Academic Engagement as Predictor of Secondary School Students’ Academic Achievement in Mathematics in Anambra State

Abstract

In the field of educational studies, academic engagement has become an important perspective in the domain of achievement motivation particularly in academic motivation. The study aimed to explore the students’ academic engagement as predictor of academic achievement in mathematics in Anambra State. The study adopted a multiple regression predictive design. The population of the study comprised of 21,204 SS II students from which a sample of 1500 were drawn. Multi-stage procedure was used to select the sample. Students’ Academic Engagement Questionnaire (SAEQ) was used for data collection. Students’ mathematics achievement scores from the state wide promotion examination were used to represent mathematics achievement. Cronbach’s alpha was used to determine the reliability of the items in the instruments. Reliability indices were found to be .75, for behavioural engagement, .83, for psychological engagement, .87, for cognitive engagement, .61, for emotional engagement, .84, for agentic engagement and .73 for social engagement. The overall reliability coefficient was .74 which shows that the instrument was reliable and good for the study. Four research questions and three nu ll hypotheses were formulated for the study. The standard multiple regression was used to analyze the collected data. The t-test for r, F-test and test of significance for β, were used to test hypotheses at .05 level of significance. Findings showed that social engagement is the most predictor of students’ academic achievement in Mathematics when compared with the other dimensions of academic engagement . Also, the analysis of variance indicated that the regression equation was significant in predicting academic achievement. This implies that at least one of the independent variables significantly predicted academic achievement in Mathematics... Based on these findings, it was recommended that using students’ academic engagement as a predictor of academic achievement would enable the academic institutions to identify at risk students much earlier compared to using only cumulative grade point average; which is a product of measure. This would enable academic institutions to formulate more effective intervention strategies to reduce attrition rate