1. ERDC Knowledge Core
  2. Engineer Research and Development Center (ERDC)
  3. Information Technology Laboratory (ITL) - (1986 - present)
  4. Publication
  5. Miscellaneous Paper
Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/40700
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dc.contributor.authorJaradat, Raed M.en_US
dc.contributor.authorGoerger, Simon R.en_US
dc.contributor.authorNagahi, Mortezaen_US
dc.contributor.authorNagahisarchoghaei, Mohammaden_US
dc.contributor.authorGhanbari, Ghodsiehen_US
dc.contributor.authorPoudyal, Sujanen_US
dc.creatorInformation Technology Laboratory (U.S.)en_US
dc.creatorUniversity of North Carolina (1793-1962)en_US
dc.creatorMississippi State University. Department of Industrial and Systems Engineeringen_US
dc.date.accessioned2021-05-21T13:29:05Zen_US
dc.date.available2021-05-21T13:29:05Zen_US
dc.date.issued2021-05en_US
dc.identifier.govdocERDC/ITL MP-21-6en_US
dc.identifier.urihttps://hdl.handle.net/11681/40700en_US
dc.identifier.urihttp://dx.doi.org/10.21079/11681/40700en_US
dc.descriptionMiscellaneous Paperen_US
dc.description.abstractThe academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a demographic survey, have been administrated for data collection. A cross-sectional web-based study applying Qualtrics has been developed to gather data from engineering students. To demonstrate the prediction power of the ST skills, FFM traits, proactive personality, academic, demographics, and family background factors on the academic performance of engineering students, two unsupervised learning algorithms applied. The study results identify that these unsupervised algorithms succeeded to cluster engineering students' performance regarding primary skills and characteristics. In other words, the variables used in this study are able to predict the academic performance of engineering students. This study also has provided significant implications and contributions to engineering education and education sustainable development bodies of knowledge. First, the study presents a better perception of engineering students' academic performance. The aim is to assist educators, teachers, mentors, college authorities, and other involved parties to discover students' individual differences for a more efficient education and guidance environment. Second, by a closer examination at the level of systemic thinking and its connection with FFM traits, proactive personality, academic, and demographic characteristics, understanding engineering students' skillset would be assisted better in the domain of sustainable education.en_US
dc.description.sponsorshipUnited States. Army. Corps of Engineersen_US
dc.format.extent13 pages / 893 kBen_US
dc.format.mediumPDF/Aen_US
dc.language.isoen_USen_US
dc.publisherEngineer Research and Development Center (U.S.)en_US
dc.relation.ispartofseriesMiscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/ITL MP-21-6en_US
dc.relation.isversionofNagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. "Effect of Individual Differences in Predicting Engineering Students' Performance: A Case of Education for Sustainable Development." In 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 925-931. IEEE, 2020.en_US
dc.rightsApproved for Public Release; Distribution is Unlimiteden_US
dc.sourceThis Digital Resource was created in Microsoft Word and Adobe Acrobaten_US
dc.subjectEngineering educationen_US
dc.subjectSustainable developmenten_US
dc.subjectEngineering studentsen_US
dc.subjectAcademic performanceen_US
dc.subjectSystems thinking skillsen_US
dc.subjectBig five personalityen_US
dc.subjectProactive personality,en_US
dc.subjectIndividual differencesen_US
dc.subjectUnsupervised learningen_US
dc.titleEffect of individual differences in predicting engineering students' performance : a case of education for sustainable developmenten_US
dc.typeReporten_US
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