Abstract
The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences between the comments in the two classes. The proposed methods basically employ latent semantic analysis (LSA) and two types of machine learning technique: SVM (support vector machine) and ANN (artificial neural network) for predicting students' final results in four grades of S, A, B and C. Moreover, an overlap method was proposed to improve the accuracy prediction results, the method allows to accept two grades for one mark to get the correct relation between LSA results and students' grades. The proposed methods achieve 50.7% and 48.7% prediction accuracy of students' grades by SVM and ANN, respectively. To this end, the results of this study reported models of students' academic performance predictors that are valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 25-30 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479941735 |
| DOIs | |
| Publication status | Published - Sep 29 2014 |
| Event | 3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014 - Kitakyushu, Japan Duration: Aug 31 2014 → Sep 4 2014 |
Publication series
| Name | Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014 |
|---|
Other
| Other | 3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014 |
|---|---|
| Country | Japan |
| City | Kitakyushu |
| Period | 8/31/14 → 9/4/14 |
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All Science Journal Classification (ASJC) codes
- Information Systems
Cite this
Comments data mining for evaluating student's performance. / Sorour, Shaymaa E.; Mine, Tsunenori; Godaz, Kazumasa; Hirokawa, Sachio.
Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 25-30 6913261 (Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Comments data mining for evaluating student's performance
AU - Sorour, Shaymaa E.
AU - Mine, Tsunenori
AU - Godaz, Kazumasa
AU - Hirokawa, Sachio
PY - 2014/9/29
Y1 - 2014/9/29
N2 - The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences between the comments in the two classes. The proposed methods basically employ latent semantic analysis (LSA) and two types of machine learning technique: SVM (support vector machine) and ANN (artificial neural network) for predicting students' final results in four grades of S, A, B and C. Moreover, an overlap method was proposed to improve the accuracy prediction results, the method allows to accept two grades for one mark to get the correct relation between LSA results and students' grades. The proposed methods achieve 50.7% and 48.7% prediction accuracy of students' grades by SVM and ANN, respectively. To this end, the results of this study reported models of students' academic performance predictors that are valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities.
AB - The present study proposes prediction approaches of student's grade based on their comments data. Students describe their learning attitudes, tendencies and behaviors by writing their comments freely after each lesson. The main difficulty of this research is to predict students' performance by separately using two class data in each lesson. Although students learn the same subject, there exist differences between the comments in the two classes. The proposed methods basically employ latent semantic analysis (LSA) and two types of machine learning technique: SVM (support vector machine) and ANN (artificial neural network) for predicting students' final results in four grades of S, A, B and C. Moreover, an overlap method was proposed to improve the accuracy prediction results, the method allows to accept two grades for one mark to get the correct relation between LSA results and students' grades. The proposed methods achieve 50.7% and 48.7% prediction accuracy of students' grades by SVM and ANN, respectively. To this end, the results of this study reported models of students' academic performance predictors that are valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities.
UR - http://www.scopus.com/inward/record.url?scp=84918495647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84918495647&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2014.17
DO - 10.1109/IIAI-AAI.2014.17
M3 - Conference contribution
AN - SCOPUS:84918495647
T3 - Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014
SP - 25
EP - 30
BT - Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -