This is the third and final installment in Bishop and Cini’s series on academic dishonesty in online education. In the second installment, they discussed some broad strategies that could help online education leaders minimize the prevalence of digital academic dishonesty. In this series, they share the specific approach UMUC has taken. More...

This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students’ patterns of language use from data, providing an accessible and non-invasive validation of student identities and student-produced content. More...