An Analysis into the Issues of Multi-Agent Data Mining
Presently instructional establishments compile and store vast volumes of information like student enrollment and attending records, moreover as their examination results. Mining such knowledge yields stimulating info that serves its handlers well. Ascension in instructional knowledge points to the very fact that distilling huge amounts of information needs an additional subtle set of algorithms. This issue led to the emergence of the sphere of Educational Data Mining (EDM). Ancient data processing algorithms cannot be directly applied to instructional issues, as they will have a particular objective and performance. This suggests that a preprocessing algorithmic rule has got to be implemented 1st and solely then some specific data processing strategies are applied to the issues. One such preprocessing algorithmic rule in EDM is cluster. Several studies on EDM have targeted on the appliance of assorted data processing algorithms to instructional attributes. Therefore, this study provides over three decades long systematic literature review on cluster algorithmic rule and its relevancy and value within the context of EDM. Future insights area unit printed supported the literature reviewed, and avenues for any analysis area unit known.
Keywords: EDM- Educational Data Mining, LMS- Learning Management Systems, EDCElectronic Data Capture
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