TY - GEN
T1 - Unlocking the Potential of Competition-Based Learning: A Case Study of Kaggle in Big Data Analytics Education
AU - Fan, Pengfei
AU - Purwanto, Erick
AU - Li, Na
AU - Wang, Jia
AU - Tay, Ting Ting
AU - Wang, Ling
AU - Wang, Qiufeng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study investigates the effectiveness of Competition-Based Learning (CBL) using Kaggle in teaching Big Data Analytics. Through a quasi-mixed research design, integrating qualitative and quantitative methods, data was collected from students who completed the Big Data Analytics module. Results indicate high satisfaction levels among participants, with the majority expressing willingness to recommend CBL using Kaggle to future students. Moreover, the CBL approach significantly enhanced student engagement and improved their understanding of Big Data Analytics concepts and techniques. The matic analysis revealed positive aspects such as real-time scoring and leaderboards, but also highlighted challenges including the lack of structured learning and guidance. Solutions proposed by students include additional support mechanisms, a balance between competition and learning, and diverse competition themes. Overall, this study underscores the potential of CBL using Kaggle to transform Big Data Analytics education, while also emphasizing the need for tailored support and refinement of course design.
AB - This study investigates the effectiveness of Competition-Based Learning (CBL) using Kaggle in teaching Big Data Analytics. Through a quasi-mixed research design, integrating qualitative and quantitative methods, data was collected from students who completed the Big Data Analytics module. Results indicate high satisfaction levels among participants, with the majority expressing willingness to recommend CBL using Kaggle to future students. Moreover, the CBL approach significantly enhanced student engagement and improved their understanding of Big Data Analytics concepts and techniques. The matic analysis revealed positive aspects such as real-time scoring and leaderboards, but also highlighted challenges including the lack of structured learning and guidance. Solutions proposed by students include additional support mechanisms, a balance between competition and learning, and diverse competition themes. Overall, this study underscores the potential of CBL using Kaggle to transform Big Data Analytics education, while also emphasizing the need for tailored support and refinement of course design.
KW - Big Data Analytics
KW - Competition-Based Learning
KW - Kaggle
KW - Student Engagement
KW - Thematic Analysis
UR - http://www.scopus.com/inward/record.url?scp=85207484060&partnerID=8YFLogxK
U2 - 10.1109/ICSCC62041.2024.10690416
DO - 10.1109/ICSCC62041.2024.10690416
M3 - Conference Proceeding
AN - SCOPUS:85207484060
T3 - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
SP - 589
EP - 595
BT - 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - AI and Education Special Session at the 10th International Conference on Smart Computing and Communication (ICSCC 2024)
Y2 - 25 July 2024 through 27 July 2024
ER -