Building an interpretable model of predicting student performance using comment data mining

Shaymaa E. Sorour, Tsunenori Mine

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

3 Citations (Scopus)

Abstract

Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.

Original languageEnglish
Title of host publicationProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
EditorsAyako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages285-291
Number of pages7
ISBN (Electronic)9781467389853
DOIs
Publication statusPublished - Aug 31 2016
Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
Duration: Jul 10 2016Jul 14 2016

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
CountryJapan
CityKumamoto
Period7/10/167/14/16

Fingerprint

Data mining
Students
Decision trees
Feedback

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Sorour, S. E., & Mine, T. (2016). Building an interpretable model of predicting student performance using comment data mining. In A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (Eds.), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 285-291). [7557619] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.114

Building an interpretable model of predicting student performance using comment data mining. / Sorour, Shaymaa E.; Mine, Tsunenori.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. ed. / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 285-291 7557619.

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

Sorour, SE & Mine, T 2016, Building an interpretable model of predicting student performance using comment data mining. in A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (eds), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557619, Institute of Electrical and Electronics Engineers Inc., pp. 285-291, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 7/10/16. https://doi.org/10.1109/IIAI-AAI.2016.114
Sorour SE, Mine T. Building an interpretable model of predicting student performance using comment data mining. In Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, editors, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 285-291. 7557619 https://doi.org/10.1109/IIAI-AAI.2016.114
Sorour, Shaymaa E. ; Mine, Tsunenori. / Building an interpretable model of predicting student performance using comment data mining. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. editor / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 285-291
@inproceedings{f8555af3c8b3454894545f80959c539d,
title = "Building an interpretable model of predicting student performance using comment data mining",
abstract = "Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.",
author = "Sorour, {Shaymaa E.} and Tsunenori Mine",
year = "2016",
month = "8",
day = "31",
doi = "10.1109/IIAI-AAI.2016.114",
language = "English",
pages = "285--291",
editor = "Ayako Hiramatsu and Tokuro Matsuo and Akimitsu Kanzaki and Norihisa Komoda",
booktitle = "Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Building an interpretable model of predicting student performance using comment data mining

AU - Sorour, Shaymaa E.

AU - Mine, Tsunenori

PY - 2016/8/31

Y1 - 2016/8/31

N2 - Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.

AB - Most current prediction models are difficult for teachers to interpret. This induces significant problems of grasping characteristics for each grade group of students, which are helpful for giving intervention and providing feedback to them. In this paper, we propose a new method to build a practical prediction model based on comment data mining. The current study classifies students' comments into six attributes (attitudes, finding, cooperation, review the lesson, understanding, and next activity plan), then extracts generic rules 'IF-THEN' about students' activities, attitudes and situations in the learning environment. Decision Tree (DT) and Random Forest (RF) models are applied to discriminate unique features related to each grade group. Evaluation results reported a set of rules for students' performance among with their situations reflected through all the course of a semester.

UR - http://www.scopus.com/inward/record.url?scp=84988905743&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84988905743&partnerID=8YFLogxK

U2 - 10.1109/IIAI-AAI.2016.114

DO - 10.1109/IIAI-AAI.2016.114

M3 - Conference contribution

AN - SCOPUS:84988905743

SP - 285

EP - 291

BT - Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

A2 - Hiramatsu, Ayako

A2 - Matsuo, Tokuro

A2 - Kanzaki, Akimitsu

A2 - Komoda, Norihisa

PB - Institute of Electrical and Electronics Engineers Inc.

ER -