Reynaldo Rivera-Baiocchi e-mail(Inicie sesión)

Contenido principal del artículo


Reynaldo Rivera-Baiocchi e-mail(Inicie sesión)


Character development requires not only high-quality curriculums, but also educators who are able to adapt programs to learners’ needs and context and staff development strategies. Big data and learning analytics strategies may improve youth character development especially in developing countries facilitating educators’ development and practical wisdom, as well as curriculum implementation’s effectiveness in countries with less knowhow in the issue. This study presents a systematic mapping literature review on the models and methods of learning analytics applied in the improvement of youth character education. Based on the literature review results, the research provides insights for future research and implementation of character education programs, and proposes a revised participatory knowledge management data-driven procedure that may facilitate educators to identify and undertake the best character formation actions in specific situations.


Althof, W. and Berkowitz, M. W. (2006). Moral education and character education: Their relationship and roles in citizenship education. Journal of Moral Education, 35(4), 495-518.

Ardelt, M. and Edwards, C. A. (2016). Wisdom at the End of Life: An Analysis of Mediating and Moderating Relations Between Wisdom and Subjective Well- Being. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 71(3), 502-513.

Avella, J. T., Kebritchi, M., Nunn, S. G. and Kanai, T. (2016). Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learning, 20(2), 13-29.

Ben Kei, D. (Ed.) (2017). Big Data and Learning Analytics in Higher Education: Current Theory and Practice. Retrieved from book/9783319065199

Berkowitz, M. W. and Bier, M. C. (2004). Research-Based Character Education. The ANNALS of the American Academy of Political and Social Science, 591(1), 72-85.

Barneveld A. van, Arnold, K. and Campbell, J. (2012). Analytics in Higher Education: Establishing a Common Language. Retrieved from a-Common-Language.pdf

Booth, A., Papaioannou, D. and Sutton, A. (2012). Systematic Approaches to a Successful Literature Review. London: Sage.

Chatti, M. A., Dyckhoff, A. L., Schroeder, U. and Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.

Choi, S. P. M., Lam, S. S., Li, K. C. and Wong, B. T.-M. (2018). Learning Analytics at Low Cost: At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions. Educational Technology & Society, 21, 273-290.

Conn, K. M. (2017). Identifying Effective Education Interventions in Sub-Saharan Africa: A Meta-Analysis of Impact Evaluations. Review of Educational Research, 87(5), 863-898.

Cooper, H. M. (1988). Organizing knowledge syntheses: A taxonomy of literature reviews. Knowledge in Society, 1(1), 104.

Coulter, D. and Wiens, J. R. (2002). Educational Judgment: Linking the Actor and the Spectator. Educational Researcher, 31(4), 15-25.

Cronin, P., Ryan, F. and Coughlan, M. (2008). Undertaking a literature review: A step-by-step approach. British Journal of Nursing (Mark Allen Publishing), 17(1), 38-43.

Dekker, G. W., Pechenizkiy, M. and Vleeshouwers, J. M. (2009). Predicting Students Drop Out: A Case Study. Retrieved from

Dishon, G. (2017). New data, old tensions: Big data, personalized learning, and the challenges of progressive education. Theory and Research in Education, 15(3), 272-289.

Drigas, A. and Leliopoulos, P. (2014). The Use of Big Data in Education. International Journal of Computer Science Issues, 11(5, 1), 58-63.

Dyckhoff, A. L. (2014). Action Research and Learning Analytics in Higher Education. Retrieved from

Education World Forum (2019, January 17). Teaching and the Power of Data - Research & Insight. Retrieved 12 June 2019, from The Education World Forum website:

Ekmekçi, A. K., Teraman, S. B. S. and Acar, P. (2014). Wisdom and Management: A Conceptual Study on Wisdom Management. Procedia - Social and Behavioral Sciences, 150, 1199-1204.

Elias, T. (2011). Learning Analytics. Retrieved from

Fägerlind, I. and Saha, L. J. (2014). Education and National Development: A Comparative Perspective. Exeter: Elsevier.

García, M. H. and Fares, J. (2008). Youth in Africa’s Labor Market. Washington: The World Bank.

Gasevic, D., Dawson, S. and Jovanovic, J. (2016). Ethics and Privacy as Enablers of Learning Analytics. Journal of Learning Analytics, 3(1), 1-4.

Greller, W. and Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15(3), 42-57.

Gyimah-Brempong, K. and Kimenyi, M. S. (2013). Youth Policy and the Future of African Development (Working Paper No. 9). Retrieved from Brookings website: development/

Harmokivi-Saloranta, P. and Parjanen, S. (2018). The knowledge-creating pattern in user-driven innovation. EAPRIL 2018 Conference Proceedings, 5, 37-51.

Harrison, T. and Khatoon, B. (2017). Virtue, practical wisdom and professional education. A pilot intervention designed to enhance virtue knowledge, understanding and reasoning in student lawyers, doctors and teachers. Birmingham: University of Birmingham.

