Intelligent data analysis of interactions and relationships among technological pedagogical content knowledge constructs via rough set analysis

his study focuses on the relationship among Content Knowledge (CK), Pedagogic Knowledge (PK), and Technological Knowledge (TK) using Technological Pedagogical Content Knowledge (TPACK). The aim of the study is to use the determined relationship to provide mathematical clarity using the Rough Set Theory, which is commonly used in areas such as Artificial Intelligence, Data Reduction, Determination of Dependencies, Estimation of Data Importance and the establishment of Decision (control) Algorithms. Accordingly, TPACK scale was applied to 340 preservice teachers who, at the time of conducting this study, were continuing their teaching at elementary (grade 5-8) and secondary (grade 9-12) Mathematics Teaching Department. The gathered data was broken into three different groups-low, medium and high. The data grouping allowed for applying of the Rough Set Analysis. This will enable TPACK constructs to assign prospective teachers to any of the three identified groups. Analysis has put forth that the CK, PK and TK components explain TPACK with a dependency degree of 0.105 and that even though the levels of significance of each component is low by itself, it cannot be removed from the data set. Lastly, decision rules have been established between CK, PK and TK with TPACK.