Abstract
Examining student learning behavior is one of the crucial educational issues. In this paper, we propose a new method to predict student performance by using comment data mining. A teacher just asks students after every lesson to freely describe and write about their learning situations, attitudes, tendencies, and behaviors. The method employs Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) to predict student grades in each lesson. In order to obtain further improvement of prediction results, we apply a majority vote method to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. Also, we evaluate the reliability of the predicted student grades to know when we can rely prediction results of student grade during the period of the semester. The experiment results show that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades compared to Probabilistic Latent Semantic Analysis (PLSA) and Latent Semantic Analysis (LSA) models. Also, considering the differences of prediction results in the two consecutive lessons helps to evaluate the reliability of the predicted results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 121-126 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781479999583 |
| DOIs | |
| Publication status | Published - Jan 6 2016 |
| Event | 4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015 - Okayama, Japan Duration: Jul 12 2015 → Jul 16 2015 |
Other
| Other | 4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015 |
|---|---|
| Country | Japan |
| City | Okayama |
| Period | 7/12/15 → 7/16/15 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
Estimation of Student Performance by Considering Consecutive Lessons. / Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori.
Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 121-126 7373887.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimation of Student Performance by Considering Consecutive Lessons
AU - Sorour, Shaymaa E.
AU - Goda, Kazumasa
AU - Mine, Tsunenori
PY - 2016/1/6
Y1 - 2016/1/6
N2 - Examining student learning behavior is one of the crucial educational issues. In this paper, we propose a new method to predict student performance by using comment data mining. A teacher just asks students after every lesson to freely describe and write about their learning situations, attitudes, tendencies, and behaviors. The method employs Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) to predict student grades in each lesson. In order to obtain further improvement of prediction results, we apply a majority vote method to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. Also, we evaluate the reliability of the predicted student grades to know when we can rely prediction results of student grade during the period of the semester. The experiment results show that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades compared to Probabilistic Latent Semantic Analysis (PLSA) and Latent Semantic Analysis (LSA) models. Also, considering the differences of prediction results in the two consecutive lessons helps to evaluate the reliability of the predicted results.
AB - Examining student learning behavior is one of the crucial educational issues. In this paper, we propose a new method to predict student performance by using comment data mining. A teacher just asks students after every lesson to freely describe and write about their learning situations, attitudes, tendencies, and behaviors. The method employs Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) to predict student grades in each lesson. In order to obtain further improvement of prediction results, we apply a majority vote method to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. Also, we evaluate the reliability of the predicted student grades to know when we can rely prediction results of student grade during the period of the semester. The experiment results show that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades compared to Probabilistic Latent Semantic Analysis (PLSA) and Latent Semantic Analysis (LSA) models. Also, considering the differences of prediction results in the two consecutive lessons helps to evaluate the reliability of the predicted results.
UR - http://www.scopus.com/inward/record.url?scp=84964389893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964389893&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2015.170
DO - 10.1109/IIAI-AAI.2015.170
M3 - Conference contribution
AN - SCOPUS:84964389893
SP - 121
EP - 126
BT - Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -