结构方程建模
人格
心理学
特质
利克特量表
五大性格特征
潜变量
样品(材料)
验证性因素分析
社会心理学
应用心理学
统计
计算机科学
数学
发展心理学
化学
色谱法
程序设计语言
作者
Fathi Said Emhemed Shaninah,Mohd Halim Mohd Noor
出处
期刊:Journal of Applied Research in Higher Education
[Emerald (MCB UP)]
日期:2023-07-13
卷期号:16 (2): 523-539
标识
DOI:10.1108/jarhe-08-2022-0274
摘要
Purpose The study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP. Design/methodology/approach The authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models. Findings The study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well. Originality/value The process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.
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