Investigating the school characteristics/attributes via the relationships between school management efficiency and representative factors of mobile e-learning: DEA and data mining methodology

References

• Al-Qirim, N. (2011). Determinants of interactive white board success in teaching in higher education institutions. Computers & Education, 56(3), 827-838. doi: 10.1016/j.compedu.2010.10.024
   Web of Science ®Google Scholar
• Asif, R., Merceron, A., Ali, S. A. and Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177-194. doi: 10.1016/j.compedu.2017.05.007
   Web of Science ®Google Scholar
• Baker, R. S. and Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
   Google Scholar
• Banker, R. D., Charnes, A. and Cooper, W. W. (1984). Some Models for Estimation of Technical and Scale Inefficiencies in Data Envelopment Analysis. Management science, 30(9), 1078-1092. doi: 10.1287/mnsc.30.9.1078
   Web of Science ®Google Scholar
• Banister, S. (2010). Integrating the iPod Touch in K–12 education: Visions and vices. Computers in the Schools, 27(2), 121-131. doi: 10.1080/07380561003801590
   Google Scholar
• Berry, M. J., and Linoff, G. (1997). Data mining Techniques: for Marketing, Sales, and Customer Support. New York: John Wiley & Sons.
   Google Scholar
• Chapman, P., Clinton, J., Keber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000). CRISP-DM 1.0 Step by step BI guide. Edited by SPSS.
   Google Scholar
• Cavanillas, J. M., Curry, E. and Wahlster, W. (2016). New horizons for a Data-Driven Economy: A Roadmap for usage and Exploitation of Big Data in Europe, Cham: Springer International Publishing.
   Google Scholar
• Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C. and Tsolakidis, A. (2014). Improving quality of educational processes providing new knowledge using data mining techniques. Procedia- Social and Behavioral Sciences, 147, 390-397. doi: 10.1016/j.sbspro.2014.07.117
   Google Scholar
• Charnes, A., Cooper, W. W. and 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
   Web of Science ®Google Scholar
• Chen, R. F. (1998). Evaluation of educational quality in universities and colleges in Taiwan using data envelopment analysis. Academic Journal of Kaohsiung Institute of Science and Technology, 28(5), 227-238.
   Google Scholar
• Chiang, W. M., (2009). Impact of Low Birth Rate on High School Education and Recommendations. Secondary Education Monthly, 60(1), 26-34.
   Google Scholar
• Chen, L. and Jia, G. (2017). Environmental Efficiency Analysis of China’s Regional Industry: A Data Envelopment Analysis (DEA) Based Approach. Journal of Cleaner Production, 142 (2), 846-853. doi: 10.1016/j.jclepro.2016.01.045
   Web of Science ®Google Scholar
• Cooper, W. W., Seiford, L. M. and Tone, K. (2007). The basic CCR model. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, 21-39.
   Google Scholar
• El-Gayar, O. F., Moran, M. and Hawkes, M. (2011). Students’ Acceptance of Tablet PCs and Implications for Educational Institutions. Educational Technology & Society, 14(2), 58-70.
   Web of Science ®Google Scholar
• Eristi, S. D., Haseski, H. I., Uluuysal, B. and Karakoyun, F. (2011). The use of mobile technologies in multimedia-supported learning environments. The Turkish Online Journal of Distance Education, 12(3), 130-141.
   Google Scholar
• Garrett, R.M. (2010). Education and Development, London: Routledge.
   Google Scholar
• Gu, Z. Y., (1999). A study on the Productivity Assessment and Integration Model of Resource Allocation in Higher Education Units. Journal of Management and Systems, 6(3), 347-364.
   Google Scholar
• Gursul, F. and Tozmaz, G. B. (2010). Which one is Smarter? Teacher or Board. Procedia-Social and Behavioral Sciences, 2(2), 5731-5737. doi: 10.1016/j.sbspro.2010.03.936
   Google Scholar
• Gikas, J. and Grant, M. M. (2013). Mobile Computing Devices in Higher Education: Student Perspectives on Learning with Cellphones, Smartphones & Social Media. The Internet and Higher Education, 19, 18-26. doi: 10.1016/j.iheduc.2013.06.002
   Web of Science ®Google Scholar
• Hwang, G. J., Tsai, C. C. and Yang, S. J. H. (2008). Criteria, Strategies and Research issues of Context-Aware Ubiquitous learning. Educational Technology and Society, 11(2), 81-91.
   Web of Science ®Google Scholar
• Hwang, Y. X. (2012). Comparative Analysis of the Operational Performance of Public and Private High Schools, Middle Schools, and Vocational Schools with Schools in the Sindian District of New Taipei City as Examples (unpublished MA thesis), Taichung: Asia University.
