Putting the pieces of the puzzle together in modeling gendered educational choices by Jakob Schwerter, Fani Lauermann, Philipp Doebler, Marjolein Fokkema

“…This study draws on five complementary theoretical perspectives—theory of circumscription and compromise, situated expectancy-value theory, the internal/external frame of reference model, the differential effects model, and the goal congruity model—to identify the strongest predictors of women's entry into STEM fields in higher education from 21 variables and their up to 441 nonlinear pairwise interactions. …”
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Hybrid SEM-ANN model for predicting undergraduates' e-learning continuance intention based on perceived educational and emotional support. by Shanshan Xu, Yangxin Wang, Wenbo Luo

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Enhancing Engineering and STEM Education With Vision and Multimodal Large Language Models to Predict Student Attention by Luis Marquez-Carpintero, Diego Viejo, Miguel Cazorla

“…Generative Artificial Intelligence (AI) and Large Language Models (LLMs), including Visual Language Models (VLMs) and Multimodal LLMs (MLLMs), have shown transformative potential in education. …”
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Ensemble machine learning for predicting academic performance in STEM education by Aklilu Mandefro Messele

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Exploring the Impact of Predictive Analytics and AI in STEM Education by Tai Ki Kim

“…The demand for STEM education is rising globally, yet high attrition rates among underrepresented groups remain a significant challenge. …”
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Factors Influencing Students’ Performance in University Mathematics Courses: A Structural Equation Modelling Approach by Moeketsi Mosia, Felix O. Egara, Fadip A. Nannim, Moses Basitere

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Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN by Alvin Muhammad ‘Ainul Yaqin, Ahmad Kamil Muqoffi, Sigit Rahmat Rizalmi, Faishal Arham Pratikno, Remba Yanuar Efranto

“…This research investigates the relevance of hybrid learning methods in the post-pandemic education context using structural equation modeling (SEM) and deep neural network (DNN) approaches. …”
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DESIGN OF STEM – PBL MODEL WITH APPLICATION IN HIGHER EDUCATION by Vasilka Krumova, Dancho Danalev, Senya Terzieva

“…According to a number of researchers, the concept of self-regulated learning is now becoming the focus of educational theory. The leading goal of bringing educational objectives closer to those of business brings to the fore one more direction for development and these are the goals for a larger share of Science – Technology – Engineering – Mathematics (STEM) skills of students as a result of training at all levels. …”
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Dual-Path Model of Team Communication and Shared Mental Models in Entrepreneurial Education: Enhancing Team Efficacy in Higher Education Using PLS-SEM by Shuangshuang Fan, Shali Wang, William Mbanyele, Yongliang Zhang

“…This study explores the influence of team communication (TC) and shared mental models (SMMs) on entrepreneurial team efficacy (ETE) within the context of Chinese higher education, introducing a dual-path model to reconcile the discrepancy between policy expectations and practical outcomes in entrepreneurship education. …”
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Factors predicting the mathematics anxiety of adolescents: a structural equation modeling approach by Suman Ahmmed, Jashodhan Saha, Maruf Ahmed Tamal, Khondaker Abdullah Al Mamun, Sajani Islam

“…Later, the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).ResultsThe findings revealed that math related negative past experiences (β = 0.241, t = 4.914, p < 0.001) and a perceived lack of teacher support (β = 0.234, t = 5.440, p < 0.001) significantly contribute to students’ low self-efficacy in mathematics. …”
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Exploring the implementation of STEM education through mathematical modelling activities in schools by Aslipah Tasarib, Roslinda Rosli, Azmin Sham Rambely

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Predicting College Student Engagement in Physical Education Classes Using Machine Learning and Structural Equation Modeling by Liguo Zhang, Jiarui Gao, Liangyu Zhao, Zetan Liu, Anlin Guan

“…This study aims to develop an interpretable machine learning model to predict student classroom engagement based on various digital technologies and construct a structural equation model (SEM) to further investigate the underlying mechanisms involving perceived usefulness (PU), perceived ease of use (PEU), and academic self-efficacy (ASE). …”
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MonteCarloSEM: An R Package to Simulate Data for SEM by Fatih Orçan

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Factors Influencing the Acceptance of ChatGPT in High Education: An Integrated Model With PLS-SEM and fsQCA Approach by Yipeng Zhao, Yan Li, Yuyao Xiao, Haodong Chang, Bo Liu

“…This paper aims to clarify the determinants influencing college students’ acceptance of ChatGPT and to facilitate its widespread adoption in higher education. To achieve this, we integrate the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) to develop a novel research framework. …”
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Understanding AI adoption among secondary education teachers: A pls-sem approach by Marta López-Costa, Belén Donate-Beby, Natividad Cabrera-Lanzo, Marcelo Fabián Maina

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Mechanism and Multilayer Perceptron prediction model of the removal of α-terpineol, terpinen-4-ol and carvone from pasteurized citrus juices by β-cyclodextrin encapsulation by Ruijie Mai, Shuo Xue, Jingnan Ren, Gang Fan, Jinchu Yang, Linhua Huang, Guijie Li, Yujiao Cheng, Qiuling Wang, Yongfeng Yang, Zhenzhen Huang, Yingjie Feng, Wenzhao Liu, Aiqun Yu, Jian Feng

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Exploring students’ experience of ChatGPT in STEM education by Federico Valeri, Pernilla Nilsson, Anne-Marie Cederqvist

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SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS by Ashabul Akbar Maulana, Achmad Fauzan

“…The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. …”
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Science and Mathematics Teachers’ Integration of TPACK in STEM Subjects in Qatar: A Structural Equation Modeling Study by Nasser Mansour, Ziad Said, Mustafa Çevik, Abdullah Abu-Tineh

“…The study examined the relationships between the subcomponents of TPACK using structural equation modeling (SEM), complemented by an analysis of additional categorical variables. …”
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Prediction and effect on relapse of natural killer cell alloreactivity based on KIR-HLA interactions in pediatric haploidentical transplantation with anti-thymoglobulin by Xiang-Feng Tang, Yan-Hui Luo, Ying-Jian Si, Mao-Quan Qin, Wei Lu, Wei Chen, Guo-Sheng Xing, Wei Cao, Hai-Fei Zhou, Xiang-Jun Liu

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