TY - GEN
T1 - Exploring Student Profile Features and Their Impact on Learning Performance in Secondary School
AU - Liang, Yicong
AU - Xie, Haoran
AU - Zou, Di
AU - Huang, Xinyi
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Data Analysis in Education
KW - Learning Achievement Predictive Model
KW - Student Learning Performance
KW - Student Profiles
UR - http://www.scopus.com/inward/record.url?scp=85177200085&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-8255-4_30
DO - 10.1007/978-981-99-8255-4_30
M3 - Conference contribution
AN - SCOPUS:85177200085
SN - 9789819982547
T3 - Communications in Computer and Information Science
SP - 349
EP - 360
BT - Technology in Education. Innovative Practices for the New Normal - 6th International Conference on Technology in Education, ICTE 2023, Proceedings
A2 - Cheung, Simon K.S.
A2 - Wang, Fu Lee
A2 - Li, Kam Cheong
A2 - Paoprasert, Naraphorn
A2 - Charnsethikul, Peerayuth
A2 - Phusavat, Kongkiti
T2 - 6th International Conference on Technology in Education, ICTE 2023
Y2 - 19 December 2023 through 21 December 2023
ER -