Prediction of students’ academic performance based on tracking logs

Anna Y.Q. Hung; Jian Xuan Weng; Jeff C.H. Huang; Owen H.T. Lu; Bin Shyan Jong; Stephen J.H. Yang

Prediction of students’ academic performance based on tracking logs

Anna Y.Q. Hung, Jian Xuan Weng, Jeff C.H. Huang, Owen H.T. Lu, Bin Shyan Jong, Stephen J.H. Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this paper, we predict students’ academic performance based on tracking log of students’ learning activities. We compare the prediction of six datasets from Kyoto University (KU), National Central University (NCU), and Chung Yuan Christian University (CYCU) by eight classification models. We use the evaluators of accuracy, recall, precision, F1-score, and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). According to the prediction results, we found that sample size and feature category influence the prediction performance of classification. We also found that the significant features based on Pearson correlation analysis have greatly influence on the prediction performance of classification.

Original language	English
Title of host publication	ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings
Editors	Lung-Hsiang Wong, Michelle Banawan, Niwat Srisawasdi, Jie Chi Yang, Ma. Mercedes T. Rodrigo, Maiga Chang, Ying-Tien Wu
Publisher	Asia-Pacific Society for Computers in Education
Pages	467-476
Number of pages	10
ISBN (Electronic)	9789869721424
State	Published - 24 Nov 2018
Event	26th International Conference on Computers in Education, ICCE 2018 - Metro Manila, Philippines Duration: 26 Nov 2018 → 30 Nov 2018

Publication series

Name	ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings

Conference

Conference	26th International Conference on Computers in Education, ICCE 2018
Country/Territory	Philippines
City	Metro Manila
Period	26/11/18 → 30/11/18

Keywords

Academic performance
Classification

The Research of Applying Big Data to Improving Moocs Learning Analytics：An Empirical Study of College Calculus(2/3)
Yang, S. J. H. (PI)
1/08/18 → 31/07/19
Project: Research
Subproject 1: Applying Big Data Technique to Moocs Learners’ Course Video Analytics and Adaptive Course Material Recommendation(3/3)
Yang, S. J. H. (PI)
1/08/18 → 31/07/19
Project: Research

Cite this

Hung, A. Y. Q., Weng, J. X., Huang, J. C. H., Lu, O. H. T., Jong, B. S., & Yang, S. J. H. (2018). Prediction of students’ academic performance based on tracking logs. In L.-H. Wong, M. Banawan, N. Srisawasdi, J. C. Yang, M. M. T. Rodrigo, M. Chang, & Y.-T. Wu (Eds.), ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings (pp. 467-476). (ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings). Asia-Pacific Society for Computers in Education.

Hung, Anna Y.Q. ; Weng, Jian Xuan ; Huang, Jeff C.H. et al. / Prediction of students’ academic performance based on tracking logs. ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings. editor / Lung-Hsiang Wong ; Michelle Banawan ; Niwat Srisawasdi ; Jie Chi Yang ; Ma. Mercedes T. Rodrigo ; Maiga Chang ; Ying-Tien Wu. Asia-Pacific Society for Computers in Education, 2018. pp. 467-476 (ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings).

@inproceedings{d43f8a58b5c446c9aae6513bc31cdca4,

title = "Prediction of students{\textquoteright} academic performance based on tracking logs",

abstract = "In this paper, we predict students{\textquoteright} academic performance based on tracking log of students{\textquoteright} learning activities. We compare the prediction of six datasets from Kyoto University (KU), National Central University (NCU), and Chung Yuan Christian University (CYCU) by eight classification models. We use the evaluators of accuracy, recall, precision, F1-score, and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). According to the prediction results, we found that sample size and feature category influence the prediction performance of classification. We also found that the significant features based on Pearson correlation analysis have greatly influence on the prediction performance of classification.",

keywords = "Academic performance, Classification",

author = "Hung, {Anna Y.Q.} and Weng, {Jian Xuan} and Huang, {Jeff C.H.} and Lu, {Owen H.T.} and Jong, {Bin Shyan} and Yang, {Stephen J.H.}",

note = "Publisher Copyright: {\textcopyright} 2018 Asia-Pacific Society for Computers in Education..All Rights Reserved.; 26th International Conference on Computers in Education, ICCE 2018 ; Conference date: 26-11-2018 Through 30-11-2018",

year = "2018",

month = nov,

day = "24",

language = "???core.languages.en_GB???",

series = "ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings",

publisher = "Asia-Pacific Society for Computers in Education",

pages = "467--476",

editor = "Lung-Hsiang Wong and Michelle Banawan and Niwat Srisawasdi and Yang, {Jie Chi} and Rodrigo, {Ma. Mercedes T.} and Maiga Chang and Ying-Tien Wu",

booktitle = "ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings",

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Hung, AYQ, Weng, JX, Huang, JCH, Lu, OHT, Jong, BS & Yang, SJH 2018, Prediction of students’ academic performance based on tracking logs. in L-H Wong, M Banawan, N Srisawasdi, JC Yang, MMT Rodrigo, M Chang & Y-T Wu (eds), ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings. ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings, Asia-Pacific Society for Computers in Education, pp. 467-476, 26th International Conference on Computers in Education, ICCE 2018, Metro Manila, Philippines, 26/11/18.

Prediction of students’ academic performance based on tracking logs. / Hung, Anna Y.Q.; Weng, Jian Xuan; Huang, Jeff C.H. et al.
ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings. ed. / Lung-Hsiang Wong; Michelle Banawan; Niwat Srisawasdi; Jie Chi Yang; Ma. Mercedes T. Rodrigo; Maiga Chang; Ying-Tien Wu. Asia-Pacific Society for Computers in Education, 2018. p. 467-476 (ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of students’ academic performance based on tracking logs

AU - Hung, Anna Y.Q.

AU - Weng, Jian Xuan

AU - Huang, Jeff C.H.

AU - Lu, Owen H.T.

AU - Jong, Bin Shyan

AU - Yang, Stephen J.H.

PY - 2018/11/24

Y1 - 2018/11/24

N2 - In this paper, we predict students’ academic performance based on tracking log of students’ learning activities. We compare the prediction of six datasets from Kyoto University (KU), National Central University (NCU), and Chung Yuan Christian University (CYCU) by eight classification models. We use the evaluators of accuracy, recall, precision, F1-score, and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). According to the prediction results, we found that sample size and feature category influence the prediction performance of classification. We also found that the significant features based on Pearson correlation analysis have greatly influence on the prediction performance of classification.

AB - In this paper, we predict students’ academic performance based on tracking log of students’ learning activities. We compare the prediction of six datasets from Kyoto University (KU), National Central University (NCU), and Chung Yuan Christian University (CYCU) by eight classification models. We use the evaluators of accuracy, recall, precision, F1-score, and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). According to the prediction results, we found that sample size and feature category influence the prediction performance of classification. We also found that the significant features based on Pearson correlation analysis have greatly influence on the prediction performance of classification.

KW - Academic performance

KW - Classification

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M3 - 會議論文篇章

AN - SCOPUS:85060038098

T3 - ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings

SP - 467

EP - 476

BT - ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings

A2 - Wong, Lung-Hsiang

A2 - Banawan, Michelle

A2 - Srisawasdi, Niwat

A2 - Yang, Jie Chi

A2 - Rodrigo, Ma. Mercedes T.

A2 - Chang, Maiga

A2 - Wu, Ying-Tien

PB - Asia-Pacific Society for Computers in Education

T2 - 26th International Conference on Computers in Education, ICCE 2018

Y2 - 26 November 2018 through 30 November 2018

ER -

Hung AYQ, Weng JX, Huang JCH, Lu OHT, Jong BS, Yang SJH. Prediction of students’ academic performance based on tracking logs. In Wong LH, Banawan M, Srisawasdi N, Yang JC, Rodrigo MMT, Chang M, Wu YT, editors, ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings. Asia-Pacific Society for Computers in Education. 2018. p. 467-476. (ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings).

Prediction of students’ academic performance based on tracking logs

Abstract

Publication series

Conference

Keywords

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Projects

The Research of Applying Big Data to Improving Moocs Learning Analytics：An Empirical Study of College Calculus(2/3)

Subproject 1: Applying Big Data Technique to Moocs Learners’ Course Video Analytics and Adaptive Course Material Recommendation(3/3)

Cite this