Modeling the effects of teaching methods on Student Academic Performance using Multilevel Statistical Techniques

This study examined the impact of various teaching methods on students’ academic performance using advanced multilevel statistical techniques. The research adopted a quantitative explanatory cross-sectional design involving 30 teachers and 300 students drawn from 15 basic schools in a selected district in Ghana. Data was collected through structured questionnaires, classroom observations, and students’ academic records. Hierarchical Linear Modeling (HLM) was employed to analyze both student-level and teacher-level predictors of academic achievement, thereby providing a comprehensive understanding of the factors influencing students’ learning outcomes. The findings revealed that lecture-based instruction remains the most frequently used teaching method in classrooms; however, it demonstrated a weak negative relationship with students’ academic performance. Conversely, inquiry-based and problem-based learning approaches showed significant positive effects on academic achievement, suggesting that student-centered pedagogies are more effective in enhancing learning outcomes. The study concludes that effective classroom practices and the adoption of learner-centered teaching methods are essential to improving academic performance among students. Consequently, the study recommends intensified teacher professional development programmes, continuous training in innovative pedagogical approaches, and educational policy reforms that promote data-driven instructional practices and active learning strategies in schools. In terms of contribution to scholarship, this study contributes to the growing body of educational research by demonstrating the applicability and effectiveness of Hierarchical Linear Modeling (HLM) in analyzing educational data within the Ghanaian basic school context.