{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:47:14Z","timestamp":1770493634196,"version":"3.49.0"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Ltd","issue":"Supp03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Inter. Net."],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:p> Recent advancements in Artificial Intelligence techniques, including machine learning models, have led to the expansion of prevailing and practical prediction simulations for various fields. The quality of teachers\u2019 performance mainly influences the quality of educational services in universities. One of the major challenges of higher education institutions is the increase of data and how to utilize them to enhance the academic program\u2019s quality and administrative decisions. Hence, in this paper, Artificial Intelligence assisted Multi-Objective Decision-Making model (AI-MODM) has been proposed to predict the instructor\u2019s performance in the higher education systems. The proposed AI-assisted prediction model analyzes the numerical values on various elements allocated for a cluster of teachers to evaluate an overall quality evaluation representing the individual instructor\u2019s performance level. Instead of replacing teachers, AI technologies would increase and motivate them. These technologies would reduce the time necessary for routine tasks to enable the faculty to focus on teaching and analysis. The usage for administrative decision-making of artificial intelligence and associated digital tools. The experimental results show that the suggested AI-MODM method enhances the accuracy (93.4%), instructor performance analysis (96.7%), specificity analysis (92.5%), RMSE (28.1 %), and precision ratio (97.9%) compared to other existing methods. <\/jats:p>","DOI":"10.1142\/s0219265921440035","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T15:41:08Z","timestamp":1640274068000},"source":"Crossref","is-referenced-by-count":12,"title":["Instructor Performance Prediction Model Using Artificial Intelligence for Higher Education Systems"],"prefix":"10.1142","volume":"22","author":[{"given":"Shuping","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Engineering Technology, Xi\u2019an Fanyi University, Xi\u2019an 710105, China"}]},{"given":"A.","family":"Shanthini","sequence":"additional","affiliation":[{"name":"SRM University, Chennai, India"}]},{"given":"Deepa","family":"Thilak","sequence":"additional","affiliation":[{"name":"Department of Computer science, SRM Institute of Science and Technology, Kattankulathur, Chennai, India"}]}],"member":"219","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"S0219265921440035BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00084"},{"key":"S0219265921440035BIB002","first-page":"1","volume-title":"ICC 2020\u20132020 IEEE International Conference on Communications (ICC)","author":"Nguyen T. 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