The impact of teaching methodology for academic achievement in mathematics subject by using predictive analytics - Ong, Shu Rou (2024)
This research investigates the profound impact of teaching strategies on students’ academic performance with a particular focus on mathematics, which is a subject foundational to critical thinking and problem-solving skills. The study delves into the application of predictive analytics to evaluate and improve instructional methodologies in mathematics education, addressing the limitations of traditional teaching approaches that often fail to cater to diverse student learning styles. By critiquing the conventional "one-size-fits-all" method, the research identifies key challenges, such as the lack of personalization, inadequate responses to evolving educational needs and inefficiencies in traditional mathematics instruction. The primary objective of this study is to use predictive analytics to examine the relationship between teaching strategies and students’ mathematics performance. Sub-objectives include identifying effective teaching techniques, addressing the inefficiencies of traditional methods and exploring personalized instructional solutions to accommodate diverse learning preferences. The study employs the Data Science Life Cycle (DSLC) methodology, which encompasses problem identification, data investigation, pre-processing, exploratory data analysis, data modelling and model evaluation to assess the influence of teaching strategies on academic outcomes. By focusing on undergraduate students from Universiti Tunku Abdul Rahman (UTAR) across the Business and Finance, Information and Communication Technology and Arts and Social Science faculties. The research aims to understand the diverse learning needs across these disciplines. The findings underscore the significant impact of tailored teaching strategies and enhanced through predictive analytics on students' understanding and performance in mathematics. The proposed solutions emphasize the need for flexible, inclusive and adaptive instructional methods to improve educational outcomes. This research contributes valuable insights to various stakeholders, including educators, policymakers and technology developers by offering evidence-based recommendations for integrating predictive analytics in education. Ultimately, the study aspires to enhance students' academic experiences and equipping them with the skills needed to succeed in a rapidly evolving educational landscape.