Data Management Computer Science Discussion Board Responses Help
Programming
I’ve provided 2 discussions that need to be responded separately. The discussion topic is the following: “Find a case study on data mining. Identify the following information:
1. Describe the data mining case and the data mining technique used.
2. Explain whether current or historical data is being used for the data mining.
3. Describe the outcomes the data mining identified, including any advantages or disadvantages of the technique used.
4. Include a web link (URL) for the data mining example that you found.”
1-)n order to improve students academic performance teachers were using data mining as a guide for who needed what type of additional educational support.The technique used was a combination of clustering, association, and classification. With every assignment and lessons that were related data was collected of what answers were wrong, grades, and attributes of each student. Clustering was used to group students by the amount of mistakes they made. Classification was given to learners who seemed to have an issue with grasping similar concepts in the lessons. Association rule was implemented to discover what mistakes were commonly been made on the same exercises.
An advantage with classification is that each student was given advice just before taking an exam based on which category they fell in. A disadvantage of clustering is that it is too broad. The same amount of mistakes does not amount to the same struggles when it comes to doing school work. Association brings an advantage of exposing where a student may be confused or not being to distinguish concepts taught in the lessons.
2-)The state of education Data mining in 2009: A review and future visions, is the title of the article from the journal of Educational Data Mining. This article was simply to explore Educational Data Mining as an emerging Discipline that was concerned with developing methods that will explore all types of data that come from any educational setting and use these methods to better understand students and the setting in which they learn. The two data mining techniques listed in this article are 1. Statistics and Visualization and 2. Web mining ( which includes classification, clustering, and outlier detection and text mining).
Historic data is used in both methods in this article. The viewpoint is focused on application of educational data mining to web data, using historical research are to a large degree, analysis and logs from student-computers interactions are examined to determine the data.
Two major advantages were noted in the article concerning the outcome.
- Public educational databases and tools for instrumenting online courses increase accessibility of educational data to wide array of individuals. Lowering previous barriers to education such as distance and unable to be physically present.
- Also Educational Data mining and accessibility to relevant and usable data has the potential to lower more barriers to entry into the work force, such as research, health care and the like.
