Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School

Yicong LIANG, Haoran XIE, Di ZOU, Fu Lee WANG*, Xinyi HUANG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


Emerging technologies have allowed researchers to easily access educational data, conduct data analysis, and predict students’ learning performance. However, the factors that are essential for the predictive model have not been identified. In the present research, based on the information entropy framework, we firstly identify the factors that influence students’ academic learning performance. Then, we adopt the explainable machine learning frameworks, which are based on logistic regression and support vector machines, to predict student learning achievements. The experiment was conducted on the real-world dataset from the secondary school within two subjects. The results reveal that the feature of the failure records from students’ past performance is a significant factor related to learning achievements. The predictive model based on student profiles achieves up to 86% accuracy for the prediction of learning outcome related to the final grade.
Original languageEnglish
Title of host publicationTechnology in Education. Innovative Practices for the New Normal : 6th International Conference on Technology in Education, ICTE 2023, Hong Kong, China, December 19–21, 2023, Proceedings
EditorsSimon K. S. CHEUNG, Fu Lee WANG, Naraphorn PAOPRASERT, Peerayuth CHARNSETHIKUL, Kam Cheong LI, Kongkiti PHUSAVAT
PublisherSpringer Singapore
Number of pages12
ISBN (Electronic)9789819982554
ISBN (Print)9789819982547
Publication statusPublished - 9 Nov 2023

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.


  • Student Learning Performance
  • Data Analysis in Education
  • Student Profiles
  • Learning Achievement Predictive Model


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