The factors affecting of digital mobile e-learning on development in senior high schools

Hsiang-Hsi LIU, Fu-Hsiang KUO


Abstract. The traditional data envelopment analysis (DEA) model ignores the cooperative relationship among decision-making units (DMUs), so it is difficult to evaluate the DMUs efficiency reasonably. In this study, we use a cross-efficiency and bootstrap truncated regression (BTR) model to analyze the determining factors of digital mobile e-learning. The empirical results of this research indicate the following results: (1) Importing digital mobile e-learning can really enhance the efficiency of school management. (2) The school size, tablet PC numbers, total equipment expenses associated with tablet PC and school location are important determinants for affecting the efficiency of school management. Owing to the government is full implementation of the new learns model, that is, to be where the students able to experience the authentic joy of new learning model and attract students join. The result of the study suggested that in order to increase the school’s cross-efficiency model efficiency. The first assist the school in upgrading the Wi-Fi technology and network equipment. In general, the school adds to the Wi-Fi technology and network equipment. That would enlarge the school network and as to attract more school will adopt the new learning. It is where the students able to experience the authentic joy of new learning model and attract students join. Thus, the schools will increase school size. However, it should be noted that total equipment expenses associated with tablet PC have the negative influence on school management efficiency due to the increasing costs for furnishing the related internet and network equipment or device to facilitate for teaching and learning among teachers and students by digital mobile e-learning. The results of this research can also be the reference for educational authorities when formulating policies and regulations for promoting digital mobile e-learning in high school in Taiwan.

Keywords. Operating efficiency, Digital mobile e-Learning, Data envelopment analysis (DEA), Truncated bootstrapped regression (TBR), Cross efficiency model.

JEL. I21, I25, I28.


Operating efficiency; Digital mobile e-Learning; Data envelopment analysis (DEA); Truncated bootstrapped regression (TBR); Cross efficiency model.

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Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for the estimation of technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. 10.1287/mnsc.30.9.1078

Badri, M. A., & Mourad, T. (2012). Determinants of School Efficiencies in Abu Dhabi Using DEA. In International Conference on Management and Education Innovation IPEDR (37).

Celen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage analysis. Energy Policy, 63, 300-310. doi. 10.1016/j.enpol.2013.09.034

Charnes, A., Cooper, W.W., Lewin, A.Y., & Seiford, L.M. (1994). Introduction. In Data Envelopment Analysis: Theory, Methodology, and Applications, (pp.3-21). Springer Netherlands.

Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. doi. 10.1016/0377-2217(78)90138-8

Crescente, M. L., & Lee, D. (2011). Critical issues of m-learning: design models, adoption processes, and future trends. Journal of the Chinese Institute of Industrial Engineers, 28(2), 111-123. doi. 10.1080/10170669.2010.548856

Chen, W., Zhou, K., & Yang, S. (2017). Evaluation of China’s electric energy efficiency under environmental constraints: A DEA cross efficiency model based on game relationship. Journal of Cleaner Production, 164, 38-44. doi. 10.1016/j.jclepro.2017.06.178

Dawabi, P., Wessner, M., & Neuhold, E. (2004). Using mobile devices for the classroom of the future. Learning with mobile devices research and development. Attawell, J. & Savill-Smith C. (Ed), pp.55-59.

Du, J., Cook, W.D., Liang, L., & Zhu, J., (2014). Fixed cost and resource allocation based on DEA cross efficiency. European Journal of Operational Research, 235(1), 206-214. doi. 10.1016/j.ejor.2013.10.002

Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and the uses. Journal of the Operational Research Society, 45(5), 567–578. doi. 10.1057/jors.1994.84

Falagario, M., Sciancalepore, F., Costantino, N., & Pietroforte, R. (2012). Using a DEA-cross efficiency approach in public procurement tenders. European Journal of Operational Research, 218(2), 523-529. doi. 10.1016/j.ejor.2011.10.031

Farrar, D.E., & Glauber, R.R. (1967). Multicollinearity in regression analysis: The problem revisited, The Journal of Economics and Statistics, 49(1), 92-107. doi. 10.2307/1937887

Goh, T., & Kinshuk, D. (2006). Getting ready for mobile learning—adaptation perspective. Journal of Educational Multimedia and Hypermedia, 15(2), 175-198.

Hausman, J. (1978). Specification tests in econometrics, Econometrics, 46(6), 1251-1271. doi. 10.2307/1913827

Kumbhakar, S.C., & Lovell, C.A.K. (2000). Stochastic Frontier Analysis, Chambridge University Press.

Liu, H.H., Kuo, F.H., & Li, L.H. (2016). The operating efficiency of vocational and senior high schools in Xindian district of New Taipei City: Three envelopment models in DEA. International Business Research, 9(11), 116-125. doi. 10.5539/ibr.v9n11p116

Milrad, M., Hoppe, U., Gottdenker, J., & Jansen, M. (2004). Exploring the use of mobile devices to facilitate educational interoperability around digitally enhanced experiments. In Wireless and Mobile Technologies in Education, 2004. Proceedings. The 2nd IEEE International Workshop on (pp.182-186).

Ozdamli, F., & Cavus, N. (2011). Basic elements and characteristics of mobile learning. Procedia-Social and Behavioral Sciences, 28, 937-942. doi. 10.1016/j.sbspro.2011.11.173

O'Malley, C., Vavoula, G., Glew, J.P., Taylor, J., Sharples, M., Lefrere, P., ... & Waycott, J. (2005). Guidelines for learning/teaching/tutoring in a mobile environment. [Retrieved from].

Oral, M., Amin, G.R., & Oukil, A., (2015). Cross-efficiency in DEA: A maximum resonated appreciative model. Measurement, 63, 159-167. doi. 10.1016/j.measurement.2014.12.006

Sexton, T.R., Silkman, R.H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. In R.H. Silk (Ed.). Measuring Efficiency: An Assessment of Data Envelopment Analysis, (32), pp. 73–105). San Francisco: Jossey-Bass.

Sexton, T.R., Leiken, A.M., Sleeper, S., & Coburn, A.F. (1989). The impact of prospective reimbursement on nursing home efficiency. Medical Care, 154-163.

Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processesi Journal of Econometrics, 136(1), 31-64. doi. 10.1016/j.jeconom.2005.07.009

Singh, M. (2010). M-Learning: A new approach to learn better. International Journal of Education and Allied Sciences, 2(2), 65-72.

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrical: Journal of the Econometric Society, 26(1), 24-36. doi. 10.2307/1907382

Wang, Y.S., Wu, M.C., & Wang, H.Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118. doi. 10.1111/j.1467-8535.2007.00809.x

Wu, J., Sun, J., & Liang, L., (2012). Cross efficiency evaluation method based on weight-balanced data envelopment analysis model. Computers & Industrial Engineering, 63(2), 513-519. doi. 10.1016/j.cie.2012.04.017

Wu, J., Chu, J., Sun, J., Zhu, Q., & Liang, L., (2016b). Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Computers & Industrial Engineering, 93(C), 143-151. doi. 10.1016/j.cie.2015.12.019



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