TY - GEN
T1 - Predictive Model with Machine Learning for Academic Performance
AU - Cecenardo-Galiano, Carlos
AU - Sumaran-Pedraza, Carolina
AU - Obregon-Palomino, Luz
AU - Iparraguirre-Villanueva, Orlando
AU - Cabanillas-Carbonell, Michael
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - Academic achievement (AP) in recent years has shown minimal progress with a difference of 0.05%, according to the report made by the Program for International Student Assessment (PISA). For this reason, the objective of this research is to build a predictive multiclass classification model for the AP of students in an elementary school. It was conducted with a dataset of 218 third-year high school students. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology was used to create the model, which consists of 6 phases and is effective in data mining (DM) projects. The random forest (RF) algorithm was also used. The results indicated that the RF model obtained the highest prediction rates compared to other studies, with an accuracy of 95% of the model, respectively. Finally, it is observed that the attributes that mostly influence prediction are the scores of Ability 02 end of I bimester, Positive Impression, Ability 01 end of I bimester, Ability 03 end of I bimester, and Adaptability. Thus, it is concluded that academic attributes are more relevant than psychological attributes in predicting RF.
AB - Academic achievement (AP) in recent years has shown minimal progress with a difference of 0.05%, according to the report made by the Program for International Student Assessment (PISA). For this reason, the objective of this research is to build a predictive multiclass classification model for the AP of students in an elementary school. It was conducted with a dataset of 218 third-year high school students. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology was used to create the model, which consists of 6 phases and is effective in data mining (DM) projects. The random forest (RF) algorithm was also used. The results indicated that the RF model obtained the highest prediction rates compared to other studies, with an accuracy of 95% of the model, respectively. Finally, it is observed that the attributes that mostly influence prediction are the scores of Ability 02 end of I bimester, Positive Impression, Ability 01 end of I bimester, Ability 03 end of I bimester, and Adaptability. Thus, it is concluded that academic attributes are more relevant than psychological attributes in predicting RF.
KW - Academic performance
KW - Machine learning
KW - Predictive modeling
UR - https://www.scopus.com/pages/publications/85174730901
U2 - 10.1007/978-981-99-3043-2_81
DO - 10.1007/978-981-99-3043-2_81
M3 - Conference contribution
AN - SCOPUS:85174730901
SN - 9789819930425
T3 - Lecture Notes in Networks and Systems
SP - 975
EP - 988
BT - Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
A2 - Yang, Xin-She
A2 - Sherratt, R. Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
T2 - 8th International Congress on Information and Communication Technology, ICICT 2023
Y2 - 20 February 2023 through 23 February 2023
ER -