The Dire Cost of Early Disengagement: A Four-Year Learning Analytics Study over a Full Program

4Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Research on online engagement is abundant. However, most of the available studies have focused on a single course. Therefore, little is known about how students’ online engagement evolves over time. Previous research in face-to-face settings has shown that early disengagement has negative consequences on students’ academic achievement and graduation rates. This study examines the longitudinal trajectory of students’ online engagement throughout a complete college degree. The study followed 99 students over 4 years of college education including all their course data (15 courses and 1383 course enrollments). Students’ engagement states for each course enrollment were identified through Latent Class Analysis (LCA). Students who were not engaged at least one course in the first term was labeled as “Early Disengagement”, whereas the remaining students were labeled as “Early Engagement”. The two groups of students were analyzed using sequence pattern mining methods. The stability (persistence of the engagement state), transition (ascending to a higher engagement state or descending to a lower state), and typology of each group trajectory of engagement are described in this study. Our results show that early disengagement is linked to higher rates of dropout, lower scores, and lower graduation rates whereas early engagement is relatively stable. Our findings indicate that it is critical to proactively address early disengagement during a program, watch the alarming signs such as presence of disengagement during the first courses, declining engagement along the program, or history of frequent disengagement states.

Cite

CITATION STYLE

APA

Saqr, M., & López-Pernas, S. (2021). The Dire Cost of Early Disengagement: A Four-Year Learning Analytics Study over a Full Program. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12884 LNCS, pp. 122–136). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86436-1_10

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free