How did the onset of the COVID-19 pandemic impact student learning in higher education? Everywhere, Sars-CoV-2 struck hardest in the most disadvantaged communities. This paper asks whether the virus's disproportionate effect on more vulnerable groups is replicated among college and university students. Data come from approximately 3800 students studying at nine higher education institutions located in six different countries around the globe. Conventional imagery of the 'Ivory Tower' treats colleges and universities as cloistered academic spaces beyond the 'real world.' Such imagery suggests that the patterns of COVID-19 inequity seen in the general population might not hold within higher education. However, the composition of the postsecondary student body has become more diverse and more representative. This could mean that patterns of inequity from the general population might hold, although perhaps at muted strength, among college and university students. We investigate the higher education context, asking how the characteristics of students, such as their gender or family background, their digital access, and their living arrangements during the COVID-19 pandemic, impacted their selfreported ability to learn. The paper finds that students in more difficult situationsno study space, too much noise, and poorer healthreported greater disruption to their learning than did their peers who experienced fewer challenging living arrangements. Vulnerability, as measured by students in traditionally marginalized positions, had smaller impacts on student's confidence in learning.
Online educators rely on asynchronous discussions to satisfy the bulk of student-student engagement that is lost when transferring from a face-to-face to an online format. However, not all discussion platforms are created equal and some specialized tools may offer advantages over standard tools embedded in more comprehensive learning management systems (LMS). In this study, we compare two online discussion platforms – one is the native discussion tool embedded within the Canvas LMS and the other is a specialized discussion platform, Packback, which supports students and instructors with gamified elements and artificial intelligence. Specifically, we assess whether post quality differs across these platforms, as measured by average word count, cited sources and weekly participation rate. The initial results are mixed and differ by course, leading us to conclude that the impact of platform on engagement and quality likely depends on the course content, instructor and protocol.
Asynchronous discussions are a popular element in online education, often used to replace the student-student and educator-student interactions that occur in physical classrooms. Most Learning Management Systems (LMSs), such as Canvas and Moodle, include native discussion tools that offer students and instructors the ability to interact and communicate at their own time and pace. Some specialized products offer a wider variety of discussion features that standard LMS tools may not have. In this study, we discuss the impact of an online discussion platform powered by artificial intelligence (AI) and gamified features. We conducted a qualitative study examining how AI elements affect the workflow, grading, and feedback experience of educators. Preliminary findings offer insights into the impact of the AI elements on graders’ perceptions, in particular on their perception of giving feedback and the perceived ambiguity of the way the AI assesses students’ post quality.
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Asynchronous discussions are a popular feature in online higher education as they enable instructor-student and student–student interactions at the users’ own time and pace. AI-driven discussion platforms are designed to relieve instructors of automatable tasks, e.g., low-stakes grading and post moderation. Our study investigated the validity of an AI-generated score compared to human-driven methods of evaluating student effort and the impact of instructor interaction on students’ discussion post quality. A series of within-subjects MANOVAs was conducted on 14,599 discussion posts among over 800 students across four classes to measure post ‘curiosity score’ (i.e., an AI-generated metric of post quality) and word count. After checking assumptions, one MANOVA was run for each type of instructor interaction: private coaching, public praising, and public featuring. Instructor coaching appears to impact curiosity scores and word count, with later posts being an average of 40 words longer and scoring an average of 15 points higher than the original post that received instructor coaching. AI-driven tools appear to free up time for more creative human interventions, particularly among instructors teaching high-enrollment classes, where a traditional discussion forum is less scalable.
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