Teacher Competence as a Predictor of Learners’ Performance in Statistics: A Quantitative Study
Document Type
Paper presentation
School Name
University of San Agustin
School Code
N/A
Abstract / Executive Summary
Mathematics forms the foundation of STEM education and plays a critical role in developing analytical and problem-solving skills; however, it continues to present significant instructional challenges worldwide. This study examined teacher competence as a predictor of learners’ performance in Statistics, providing empirical evidence to enhance instructional quality and advance Sustainable Development Goal 4 (Quality Education). A quantitative correlational research design was employed, involving 251 senior high school teachers selected through stratified random sampling. Teacher competence was assessed using a validated researcher-developed instrument anchored on the Results-Based Performance Management System (RPMS), while learners’ academic performance data were obtained from official school records in Statistics subject. Descriptive statistics and simple linear regression analysis were utilized to examine the predictive relationship between teacher competence and student achievement. Results indicated that teacher competence significantly predicted learners’ performance in Statistics, accounting for 3.6% of the variance in academic outcomes. Although the effect size was modest, the findings highlight the essential role of competent teachers in fostering effective Mathematics instruction and supporting student learning in STEM contexts. The study underscores the importance of evidence-based policymaking and targeted professional development programs aimed at strengthening teacher competence, improving instructional effectiveness, and promoting inclusive and equitable quality education consistent with the goals of SDG 4.
Keywords:
Teacher competence; Mathematics education; Statistics achievement; STEM education; Learners’ performance; Quantitative correlational study; Sustainable Development Goal 4
Teacher Competence as a Predictor of Learners’ Performance in Statistics: A Quantitative Study
Mathematics forms the foundation of STEM education and plays a critical role in developing analytical and problem-solving skills; however, it continues to present significant instructional challenges worldwide. This study examined teacher competence as a predictor of learners’ performance in Statistics, providing empirical evidence to enhance instructional quality and advance Sustainable Development Goal 4 (Quality Education). A quantitative correlational research design was employed, involving 251 senior high school teachers selected through stratified random sampling. Teacher competence was assessed using a validated researcher-developed instrument anchored on the Results-Based Performance Management System (RPMS), while learners’ academic performance data were obtained from official school records in Statistics subject. Descriptive statistics and simple linear regression analysis were utilized to examine the predictive relationship between teacher competence and student achievement. Results indicated that teacher competence significantly predicted learners’ performance in Statistics, accounting for 3.6% of the variance in academic outcomes. Although the effect size was modest, the findings highlight the essential role of competent teachers in fostering effective Mathematics instruction and supporting student learning in STEM contexts. The study underscores the importance of evidence-based policymaking and targeted professional development programs aimed at strengthening teacher competence, improving instructional effectiveness, and promoting inclusive and equitable quality education consistent with the goals of SDG 4.
