ABSTRACT
The Covid-19 pandemic, as Henry Kissinger mentions, will not only "forever alter the world order,"but also potentially transform the ever-changing higher education world. The recent increase in technological innovations in information, communications, and computer technology has profoundly transformed traditional teaching-learning processes and peer-to-peer interactions for knowledge transfer. One such radical change in technology that researchers are continually working on is motivating collaborative learning and student interactions to improve their learning experiences. Collaborative Learning (CL), where students work in groups to achieve a specific learning objective, can facilitate a deep learning activity that promotes student participation. However, the potential of discussion forums is limited due to their unstructured nature in LMSs like Canvas.We propose and develop a structured discussion forum that can offer a platform to communicate and discuss problems and receive feedback, discuss solutions, and suggestions online. Students who participate in these discussion forums can benefit in multiple ways, including increased class preparedness and more active learning. The twofold objectives and outcomes include 1) analyzing discussion board data to reveal students' interaction and their degree of participation in the course, and 2) developing a toolset to draw useful inferences from such collaboration networks. Specifically, our schema-based model can help students visualize the discussion board networks creating an engaged learning environment. Furthermore, the model can help draw valuable inferences of the patterns of student interactions and assess student participation and belonging in the course with greater precision.This paper demonstrates a schema-based discussion board model that can allow researchers to collect better-formatted discussion data and more reliable information about the posts, such as the type of posts and the relationships of each post with others. The reimagined discussion boards include the ability to classify discussion posts using various parameters, visualize the posts' patterns of interactions, identify their relationships with other discussion posts, and precisely evaluate student participation in discussions to monitor the major topics of discussion. We believe that the result of increased participation in discussions with other students will have the effect of increasing students' sense of belonging to the community of scholars. © 2022 IEEE.
ABSTRACT
There are many ways to go wrong when evaluating new information, e.g. by putting unwarranted trust in non-experts, or failing to scrutinize information about threats. We examined how effective people were at evaluating information about the COVID-19 pandemic. Early in the course of the pandemic, we recruited 1791 participants from six countries with varying levels of pandemic severity, and asked them to evaluate true and false pandemic-related statements (assertions and prescriptions) sampled from the media. We experimentally manipulated the source of each statement (a doctor, a political/religious leader, social media, etc.). Overall, people proved to be epistemically vigilant: they distinguished between true and false statements, especially prescriptions, and they trusted doctors more than other sources. These effects were moderated by feeling threatened by the pandemic, and by strong identification with some sources (political/religious leaders). These findings provide optimism in the fight against misinformation, while highlighting challenges posed by politics and ideology. © Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021.All rights reserved.