{"title":"Dunning–Kruger Effect in Knowledge Management Examination of BSc Level Business Students","authors":"A. Kun, C. Juhász, J. Farkas","doi":"10.21791/ijems.2023.1.3.","DOIUrl":null,"url":null,"abstract":"The Dunning-Kruger effect (DKE) in higher education evaluation is one of the current research areas of psychology, educational science, and management science (in our case). Its importance is that the less prepared one is, the less accurately one can judge what performance is expected of him. What is more, he will err more and will overestimate himself. The present study aims better to understand the phenomenon with new, small-sample empirical results. The study is part of a research series that has been ongoing at the University of Debrecen since 2015. It not only quantitatively expands the literature but also includes the course of Knowledge Management among those examined. During the research, students were asked both before the examination (N = 63) and after the examination (N = 76) to guess how many points they would achieve on a multiple-choice test. It supports the presence of DKE, both in the case of pre-examination and post-examination self-evaluations. Using four multivariate linear regression models, we examined whether the sign value or absolute value of the errors made during the guesses show a correlation - in addition to the available control variables - with the test score. Our results showed that the more accurate the pre-examination and post-examination estimations were, the higher the students' actual score was, while the less they tended to overestimate their preparation. This supports the presence of DKE, both in the case of pre-exam and post-exam self-evaluation.","PeriodicalId":44185,"journal":{"name":"International Journal of Mathematical Engineering and Management Sciences","volume":"81 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Mathematical Engineering and Management Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21791/ijems.2023.1.3.","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The Dunning-Kruger effect (DKE) in higher education evaluation is one of the current research areas of psychology, educational science, and management science (in our case). Its importance is that the less prepared one is, the less accurately one can judge what performance is expected of him. What is more, he will err more and will overestimate himself. The present study aims better to understand the phenomenon with new, small-sample empirical results. The study is part of a research series that has been ongoing at the University of Debrecen since 2015. It not only quantitatively expands the literature but also includes the course of Knowledge Management among those examined. During the research, students were asked both before the examination (N = 63) and after the examination (N = 76) to guess how many points they would achieve on a multiple-choice test. It supports the presence of DKE, both in the case of pre-examination and post-examination self-evaluations. Using four multivariate linear regression models, we examined whether the sign value or absolute value of the errors made during the guesses show a correlation - in addition to the available control variables - with the test score. Our results showed that the more accurate the pre-examination and post-examination estimations were, the higher the students' actual score was, while the less they tended to overestimate their preparation. This supports the presence of DKE, both in the case of pre-exam and post-exam self-evaluation.
期刊介绍:
IJMEMS is a peer reviewed international journal aiming on both the theoretical and practical aspects of mathematical, engineering and management sciences. The original, not-previously published, research manuscripts on topics such as the following (but not limited to) will be considered for publication: *Mathematical Sciences- applied mathematics and allied fields, operations research, mathematical statistics. *Engineering Sciences- computer science engineering, mechanical engineering, information technology engineering, civil engineering, aeronautical engineering, industrial engineering, systems engineering, reliability engineering, production engineering. *Management Sciences- engineering management, risk management, business models, supply chain management.