Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. Generally, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this study, four different classification techniques, -decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis-are used to build classifier models. Their performances are compared over a dataset composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The findings of the study indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve measurement instruments.

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The aim of the research is to determine adoption level of users to the e-transformation model applied in the process of Electronic Records Management System transformation by the Extended Technology Acceptance Model and to analyze users' different characteristics on the acceptance of e-transformation in a public institution. In this research, correlational comparative survey was used as a quantitative research method. The sample of the survey research was 469 users among the staff and faculty of the Marmara University which is a state university in Istanbul. Demographics Questionnaire and Extended Technology Acceptance Model Survey was used to collect data. The quantitative data collected by these tools were analyzed by descriptive statistics and inferential statistical tests. The findings of the study imply that the e-transformation model was accepted by the users but there are still some

This research was conducted to understand university and program preference criteria used by students and to find out differences in preference criteria with regard to students' study areas, academic units, education type,