Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response

Abstract

Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher features for example, capacity and clarity to encourage the students to make them study of his subjects, capacity to establish the lesson also with trainings and lectures. These aspects are not only attributable to motivate students, but also age, gender, prior experiences, As more and more the effectiveness of teaching is becoming even more significant in school evaluation system, it is, indeed, essential to discover how to assess it and find the significant related factors for giving the best rank of feedback to their tutors. This paper focuses the assessment of teaching effectiveness forum from students’ perspective “Feedback”, examine the questionnaires provided to the students of Cihan University at the end of their courses and discover the most effected teacher’s characteristics. The outcome variable (Student’s Feedback) was recorded on well-ordered, five-point scale of Likert provided by the students, to a set of independent variables to teacher level. The major techniques elaborated in the model fitting for ordinal regression were stating which independent variables are most likely to be kept in the model and selecting the link function such as, (logit link, complementary log-log link, negative log-log link and probit link) which verified the model suitability. Further to that, several statistical diagnoses have conducted like the model fitting, classification accuracy and the validity assumptions of the model, which is parallel lines, were fundamentally calculated in choosing the best fitted model. The dataset implemented in the analysis entails of almost (21566) respondents to the formed questionnaire in relation to courses of Cihan University for the 2018 – 2019 academic year.