25 May 2021

Holistic and Reductionist Thinker: A Comparison Study Based on Individuals’ Skillset and Personality Types

Abstract: As organizations operate in turbulent and complex environments, it has become a necessity to assess the systems thinking (ST) skills, personality types (PTs), and demographics of practitioners. In this study, we investigated the relationship between practitioners’ ST profile, their PTs profiles and demographic characteristics in the domain of complex system problems. The objective of this study is to address the current gap in the literature – lack of studies dedicated to predicting practitioners’ ST profile based on their PTs and demographics characteristics. A total of 258 practitioners with different demographics and PTs provided the data. The results show that (1) practitioners can be classified based on their ST skills scores into two clusters: holistic and reductionist (that is, ST profile), (2) each cluster has different PTs profiles and demographic characteristics, and (3) practitioner’s ST profile can be predicted, with good accuracy, based on their PTs profile and demographic characteristics.

21 May 2021

Effect of Individual Differences in Predicting Engineering Students' Performance: A Case of Education for Sustainable Development

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.