A FRAMEWORK FOR DATA INTELLIGENCE AND ITS APPLICABILITY IN EDUCATIONAL SYSTEM

Authors

  • Esther S. Alu
  • Joshua Abah
    Federal University Lafia, Nasarawa State
  • David O. Adewumi

Keywords:

Academic effectiveness, decision making, data intelligence, educational development, machine learning.

Abstract

Data as well as information is the most valuable tools used for academic effectiveness and for the organization to achieve its aim and objectives. Data is very essential and the cognitive means to collect and organize this data cannot be achieved without human intelligence. In the contemporary society, the means for reliability, accuracy, effectiveness and for a better recognition of an organization has to do with the reliable information and the means to which data can be collected and organized for future use and for the effective management of the organization. Data intelligence guarantees the integral and sustainable development in all aspect of human endeavors; Science and Technologyy, Arts and Humanities and in every other aspect of human development, data intelligence serves as the master key for decision making. This paper critically reviews the necessity of data intelligence and its application areas in harmony with the effectiveness of academic and educational institutions. This work concludes that data intelligence is integral to the effectiveness of any organization including educational institutions and no organizational problem can be tackled without data intelligence. It is recommended that data intelligence should be applied in the Nigeria educational system and machine learning techniques be applied so as to derive meaning from available data banks. This work developed a framework for application of data intelligence in educational system

Dimensions

Agasisti, T., Bowers, A.J. (2017). Data Analytics and Decision Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions. In Johnes, G., Johnes, J., Agasisti, T., López-Torres, L. (Eds.) Handbook of Contemporary Education Economics (p.184-210). Cheltenham, UK: Edward Elgar Publishing. ISBN: 978-178536-906-3 http://www.e-elgar.com/shop/handbook-ofcontemporary-education-economics

Baker, R.S.J.D. et al., (2009) “Educational Software Features that Encourage and Discourage ‘Gaming the System’,†Proc. 14th Int’l Conf. Artificial Intelligence in Education, 2009, pp. 475–482.

Baker, R.S., & Inventado, P.S. (2014). ‘Educational data mining and learning analytics’. In J.A. Larusson & B. White (Eds.), Learning Analytics pp. 61-75. New York: Springer.

Baker, R.S., & Yacef, K. (2009). ‘The State of Educational Data Mining in 2009: A Review and Future Visions’. Journal of Educational Data Mining, 1(1), 3-16.

Baker R. & Yacef, K., (2009). “The State of Educational Data Mining in 2009: A Review and Future Visions,†J. Educational Data Mining, vol. 1, no. 1, 2009, pp. 3–17.

Baker, R.S.J.D. et al., (2013) “Predicting Robust Learning with the Visual Form of the Moment-by-Moment Learning Curve,†J.Learning Sciences, vol. 22, no. 4, 2013, pp. 639–666.

Balfanz, R., Herzog, L., & MacIver, D. J. (2007). ‘Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions’. Educational Psychologist, 42(4), 223-235.

Behrens, J. T., & DiCerbo, K. E. (2014). Technological Implications for Assessment Ecosystems: Opportunities for Digital Technology to Advance Assessment. Teachers College Record, 116(11), 1-22.

Bienkowski, M., Feng, M., & Means, B. (2012). ‘Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief’. Washington, DC: U.S. Department of Education, Office of Educational Technology.

Bowers, A.J., Krumm, A.E., Feng, M., & Podkul, T. (2016). ‘Building a Data Analytics Partnership to Inform School Leadership Evidence-Based Improvement Cycles’. Paper presented at the Annual meeting of the American Educational Research Association, Washington, DC.

Bowers, A.J. (2017). ‘Quantitative Research Methods Training in Education Leadership and Administration Preparation Programs as Disciplined Inquiry for Building School Improvement Capacity. Journal of Research on Leadership Education, 12(1), pp.72-96. http://doi.org/10.1177/1942775116659462.

Bowers, A.J., Shoho, A.R., & Barnett, B.G. (2014). ‘Considering the Use of Data by School Leaders for Decision Making’. In A.J. Bowers, A.R. Shoho & B.G. Barnett (Eds.), Using Data in Schools to Inform Leadership and Decision Making. pp. 1-16. Charlotte, NC: Information Age Publishing.

