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The presented article “Algorithm of Problem Solving in Educational Data Mining Approach” is following in the previous article “Data Mining Tools in Science Education” (Zaskodny, 2012, JSCI). The main principle of previous article was data mining in science education as problem solving. The main goal was consisting in delimitation of complex data mining tool and partial data mining tool in area of science education. The procedure of previous article was consisting of data preprocessing in science education, data processing in science education, description of curricular process as complex data mining tool, description of analytical synthetic modeling as partial data mining tool and finally the application via physics education.
The presented article is based on partly the widening of previous article, partly the innovation of previous article procedure, partly the presentation of new results. The presented article is respecting all the quoted sources which were utilized in the previous article (Zaskodny, 2012). The presented article is also closely issuing from monographs processed by Zaskodny et al,. 2014, Zaskodny, 2016.
The presented article is describing the role of algorithms in problem solving as significant result mainly of educational data mining approach, but also marginally of data mining approach in statistics and theory of financial derivatives (an expression of inter-disciplinary communication) The problem solving is expressing very often the essence of data mining and the algorithm of problem solving is showing the way how to reach the concrete results. It is showing not only how to substantiate the concrete results, but also how to continue by an expression of needful textbook structure (in the case of educational data mining approach) or how to continue in the form of programming language application (in the case of data mining in statistics and financial derivatives theory).
Within presented article it will be shown the concrete applications of problem solving by means of the algorithm of curricular process as complex tool of educational data mining. Also the algorithms of statistics and financial derivatives theory will be indicated.
The structure of delimitation of the role of algorithm in problem solving (within educational data mining approach) will be described through following succession of steps:
1. Data Mining Approach as Realization of Data Mining Cycle 2. Complex Tool of Educational Data Mining – Curricular Process 3. Significant Partial Tool of Data Mining – Analytical Synthetic Modeling 4. Significant Partial Tool of Data Mining – Matrix Modeling and Main Diagonal of Matrix 5. Algorithm of Curricular Process 6. General Role of Algorithms in Data Mining Approach
