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. In order 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 progress rates of students enrolled 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 collected from an anonymous online survey as well as case study interviews. We aim to examine how this novel phenomenon affected the perceptions and teaching experiences among faculty members who previously taught courses on campus and then suddenly switched to remote delivery of their courses during Spring 2020.

Within the last year, the number of students who participate in online learning platforms has exploded. The transition to online platforms has moved discussion out of the classroom and onto digital spaces.

Although MicroMasters courses differ from traditional undergraduate level MOOCs in student demographics, course design, and outcomes, the various aspects of this type of program have not yet been sufficiently investigated. This study aims to pave the path towards enhancing the design of constituent courses of MicroMasters programs with the focus on the application of Machine Learning algorithms.

The coronavirus pandemic prompted the Georgia Institute of Technology (Georgia Tech) to design a set of innovative trials focused on novel problems in delivering at-scale learning horizontally. This chapter provides insight into two specific technological tools that adapt solutions for vertical scaling and how these tools can be scaled across many classes. We explain how Georgia Tech identified strategic needs that emerged from the remote learning environment based on faculty survey findings.

This work reports on progress made towards building an equitable model to predict the success of an applicant to Georgia Tech's Online Master's in Analytics program. As a first step, we have collected and processed data on 9,044 applications and have trained a predictive model with a ROC-AUC score of 0.81, which predicts whether an applicant would be admitted to the program. Our next steps will include using applicant data to model the successful completion of the Analytics program's three core courses, graduation, and finally job placement.