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Title: Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development
Authors: Nagahi, Morteza.
Jaradat, Raed M.
Nagahisarchoghaei, Mohammad.
Ghanbari, Ghodsieh.
Poudyal, Sujan.
Goerger, Simon R.
Keywords: Engineering education
Sustainable development
Engineering students
Academic performance
Systems thinking skills
Big five personality
Proactive personality,
Individual differences
Unsupervised learning
Publisher: Information Technology Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/ITL MP-21-6
Is Version Of: Nagahi, 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.
Abstract: The 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.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/ITL MP-21-6
Rights: Approved for Public Release; Distribution is Unlimited
Size: 13 pages / 893 kB
Types of Materials: PDF/A
Appears in Collections:Miscellaneous Paper

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