ABSTRACT
The ongoing COVID-19 pandemic has disrupted vital elements of personal and public health, society, and education. Increasingly with the viral pandemic, misinformation on health and science issues has been disseminated online. We developed an undergraduate training program focused on producing and presenting research to combat the rampant spread of this misinformation. Online misinformation represents a complex, multidisciplinary problem. Consequently, recruitment of students to the program was not exclusive to those from Computer Science or Science, Technology, Engineering, and Math (STEM) educational backgrounds. Participants were actively recruited from fields such as Linguistics, Social and Political sciences. This data analytics outreach program aimed to train educationally and demographically diverse undergraduate students in computational techniques and presentation skills through guided research regarding the current burst of misinformation. Over ten weeks, participants were instructed in an online curriculum covering five milestones: Python programming, data processing, machine learning with natural language processing, visualization, and presentation. Subsequently, participants were engaged in Computer Science research analyzing a real-world data set gathered from Twitter™ 1 between January and June 2020. Participants were organized into teams to investigate subtopics within the broader subject of misinformation: 1) detecting social media bot accounts, 2) identifying propaganda with computational methods, and 3) studying the discourse surrounding science preprints (i.e., papers that have been posted to the Internet but have not been peer reviewed). The program culminated in an exposition where each team presented research results to program officers, senior faculty, deans, government officials, and industry experts. Here we present the program curriculum, metrics of educational effectiveness, and feedback collected from participants. © American Society for Engineering Education, 2022.
ABSTRACT
Without a sense of belonging, students may become disheartened and give up when faced with new challenges. Moreover, with the sudden growth of remote learning due to COVID-19, it may be even more difficult for students to feel connected to the course and peers in isolation. Therefore, we propose a recommendation system to build connections between students while recommending solutions to challenges. This pilot system utilizes students' reflections from previous semesters, asking about learning challenges and potential solutions. It then generates sentence embeddings and calculates cosine similarities between the challenges of current and prior students. The possible solutions given by previous students are then recommended to present students with similar challenges. Self-reflection encourages students to think deeply about their learning experiences and benefit both learners and instructors. This system has the potential to allow reflections also to help future learners. By demonstrating that previous students encountered and overcame similar challenges, we could help improve students' sense of belonging. We then perform user studies to evaluate this system's potential and find that participants rated 70% of the recommended solutions as useful. Our findings suggest an increase in students' sense of membership and acceptance, and a decrease in the desire to withdraw. © 2022 ACM.