scholarly journals The Adoption of a Virtual Reality–Assisted Training System for Mental Rotation: A Partial Least Squares Structural Equation Modeling Approach (Preprint)

2019 ◽  
Author(s):  
Chen-Wei Chang ◽  
Shih-Ching Yeh ◽  
Mengtong Li
Keyword(s):  
Virtual Reality ◽  
Structural Equation Modeling ◽  
Least Squares ◽  
Mental Rotation ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Practical Importance ◽  
Training System ◽  
Equation Modeling ◽  
And Training

BACKGROUND Virtual reality (VR) technologies have been developed to assist education and training. Although recent research suggested that the application of VR led to effective learning and training outcomes, investigations concerning the acceptance of these VR systems are needed to better urge learners and trainees to be active adopters. OBJECTIVE This study aimed to create a theoretical model to examine how determining factors from relevant theories of technology acceptance can be used to explain the acceptance of a novel VR-assisted mental rotation (MR) training system created by our research team to better understand how to encourage learners to use VR technology to enhance their spatial ability. METHODS Stereo and interactive MR tasks based on Shepard and Metzler’s pencil and paper test for MR ability were created. The participants completed a set of MR tasks using 3D glasses and stereoscopic display and a 6-degree-of-freedom joystick controller. Following task completion, psychometric constructs from theories and previous studies (ie, perceived ease of use, perceived enjoyment, attitude, satisfaction, and behavioral intention to use the system) were used to measure relevant factors influencing behavior intentions. RESULTS The statistical technique of partial least squares structural equation modeling was applied to analyze the data. The model explained 47.7% of the novel, VR-assisted MR training system’s adoption intention, which suggests that the model has moderate explanatory power. Direct and indirect effects were also interpreted. CONCLUSIONS The findings of this study have both theoretical and practical importance not only for MR training but also for other VR-assisted education. The results can extend current theories from the context of information systems to educational and training technology, specifically for the use of VR-assisted systems and devices. The empirical evidence has practical implications for educators, technology developers, and policy makers regarding MR training.

scholarly journals The Adoption of a Virtual Reality–Assisted Training System for Mental Rotation: A Partial Least Squares Structural Equation Modeling Approach

10.2196/14548 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e14548
Author(s):  
Chen-Wei Chang ◽  
Shih-Ching Yeh ◽  
Mengtong Li
Keyword(s):  
Virtual Reality ◽  
Structural Equation Modeling ◽  
Least Squares ◽  
Mental Rotation ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Practical Importance ◽  
Training System ◽  
Equation Modeling ◽  
And Training

Background Virtual reality (VR) technologies have been developed to assist education and training. Although recent research suggested that the application of VR led to effective learning and training outcomes, investigations concerning the acceptance of these VR systems are needed to better urge learners and trainees to be active adopters. Objective This study aimed to create a theoretical model to examine how determining factors from relevant theories of technology acceptance can be used to explain the acceptance of a novel VR-assisted mental rotation (MR) training system created by our research team to better understand how to encourage learners to use VR technology to enhance their spatial ability. Methods Stereo and interactive MR tasks based on Shepard and Metzler’s pencil and paper test for MR ability were created. The participants completed a set of MR tasks using 3D glasses and stereoscopic display and a 6-degree-of-freedom joystick controller. Following task completion, psychometric constructs from theories and previous studies (ie, perceived ease of use, perceived enjoyment, attitude, satisfaction, and behavioral intention to use the system) were used to measure relevant factors influencing behavior intentions. Results The statistical technique of partial least squares structural equation modeling was applied to analyze the data. The model explained 47.7% of the novel, VR-assisted MR training system’s adoption intention, which suggests that the model has moderate explanatory power. Direct and indirect effects were also interpreted. Conclusions The findings of this study have both theoretical and practical importance not only for MR training but also for other VR-assisted education. The results can extend current theories from the context of information systems to educational and training technology, specifically for the use of VR-assisted systems and devices. The empirical evidence has practical implications for educators, technology developers, and policy makers regarding MR training.

