Manual Label and Machine Learning in Clustering and Predicting Student Performance: A Practice Based on Web-Interactive Teaching Systems

Mengjiao Yin, Hengshan Cao, Zuhong Yu and Xianyu Pan
Additional contact information
Mengjiao Yin: Wuxi Taihu University, China
Hengshan Cao: Wuxi Taihu University, China
Zuhong Yu: Wuxi Taihu University, China
Xianyu Pan: Wuxi Taihu University, China

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2024, vol. 19, issue 1, 1-33

Abstract: This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a four-quadrant clustering technique. The AIM model delineates student investment into four quadrants based on their time and energy commitment to academic pursuits, distinguishing between result-oriented and process-oriented investments. The findings reveal that the four-quadrant method surpasses machine learning clustering in predictive accuracy, highlighting the robustness of manual feature engineering. The study's significance lies in its potential to guide educators in designing targeted interventions and personalized learning strategies, emphasizing the importance of process-oriented assessment in education. Future research is recommended to expand the sample size and explore the integration of deep learning models for validation.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWLTT.347661 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jwltt0:v:19:y:2024:i:1:p:1-33

Access Statistics for this article

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) is currently edited by Mahesh S. Raisinghani

More articles in International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().