Attitudes wards Online Learning ring COVID-19: A Cluster and Sentiment Analyses

This paper investigated the attitudes of 702 college students toward the implementation of fully online learning during the COVID-19 pandemic. Toward this goal, responses of the students were collected and analyzed through hierarchical cluster and sentiment analyses using the R software. Hierarchical cluster analysis revealed hopeful and apprehensive attitudes toward online learning. Advantages of online learning emerged as positive sentiments while challenges and their impact on mental health emerged as negative sentiments. It is concluded that online learning is a promising platform of learning provided that its shortcomings are addressed. Implications to teaching are offered. © 2022 The authors and IOS Press.

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

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. © 2021 ACM.