The Antecedent of Student Academic Achievement Prediction

Abstract

The research goal was set to determine to what extent the influences of learning analytics and academic analytics, the antecedent factors in predicting student academic achievement through the use of big data were. There has been no discussion on progress, success, retention, or decline of this achievement. Therefore, this research has significance for the improvement of higher education institutions. The research was in the form of online surveys involving 203 respondents, i.e., leaders, structural staff, and academic advisors from each of these institutions in Pontianak. Tests of eight hypotheses were conducted through SEM-PLS Method, and two of them had no direct influences. The results show that the two antecedent factors, directly and indirectly, have different influences and significance values on student academic achievement prediction despite the critical roles of big data. In addition, results obtained through the application of learning analytics and academic analytics in relation to big data of higher education institutions, especially for the need to predict student academic achievement, are infrequently similar.