   Google Scholar
• Ifenthaler, D. and Schweinbenz, V. (2013). The Acceptance of Tablet- PCs in Classroom Instruction: The Teachers’ Perspective. Computers in Human Behavior, 29(3), 525-534. doi: 10.1016/j.chb.2012.11.004
   Web of Science ®Google Scholar
• Ifenthaler, D. and Schweinbenz, V. (2016). Students’ acceptance of tablet pcs in the classroom. Journal of Research on Technology in Education, 48(4), 306-321. doi: 10.1080/15391523.2016.1215172
   Web of Science ®Google Scholar
• Kuo, F. H. and Liu, H. H. (2017). Upgrading School Efficiencies and Learning Interests Through Innovative Teaching of Digital Mobile E-learning. Journal of Social and Administrative Sciences, 4(4), 382-397.
   Google Scholar
• Liu, H. H. and Kuo, F. H. (2017). Operating Efficiency and its Effect from Innovative Teaching through Digital Mobile E-Learning for Public and Private High Schools. Research in Applied Economics, 9(3), 70-90. doi: 10.5296/rae.v9i3.11828
   Google Scholar
• Liu, H. H. and Kuo, F. H. (2017). Determinants of School Efficiencies from Innovative Teaching through Digital Mobile E-Learning for High Schools: Application of Bootstrap Truncated Regression Model, Asian Journal of Economic Modelling, 5(4), 431-449. doi: 10.18488/journal.8.2017.54.431.449
   Google Scholar
• Martin, S., Diaz, G., Sancristobal, E., Gil, R. Castro, M. and Peire, J. (2011). New Technology Trends in Education: Seven years of Forecasts and Convergence. Computers and Education, 57(3), 1893-1906. doi: 10.1016/j.compedu.2011.04.003
   Web of Science ®Google Scholar
• Ohlhorst, F. J. (2012). Big Data analytics: Turning Big Data into Big Money. New York: John Wiley & Sons.
   Google Scholar
• Priyadharsini, C. and Thanamani, A. S. (2014). An Overview of Knowledge Discovery Database and Data mining Techniques. International Journal of Innovative Research in Computer and Communication Engineering, 2(1), 1571-1572.
   Google Scholar
• Price, S., Jewitt, C. and Crescenzi, L. (2015). The Role of iPads in Pre- School Children’s mark making Development. Journal Computers & Education, 87, 131-141. doi: 10.1016/j.compedu.2015.04.003
   Web of Science ®Google Scholar
• Rohanizadeh, S. S. and Bameni, M. M. (2009). A Proposed Data Mining Methodology and its Application to Industrial Procedures. Journal of Industrial Engineering, 4, 37-50.
   Google Scholar
• Simsek, A., İskenderoğlu, T. and İskenderoğlu, M. (2010). Investigating pre-service computer teachers’ attitudes towards distance education. Procedia-Social and Behavioral Sciences, 9, 324-328. doi: 10.1016/j.sbspro.2010.12.158
   Google Scholar
• Suki, N. M. and Suki, N. M. (2011). Users’ Behavior towards Ubiquitous M-Learning. The Turkish Online Journal of Distance Education, 12(3), 118-129.
   Google Scholar
• Stacey, B., Michelle, D., Cooch, J. and Paul, R. (2002). Data Mining. University of Iowa, 26-43.
   Google Scholar
• Vallurupalli, S., Paydak, H., Agarwal, S. K., Agrawal, M. and Assad- Kottner, C. (2013). Wearable Technology to Improve Education and Patient Outcomes in a Cardiology Fellowship Program- A Feasibility Study. Health and Technology, 3(4), 267-270. doi: 10.1007/s12553-013-0065-4
   Google Scholar
• Wang, G. M. and Gu, Z. Y., (1991). A Study on the Application of DEA in Educational Assessment. Modern Education, 6(1), 118-127.
   Google Scholar
• Williams, A. J. and Pence, H. E. (2011). Smart phones, a Powerful tool in the chemistry classroom. Journal of Chemical Education, 88(6), 683-686. doi: 10.1021/ed200029p
   Web of Science ®Google Scholar
• Wirth, R. and Hipp, J. (2000). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (pp. 29-39). London, UK: Springer-Verlag.
   Google Scholar
• Xiong, Y., Li, Z. and Fang, X. (2017). Performance Evaluation of Introducing Group Technology into Machining Industry with Data Envelopment Analysis. Journal of Interdisciplinary Mathematics, 20(1), 295-305. doi: 10.1080/09720502.2016.1258834
   Web of Science ®Google Scholar
• Zeyu, J., Shuiping, Y., Mingduan, Z., Yongqiang, C. and Yi, L. (2017). Model study for intelligent transportation system with big data. Procedia Computer Science, 107, 418-426. doi: 10.1016/j.procs.2017.03.132
   Google Scholar