Harzing A.-W. (2007). Publish or Perish, available from Retrieved from

Harzing, A.-W. and Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison. Scientometrics, 106(2), 787-804.

Hislop, D., Bosua, R. and Helms, R. (2018). Knowledge Management in Organizations: A Critical Introduction. Oxford: Oxford University Press.

Hubers, M. D., Poortman, C. L., Schildkamp, K., Pieters, J. M. and Handelzalts, A. (2016). Opening the black box: Knowledge creation in data teams. Journal of Professional Capital and Community, 1(1), 41-68.

Hunter, J. D. (2008). The Death of Character: Moral Education in an Age Without Good Or Evil. New York: Basic Books.

IBM Software Group (2001). Analyticss for Achievement. Understand success and boost performance in primary and secondary education. Retrieved from

Jayaprakask, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R. and Baron, J. D. (2014). Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. Journal of Learning Analytics, 1(1), 6-47.

Jeynes, W. H. (2019). A Meta-Analysis on the Relationship Between Character Education and Student Achievement and Behavioral Outcomes. Education and Urban Society, 51(1), 33-71.

Klašnja-Milićević, A., Ivanović, M. and Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25(6), 1066-1078.

Kondo, N., Okubo, M. and Hatanaka, T. (2017). Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data. 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 198-201.

Lassiter, W. L. and Perry, D. C. (2009). Preventing Violence and Crime in America’s Schools: From Put-Downs to Lock-Downs: From Put-Downs to Lock-Downs. Santa Barbara: ABC-CLIO.

Leitner, P., Khalil, M. and Ebner, M. (2017). Learning Analytics in Higher Education- A Literature Review. In A. Peña-Ayala (Ed.), Studies in Systems, Decision and Control. Learning Analytics: Fundaments, Applications, and Trends (pp. 1-23). Heidelberg: Springer.

Lewis, S. V., Robinson, E. H. and Hayes, B. G. (2011). Implementing an Authentic Character Education Curriculum. Childhood Education; Olney, 87(4), 227-231.

Li, Y. and Zhai, X. (2018). Review and Prospect of Modern Education using Big Data. Procedia Computer Science, 129, 341-347.

Lickona, T. (1996). Eleven Principles of Effective Character Education. Journal of Moral Education, 25(1), 93-100.

Lickona, T. (2004). Character Matters: How to Help Our Children Develop Good Judgment, Integrity, and Other Essential Virtues. New York: Simon and Schuster.

Lickona, T., Schaps, E. and Lewis, C. (2007). CEP’s Eleven Principles of Effective Character Education. Retrieved from

Lonn, S., Aguilar, S. and Teasley, S. D. (2013). Issues, Challenges, and Lessons Learned when Scaling Up a Learning Analytics Intervention. Proceedings of the Third International Conference on Learning Analytics and Knowledge, 235- 239.

Marsh, J. A., Pane, J. F. and Hamilton, L. S. (2006). Making Sense of Data-Driven Decision Making in Education. Retrieved from

Marsh, O., Maurovich-Horvat, L. and Stevenson, O. (2014). Big Data and Education: What’s the Big Idea? London: University College London Public Policy.

Martens, K. and Niemann, D. (2013). When Do Numbers Count? The Differential Impact of the PISA Rating and Ranking on Education Policy in Germany and the US. German Politics, 22(3), 314-332.

McCoy, C. and Shih, P. (2016). Teachers as Producers of Data Analytics: A Case Study of a Teacher-Focused Educational Data Science Program. Journal of Learning Analytics, 3(3), 193-214.

Muthukrishnan, S. M., Govindasamy, M. K. and Mustapha, M. N. (2017). Systematic mapping review on student’s performance analysis using big data predictive model. Journal of Fundamental and Applied Sciences, 9(4S), 730-758.

Nonaka, I. (1991). The Knowledge-Creating Company. Harvard Business Review, 69(6), 96-104.

Nonaka, I. (1994). A Dynamic Theory of Organizational Knowledge Creation. Organization Science, (1), 14.

Nonaka, I., Takeuchi, H. and Umemoto, K. (1996). A theory of organizational knowledge creation. International Journal of Technology Management, 11(7-8), 833-845.

Nsamenang, A. B. and Tchombé, T. M. (2012). Handbook of African Educational Theories and Practices: A Generative Teacher Education Curriculum. Bamenda: Human Development Resource Centre.

OECD (2013). Exploring data-driven innovation as a new source of growth: Mapping the policy issues raised by “big data”. 319-356. Retrieved from

Pauleen, D. J. and Wang, W. Y. C. (2017). Does big data mean big knowledge? KM perspectives on big data and analytics. Journal of Knowledge Management, 21(1), 1-6.

Picciano, A. G. (2012). The Evolution of Big Data and Learning Analytics in American Higher Education. Journal of Asynchronous Learning Networks, 16(3), 9-20.

