, conducted in July 2024/25. Utilizing a two-way ANOVA on simulated test score data, the research assessed the effects of interactive versus traditional teaching methods and collaborative versus competitive classroom environments. Results revealed significant main effects for teacher practices (F (1, 96) = 30.56, p = 2.78e-07), with interactive methods yielding higher mean scores (72.15) compared to traditional methods (61.95), and classroom dynamics (F (1, 96) = 18.12, p = 4.82e-05), where collaborative settings outperformed competitive ones. The interaction effect was non-significant (F (1, 96) = 0.02, p = 0.885), indicating independent contributions. A box plot visually confirmed these trends, showing elevated medians for Interactive/Collaborative groups. These findings suggest that interactive teaching and collaborative dynamics independently enhance science achievement, with teacher practices explaining greater variance (1621.66 sums of squares) than dynamics (961.69). Limitations include the use of simulated data and a single-gender focus, restricting generalizability. The study aligns with social constructivist and cooperative learning theories, advocating for pedagogical innovation in under-resourced settings. Recommendations include teacher training in interactive methods and fostering collaborative classrooms, supported by resource allocation and further longitudinal research. This research provides a foundation for improving science education for female students in similar contexts.

Analysis of Defamation on the X Account of Gibran Rakabuming Raka

2026-03-13T02:31:08+00:00

This study examines the practice of defamation in the comment section of Gibran Rakabuming Raka’s social media account on X during the period from December 2023 to February 2024. A qualitative method was employed using reading and note-taking techniques, alongside theoretical triangulation combining Defamation Theory and Syntax Theory to ensure data validity. Data analysis was conducted using equivalence (padan) and distribution (agih) methods. The findings reveal that the linguistic forms of defamation consist of words (57 instances), phrases (82 instances), clauses (56 instances), and sentences (8 instances), with phrases and words dominating as concise yet sharply meaningful expressions. In terms of defamation types, insults (142 instances), defamation of character (38 instances), slander (22 instances), and blasphemy (1 instance) were identified, with insults being the most prevalent form. These findings indicate that defamation on social media tends to be direct, brief, and targets both the personal image and public reputation of the subject.

The Role of Classroom Emotional Climate in Predicting Student Engagement and Learning Satisfaction

2026-03-17T07:59:21+00:00

The emotional climate of university classrooms has emerged as an important factor influencing student engagement and learning experiences. This study explores how classroom emotional climate influences student engagement and learning satisfaction among undergraduate students. The study involved 312 undergraduate students enrolled in social science courses at University of Copenhagen. Students completed validated scales measuring perceived classroom emotional climate, engagement, and learning satisfaction. Data were analyzed using multiple regression and path analysis. Classroom emotional climate significantly predicted student engagement (β = 0.483, p < 0.001) and learning satisfaction (β = 0.451, p < 0.001). Engagement also significantly predicted satisfaction (β = 0.372, p < 0.001), suggesting a partial mediation relationship. Positive classroom emotional climates enhance student engagement and improve overall learning satisfaction. Universities should support teaching practices that foster supportive and emotionally positive classroom environments.

Impact of AI-Driven Personalized Learning in Educational Systems on Policy Development

2026-04-18T07:33:43+00:00

The integration of AI-driven personalized learning within educational systems is transforming the landscape of policy development. This abstract explores the profound implications of these technologies on various educational policies, including curriculum design, assessment practices, and teacher training initiatives. Personalized learning models, powered by artificial intelligence, offering tailored educational experiences that cater to individual student needs, thereby enhancing engagement and academic achievement. As educators leverage data analytics and adaptive learning technologies, policymakers are prompted to rethink traditional frameworks to create adaptable, inclusive, and equitable educational environments. The findings will underscore the necessity for policies that facilitate the integration of AI tools while ensuring responsible use of data, equity in access, and support for teachers. This discourse will also highlight the importance of ongoing professional development for educators to navigate these innovations effectively. Ultimately, AI-driven personalized learning not only reshapes instructional methodologies but also necessitates a re-evaluation of policy structures to foster an ecosystem conducive to continuous improvement and innovation in education.