Educational Data Mining



  • Educational Data Mining is a significant research field called EDM.
  • It uses multiple algorithms to improve educational results and explain educational procedures for further decision making.
  • In these algorithms are used to mine knowledge from educational data and study attributes that can add to boost performance.
  • Behavioral models depend observable changes in behavior of student to determine learning results.
  • Psychological models based effective involvement of teacher in learning procedure viewed as a demonstration in a network nodes that improves learning experience of students and diminishes the requirement for the immediate inclusion of a professor.

What is Educational Data Mining

  • EDM stands for Educational Data Mining.
  • Educational Data Mining can be defined as the technique for finding the specific types of data that come from the education system and implementing those techniques to understand students and the system better.
  • It process of transforming raw data obtained from educational systems into useful data that can be used to make data-driven decisions. It is challenging for educational data mining via the web because of its specific features on data. Distribution of educational information over time has extraordinary attributes.
  • Now a day, the utilization of information collected through these advancement educational data, is developing acknowledgment that is not all key data is stored in one data stream. Data mining is most powerful technique with incredible potential to help schools and universities focus most significant information in data sets they have collected about student's behavior and potential learners.
  • Data mining uses its tools to find relationships in huge data sets and previously unknown patterns.
  • These tools can incorporate statistical models, machine learning techniques, mathematical algorithms.

Approaches of Data Mining in Educational Data

  • The data may include teacher's data, accounts data, student's data, alumni data, etc. Educational data mining focuses development techniques for exploring special types of data that originate from an educational context.
  • algorithms such Clustering, Association Rules, Genetic Algorithms, Decision tree, Classification, Regression, Neural Networks , Artificial Intelligence , etc. are used for knowledge discovery from databases.

Clustering

  • It refers to the process of identification and classification of objects into different groups, segmentation of a data set into subsets
  • In that the data in each subset share some common characteristic of similar classes of objects.

Classification

  • It refers to the process of describing data relationships and expresses values for future observation.
  • It is the task of learning an objective function that maps each attribute set A to one of the predefined class level B.
  • There are various classification techniques such as Memory-based reasoning, Rule-based methods, namely Decision Tree-based Methods, Na├»ve Bayes and Bayesian Belief Networks, Neural Networks.
  • In classification, test data is used to estimate the certainty of the classification rules.

Prediction

  • Regression techniques can be adapted for prediction.
  • It can be used to demonstrate connection between one or more independent and dependent variables. The advanced techniques such as logistic regression, neural nets, and decision trees.

Education data mining in the coming future

  • Educational Data Mining concerns with creating strategies that find useful data is originated from educational environment.
 Education in Datamining

Education in Datamining



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