Title: Mining High Dimensional Educational (Student) Data to Extract Patterns for Interestingness
Title: Mining High Dimensional Educational (Student) Data to Extract Patterns for Interestingness
An Introduction to Data Mining
Overview
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Educational Data Mining
Educational Data Mining (EDM) is the application of Data Mining (DM) techniques to educational data, and so, its objective is to analyze these type of data in order to resolve educational research issues. EDM is concerned with developing methods to explore the unique types of data in educational settings and, using these methods, to better understand students and the settings in which they learn.
EDM has emerged as a research area in recent years for researchers all over the world from different and related research areas such as:
- Offline education try to transmit knowledge and skills based on face-to-face contact and also study psychologically on how humans learn. Psychometrics and statistical techniques have been applied to data like student behavior/performance, curriculum, etc. that was gathered in classroom environments
- E-learning and Learning Management System (LMS). Elearning provides online instruction and LMS also provides communication, collaboration, administration and reporting tools. Web Mining (WM) techniques have been applied to student data stored by these systems in log files and databases.
- Intelligent Tutoring (ITS) and Adaptive Educational Hypermedia System (AEHS) are an alternative to the
An Introduction to Data Mining
Overview
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Educational Data Mining
Educational Data Mining (EDM) is the application of Data Mining (DM) techniques to educational data, and so, its objective is to analyze these type of data in order to resolve educational research issues. EDM is concerned with developing methods to explore the unique types of data in educational settings and, using these methods, to better understand students and the settings in which they learn.
EDM has emerged as a research area in recent years for researchers all over the world from different and related research areas such as:
- Offline education try to transmit knowledge and skills based on face-to-face contact and also study psychologically on how humans learn. Psychometrics and statistical techniques have been applied to data like student behavior/performance, curriculum, etc. that was gathered in classroom environments
- E-learning and Learning Management System (LMS). Elearning provides online instruction and LMS also provides communication, collaboration, administration and reporting tools. Web Mining (WM) techniques have been applied to student data stored by these systems in log files and databases.
- Intelligent Tutoring (ITS) and Adaptive Educational Hypermedia System (AEHS) are an alternative to the
References: [2]. Surjeet Kumar Yadav, Brijesh Bharadwaj, Saurabh Pal, “Mining Education Data to Predict Student’s Retention: A comparative Study” IJACSA, Vol.10, No.2, 2012. [3]. Cristóbal Romero, Sebastián Ventura, Enrique García (2007),” Data mining in course management systems: Moodle case study and tutorial “ Elsevier Science. [4]. Alaa el-Halees, “Mining students data to analyze e-Learning behavior: A Case Study”, 2009. [9].Marta Zorrilla, et al.,(2010) “A Decision Support System to improve e-Learning Environments” EDBT Copyright 2010 ACM 978-1-60558-945-9/10/0003. [10] Brijesh Kumar Baradwaj, Saurabh Pal, “Mining Educational Data to Analyse Students Performance”, IJACSA, Vol.2, No.6, 2011. [11]. Nikolaos Dimokas, Nikolaos Mittas, Alexandros Nanopoulos, Lefteris Angelis(2008), "A Prototype System for Educational Data Warehousing and Mining," Informatics, Panhellenic Conference on, pp. 199-203. [14]. Nathaniel Anozie,Brian W. Junker(2006), ” Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system”.