Bouwma-Gearhart, J., & Collins, J. (2015, October). ‘What We Know About Data-Driven Decision Making In Higher Education: Informing Educational Policy and Practice’. In Proceedings of International Academic Conferences No. 2805154. International Institute of Social and Economic Sciences.

Bowers, A.J. (2008). ‘Promoting Excellence: Good to great, NYC's district 2, and the case of a high performing school district’. Leadership and Policy in Schools, 7(2), 154-177.

Bowers, A. J., Shoho, A. R., & Barnett, B. G. (2014). ‘Considering the Use of Data by School Leaders for Decision Making’. In A. J. Bowers, A. R. Shoho & B. G. Barnett (Eds.), Using Data in Schools to Inform Leadership and Decision Making. pp. 1-16. Charlotte, NC: Information Age Publishing.

Bowers, A.J., Sprott, R., & Taff, S. (2013). ‘Do we know who will drop out? A review of the predictors of dropping out of high school: Precision, sensitivity and specificity’. The High School Journal, 96(2), 77-100.

Braun, M.L., & Ong, C.S. (2014). ‘Open Science in Machine Learning’. In V. Stodde, F. Leisch & R. D. Peng (Eds.), Implementing Reproducible Research. pp. 343365: Chapman and Hall/CRC.

Brunello, G., & Checchi, D. (2007). ‘Does school tracking affect equality of opportunity? New international evidence’. Economic Policy, 22(52), 782-861.

Campbell, J. P., & Oblinger, D. G. (2007). ‘Academic analytics’. EDUCAUSE Review, 42(4), 40-57.

Cosner, S. (2014). ‘Strengthening Collaborative Practices in Schools: The Need to Cultivate Development

Daniel, B. (2015). ‘Big Data and analytics in higher education: Opportunities and challenges’. British Journal of Educational Technology, 46(5), 904-920. Datnow, A., & Hubbard, L. (2015). ‘Teachers’ Use of Assessment Data to Inform Instruction: Lessons From the Past and Prospects for the Future’. Teachers College Record, 117(4), 1-26.

Farley-Ripple, E.N., & Buttram, J.L. (2015). ‘The Development of Capacity for Data Use: The Role of Teacher Networks in an Elementary School’. Teachers College Record, 117(4), 1-34.

Gandomi, A., & Haider, M. (2015). ‘Beyond the hype: Big data concepts, methods, and analytics’. International Journal of Information Management, 35(2), 137-144.

Perspectives and Diagnostic Approaches’. In A. J. Bowers, A. R. Shoho & B. G. Barnett (Eds.), Using Data in Schools to Inform Leadership and Decision Making. Charlotte, NC: Information Age Publishing.

Schutt, R., & O'Neil, C. (2013). Doing Data Science: Straight Talk from the Frontline. Cambridge, MA:

Uwezo. 2016. Are Our Children Learning? Uwezo Uganda 6th Learning Assessment Report. Kampala: Twaweza East Africa.

World Bank. 2018. World Development Report 2018: Learning to Realize Education’s Promise. Washington, DC: World Bank.

https://www.researchgate.net/publication/322447978_Data_Analytics_and_Decision-making_in_Education_Towards_the_Educational_Data_Scientist_as_a_Key_Actor in_Schools_and_Higher_Education_Institutions/link/5a593645aca2727d60815f64/download

https://www.sisense.com/glossary/data-intelligence/

Published

30-09-2020

How to Cite

A FRAMEWORK FOR DATA INTELLIGENCE AND ITS APPLICABILITY IN EDUCATIONAL SYSTEM. (2020). FUDMA JOURNAL OF SCIENCES, 4(3), 631-635. https://doi.org/10.33003/fjs-2020-0403-293

Issue

Vol. 4 No. 3 (2020): FUDMA Journal of Sciences - Vol. 4 No. 3

Section

Review Articles
Copyright & Licensing

How to Cite

A FRAMEWORK FOR DATA INTELLIGENCE AND ITS APPLICABILITY IN EDUCATIONAL SYSTEM. (2020). FUDMA JOURNAL OF SCIENCES, 4(3), 631-635. https://doi.org/10.33003/fjs-2020-0403-293

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