Data-Driven Interventions for Capstone Projects

被引:0
作者
Naeem, Usman [1 ]
Shu, Chao [1 ]
Ma, Ling [1 ]
Chen, Yue [1 ]
Zou, Yixuan [1 ]
Sagor, Md Hasanuzzaman [1 ]
Akter, Habiba [1 ]
FinesilverSmith, Karen [1 ]
机构
[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England
来源
2024 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE, EDUCON 2024 | 2024年
关键词
Learner Engagement Analytics; Interventions; Capstone Projects; EMPLOYABILITY SKILLS; SUPERVISION; SYSTEM; WORK;
D O I
10.1109/EDUCON60312.2024.10578897
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The capstone project is a crucial element of a degree programme and plays a vital role in the growth of learners, as it enables them to enhance their problem-solving skills and improve their employability prospects. In addition to this, the project provides the learners with an opportunity to demonstrate and showcase their critical thinking abilities and creativity. However, due to the year-long independent nature of these projects, learners can disengage due to a lack of motivation or self-regulated skills throughout the project. To address this problem, we formulated a data-driven intervention approach that conducts learner engagement analytics to identify and support disengaged learners, ensuring they maximise the benefits of completing a capstone project. The motivation was also to provide these learners with the necessary resources and support to get them back on track. This approach was implemented in the capstone projects conducted by learners at Queen Mary University of London within the School of Electronic Engineering and Computer Science. Based on the data of the three cohorts in 2020-21, 2021-22 and 2022-23, our analysis shows that the proposed data-driven intervention approach for capstone projects can effectively identify less-engaged learners and targeted interventions are shown to improve the overall performance of these less-engaged learners on capstone projects.
引用
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页数:7
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