Predicting Faculty Performance in Higher Education using Machine Learning
T. Manjunath Kumar1, R. Murugeswari2
1T. Manjunath Kumar*, Computer Science and Engineering, Kalasalingam Academy of Research and Education, KrishnanKoil, India.
2Dr.R. Murugeswari, Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9472-9478 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9750118419/2019©BEIESP | DOI: 10.35940/ijrte.D9750.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Higher education is witnessing significant change in transferring of knowledge and experience from the faculty to the student community on a large scale. Domains requiring a high degree of skill development such as engineering technology education need a place in the university of deeper faculty communication and information percolation process. Despite various measurement and accounting system are prevailing in the current educational system. Known advanced methods for automated evaluation of faculty performance has emanated successfully yet. Here we propose a Machine Learning technique which can interact both with faculty and students to collect feature-oriented parameters of the academic processes prevailing in engineering domain to qualitative and quantitatively access the faculty performance in the modern ways. By using various machine learning methods such as Support Vector Machine, Logistic Regression, Naïve Bayes, Random Forest and Deep learning are used to build the models. The proposed model could identify the faculty’s performance who are likely to improve the course teaching for the students to enhance the teaching – learning process. Model accuracy is used to evaluate the performance of the faculty in the course. To identify the Machine learning algorithm performance F1-Measure and Area under Curve (AUC) value are compared. Evaluation result indicates that Random Forest algorithm is best suitable algorithm for faculty performance prediction for course evaluation through feedback questionaries’ given by the students. It has a 73.1% higher accuracy, 78.7% F1- measure and 0.73 value of AUC when compared to other models. This model is used for decision making support in the higher educational university.
Keywords: Machine Learning, Performance Prediction, Feature Selection, Feedback questionnaires, Decision making, Course evaluation.
Scope of the Article: Machine Learning.