Title: Presentation of the Data Analytics Scholarship Working Group (SWG) in EECS
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.
Short Bio: Dr Chao Shu is a lecturer of Telecommunication Engineering in the School of Electronic Engineering and Computer Science at the Queen Mary University of London, teaching on the QMUL-BUPT Joint Programme. He is a data enthusiast and is currently the co-head of the data analytics scholarship working group in the School of EECS at QMUL. He did his PhD research in the Antenna Research Group at QMUL and received his PhD degree in 2021.
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.
Short bio: Dr. Marie-Luce Bourguet teaches multimedia modules on the Joint Programme with BUPT. She is Director of the EECS Scholarship Centre (ESC) and Deputy Director for Scholarship, the Centre for Academic Inclusion in Science and Engineering (CAISE).