A Path Model to Infer Mathematics Performance: The Interrelated Impact of Motivation, Attitude, Learning Style and Teaching Strategies Variables

TL;DR

This study develops a path model to understand how motivation, attitude, learning style, and teaching strategies collectively influence students' mathematics performance, highlighting the importance of psychological and instructional factors.

Contribution

It introduces a comprehensive path model that integrates multiple psychological and instructional variables to predict mathematics performance.

Findings

Attitude towards mathematics significantly predicts performance.

Learning style and teaching strategies have direct and indirect effects.

The model fits the observed data well.

Abstract

The present study aims at exploring predictors influencing mathematics performance. In particular, the research focuses on four subject components such as motivation, attitude towards mathematics, learning style, and teaching strategies. The study respondents have involved a sample of 240 students from Agusan del Sur State College of Agriculture and Technology (ASSCAT). Path analysis will be used to test the direct and indirect relations between the predictors and mathematics performance. Based on the result, the calculation of reproduced correlation through path decompositions and subsequent comparison to the empirical correlation indicated that the path model fits the observed data. Results also revealed that a large proportion of mathematics performance could be predicted from the attitude towards mathematics, learning style, and teaching strategies. Moreover, attitude towards mathematics, learning style, and teaching strategies influence mathematics performance directly and indirectly.