William Cope
May Thet Khine Nyein
Obie Ellmoos.png "Nathan Luise")
Nathan Luise
Olive SabiitiAssessment for Learning MOOC’s Updates
Educational Data Mining
Data mining, also called Knowledge Discovery in Databases (KDD), is the field of discovering novel and potentially useful information from large amounts of data. Data mining has been applied in a great number of fields, and education is one of them. Therefore, educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings, and using those methods to better understand students and the settings which they learn in.
Psychometric analysis of item & test performance referees to utilizing best practices and leading-edge software to perform the complex quantitative analyses and providing the results with understandable explanations.
The Psychometric analysis of item & test performance is an essential step in developing a defensible test is psychometric analysis of response data. This step provides detailed feedback on what is working well in the test, what is not, and why. This drives continual improvement, but also provides documentation of validity evidence. In universities, this application is used to analyze student’s results and it helps the tutors to identify what skills and knowledge have been acquired and what have been not.
In fact, educational data mining techniques can help to improve students’ achievement and success more effectively in an efficient way using educational data mining. It could bring the benefits and impacts to students, educators and academic institutions. education data mining helps in understanding learning outcomes, identify students; behavior, and characterize groups of students.
Resources:
Algarni A. Data mining in education. Int J Adv Comput Sci Appl. 2016;7(6):456–461.
T. Mishra, D. Kumar, S. Gupta, Mining students’ data for prediction performance, in: Proceedings of the 2014 Fourth International Conference on Advanced Computing & Communication Technologies, ACCT ‘14, IEEE Computer Society, Washington, DC, USA, 2014, pp. 255-262. doi:10.1109/ACCT. 2014.105. URL http://dx.doi.org/10.1109/ACCT. 2014.105.
The discussion on the role of data mining, particularly in the context of education and psychometric analysis, is incredibly pertinent in today's data-driven world. Data mining, or Knowledge Discovery in Databases (KDD), indeed offers a powerful tool for extracting valuable insights from large datasets, and educational data mining (EDM) is a promising field within it.
Psychometric analysis, as described, is crucial for developing reliable and valid assessments in education. It not only helps in understanding what's working well in tests but also provides a foundation for continuous improvement. This kind of analysis is essential in educational settings to gauge student performance accurately.
Moreover, the application of EDM in universities for analyzing student results and identifying the acquisition of skills and knowledge is a fantastic example of how data mining can be practically beneficial in education. It empowers educators to make informed decisions, tailor teaching methods, and, ultimately, enhance student achievement.