Predictive model for college students’ performance in higher mathematics - Donalyn Sabanal; Mariza Gako; Herson Dela Torre; Jamaica Sabanal; Rex Boi So; John Bricster Bacal; Lyvelle Dim Corgio; Jen Frances Laroga; Cecil Camallere; Mary Joy Pagador; Reza Jean Barino; Kryzdale Mameng; et al. (Social Sciences and Humanities Open)
This study investigated the influence of psychological factors (attitude, learning engagement, motivation, and self-efficacy) and instructional factors (teacher-related factors and learning environment) on college students' performance in higher mathematics. It developed a predictive model for higher math performance. This utilized a cross-sectional research design using a survey to gather data at a university in Cebu province, Philippines, with 391 students from Bachelor of Science in Industrial Engineering and Bachelor of Secondary Education Major in Mathematics programs selected through purposive sampling, resulting in an 87% response rate. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. Findings revealed that students generally held positive attitudes towards math, demonstrated average learning engagement, were highly motivated, and reported average self-efficacy. Both teacher-related factors and the learning environment were perceived very positively. While both psychological and instructional factors showed significant positive correlations with mathematics performance, multiple regression analysis indicated that only attitude was not a significant predictor. Learning engagement emerged as the strongest predictor, followed by learning environment and self-efficacy. The main contribution of this study is the creation of a predictive model, Higher Mathematics Performance = 1.82 + 0.008 (attitude) + 0.257 (learning engagement) + 0.039 (motivation) + 0.098 (self-efficacy) + 0.084 (teacher-related factors) + 0.155 (learning environment) + e, which accounted for 82.7% of the variance in higher mathematics performance. Hence, the predictive model developed in this study can serve as a valuable tool to design interventions and effective strategies for improving mathematics performance and overall academic achievement in higher education. This leads to a broad picture of the fact that these factors are correlated and how the various correlations can describe strategies for proposing relevant programs necessary for the improvement of performance in higher mathematics.