PreFIC: Predictability of Faculty Instructional Performance Through Hybrid Prediction Model
Unife O. Cagas1, Allemar Jhone P. Delima2, Teresita L. Toledo3
1Unife O. Cagas, College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines.
2Allemar Jhone P. Delima, College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines.
3Teresita L. Toledo, College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 22-25 | Volume-8 Issue-7, May 2019 | Retrieval Number: E3211038519/19©BEIESP
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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: The Higher Education Institutions have amplified the practice of incorporating datamining in extracting information from the data in relation to educational context. As one of the regarded quest of HEIs, predicting faculty instructional performance has made easy; and the accuracy of the result has become more reliable through the application of data mining algorithms and techniques. This study proposed a hybrid model in predicting the instructional performance of faculty in the four State Universities and Colleges (SUC) in Caraga Region, Philippines by integrating k-means segmentation on the C4.5 algorithm prior to prediction. A total of 597 records of student-respondents was used for simulation using the 10-folds cross validation scheme. Simulation result showed that with integration of k-means algorithm, the identified prediction accuracy of 86.09% using C4.5 algorithm alone has increased to 87.93%. Future researchers may utilize other hybrid algorithms in the quest on improving the literature of educational data mining.
Keyword: Accuracy Enhancement, Hybrid Model, Instructional Performance, Prediction
Scope of the Article: Artificial Intelligent Methods, Models, Techniques.