Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters
<p dir="ltr">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...
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2023
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| _version_ | 1864513527073472512 |
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| author | Bilal Ahmed (341166) |
| author2 | Li Wang (15202) Ahmad Sami Al-Shamayleh (17541495) Muhammad Tanvir Afzal (4162504) Ghulam Mustafa (458105) Wagdi Alrawagfeh (17271664) Adnan Akhunzada (3134064) |
| author2_role | author author author author author author |
| author_facet | Bilal Ahmed (341166) Li Wang (15202) Ahmad Sami Al-Shamayleh (17541495) Muhammad Tanvir Afzal (4162504) Ghulam Mustafa (458105) Wagdi Alrawagfeh (17271664) Adnan Akhunzada (3134064) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bilal Ahmed (341166) Li Wang (15202) Ahmad Sami Al-Shamayleh (17541495) Muhammad Tanvir Afzal (4162504) Ghulam Mustafa (458105) Wagdi Alrawagfeh (17271664) Adnan Akhunzada (3134064) |
| dc.date.none.fl_str_mv | 2023-11-27T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3336950 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning_Approach_for_Effective_Ranking_of_Researcher_Assessment_Parameters/25239748 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Measurement Indexes Mathematics Metadata Reliability Random forests Current measurement Research evaluation H index and variants research assessment parameters ranking of researchers math subject classification |
| dc.title.none.fl_str_mv | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3336950" target="_blank">https://dx.doi.org/10.1109/access.2023.3336950</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5e8dd1afbe142092867f222a3902f10f |
| identifier_str_mv | 10.1109/access.2023.3336950 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239748 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning Approach for Effective Ranking of Researcher Assessment ParametersBilal Ahmed (341166)Li Wang (15202)Ahmad Sami Al-Shamayleh (17541495)Muhammad Tanvir Afzal (4162504)Ghulam Mustafa (458105)Wagdi Alrawagfeh (17271664)Adnan Akhunzada (3134064)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringMeasurementIndexesMathematicsMetadataReliabilityRandom forestsCurrent measurementResearch evaluationH index and variantsresearch assessment parametersranking of researchersmath subject classification<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3336950" target="_blank">https://dx.doi.org/10.1109/access.2023.3336950</a></p>2023-11-27T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3336950https://figshare.com/articles/journal_contribution/Machine_Learning_Approach_for_Effective_Ranking_of_Researcher_Assessment_Parameters/25239748CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252397482023-11-27T09:00:00Z |
| spellingShingle | Machine Learning Approach for Effective Ranking of Researcher Assessment Parameters Bilal Ahmed (341166) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Measurement Indexes Mathematics Metadata Reliability Random forests Current measurement Research evaluation H index and variants research assessment parameters ranking of researchers math subject classification |
| status_str | publishedVersion |
| 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 | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Measurement Indexes Mathematics Metadata Reliability Random forests Current measurement Research evaluation H index and variants research assessment parameters ranking of researchers math subject classification |