DOI: 10.14704/nq.2018.16.5.1305

Influencing Factors of Students’ Acceptance of Blended Learning Based on Cognitive Neural Network

Yongchang Zhang

Abstract


In order to study the influencing factors of students’ acceptance of blended learning, the structural equation model is used to establish a model of students’ acceptance of blended learning, BP neural network is applied to analyze the effect strength of each factor on the acceptance of blended learning, and empirical study is conducted to verify the impact of perceived ease of use, perceived usefulness, learning atmosphere, and interactive behavior on the students’ acceptance of blended learning. As the research results show, perceived ease of use and perceived usefulness are important factors affecting the acceptance of blended learning; Factors such as learning atmosphere and interactive behavior also affect the acceptance of blended learning, as the former can effectively enhance learning interest and stimulate learning enthusiasm, while the latter determines the frequency and intensity of blended learning exchanges and is an important influence factor for deepened learning; When learning background is introduced into the study of influencing factors, it is found that learners' learning background plays an important role in learning process and learning effect, and is also a key factor among many influencing factors; Learning background has a direct impact on the quality of learning. It has a clear role in adjusting perceived ease of use and learning atmosphere, but it does not have an obvious regulatory effect on perceived usefulness and interactive behavior.

Keywords


Structural Equation Model, Neural Network, Blended Learning, Acceptance, Learning Effect

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References


Babič M. New hybrid method of intelligent systems using to predict porosity of heat treatment materials with network and fractal geometry, Academic Journal of Manufacturing Engineering 2017; 15(1): 29-34.

Bersin J. The blended learning book: best practices, proven methodologies and lessons learned. Turkish Online Journal of Distance Education 2016; 11(3): 1-9.

Detilleux J, Theron L, Beduin JM. A structural equation model to evaluate direct and indirect factors associated with a latent measure of mastitis in Belgian dairy herds. Preventive Veterinary Medicine 2012; 107(3): 170-79.

Ertmer P, Gedik NT, Richardson JC. Perceived value of online discussions: perceptions of engineering and education students. Proceeding of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2008; 4679-87

Joo YJ, Lim KY, Kim EK. Online university students’ satisfaction and persistence: examine perceived level of presence usefulness and ease of use as predictors in a structural moded. Computers & Education 2011; 57(2): 1654-64.

Kadri O, Mouss LH. Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization, Academic Journal of Manufacturing Engineering 2017; 15(2): 43-50.

Kassab SE, Alshafei AI, Salem AH. Relationships between the quality of blended learning experience, self-regulated learning, and academic achievement of medical students: a path analysis. Advances in Medical Education & Practice 2015; 6: 27-32.

Liu GP, Kadirkamanathan V. Predictive control for non-linear systems using neural networks. International Journal of Control 1998; 71(6): 1119-32.

Meyer JP, Zhu S. Fair and equitable measurement of student learning in MOOCs: an introduction to item response theory, scale linking, and score equating. Research & Practice in Assessment 2013; 8(1): 26-39.

Olives RL. Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing 1981; 57(3): 25-48.

Owston R, York D, Murtha S. Student perceptions and achievement in a university blended learning strategic initiative. The Internet and Higher Education 2013; 18 (18): 38-46

Picciano A. Beyond student perceptions: issues of interaction, presence and performance in an online course. Journal of Asynchronous Learning Networks 2012; 6(1): 21-38.

Poznyak AS, Learning for Dynamic Neural Networks. In 10th Yale Workshop. Adaptive Learning Systems 1998; 38-47.

Prietio IM, Revilla E. Formal and informal facilitators of learning capability: the moderating effect of learning climate. Working Papers Economic 2006; 6-9.

Sahin I, Shelley M. Considering students perceptions: the distance education student satisfaction model. Journal of Educational Technology & Society 2008; 1(3): 216-23.

Sher A. Assessing the relationship of student-instructor and student-student interaction to student learning and satisfaction in web-based online learning environment. Journal of Interactive Online Learning 2009; 8(2):102-20.

Singh H, Reed C. A white paper: achieving success with blended learning. Centre Software Retrieved 2001; 12(3): 206-07.

Small F, Dowell D, Simmons P. Teacher communication preferred over peer interaction: student satisfaction with different tools in a virtual learning environment. Journal of International Education in Business 2012,5(2): 114-28.

Sun YS, Moon TH, Sohn SY. Structural equation model for effective CRM of information infrastructure industry in Korea. Expert Systems with Applications 2009; 36(2): 1695-705.

Wang YS. Assessment of learner satisfaction with asynchronous electronic learning systems. Information and Management 2003; 41(1): 75-86.

Wu J, Liu W. An empirical investigation of the critical factors affecting students satisfaction in EFL blended learning. Journal of Language Teaching & Research 2013; 4(1): 68-81.


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