Events
Scholarship Exchange Webinar
Date: Wednesday 31 January 2024 12:00 - 13:00
Location: Online (Teams link below)
Session: The Data Analytics Scholarship Working Group (SWG) in EECS has a motto - "Every course/module generates data, and within that data lies a story waiting to be told." The SWG comprises educators who have been working on various scholarship projects based on analytics related to their modules/courses. In this session, Usman Naeem, Chao Shu, and Marie-Luce Bourguet, members of this SWG, will give an overview of the group's objectives. They will explain how the group has empowered educators to lead projects and share insights from projects conducted by Chao Shu and Marie-Luce Bourguet. Below are descriptions of the projects conducted by Chao Shu and Marie-Luce Bourguet.
Title: A Data-Driven Analysis of the Correlation between English Language Proficiency and Academic Performance in Transnational Education
Speaker: Chao Shu
Language barriers pose a unique challenge in Transnational Education (TNE) as TNE students pursue their degrees within their home country without being exposed to an English language environment beyond the classroom. This research aims to evaluate the quantitative impact of English language proficiency on the academic performance of TNE students based on a comprehensive dataset collected from a UK-China transnational Engineering degree programme. A data-driven approach is also proposed to help TNE educational institutions gain a better understanding of how English language proficiency is associated with students' academic outcomes at the module level, so that they can optimise curriculum design with integrated language development opportunities, provide targeted interventions, ultimately improve the overall educational experience for TNE students.
Title: Demonstrating the impact of study regularity on academic success using learning analytics
Speaker: Marie-Luce Bourguet
Flipping the classroom requires from students good self-regulated learning skills, primarily time management and study regularity, as they must have engaged in learning activities prior to attending live classes. I will present my approach of using learning analytics to demonstrate the impact of study regularity on academic success in a flipped learning environment. A key contribution is the definition of a measure of study regularity that can uncover various students’ learning profiles during flipped learning, and that strongly correlates with academic success. I will then discuss how such a measure can also be used to raise student’s awareness about their learning behaviour and lack of appropriate strategy, to nudge the students into modifying their learning behaviour, and to monitor class behaviour, such as detecting a worrying students’ disengagement trend.