TY - GEN
T1 - Course-Graph Discovery from Academic Performance Using Nonnegative LassoNet
AU - Liu, Mengfei
AU - Wei, Shuangshuang
AU - Liu, Shuhui
AU - Shang, Xuequn
AU - Zhang, Yupei
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This paper focuses on the problem of mining a course graph from students’ academic grades in formal education, which is an essential topic for artificial intelligence in education (AIED). However, most current methods often suffer from hardly understanding associations in practice. To this end, we formulate this problem into a feature selection schema that the proposed nonnegative LassoNet can solve. In the study case, we use the course scores of 4,577 records in the computer science department at our university. From the study results, our method achieves about 78% accuracy in score prediction with an acceptable error, which is better than traditional regression models with shrinkage. Based on the sparse self-expressive representation, we create a course map to show the associations behind the student’s academic performance, providing pieces of evidence for education studies and triggering exciting discoveries.
AB - This paper focuses on the problem of mining a course graph from students’ academic grades in formal education, which is an essential topic for artificial intelligence in education (AIED). However, most current methods often suffer from hardly understanding associations in practice. To this end, we formulate this problem into a feature selection schema that the proposed nonnegative LassoNet can solve. In the study case, we use the course scores of 4,577 records in the computer science department at our university. From the study results, our method achieves about 78% accuracy in score prediction with an acceptable error, which is better than traditional regression models with shrinkage. Based on the sparse self-expressive representation, we create a course map to show the associations behind the student’s academic performance, providing pieces of evidence for education studies and triggering exciting discoveries.
KW - Academic performance prediction
KW - Course-graph discovery
KW - Nonnegative LassoNet
KW - Self-expressive representation
UR - http://www.scopus.com/inward/record.url?scp=85187795403&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-0737-9_32
DO - 10.1007/978-981-97-0737-9_32
M3 - 会议稿件
AN - SCOPUS:85187795403
SN - 9789819707362
T3 - Communications in Computer and Information Science
SP - 364
EP - 370
BT - Computer Science and Education. Educational Digitalization - 18th International Conference, ICCSE 2023, Proceedings
A2 - Hong, Wenxing
A2 - Kanaparan, Geetha
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Computer Science and Education, ICCSE 2023
Y2 - 1 December 2023 through 7 December 2023
ER -