Volume 4, Issue 2, December 2020, Page: 78-85
Design and Validation of School ICT Monitoring & Evaluation Framework: A Case Study
Xu Chaojun, School of Education Science, Nanjing Normal University, Nanjing, China
Yan Wei, Linyi No. 19 Middle School, Linyi, China
Hu Chenlin, Jurong Huayang Middle School, Zhenjiang, China
Shi Congying, School of Psychology, Nanjing Normal University, Nanjing, China
Received: Sep. 24, 2020;       Accepted: Oct. 17, 2020;       Published: Oct. 23, 2020
DOI: 10.11648/j.ajeit.20200402.16      View  92      Downloads  18
With the rapid development of school ICT, students and stuffs ICT activity data are rich enough for school ICT monitor and evaluation. This paper proposes an evidence based school ICT evaluation framework, which includes four parts: data collection, data security and privacy protection, information analysis and evaluation analysis, evaluation and decision-making recommendations. Combined with the iso8000-61 data quality standard and the core elements of school ICT evaluation, the reliability of data collection technology and the availability of collected data are verified. Through interviews, the school teachers and ICT are responsible for the concerns of data security and privacy protection in the research and application of education big data. The experimental results show that the school ICT evaluation based on the fact data has outstanding performance in the aspect of problem diagnosis and the guiding significance of the evaluation conclusion, and has more practical guiding significance. At the end of the paper, some suggestions are put forward for the promotion of technical framework, data security and privacy protection.
Evidence Data, Data Usability, School ICT, Evaluation Framework, Data Analysis
To cite this article
Xu Chaojun, Yan Wei, Hu Chenlin, Shi Congying, Design and Validation of School ICT Monitoring & Evaluation Framework: A Case Study, American Journal of Education and Information Technology. Vol. 4, No. 2, 2020, pp. 78-85. doi: 10.11648/j.ajeit.20200402.16
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Karimi, S. S., Mulwa, A. S., & Kyalo, D. N. (2020). Stakeholder engagement in monitoring and evaluation and performance of literacy and numeracy educational programme in public primary schools in nairobi county, kenya. Journal of Educational and Developmental Psychology, 10 (2), 10.
Hey, T. (2012). The Fourth Paradigm – Data-Intensive Scientific Discovery. International Symposium on Information Management in A Changing World. Springer, Berlin, Heidelberg.
Eduardo, V. & Aponte, M. (2014). Indicators for Monitoring and Evaluation of ICT for education: a systematic review. WOSC 2014, 16th Congress of the World Organization of Systems and Cybernetics.
Delavari, N., Shirazi, M. R. A., & Beikzadeh, M. R. (2004). A new model for using data mining technology in higher educational systems. International Conference on Information Technology Based Higher Education & Training (pp. 321-326). IEEE.
Mandinach, E. B., & Schildkamp, K. (2020). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 2020, 100842, ISSN 0191-491X, https://doi.org/10.1016/j.stueduc.2020.100842.
Cherian, T., Hwang, A., Mantel, C., Veira, C., & Hinman, A. (2020). Global Vaccine Action Plan lessons learned III: Monitoring and evaluation/accountability framework. Vaccine, Volume 38, Issue 33, 2020, Pages 5379-5383, ISSN 0264-410X, https://doi.org/10.1016/j.vaccine.2020.05.028.
Liu, T., Zhang, W., Yuwono, M., Zhang, M., & Su, S. W. (2020). A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models. Sensors and Actuators B: Chemical, Volume 311, 2020, 127868, ISSN 0925-4005, https://doi.org/10.1016/j.snb.2020.127868.
Odhiambo, J. O., Wakibia, J., & Sakwa, M. M. (2020). Effects of monitoring and evaluation planning on implementation of poverty alleviation mariculture projects in the coast of Kenya. Marine Policy, Volume 119, 2020, 104050, ISSN 0308-597X, https://doi.org/10.1016/j.marpol.2020.104050.
Shewbridge, Jang, E. E., Matthews, P. & Santiago, P. (2011). OECD Reviews on evaluation and assessment in education: Denmark. OECD.
Republic of the Philippines Republic Act No. 9155 (2001). An Act Instituting a Framework of Governance for Basic Education, Establishing Authority and Accountability, Renaming the Department of Education, Culture and Sports as the Department of Education, and for Other Purposes. National Legislative Bodies.
Safsmsadmin (2016). School Monitoring & Evaluation: What Works and What Doesn’t. Retrieved 2020-09-18 from https://safsms.com/blog/7-things-look-school-monitoring-evaluation-works-doesnt.
Palombi O., Jouanot F., Nziengam N., Behrooz O. T., Rousset M. C., et al. Onto SIDES: Ontology-based student progress monitoring on the national evaluation system of French Medical Schools. Artificial Intelligence in Medicine, Elsevier, 2019, 96, pp. 59-67. <10.1016/j.artmed.2019.03.006>. .
Ikemoto, G. S., & Marsh, J. A. (2007). “Mantra: Different Conceptions of Data-Driven Decision Making." yearbook of the national society for the study of ducation, 106 (1), 105-131.
Brien, S. O., Mcnamara, G., Hara, J. O. & Brown, M. (2018). Irish teachers, starting on a journey of data use for school self-evaluation. Studies in Educational Evaluation, 60, 1-13, https://doi.org/10.1016/j.stueduc.2018.11.001.
Neeley, S. J., Scott, R. Barnes, S. & et al. (2006). Long-Range Plan for Technology, 2006-2020: A Report to the 80th Texas Legislature from the Texas Education Agency. Viewed September 19, 2020 https://tea.texas.gov/sites/default/files/FinalCombinedLRPT2020.pdf.
D&T Association (2016). D&T Primary Self-Review Framework – making monitoring and evaluation easier and more effective. Viewed September 19, 2020 https://primarysrf.data.org.uk.
Fernández-Gutiérreza M., Gimenezb G., Caleroc J. Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish Autonomous Communities. Computers & Education. Vol 157, November 2020. https://doi.org/10.1016/j.compedu.2020.103969.
Wagner, D. A., Day B., James T., Kozma B. R., Miller J. & Unwin T. (2005). Monitoring and Evaluation of ICT in Education Projects: A Handbook for Developing Countries. Washington, DC: infoDev / World Bank. Available at: http://www.infodev.org/en/Publication.9.html.
Efron, B. & Hastie, T. (2016). Computer age statistical inference: Algorithms, evidence, and data science. Cambridge University Press. DOI: 10.1017/CBO9781316576533.
Breiman, L. (2001). Statistical modeling: the two cultures (with comments and a rejoinder by the author). Statistical Science, 16 (3), 199-215.
Canadian Education Statistics Council (2016). The Pan-Canadian Education Indicators Program (PCEIP). Viewed September 19, 2020. https://www.cmec.ca/259/Overview.html.
Browse journals by subject