Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters
The measurement and assessment of academic performance is now a fact of scientific life. This assessment guides the scientific community in making significant judgments such as selecting appropriate candidates for various positions, nominating individuals for scientific awards, and awarding scholars...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10328867/ |
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| author | Bilal Ahmed Li Wang Ahmad Sami Al-Shamayleh Muhammad Tanvir Afzal Ghulam Mustafa Wagdi Alrawagfeh Adnan Akhunzada |
| author_facet | Bilal Ahmed Li Wang Ahmad Sami Al-Shamayleh Muhammad Tanvir Afzal Ghulam Mustafa Wagdi Alrawagfeh Adnan Akhunzada |
| author_sort | Bilal Ahmed |
| collection | DOAJ |
| description | The measurement and assessment of academic performance is now a fact of scientific life. This assessment guides the scientific community in making significant judgments such as selecting appropriate candidates for various positions, nominating individuals for scientific awards, and awarding scholarships or grants. Several research assessment parameters have been proposed by researchers to identify the most influential scholars. In the literature, researchers have employed a combination of hypothetical and fictional scenarios, as well as manual approaches, to identify the best assessment parameters. Moreover, there is no established benchmark available for assessing these parameters. The current study employs an innovative machine learning approach, the Dynamic Random Forest with Brouta Optimizer called “BorutaRanked Forest”, to prioritize the assessment metrics for researchers by calculating the importance score for each metric. Thirty different assessment metrics have been evaluated on a comprehensive dataset of researchers that contains awardees researchers and non-awardees researchers of three decades from (1990 to 2023). The main purpose of this evaluation is to determine the potential value and significance of each parameter relative to others. In addition, the position of awardees researchers is examined at different percentile ranges form Top 10% to Top 100% in the ranked lists of each parameter. During the individual evaluation of each parameter, we uncovered several intriguing patterns in the data. Our findings indicate that the normalized h-index is a particularly effective assessment parameter for the impact evaluation of researchers in the domain of mathematics. An analysis has been conducted to explore the correlation between parameters and awarding societies, examining the associations between different metrics and specific awarding societies. |
| first_indexed | 2024-03-08T04:53:04Z |
| format | Article |
| id | doaj.art-ef3985f7b5734ca7b6299a92089e7fb5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:53:04Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ef3985f7b5734ca7b6299a92089e7fb52024-02-08T00:01:32ZengIEEEIEEE Access2169-35362023-01-011113329413331210.1109/ACCESS.2023.333695010328867Machine Learning Approach for Effective Ranking of Researcher Assessment ParametersBilal Ahmed0https://orcid.org/0000-0002-9458-9412Li Wang1https://orcid.org/0000-0002-7385-1426Ahmad Sami Al-Shamayleh2https://orcid.org/0000-0002-7222-2433Muhammad Tanvir Afzal3Ghulam Mustafa4https://orcid.org/0000-0002-0354-8229Wagdi Alrawagfeh5https://orcid.org/0000-0003-4227-9276Adnan Akhunzada6https://orcid.org/0000-0001-8370-9290College of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, JordanDepartment of Computing, Shifa Tameer-e-Millat University, Islamabad, PakistanDepartment of Computing, Shifa Tameer-e-Millat University, Islamabad, PakistanCollege of Computing and IT, University of Doha for Science and Technology, Doha, QatarCollege of Computing and IT, University of Doha for Science and Technology, Doha, QatarThe measurement and assessment of academic performance is now a fact of scientific life. This assessment guides the scientific community in making significant judgments such as selecting appropriate candidates for various positions, nominating individuals for scientific awards, and awarding scholarships or grants. Several research assessment parameters have been proposed by researchers to identify the most influential scholars. In the literature, researchers have employed a combination of hypothetical and fictional scenarios, as well as manual approaches, to identify the best assessment parameters. Moreover, there is no established benchmark available for assessing these parameters. The current study employs an innovative machine learning approach, the Dynamic Random Forest with Brouta Optimizer called “BorutaRanked Forest”, to prioritize the assessment metrics for researchers by calculating the importance score for each metric. Thirty different assessment metrics have been evaluated on a comprehensive dataset of researchers that contains awardees researchers and non-awardees researchers of three decades from (1990 to 2023). The main purpose of this evaluation is to determine the potential value and significance of each parameter relative to others. In addition, the position of awardees researchers is examined at different percentile ranges form Top 10% to Top 100% in the ranked lists of each parameter. During the individual evaluation of each parameter, we uncovered several intriguing patterns in the data. Our findings indicate that the normalized h-index is a particularly effective assessment parameter for the impact evaluation of researchers in the domain of mathematics. An analysis has been conducted to explore the correlation between parameters and awarding societies, examining the associations between different metrics and specific awarding societies.https://ieeexplore.ieee.org/document/10328867/Research evaluationH index and variantsresearch assessment parametersranking of researchersmath subject classification |
| spellingShingle | Bilal Ahmed Li Wang Ahmad Sami Al-Shamayleh Muhammad Tanvir Afzal Ghulam Mustafa Wagdi Alrawagfeh Adnan Akhunzada Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters IEEE Access Research evaluation H index and variants research assessment parameters ranking of researchers math subject classification |
| title | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters |
| title_full | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters |
| title_fullStr | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters |
| title_full_unstemmed | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters |
| title_short | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters |
| title_sort | machine learning approach for effective ranking of researcher assessment parameters |
| topic | Research evaluation H index and variants research assessment parameters ranking of researchers math subject classification |
| url | https://ieeexplore.ieee.org/document/10328867/ |
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