Marizel Villanueva | De La Salle University (original) (raw)
aspiring data scientist
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Papers by Marizel Villanueva
– Vast amounts of data is now being collected and educational data belongs to one that contribute... more – Vast amounts of data is now being collected and educational data belongs to one that contributes to this voluminous content that is unprocessed. Available and plentiful, the researchers sought to look at the successes of mining educational data. This research paper aimed to do a review of different Educational Data Mining Researches and compare them. It has been seen that most algorithms used in Educational Data Mining were meant to produce clustering for data predictions. Monitoring student performance have been the core of most of the researches. Majority of them used Weka as a tool, and one used SPSS. Generally, most of the researches were successful, but the search for better hybridized algorithms would be more useful for them if they were able to get more meaningful and historical databases. Future recommendations in educational data mining are presented in terms of future scope of the researches related to it, together with suggested area of algorithms and data mining software needed to mine useful data as well.
– Vast amounts of data is now being collected and educational data belongs to one that contribute... more – Vast amounts of data is now being collected and educational data belongs to one that contributes to this voluminous content that is unprocessed. Available and plentiful, the researchers sought to look at the successes of mining educational data. This research paper aimed to do a review of different Educational Data Mining Researches and compare them. It has been seen that most algorithms used in Educational Data Mining were meant to produce clustering for data predictions. Monitoring student performance have been the core of most of the researches. Majority of them used Weka as a tool, and one used SPSS. Generally, most of the researches were successful, but the search for better hybridized algorithms would be more useful for them if they were able to get more meaningful and historical databases. Future recommendations in educational data mining are presented in terms of future scope of the researches related to it, together with suggested area of algorithms and data mining software needed to mine useful data as well.