•Research paper: Ruangsak Trakunphutthirak, Vincent C. S. Lee, and Yen Cheung. A Study of Educational Data Mining: Evidence from a Thai University: Article in Proceedings of the AAAI Conference on Artificial Intelligence· July 2019 : DOI: 10.1609/aaai.v33i01.3301734 :
•https://www.researchgate.net/publication/335805027
•https://www.aaai.org/ojs/index.php/AAAI/article/view/3851

•A Study of Educational Data Mining: Evidence from a Thai University :
•Abstract
•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 generalized 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 reveal 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, analyzing it over a longer period and relating the period of data collection with events during the academic year.
 

What kinds of things can educational data mining tell us, or not tell us?

•Data mining is about explaining the past and predicting the future by means of data analysis. Educational Data Mining is a promising discipline which has an imperative impact on predicting students’ academic performance.
•Thousands of students take admissions in Universities and colleges every year, at the time of admissions they collect the students’ data. In the same way while the Teachers join in the institution they collect their personal and professional data.
•Understand the importance of data is essential from a business point of view. Data collected at the time of admission can be used for classifying and predicting students’ behavior and performance as well as teachers’ performance.

•The following the four goals of EDM:
•Predicting learner’s behaviors by improving student models. Modeling is characterizing and categorizing a student’s characteristics or states that make up the student’s knowledge, motivation, meta-cognition, and attitudes.
•Discovering or improving knowledge domain structure models. For example, there are concept models of the materials being taught and models that explain the interrelationships of knowledge in a domain (Barnes, 2005).
•Studying the most effective pedagogical support for student learning that can be achieved through learning systems.
•Establishing empirical evidence to support or articulate pedagogical theories, frameworks, and educational phenomena to determine core influential components of learning to enable the designing of better learning systems.
•EDM goals are achieved by adapting psychometrics, employing statistical techniques, and mining log data stored in offline educational settings, including face-to-face contacts, studying the psychology of how humans learn, participating in online learning obtained from E-learning and Learning Management System (LMS), and using Intelligent Tutoring System (ITS) (Romero & Ventura 2010).

•Furthermore, the empirical study about EDM has the needs in pedagogical industry, especially higher education. This is because higher educational institutions have a large set of data enough to conduct the analysis. (Kollias et al., 2005) although educators are not aware of how to conduct EDM in their own practice. They might also not know how to use the latest technology and why it is important due to a lack of technological training. (Selwyn, 2011) The complicated and new machine learning techniques, which are the main analysis techniques of EDM, might puzzle educator

• Some Ethical Considerations
• Educational data mining offers several advantages, vis-à-vis more traditional educational research paradigms, such as laboratory experiments,in-vivo experiments, and design research.

•Reference:

1) Jesse Tetsuya, Why is Educational Data Mining important in the research? : https://towardsdatascience.com/why-is-educational-data-mining-important-in-the-research-e78ed1a17908
2) P. Meena Kumari et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2458-2461

3) Baker, R.S.J.d. (in press) Data Mining for Education. To appear in McGaw, B., Peterson, P.,

4) Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.

5) Ryan S.J.d. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA rsbaker@cmu.edu, Data Mining for Education.