Predictors of Students’ Mathematics Performance in Online Distance Learning

Authors : Roj Z. Compo

Volume/Issue : Volume 7 - 2022, Issue 12 - December

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Students’ academic performance in online learning can be affected by several factors. This study investigated the predictors of students’ mathematics performance in online distance learning in terms of student engagement, instructional quality, online learning environment effectiveness, and parental involvement. The descriptive-correlational design was used in this study. A total of 223 Grade 11 Senior High School Students in the Basic Education Department of Misamis University served as respondents who were selected through stratified random sampling. Survey questionnaires were used in gathering the data from the respondents. Mean, Standard Deviation, Pearson Product – Moment Correlation Coefficient, and Stepwise Multiple Regression were used to analyze the study's data. Findings revealed that students were highly engaged and demonstrated outstanding performance in their mathematics subject. Students perceived a satisfactory level of teachers’ instructional quality and effective use of MS teams as a learning platform. Parents were found to be highly involved in their children’s education in dealing with online learning. Moreover, students’ behavioral engagement, teachers’ professional knowledge, and classroom management in online distance learning significantly correlated to students’ mathematics performance. Multiple regression showed that students’ behavioral engagement and teachers’ professional knowledge significantly predicted the students’ mathematics performance. This suggests that teachers’ mastery of the course and students’ direct participation affects students’ mathematics performance in online distance learning. This study recommends investigating other significant predictors of students’ mathematics performance in online distance learning.

Keywords : behavioral engagement, instructional quality, mathematics performance, professional knowledge


Paper Submission Last Date
31 - March - 2023

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