Partial least squares structural equation modeling of chemistry attitude in introductory college chemistry

2018 ◽  
Vol 19 (4) ◽  
pp. 1270-1286 ◽  
Author(s):  
James Ross ◽  
Leslie Nuñez ◽  
Chinh Chu Lai
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
At Risk Students ◽  
Affective Domain ◽  
Equation Modeling ◽  
College Chemistry ◽  
The Subject ◽  
Introductory College

Students’ decisions to enter or persist in STEM courses is linked with their affective domain. The influence of factors impacting students’ affective domain in introductory college chemistry classes, such as attitude, is often overlooked by instructors, who instead focus on students’ mathematical abilities as sole predictors of academic achievement. The current academic barrier to enrollment in introductory college chemistry classes is typically a passing grade in a mathematics prerequisite class. However, mathematical ability is only a piece of the puzzle in predicting preparedness for college chemistry. Herein, students’ attitude toward the subject of chemistry was measured using the original Attitudes toward the Subject of Chemistry Inventory (ASCI). Partial least squares structural equation modeling (PLS-SEM) was used to chart and monitor the development of students’ attitude toward the subject of chemistry during an introductory college chemistry course. Results from PLS-SEM support a 3-factor (intellectual accessibility,emotional satisfaction, andinterestandutility) structure, which could signal the distinct cognitive, affective, and behavioral components of attitude, according to its theoretical tripartite framework. Evidence of a low-involvement hierarchy of attitude effect is also presented herein. This study provides a pathway for instructors to identify at-risk students, exhibiting low affective characteristics, early in a course so that academic interventions are feasible. The results presented here have implications for the design and implementation of teaching strategies geared toward optimizing student achievement in introductory college chemistry.

An Introduction to Structural Equation Modeling (SEM) and the Partial Least Squares (PLS) Methodology

2011 ◽  
pp. 110-129 ◽  
Author(s):  
Nicholas J. Ashill
Keyword(s):  
Structural Equation Modeling ◽  
Information Systems ◽  
Organizational Behavior ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Academic Research ◽  
Equation Modeling ◽  
Formative Measurement ◽  
Statistical Assumptions

Over the past 15 years, the use of Partial Least Squares (PLS) in academic research has enjoyed increasing popularity in many social sciences including Information Systems, marketing, and organizational behavior. PLS can be considered an alternative to covariance-based SEM and has greater flexibility in handling various modeling problems in situations where it is difficult to meet the hard assumptions of more traditional multivariate statistics. This chapter focuses on PLS for beginners. Several topics are covered and include foundational concepts in SEM, the statistical assumptions of PLS, a LISREL-PLS comparison and reflective and formative measurement.

Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM) (Segunda Edición)

2019 ◽  
Author(s):  
Joseph F. Hair Jr. ◽  
G. Tomas M. Hult ◽  
Christian M. Ringle ◽  
Marko Sarstedt ◽  
Julen Castillo Apraiz ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling

How Do Fans Feel? A Case Analysis of Measuring and Modeling Attitudes Using Partial Least Squares Structural Equation Modeling

2016 ◽  
Vol 5 (1) ◽  
pp. 85-88 ◽  
Author(s):  
Lane Wakefield ◽  
Gregg Bennett
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling ◽  
Sport Organizations ◽  
Sports Teams ◽  
Know How ◽  
Fan Communities

Virtual fan communities (VFC) have become very popular among fans of sports teams. A VFC provides an online place for fans to meet and discuss the team, consume media, and develop friendships. Students will learn, in this case study, how to use partial least squares structural equation modeling (PLS-SEM) to assess fan attitudes toward the VFC and sponsors of the firm. Students will also learn how sport organizations can benefit from leadership with statistical know-how. The case is fictional, but it is based on an actual research study conducted in conjunction with a prominent virtual fan community in which ownership had an interest in fans’ attitudes toward their service.

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