Analysis of Faculty Performance Evaluation Using Classification

Abstract

In order to increase the effectiveness of delivery of quality education, it is important to evaluate the performance of two major stackholders namely students and faculty. Presently, Data Mining has emerged an important area of research in Higher and Technical Education. Data mining techniques are applied in higher education to address and give an insight to educational and administrative problems in HEIs. However, a large portion of the instructive mining research concentrates on modelling and predicting student's performance and a very few research models are available on faculty performance. While evaluating faculty performance, majority of the research used questionnaire as an important tool for collecting feedback from the students. The same method is being used in this research also. In this study, we have applied five Classification Techniques namely Logistic Regression, Decision Tree, Linear SVM, Neural Network and Naive Bayes and used student's results along with filled questionnaires to predict the performance of faculty. The accuracy, sensitivity and specificity of classification rules were estimated. The findings of the study indicate the effectiveness of classification in evaluation of faculty.

An Android Based Automated Tool for Performance Evaluation of a Course Teacher (CTE)

“…Advanced education organizations have begun to embrace the utilization of innovation into their conventional showing components for upgrading learning and instructing. There are several research works like [1,6,10,11,12] which uses machine learning algorithms to predict, classify, and analyze student reviews for faculty evaluation.…”

“…Understudy fulfillment reviews are ordinarily utilized as an apparatus to catch the nature of instructing and learning. They can without a doubt give significant data to the senior administration of HEIs giving bits of knowledge on their qualities and shortcomings [1].…”

Visual Analytics for Faculty Teaching Practice and Performance: A Case Study of Educational Data Mining

“…al., 2016). Similarly, to increase the efficiency of delivering quality education, it is important to assess the performance of two major stakeholders, namely students and faculty (Saxena and Bhatnagar, 2018). The term faculty teaching practice and performance is based on the learning experience, student overall performance, and actual subject performance.…”

“…Nonetheless, the questions of what "effective" means and how it is measured continue to challenge faculty and administrators in university. The main problem faced by Higher Educational Institutions (HEIs) is the challenge of improving academic quality (Saxena and Bhatnagar, 2018). Higher education has an important role both for the student and the university itself.…”

“…Higher education has an important role both for the student and the university itself. However, most of the instructive mining research focus on modelling and forecasting student performance, and very few research models on faculty performance are available (Saxena and Bhatnagar, 2018). Moreover, according to the findings found by Lyde et al, evaluation rating forms filled by students are the most commonly used indicator of success in faculty members' teaching performance yet many parties did not make the best use of these datasets (Lyde et.…”