Conceptualizing Mining of Firm’s Web Log Files

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

doi:10.21078/jssi-2017-489-22

Cited by 7 publications

(3 citation statements)

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“…From log files, we can use basic statistical analysis to find more information, but the knowledge extracted from this kind of analysis is very limited (Trakunphutthirak et al., 2017). Machine learning techniques enable us to extract more knowledge from this data: they allow us to mine the log file data to better understand and classify patterns in Internet usage behaviour.…”

Section: Some Related Workmentioning

confidence: 99%

Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

Trakunphutthirak

Lee

2021

Journal of Educational Computing Research

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Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy.

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Section: Some Related Workmentioning

confidence: 99%

Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

Trakunphutthirak

Lee

2021

Journal of Educational Computing Research

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“…Basic statistical analysis can be performed to discover more information from log files. The key problem with this analysis is that the knowledge extracted from this kind of analysis is very limited (Trakunphutthirak, Cheung, and Lee 2017). Many recent studies (Amornsinlaphachai 2016;Senthil and Lin 2017;Zhou et al 2018) have shown that machine learning techniques are mostly able to extract more knowledge from that data.…”

Section: Machine Data: Internet Access Log Filesmentioning

confidence: 99%

“…Firewalls automatically keep an internet access log file in a systematic way (Trakunphutthirak, Cheung, and Lee 2017). The log file is used to create a data model as a schema on read (Dong and Srivastava 2013).…”

Section: Introductionmentioning

confidence: 99%

A Study of Educational Data Mining: Evidence from a Thai University

Trakunphutthirak

Cheung

Lee

2019

AAAI

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Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.

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