Matematiksel Becerilere İlişkin Birliktelik Kurallarının Çıkarılması
Erdoğan, Hanife Merve
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The cumulative nature of mathematics is one of the most important aspects of mathematics teaching. The preliminary learning required in the teaching of a mathematics subject should be determined first, then the deficiencies in the preliminary learning should be eliminated, if any, and the teaching of the new subject should be carried out with the necessary associations. The aim of this study is to classify the questions in the mathematics subtest of the Transition System from Basic Education to Secondary Education according to MATH Taxonomy and to make association analyzes for these questions and the skills they belong to. The data used in the study consisted of the data of 10000 students who participated in TEOG in the 2016-2017 academic year and answered the first term mathematics subtest and 10154 students who answered the second term mathematics subtest of the same year. The study continued with the analysis of the data set containing the answer patterns of the students according to the rules of association of data mining methods. At this stage, RapidMiner program was used and FP-Growth algorithm was used. As a result of the study, it was noted that the mathematics subtests included the items that measure the skills in MATH Use of Routine Procedures which is the last category of group A of MATH taxonomy. In addition, the most correctly answered items were predominantly in group A; the least correct answer is in group C. The items in the sub-tests, in the areas of learning in the mathematics curriculum, the first term “Numbers and Operations”; and the second semester was collected under the title of Geometry and Measurement. It was found that students with any level of skill in MATH taxonomy related to mathematical skills were able to perform the skills in subcategories at a high rate. The other association recorded is that the students who have the gains in the learning fields given after the mathematics curriculum have a high percentage of the gains in the given learning fields. However, this result is not valid for Probability of Simple Events.
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