Applying the Product Design of Learning and Management for Innovation Development

Liao,, S. (2015). Applying the Product Design of Learning and Management for Innovation Development. The Journal of Distribution Science, 13(6), 25-33, https://doi.org/https://doi.org/10.15722/jds.13.6.201506.25

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Abstract

Purpose - This paper's goal is to assess and promote several good teaching product designs and several learning environments. The paper discusses research product design learning and management. Research design, data, and methodology - As part of information science and technology, a school uses several teaching networks for auxiliary teaching, taking several designs as the teaching foundation, and creating multimedia curricula. Results - The results indicate that in the best learning designs and environments, the learner can maintain a high interest, which not only attracts all levels in the schools, but also has a pivotal influence on teaching around the world. The research study answers the question, was the atmosphere already luxurious? Conclusions - This study introduces several methodologies that are widely used for experimental processes. Using multi-criterion decision-making technology in studies of language product evaluation systems, the language teaching quality and space design is developed, and the language classroom learning system, the machine operation, the classroom environment design method, etc., conform to specifics of the study, the best choices, the most effective utilization, and are the most efficient.

keywords: Language Classroom, Traditional Classroom, Network Study, Hardware Engineering, Software Management, Innovative Design

Reference

Atchley, P., and Dressel, J. (2004) Conversation limits the functional field of view. Human factors, 46(4), 664-673.

Carlos, R. (2004). A general structure of achievement function for a goal programming model. European Journal of Operational Research, 153(3), 675-686.

Helen, M., and Jim, W. (2002). Experimental comparisons of data entry by automated speech recognition, keyboard, and mouse. Human factors,. 44(2), 198-209.

Hirotaka, N., Yun, Y. B., Takeshi, A., and Min, Y. (2005). MOP/GP models for machine learning. European Journal of Operational Research, 166(3), 756-768.

Hu, Y. C., Chen, R. S., Tzeng, G. H., and Chiu, Y. J. (2003). Acquisition of compound skills and learning costs for expanding competence sets. Computers and Mathematics with Applications, 46(5), 831-848.

Huang, J. J., Ong, C. S., and Tzeng, G. H. (2005).Interval multidimensional scaling for group decision using rough set concept. Expert Systems with Appllications, 31(3), 525-530.

Justin, A. M., Balakrishnan, J. D., Michael, D. O., and Walter, J. K. (2002). Intelligibility of speech in a virtual 3-D environment. Human factors, 44(2), 272-86.

Maury, A. N., and Hope, J. (2002). Determination and evaluation of acceptable force limits in single-digit tasks. Human factors, 44(4), 545-563.

Mehrdad, T., Dylan, J., and Carlos, R. (1997). Goal programming for decision making; An overview of the current state-of-the-art. European Journal of Operational Research, 111(3), 569-581.

10.

Peter, I. S., Hongwei, H. B., Dotson, W., and Douglas, E. A. (2003). Control and perception of balance at elevated and sloped surfaces. Human Factors and Ergonomics Society, 45(1), 136-147.

11.

Steven, A. Jax., David, A. R., Jonathan, V., and Ruud, G. M. (2003). Computational motor control and human factors:Modeling movements in real and possible environments. Human factors, 45(2), 5-27.

12.

Tseng, F. M., and Tzeng, G. H. (2002). A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets and Systems, 126(3), 367-376.

13.

Tzeng, G. H., Chiang, C. H., and Li, C. W. (2006). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044.

14.

William, K. K., and Michael, J. S. (2002). Psychophysical assessments of image-sensor fused imagery. Human factors, 44(2), 257-271.

15.

Yu, R., and Tzeng, G. H. (2006). A soft computing method for multi-criteria decision making with dependence and feedback. Applied Mathematics and Computation, 180(1), 63-75.

16.

Yun, Y. B., Nakayama, H., and Arakawa, M. (2003). Multiple criteria making with generalized DEA and an aspiration level method. European Journal of Operational Research, 158(3), 697-706.

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Article Contents

Vol.13 No.6

Applying the Product Design of Learning and Management for Innovation Development

Abstract

Reference

The Journal of Distribution Science