Piety, P. J., Hickey, D. T. and Bishop, M. J. (2014). Educational Data Sciences: Framing Emergent Practices for Analytics of Learning, Organizations, and Systems. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, 193-202.

Rivas, A. (2016). Latin America after PISA. Lessons Learned about Education in Seven Countries (2000-2015). Buenos Aires: CIPPEC.

Rivera, R. (2016). Segmentación Relacional (Relational Segmentation). Tesis doctoral, Universidad de Navarra, Facultad de Comunicación.

Rivera, R., Santos, D., Brändle, G. and Cárdaba, M. A. M. (2016). Design Effectiveness Analysis of a Media Literacy Intervention to Reduce Violent Video Games Consumption Among Adolescents The Relevance of Lifestyles Segmentation. Evaluation Review, 40(2), 142-161.

Robertson, S. L. (2019). Can Big Data Bridge the Gap between Knowing and Doing? - Research & Insight. Education World Forum. Retrieved from

Rodríguez, P., Palomino, N. and Mondaca, J. (2017). El uso de datos masivos y sus técnicas analíticas para el diseño e implementación de políticas públicas en Latinoamérica y el Caribe. Retrieved from

Rodríguez, P., Truffello, R., Suchan, K., Varela, F., Matas, M., Mondaca, J., … Allende, C. (2016). Apoyando la formulación de políticas públicas y toma de decisiones en educación utilizando técnicas de análisis de datos masivos : El caso de Chile. MINISTERIO DE EDUCACION. Retrieved from

Rojas-Castro, P. (2017). Learning Analytics: A Literature Review. Educación y Educadores, 20(1), 106-128.

Romera, D. D. M. (2018). Evaluar el pensamiento crítico en Educación para la Ciudadanía: Propuesta para contextos masificados. Didacticae: Revista de Investigación en Didácticas Específicas, 0(3), 131-144.

Romero, C. and Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.

Romero, C., Ventura, S., Pechenizkiy, M. and Baker, R. S. J. d. (2010). Handbook of Educational Data Mining. Boca Ratón: CRC Press.

Rowley, J. and Gibbs, P. (2008). From learning organization to practically wise organization. The Learning Organization, 15(5), 356-372.

Schildkamp, K. and Poortman, C. (2015). Factors Influencing the Functioning of Data Teams. Teachers College Record, 117(4), 040310.

Schildkamp, K., Poortman, C. L. and Handelzalts, A. (2016). Data teams for school improvement. School Effectiveness and School Improvement, 27(2), 228-254.

Schwartz, B. (2011). Practical wisdom and organizations. Research in Organizational Behavior, 31, 3-23.

Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400.

Silva, C. and Fonseca, J. (2017). Educational Data Mining: A Literature Review. In A. Rocha, M. Serrhini and C. Felgueiras (Eds.), Advances in Intelligent Systems and Computing. Europe and MENA Cooperation Advances in Information and Communication Technologies (pp. 87-94). New York: Springer.

Sin, K. and Muthu, L. (2015). Application Of Big Data In Education Data Mining And Learning Analytics-A Literature Review. ICTACT Journal on Soft Computing, 5(4), 1035-1049.

Smagorinsky, P. and Taxel, J. (2005). The Discourse of Character Education: Culture Wars in the Classroom. London: Routledge.

Uggerhøj, L. (2012). Theorizing practice research in social work. Social Work and Social Sciences Review, 15(1), 49-73.

UN Global Pulse (2012). Big Data for Development: Challenges and Opportunities. New York: United Nations Global Pulse.

Waldron, J. (1995). The Wisdom of the Multitude: Some Reflections on Book 3, Chapter 11 of Aristotle’s Politics. Political Theory, 23(4), 563-584.

Watson, R. J. and Christensen, J. L. (2017). Big data and student engagement among vulnerable youth: A review. Current Opinion in Behavioral Sciences, 18, 23-27.

Wayman, J. C., Midgley, S. and Stringfield, S. (2006). Leadership for data-based decision making: collaborative educator teams. In A. B. Danzig, K. M. Borman, B. A. Jones and W. F. Wright (Eds.), Leadership. Learner-Centred Leadership: Research, Policy and Practice (pp. 189-205). Mahwah: Lawrence Erlbaum.

World Bank (2008). Curricula, Examinations, and Assessment in Secondary Education in Sub-Saharan Africa. Washington: World Bank Publications.

Song, Y., Ren, J. and Zhang, K. (2017). Study on the Interactive Education Model of College Students Ideological and Political Class and Supervision of Daily Behavior in the Era of Big Data. Boletin Tecnico, 55(20), 650-655.

Yu, X. and Wu, S. (2015). Typical Applications of Big Data in Education. 2015 International Conference of Educational Innovation through Technology (EITT), 103-106.


Search GoogleScholar


Detalles del artículo

Biografía del autor/a

Reynaldo Rivera-Baiocchi, Universidad Austral. CERRITO 1250