Automated evaluation of student comments on their learning behavior

研究成果: ジャーナルへの寄稿Conference article

3 引用 (Scopus)

抄録

Learning comments are valuable sources of interpreting student status of understanding. The PCN method introduced in [Gouda2011] analyzes the attitudes of a student from a view point of time series. Each sentence of a comment is manually classified as one of P,C,N or O sentence. P(previous) indicates learning activities before the classtime, C(current) represents understanding or achievements during the classtime, and N(next) means a learning activity plan or goal until next class. The present paper applies SVM(Support Vecotor Machine) to predict the category to which a given sentence belongs. Empirical evaluation using 4,086 sentences was conducted. By selecting feature words of each category, the prediction performance was satisfactory with F-measures 0.8203, 0.7352, 0.8416 and 0.8612 for P,C,N and O respectively.

元の言語英語
ページ(範囲)131-140
ページ数10
ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8167 LNCS
DOI
出版物ステータス出版済み - 10 23 2013
イベント12th International Conference on Web-based Learning, ICWL 2013 - Kenting, 台湾省、中華民国
継続期間: 10 6 201310 9 2013

Fingerprint

Students
Evaluation
Time series
Performance Prediction
Predict
Learning
Class

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

これを引用

@article{33666067525d4784a6358a2512f6388b,
title = "Automated evaluation of student comments on their learning behavior",
abstract = "Learning comments are valuable sources of interpreting student status of understanding. The PCN method introduced in [Gouda2011] analyzes the attitudes of a student from a view point of time series. Each sentence of a comment is manually classified as one of P,C,N or O sentence. P(previous) indicates learning activities before the classtime, C(current) represents understanding or achievements during the classtime, and N(next) means a learning activity plan or goal until next class. The present paper applies SVM(Support Vecotor Machine) to predict the category to which a given sentence belongs. Empirical evaluation using 4,086 sentences was conducted. By selecting feature words of each category, the prediction performance was satisfactory with F-measures 0.8203, 0.7352, 0.8416 and 0.8612 for P,C,N and O respectively.",
author = "Kazumasa Goda and Sachio Hirokawa and Tsunenori Mine",
year = "2013",
month = "10",
day = "23",
doi = "10.1007/978-3-642-41175-5_14",
language = "English",
volume = "8167 LNCS",
pages = "131--140",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Automated evaluation of student comments on their learning behavior

AU - Goda, Kazumasa

AU - Hirokawa, Sachio

AU - Mine, Tsunenori

PY - 2013/10/23

Y1 - 2013/10/23

N2 - Learning comments are valuable sources of interpreting student status of understanding. The PCN method introduced in [Gouda2011] analyzes the attitudes of a student from a view point of time series. Each sentence of a comment is manually classified as one of P,C,N or O sentence. P(previous) indicates learning activities before the classtime, C(current) represents understanding or achievements during the classtime, and N(next) means a learning activity plan or goal until next class. The present paper applies SVM(Support Vecotor Machine) to predict the category to which a given sentence belongs. Empirical evaluation using 4,086 sentences was conducted. By selecting feature words of each category, the prediction performance was satisfactory with F-measures 0.8203, 0.7352, 0.8416 and 0.8612 for P,C,N and O respectively.

AB - Learning comments are valuable sources of interpreting student status of understanding. The PCN method introduced in [Gouda2011] analyzes the attitudes of a student from a view point of time series. Each sentence of a comment is manually classified as one of P,C,N or O sentence. P(previous) indicates learning activities before the classtime, C(current) represents understanding or achievements during the classtime, and N(next) means a learning activity plan or goal until next class. The present paper applies SVM(Support Vecotor Machine) to predict the category to which a given sentence belongs. Empirical evaluation using 4,086 sentences was conducted. By selecting feature words of each category, the prediction performance was satisfactory with F-measures 0.8203, 0.7352, 0.8416 and 0.8612 for P,C,N and O respectively.

UR - http://www.scopus.com/inward/record.url?scp=84885820055&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885820055&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-41175-5_14

DO - 10.1007/978-3-642-41175-5_14

M3 - Conference article

AN - SCOPUS:84885820055

VL - 8167 LNCS

SP - 131

EP - 140

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -