Detecting student at risk of failure: a case study of conceptualizing mining from internet access log files

Ruangsak Trakunphutthirak, Yen Cheung, Vincent C.S. Lee

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

1 Citation (Scopus)

Abstract

Predicting student academic performance can be done by using educational data mining. Machine learning techniques play an important role for predicting academic performance from the large-scale data like the internet access log files from a university. Current data sources are mainly manual collections of data or data from a single unit of study. This study highlights the use of a new data source by transforming a university log file to predict academic performance. The log file comprises student internet access activities and browsing categories. To detect overall student academic performance, we select the best prediction accuracy by enhancing two datasets and comparing different weights in the time and frequency domains. We found that the random forest technique provides the best way in these datasets to predict students at risk-of-failure. We also found that data from internet access activities reveals a better accuracy than data from browsing categories. The combination of two datasets reveals a better picture of students' internet utilization and thus indicates how students at risk-of-failure can be detected by their internet access activities and browsing behavior.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Subtitle of host publication17–20 November 2018 Singapore
EditorsHanghang Tong, Zhenhui (Jessie) Li, Feida Zhu, Jeffrey Yu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages365-371
Number of pages7
ISBN (Electronic)9781538692882
ISBN (Print)9781538692899
DOIs
Publication statusPublished - 2018
EventWorkshop on Data Mining for eLearning Personalization 2018 - Singapore, Singapore
Duration: 17 Nov 201817 Nov 2018
Conference number: 18th

Conference

ConferenceWorkshop on Data Mining for eLearning Personalization 2018
Abbreviated titleDEEP 2018
CountrySingapore
CitySingapore
Period17/11/1817/11/18
OtherWorkshop as part of International Conference on Data Mining 2018.

Keywords

  • educational data mining
  • internet access activities
  • log file
  • students at risk-of-failure

Cite this

Trakunphutthirak, R., Cheung, Y., & Lee, V. C. S. (2018). Detecting student at risk of failure: a case study of conceptualizing mining from internet access log files. In H. Tong, Z. J. Li, F. Zhu, & J. Yu (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018: 17–20 November 2018 Singapore (pp. 365-371). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2018.00060