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2022 | OriginalPaper | Buchkapitel

PSRE Self-assessment Approach for Predicting the Educators’ Performance Using Classification Techniques

verfasst von : Sapna Arora, Manisha Agarwal, Shweta Mongia, Ruchi Kawatra

Erschienen in: Artificial Intelligence and Speech Technology

Verlag: Springer International Publishing

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Abstract

With the growing interest in and significance of Educational Data Mining to educators’ performance, there is a vital need to comprehend the full scope of job performance that can substantially impact teaching quality. However, a few educational institutions are attempting to improve educator effectiveness to improve student outcomes. Furthermore, for reasons of confidentiality, most institutions do not share their data. As a result, an assessment of a self-assessment strategy is required to improve educators’ performance. With four input parameters and five classifiers (Logistics Regression, Naive Bayes, K-nearest Neighbor, Support Vector Machine- Linear, and Radial Basis Function), the proposed PSRE (Professional, Social, Research, and Emotional behavior) self-assessment approach is modeled to predict the overall performance of educators working in various Higher Educational Institutions. Overall, K-nearest neighbor has a high accuracy of 95.43%, which may help determine educators’ progress and assist them in reaching new professional heights.

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Vorheriges Kapitel Applying XGBoost Machine Learning Model to Succor Astronomers Detect Exoplanets in Distant Galaxies

Nächstes Kapitel Comparative Analysis of Traditional and Optimization Algorithms for Feature Selection

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Titel: PSRE Self-assessment Approach for Predicting the Educators’ Performance Using Classification Techniques
verfasst von: Sapna Arora
Manisha Agarwal
Shweta Mongia
Ruchi Kawatra
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Speech Technology
Print ISBN: 978-3-030-95710-0

Electronic ISBN: 978-3-030-95711-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-95711-7_34

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