Modeling Computing Students' Perceived Academic Performance in an Online Learning Environment

Abstract

This study attempted to develop a model that characterized the perceived academic performance of computing students (subsequently referred to as students) in an online learning environment. It was hypothesized that students' academic performance in online learning could be modeled through their online learning capabilities, attitudes towards online learning, and online learning academic self-concept. Toward this goal, 264 students answered a validated survey form. Multinomial logistic regression analyses showed that perceived academic performance in terms of perceived grade attainment and perceived learning achievements had different sets of predictors. This finding indicates that perceived academic performance in an online learning environment has two distinct measures with distinct sets of predictors. Additional analyses revealed that the students are further distinguished when the predictors were categorized by levels of academic performance. Implications to online teaching and recommendations are discussed.