The Study of Learning Computer Programming for Students with Medical Fields of Specification—An Analysis via Structural Equation Modeling

Abstract

In this study, the authors constructed structural equation models in order to determine the relationship between students’ learning attitudes and their computational thinking perspectives and programming empowerment. The purpose is to understand students’ perceived competence to use computational thinking effectively, along with their computer programming learning attitude regarding the C++ programming language for one semester (2 hours per week, 36 total learning hours). A total of 495 students specializing in the medical field participated in the study. Structural equation models were constructed according to three adapted scales: the computer programming learning attitude scale, the computational thinking perspectives scale, and the programming empowerment scale. The computer programming learning attitude scale is based on three factors: willingness, negativity, and necessity. The computational thinking perspectives scale also considers three factors: the ability to express, the ability to connect, and the ability to question. The programming empowerment scale is composed of four factors: meaningfulness, impact, creative self-efficacy, and programming self-efficacy. The results showed that a positive learning attitude will positively affect computational thinking perspectives and programming empowerment. However, when students have a negativity attitude, feeling that they are being forced to learn the C++ programming language, their computational thinking perspectives and programming empowerment will be negatively affected. In order to promote students’ learning attitude, various teaching strategies, teaching curriculum design, and pedagogy design could be further explored.

Assessing potential driving factors of the ecosystem service value of mariculture shellfish in China using a structural equation modeling approach

“…Therefore, it is imperative to choose suitable statistical approaches to determine the potential driving factors of the ecosystem service value of mariculture shellfish in China. As a novel and promising modeling approach, structural equation model (SEM) integrates path analysis with multiple factor analysis (Yang et al, 2018;Lai et al, 2022a;Ou et al, 2022). Compared with traditional statistical methods, SEM can not only reveal the interrelationship and strength of individual influencing factors, but also fit and evaluate the overall model, thus providing a more comprehensive understanding of the underlying mechanisms that affect response variables (Cao et al, 2020).…”