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Primary science curriculum student acceptance of blended learning: structural equation modeling and visual analytics

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Abstract

This paper focuses on the analysis of perceived usefulness (PU), perceived ease-of-use (PE), perceived playfulness (PP), community support (CS), and other factors that affect the acceptance of Chinese students (SA) in Blended learning of primary science curriculum. Based on technology acceptance model and Unified Theory of Acceptance and Use of Technology, an initial structural equation model is proposed. The initial structural model is for blended learning student acceptance (SA) in primary science curriculum. It contains five latent variables, and 4 latent variables can affect SA. Questionnaire responses are collected through blended learning SA questionnaire survey and analyzed using statistical methods. The questionnaire has 25 questions and collects 357 answers from all over China. Based on the reliability analysis, exploratory factor analysis, and confirmatory factor analysis of the data, the initial structural equation model is improved. According to the final structural equation model, the influence order of influencing factors on primary science curriculum blended learning SA is CS > PP > PU > PE. Based on the final model, an interactive visualization application is designed and implemented using SAP Analytics Cloud to allow users to understand the model easily and explore interactions among these factors visually. Teachers can directly see the changes of various factors through visualization, and do not need to pay attention to complex model details. This approach provides new practice for the application of theoretical models in Pedagogy.

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Data availability

The data that support the findings of this study are openly available in IEEE DataPort at http://doi.org/10.21227/kx61-7732, reference (Xu., 2020).

Abbreviations

TAM:

Technology acceptance model

UTAUT:

Unified Theory of Acceptance and Use of Technology

PE:

Performance expectancy

EE:

Effort expectancy

SI:

Social influence

FC:

Facilitating conditions

PU:

Perceived usefulness

PE:

Perceived ease-of-use

PP:

Perceived playfulness

CS:

Community support

SA:

Student acceptance

ML:

Maximum likelihood

GLS:

Generalized least squares

KMO:

Kaiser–Meyer–Olkin

SAC:

SAP Analytics Cloud

VR:

Virtual reality

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Acknowledgements

The author would like to thank teachers and students of Institute of Vocational Education, Tongji University, especially Prof. Dr. Yue, CAI for his guidance on educational theory and statistics, and Ms. Gao-Le, LI for her assistance in collecting survey data for this paper.

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  1. SAP Labs China, No. 1001 Chenhui Road, Pudong, Shanghai, 201203, China

    Xu Liu

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Contributions

The main contribution of the author in the paper is designing one new structural equation modeling for primary science curriculum students and using collected survey data to improve and verify the model. The author also gives one new visual analytics approach to explore the structural model interactively.

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Correspondence to Xu Liu.

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In accordance with my ethical obligation as a researcher, I am reporting that I have business interests in a company that may be affected by the research reported in the enclosed paper.

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Liu, X. Primary science curriculum student acceptance of blended learning: structural equation modeling and visual analytics. J. Comput. Educ. 9, 351–377 (2022). https://doi.org/10.1007/s40692-021-00206-8

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