Stemming Implementation in Preprocessing Phase for Evaluating of Exams Using Data Mining Approach
AbstractIn educational activities, examinations are sometimes carried out in the form of multiple-choice tests or sometimes as open-ended long texts. When multiple-choice tests are performed, evaluating process is carried out either manual or computer-assisted. Exam questions prepared in the form of multiple choice tests are not suitable for every course. It may be necessary to use open-ended questionnaires in order for pupils to accurately measure their achievement in relation to the course. It can take a long time to evaluate examinations made with such questions. However, this process can create problems in terms of objective evaluation. Data mining, defined as the extraction of useful information from large quantities of data, can be used to process all kinds of data. The data mining method used in the processing of textual data is called text mining. In text processing studies, data is subject to preprocessing in order to obtain a high quality data set. The most important stage of preprocessing is stemming. In this study, stemming process is implemented to questions and correct answers taken from students. The results obtained in 2 different samples and 4 sentences are 71%, 69%, 86% and 78% correct. In order to be able to distinguish what the textual data written in the natural language really is, it is necessary to use the states of the words which are made up of construction and free from the suffixes. Therefore, in the pre-processing phase, stemming process is applied to the textual data in accordance with the grammar rules of the language they are written on, and stems of every word are found. Text processing is used in many areas of the natural language. Computer-aided solutions will be inevitable so that problems can be eliminated and open-ended questions can be quickly assessed. Despite the desirability of a computer aided solution for this measurement technique, studies of this solution are not included in the literature very much.
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