Presenting and publishing our research is crucial to advancing knowledge in our field and contributing to the global academic conversation. It also allows us to showcase our expertise and establish ourselves as thought leaders in our respective fields, attracting top talent and funding opportunities.
This study examined how graduate students’ sense of belonging reflected their cognitive and affective experiences and their discursive engagement in three classroom discussion environments: face-to-face, and synchronous and asynchronous computer-mediated discussions. Self-report surveys at mid-semester identified higher and lower belongingness students. Mid-semester and end-of-semester ratings allowed exploration of cognitive/affective factors.
As enrollment increases, teaching assistants (TAs) need help prioritizing responding to students’ posts in on-line forums. In a graduate level Artificial Intelligence forum, three instructors rank the urgency of posts which is compared to the ratings of 13 course TAs; correlation with the instructors’ scores have an r=0.55, with a TA inter-rater reliability of 35%. However, when TAs used a codebook containing seven dimensions created by the instructors to define urgency levels, correlation increased to r=0.73 and reliability to 53%.
In this white paper, we mainly discuss the impact of generative language models on the assessment of students’ learning in the higher education setting and provide example alternative or other transformative assessment methods that faculty can consider adopting in response to threats posed by ChatGPT. Next, we discuss ideas on how faculty should adapt to the emergence of AI language models like ChatGPT.
This study aimed to understand the relationship between course activities and learning progress among students enrolled in the MicroMasters certificate program offered in an affordable MOOC-based learning platform. To capture the relationship, the differences between the engagement patterns of learners in the MicroMasters program compared to a non-degree MOOC were examined by utilizing machine-learning (ML) techniques in the clickstream database. The ML analyses revealed discrepancies in activity patterns and student progress rates in MicroMaster and MOOC courses.
This paper presents the progress made towards developing an equitable predictive model for admission success to an online Master's program with a large pool of applicants. The overarching goal of this project is to help the future development of a systematic evaluation tool for programs with large applications. In the first phase of the project, we collected and processed data on 9,044 applications and have trained a predictive model using applicants' profile information such as demographic data, academic background, and test scores.
In this study, we work towards a strategy to measure and enhance the quality of interactions in discussion forums at scale. We present a machine learning (ML) model which identifies the phase of cognitive presence exhibited by a student’s post and suggest future applications of such a model to help online students develop higher-order thinking. We collect discussion forum transcript data from two online courses: CS1301 (an introductory computer programming MOOC) offered by edX and CS6601 (a graduate course on artificial intelligence) which uses the Piazza online discussion tool.
This study focuses on the recent emergency move from face-to-face to remote teaching in higher education due to the coronavirus disease pandemic (COVID-19). This mixed-method study uses data from an anonymous online survey and case study interviews. We aim to examine how this novel phenomenon affected the perceptions and teaching experiences of faculty members who previously taught courses on campus and suddenly switched to remote delivery of their courses during Spring 2020.