Prediction of students' grades based on free-style comments data

Shaymaa E. Sorour, Tsunenori Mine, Kazumasa Goda, Sachio Hirokawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper we propose a new approach based on text mining technique to predict student's performance using LSA (latent semantic analysis) and K-means clustering method. The present study uses free style comments written by students after each lesson. Since the potentials of these comments can reflect students' learning attitudes, understanding and difficulties to the lessons, they enable teachers to grasp the tendencies of students' learning activities.To improve this basic approach, overlap method and similarity measuring technique are proposed. We conducted experiments to validate our proposed methods. The experimental results illustrated that prediction accuracy was 73.6% after applying the overlap method and that was 78.5% by adding the similarity measuring.

Original languageEnglish
Title of host publicationAdvances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings
PublisherSpringer Verlag
Pages142-151
Number of pages10
ISBN (Print)9783319096346
DOIs
Publication statusPublished - Jan 1 2014
Event13th International Conference on Advances in Web-Based Learning, ICWL 2014 - Tallinn, Estonia
Duration: Aug 14 2014Aug 17 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8613 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Advances in Web-Based Learning, ICWL 2014
CountryEstonia
CityTallinn
Period8/14/148/17/14

Fingerprint

Student Learning
Students
Overlap
Prediction
Latent Semantic Analysis
K-means Clustering
Text Mining
Clustering Methods
Predict
Semantics
Experimental Results
Experiment
Style
Similarity
Experiments

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Sorour, S. E., Mine, T., Goda, K., & Hirokawa, S. (2014). Prediction of students' grades based on free-style comments data. In Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings (pp. 142-151). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8613 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-09635-3_15

Prediction of students' grades based on free-style comments data. / Sorour, Shaymaa E.; Mine, Tsunenori; Goda, Kazumasa; Hirokawa, Sachio.

Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings. Springer Verlag, 2014. p. 142-151 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8613 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sorour, SE, Mine, T, Goda, K & Hirokawa, S 2014, Prediction of students' grades based on free-style comments data. in Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8613 LNCS, Springer Verlag, pp. 142-151, 13th International Conference on Advances in Web-Based Learning, ICWL 2014, Tallinn, Estonia, 8/14/14. https://doi.org/10.1007/978-3-319-09635-3_15
Sorour SE, Mine T, Goda K, Hirokawa S. Prediction of students' grades based on free-style comments data. In Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings. Springer Verlag. 2014. p. 142-151. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-09635-3_15
Sorour, Shaymaa E. ; Mine, Tsunenori ; Goda, Kazumasa ; Hirokawa, Sachio. / Prediction of students' grades based on free-style comments data. Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings. Springer Verlag, 2014. pp. 142-151 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{107332a037ca49d08dd451c5260070eb,
title = "Prediction of students' grades based on free-style comments data",
abstract = "In this paper we propose a new approach based on text mining technique to predict student's performance using LSA (latent semantic analysis) and K-means clustering method. The present study uses free style comments written by students after each lesson. Since the potentials of these comments can reflect students' learning attitudes, understanding and difficulties to the lessons, they enable teachers to grasp the tendencies of students' learning activities.To improve this basic approach, overlap method and similarity measuring technique are proposed. We conducted experiments to validate our proposed methods. The experimental results illustrated that prediction accuracy was 73.6{\%} after applying the overlap method and that was 78.5{\%} by adding the similarity measuring.",
author = "Sorour, {Shaymaa E.} and Tsunenori Mine and Kazumasa Goda and Sachio Hirokawa",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-09635-3_15",
language = "English",
isbn = "9783319096346",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "142--151",
booktitle = "Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings",
address = "Germany",

}

TY - GEN

T1 - Prediction of students' grades based on free-style comments data

AU - Sorour, Shaymaa E.

AU - Mine, Tsunenori

AU - Goda, Kazumasa

AU - Hirokawa, Sachio

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper we propose a new approach based on text mining technique to predict student's performance using LSA (latent semantic analysis) and K-means clustering method. The present study uses free style comments written by students after each lesson. Since the potentials of these comments can reflect students' learning attitudes, understanding and difficulties to the lessons, they enable teachers to grasp the tendencies of students' learning activities.To improve this basic approach, overlap method and similarity measuring technique are proposed. We conducted experiments to validate our proposed methods. The experimental results illustrated that prediction accuracy was 73.6% after applying the overlap method and that was 78.5% by adding the similarity measuring.

AB - In this paper we propose a new approach based on text mining technique to predict student's performance using LSA (latent semantic analysis) and K-means clustering method. The present study uses free style comments written by students after each lesson. Since the potentials of these comments can reflect students' learning attitudes, understanding and difficulties to the lessons, they enable teachers to grasp the tendencies of students' learning activities.To improve this basic approach, overlap method and similarity measuring technique are proposed. We conducted experiments to validate our proposed methods. The experimental results illustrated that prediction accuracy was 73.6% after applying the overlap method and that was 78.5% by adding the similarity measuring.

UR - http://www.scopus.com/inward/record.url?scp=84905851384&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905851384&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-09635-3_15

DO - 10.1007/978-3-319-09635-3_15

M3 - Conference contribution

AN - SCOPUS:84905851384

SN - 9783319096346

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 142

EP - 151

BT - Advances in Web-Based Learning, ICWL 2014 - 13th International Conference, Proceedings

PB - Springer Verlag

ER -