TY - GEN
T1 - Understandable prediction models of student performance using an attribute dictionary
AU - Sorour, Shaymaa E.
AU - El Rahman, Shaimaa Abd
AU - Kahouf, Samir A.
AU - Mine, Tsunenori
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant Number 26350357, 26540183 and 16H02926.
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - This paper proposes a new approach for predicting final student grade with high accuracy. It builds an attribute dictionary (AD) automatically from students’ comments collected after every lesson. Furthermore, it combines white-box models: Decision Tree (DT) and Random Forest (RF), and a black-box model: Support Vector Machine (SVM) to construct an interpretable prediction model and carry out eclectic rule-extraction. First, the AD is built from students’ comments, which are converted to attribute vectors. Second, the output decision is generated by SVM using the attribute vectors in the training phase and then DT and RF are applied to the output decision to extract symbolic rules. Experimental results illustrate the validity of the AD constructed automatically and the superiority of the proposed approach compared to single machine learning techniques: DT, RF and SVM.
AB - This paper proposes a new approach for predicting final student grade with high accuracy. It builds an attribute dictionary (AD) automatically from students’ comments collected after every lesson. Furthermore, it combines white-box models: Decision Tree (DT) and Random Forest (RF), and a black-box model: Support Vector Machine (SVM) to construct an interpretable prediction model and carry out eclectic rule-extraction. First, the AD is built from students’ comments, which are converted to attribute vectors. Second, the output decision is generated by SVM using the attribute vectors in the training phase and then DT and RF are applied to the output decision to extract symbolic rules. Experimental results illustrate the validity of the AD constructed automatically and the superiority of the proposed approach compared to single machine learning techniques: DT, RF and SVM.
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U2 - 10.1007/978-3-319-47440-3_18
DO - 10.1007/978-3-319-47440-3_18
M3 - Conference contribution
AN - SCOPUS:84995969111
SN - 9783319474397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 171
BT - Advances in Web-Based Learning - ICWL 2016 - 15th International Conference, Proceedings
A2 - Nanni, Umberto
A2 - Temperini, Marco
A2 - Spaniol, Marc
A2 - Chiu, Dickson K.W.
A2 - Marenzi, Ivana
PB - Springer Verlag
T2 - 15th International Conference on Advances in Web-Based Learning, ICWL 2016
Y2 - 26 October 2016 through 29 October 2016
ER -