ADAPTIVE RELEASE LEARNING PATHS TO MOTIVATE ACTIVE LEARNING AND ENGAGEMENT IN STUDENTS

Abstract

Learning Analytics (LA), a decade old emerging filed, has the potential to make data-informed decisions to improve the quality of Higher Education (HE). It can be a good tool for HE institutions to tackle problems like student retention and promote student success rates. While LA could involve studying the impact of socioeconomic variables such as age, work, gender, stage, status, etc., on student success;these variables cannot be addressed by a teacher. Study attitude on the other hand, may be affected by instructional design, study counselling and guidance with theory informed teaching interventions. Grounding first year bachelor's students in the culture of active learning in their first year itself, will help develop self-regulation strategies which will thereby improve success and retention for not just the first year but also to complete the bachelor program in the stipulated period. In this study, we analyze data sourced from across all the first-year bachelor's courses of an Economics and Business Faculty. The students are classified into different groups according to their summative scores and their LMS interaction behaviors are studied. in future work, the collection of data across different campuses, courses and student programs allows for a comparative analysis across different dimensions, thus allowing for the investigation of the generalizability of results by means of out-of-sample testing or models built on a single course's data. Additionally, the collection of data across three successive academic years will also allow for the out-of-time validation of findings, including the analysis of the impact of the COVID-19 pandemic on the students' behavior. © 2021 Virtual Simulation Innovation Workshop, SIW 2021. All rights reserved.