Robin Yap’s research while affiliated with George Brown College and other places

The current study sought to investigate behavioral factors that affect students’ trust perception in using AI-supported Edtech in a postsecondary institution. A model and research hypotheses were developed based on extensive literature and theoretical reviews on technology acceptance theories to affirm the nature of the hypotheses for the study. The Theory of Planned Behavior (TPB) was adopted as the model for the current study by expanding it with trust, and perceived risk. Data was collected from students at the studying at a public institution in Ontario, Canada. The data collected were analyzed using structural equation modelling (SEM) techniques to affirm the study hypotheses. The findings revealed that all the independent variables of Perceived Compatibility, Ease of Use, Usefulness, Facilitating Condition, Risk, and Subjective Norms had a significant and positive influence on students' Trust perception in using AI-supported Edtech. By examining the influential factors of students' behavioral intention to use Edtech, this study will practically position academicians and policymakers in higher educational institutions in their quest to invest in AI-supported Edtech to prioritize variables aimed at influencing students' trust perception to use AI-supported Edtech.

The current educational disruption caused by the COVID‐19 pandemic has fuelled a plethora of investments and the use of educational technologies for Emergency Remote Learning (ERL). Despite the significance of online learning for ERL across most educational institutions, there are wide mixed perceptions about online learning during this pandemic. This study, therefore, aims at examining public perception about online learning for ERL during COVID‐19. The study sample included 31,009 English language Tweets extracted and cleaned using Twitter API, Python libraries and NVivo, from 10 March 2020 to 25 July 2020, using keywords: COVID‐19, Corona, e‐learning, online learning, distance learning. Collected tweets were analysed using word frequencies of unigrams and bigrams, sentiment analysis, topic modelling, and sentiment labeling, cluster, and trend analysis. The results identified more positive and negative sentiments within the dataset and identified topics. Further, the identified topics which are learning support, COVID‐19, online learning, schools, distance learning, e‐learning, students, and education were clustered among each other. The number of daily COVID‐19 related cases had a weak linear relationship with the number of online learning tweets due to the low number of tweets during the vacation period from April to June 2020. The number of tweets increased during the early weeks of July 2020 as a result of the increasing number of mixed reactions to the reopening of schools. The study findings and recommendations underscore the need for educational systems, government agencies, and other stakeholders to practically implement online learning measures and strategies for ERL in the quest of reopening of schools.