Knowledge discovery in databases (KDD)

Knowledge discovery in databases (KDD)

Knowledge discovery in databases (KDD). We may call "Knowledge discovery" which aims at finding interesting regularities,patterns, and concepts in data. IGOABROAD has the same as system. we are using different statistical and heuristic techniques called data mining techniques. Knowledge discovery has bee applied in academic research. For example, When i design IGOABROAD, i predicted workds on student's success,errors,or help requsts are: predicting the time spent insolving an exercise task by using networks and predicting in which word the student asks help in reading English.

Accroding to Salazar & Vergara's paper, The process of knowledge discovery covered the following stages:

1. Building a reliable data warehouse, by filtering data inconsistencies, solving data heterogeneity problems,and processing data.
2. Obtaining and interpreting patterns of the student behaviour in eLearning activities by using independent component analysis, neural networks, and linear regression analysis.
3. obtaining homogeneous data groups by applying clustering processing and selecting data groups,sorted out by research topics,for the definition of decision rules.
4. Applying a knowledge representation on selected groups using decision trees to obtain the decision rules of the factors that influence on academic achievement success and failure.
5. Evaluating knowledge findings by experts,from the point of view of their validity,novelty, and simplicity.
6. outlining strategies for the improvement of academic processes.