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

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Artificial Intelligence and Speech Technology (AIST 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1546))

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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Author information

Authors and Affiliations

  1. Banasthali Vidyapith, P.O. Banasthali Vidyapith, Tonk, 304022, Rajasthan, India

    Sapna Arora & Manisha Agarwal

  2. University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Shweta Mongia

  3. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Ruchi Kawatra

Authors
  1. Sapna Arora
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  2. Manisha Agarwal
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  3. Shweta Mongia
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  4. Ruchi Kawatra
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Editor information

Editors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Amita Dev

  2. Kamrah Institute of Information Technology, Gurgaon, India

    S. S. Agrawal

  3. Indira Gandhi Delhi Technical University for Women, Delhi, India

    Arun Sharma

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Arora, S., Agarwal, M., Mongia, S., Kawatra, R. (2022). PSRE Self-assessment Approach for Predicting the Educators’ Performance Using Classification Techniques. In: Dev, A., Agrawal, S.S., Sharma, A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_34

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  • DOI: https://doi.org/10.1007/978-3-030-95711-7_34

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  • Online ISBN: 978-3-030-95711-7

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