Abstract
To grasp a student's lesson attitude and learning situation and to give a feed back to each student are educational foundations. Goda et al. proposed the PCN method to estimate a learning situation from a comment freely written by students[6, 7]. The PCN method categorizes comments into three items of P (previous), C(current) and N(next). They pointed out a correlation between the student's final results and the validity of a descriptive content of item C, that is something related to understanding of the lesson and learning attitudes to the lesson. However, a problem left in their work is the badness of performance in prediction for upper grade students. This paper proposes two manners of utilization of PCN scores: the validity level determination for assessment, and for prediction performance of students' final grades. In order to validate the proposed manners of utilization, we conducted two experiments. First, we employed multiple regression analysis to calculate PCN scores that determine the validity level with respect to each viewpoint. Students who wrote comments with a high PCN score are considered as those who describe their learning attitude appropriately. We also applied a machine learning method SVM (support vector machine) to students' comments for predicting their final results in five grades of S, A, B, C and D. Experimental results illustrated that as comments of students get higher PCN scores, the prediction performance of the students' grades becomes higher.
| Original language | English |
|---|---|
| Title of host publication | SIGITE 2013 - Proceedings of the 2013 ACM SIGITE Annual Conference on Information Technology Education |
| Pages | 35-42 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - Nov 14 2013 |
| Event | 2013 13th ACM SIGITE Annual Conference on Information Technology Education, SIGITE 2013 - Orlando, FL, United States Duration: Oct 10 2013 → Oct 12 2013 |
Other
| Other | 2013 13th ACM SIGITE Annual Conference on Information Technology Education, SIGITE 2013 |
|---|---|
| Country | United States |
| City | Orlando, FL |
| Period | 10/10/13 → 10/12/13 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Education
Cite this
Correlation of grade prediction performance and validity of self-evaluation comments. / Goda, Kazumasa; Hirokawa, Sachio; Mine, Tsunenori.
SIGITE 2013 - Proceedings of the 2013 ACM SIGITE Annual Conference on Information Technology Education. 2013. p. 35-42.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Correlation of grade prediction performance and validity of self-evaluation comments
AU - Goda, Kazumasa
AU - Hirokawa, Sachio
AU - Mine, Tsunenori
PY - 2013/11/14
Y1 - 2013/11/14
N2 - To grasp a student's lesson attitude and learning situation and to give a feed back to each student are educational foundations. Goda et al. proposed the PCN method to estimate a learning situation from a comment freely written by students[6, 7]. The PCN method categorizes comments into three items of P (previous), C(current) and N(next). They pointed out a correlation between the student's final results and the validity of a descriptive content of item C, that is something related to understanding of the lesson and learning attitudes to the lesson. However, a problem left in their work is the badness of performance in prediction for upper grade students. This paper proposes two manners of utilization of PCN scores: the validity level determination for assessment, and for prediction performance of students' final grades. In order to validate the proposed manners of utilization, we conducted two experiments. First, we employed multiple regression analysis to calculate PCN scores that determine the validity level with respect to each viewpoint. Students who wrote comments with a high PCN score are considered as those who describe their learning attitude appropriately. We also applied a machine learning method SVM (support vector machine) to students' comments for predicting their final results in five grades of S, A, B, C and D. Experimental results illustrated that as comments of students get higher PCN scores, the prediction performance of the students' grades becomes higher.
AB - To grasp a student's lesson attitude and learning situation and to give a feed back to each student are educational foundations. Goda et al. proposed the PCN method to estimate a learning situation from a comment freely written by students[6, 7]. The PCN method categorizes comments into three items of P (previous), C(current) and N(next). They pointed out a correlation between the student's final results and the validity of a descriptive content of item C, that is something related to understanding of the lesson and learning attitudes to the lesson. However, a problem left in their work is the badness of performance in prediction for upper grade students. This paper proposes two manners of utilization of PCN scores: the validity level determination for assessment, and for prediction performance of students' final grades. In order to validate the proposed manners of utilization, we conducted two experiments. First, we employed multiple regression analysis to calculate PCN scores that determine the validity level with respect to each viewpoint. Students who wrote comments with a high PCN score are considered as those who describe their learning attitude appropriately. We also applied a machine learning method SVM (support vector machine) to students' comments for predicting their final results in five grades of S, A, B, C and D. Experimental results illustrated that as comments of students get higher PCN scores, the prediction performance of the students' grades becomes higher.
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UR - http://www.scopus.com/inward/citedby.url?scp=84887315481&partnerID=8YFLogxK
U2 - 10.1145/2512276.2512294
DO - 10.1145/2512276.2512294
M3 - Conference contribution
AN - SCOPUS:84887315481
SN - 9781450322393
SP - 35
EP - 42
BT - SIGITE 2013 - Proceedings of the 2013 ACM SIGITE Annual Conference on Information Technology Education
ER -