An Exploratory Study of Factors Affecting Research Productivity in Higher Educational Institutes Using Regression and Deep Learning Techniques

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DOI:

https://doi.org/10.47852/bonviewAIA3202660

Keywords:

High Education, scientific research, research performance, educational institutes, funded researches

Abstract

Higher education is grappling with challenges from globalization. The competition between worldwide universities depends not only on the availability of infrastructure and faculty members' teaching quality, but also on their research performance. The research produced by faculty members has a significant impact on a university's standing, ability to acquire funds, and ability to enroll both domestic and international students. The objective of this paper is to identify factors affecting scientific research productivity in selected higher educational institutes. The paper reports the views of academic staff from different educational institutes on such issues as the determinants of research performance. A quantitative analysis approach, including correlation and regression, in addition to deep learning, was utilized to achieve the aim of the paper. The findings of this research demonstrate that the support of academic institutes for enhancing research and providing facilities and funds for such purpose has a great impact on research performance. The allocation of hours of scientific research to the faculty member also had a positive impact on the improvement of scientific research. Linking career promotion and scientific research encourages faculty members to publish more papers. Moreover, the level of qualification for faculty members has a great impact on their rate of publishing papers.

 

Received: 13 January 2023 | Revised: 2 June 2023 | Accepted: 9 June 2023

 

Conflicts of Interest

The author declares that she has no conflicts of interest to this work.

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Published

2023-06-16

How to Cite

M. Helali, R. G. . (2023). An Exploratory Study of Factors Affecting Research Productivity in Higher Educational Institutes Using Regression and Deep Learning Techniques. Artificial Intelligence and Applications. https://doi.org/10.47852/bonviewAIA3202660

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Online First Articles