Using Learning Analytics to Improve Students’ Reading Skills: A case study in an American International School with EAL Students

  • Jorge Alexander Aristizábal ISHCMC American Academy, Vietnam
Keywords: Data, reading, Student learning, learning analytics



This paper shows how an American International School in Vietnam has been using data and Learning Analytics to learn about students learning from their assessment data and how to use these data to improve, among other areas, the reading skills of their mostly EAL student population. The source of data comes primarily from a Computer Adaptive Testing platform, commonly known as the MAP Growth test, which provides information about Math and Reading skills for each particular student.

The data provided is transformed and presented to educational stakeholders through visualizations created in a specialized software in order to dig into the data and answer the pedagogical questions emerged from teachers and administrators. This process involves a new field know and Learning Analytics and Visual Data Mining in order to find new information not usually evident in school datasets. The results indicate that when teachers identify specific strengths and areas for improvement get into a reflective process that end up in actions plans for overall school and student learning improvement. In addition, learning analytics proves itself to be an effective way to understand what students learn and engage in actions to improve the conditions where learning happens.




Download data is not yet available.

Author Biography

Jorge Alexander Aristizábal, ISHCMC American Academy, Vietnam

is currently the Associate Director of Teaching and Learning at the International School Ho Chi Minh City American Academy in Vietnam. Prior to this post, he was the Assessment Coordinator at Colegio Nueva Granada and Professor of Education and Technology at UNICA, both in Bogotá, Colombia. He holds a Doctorate in Education from Universidad Santo Tomás; an MBA from Universidad de Alcalá, Spain; M.Ed from Universidad Externado de Colombia; and a MDQ and B.A. in Chemistry from Universidad Pedagógica Nacional in Colombia. He is an educator with 20+ years of experience in international schools, has consulted for the National Ministry of Education and private corporations. He is certified in DataWise from the Harvard Graduate School of Education and holds certificates in Curriculum and Assessment from the Principals´ Training Center in Miami, USA.


Aristizabal, J. A. (2016). Analítica de datos de aprendizaje (ADA) y gestión educativa. Gestión de la educación, 1(2), 149-168. DOI:

Aristizábal, J. A. (2017). Diseño y aportes de un modelo para minería de datos educativos en aulas de educación media de carácter presencial. (Tesis de doctorado). Universidad Santo Tomás, Bogotá, Colombia. Retrieved from

Baker, R., & Yacef, K. (2009). Editorial Welcome. JEDM | Journal of Educational Data Mining, 1(1), 1-3. Retrieved from

Chen, M., et al (2009). Data, information, and knowledge in visualization. Computer Graphics and Applications, IEEE, 29(1), 12-19. Retrieved from

Cook, K. A., & Thomas, J. J. (Eds). (2005). Illuminating the path: The research and development agenda for visual analytics. Richland, WA: Pacific Northwest National Laboratory.

Elias, T. (2011). Learning Analytics: Definitions, Processes and Potential. Retrieved from 452659685fe3950b0e515a28ce89d9c5592a.pdf

Goebel, M., & Gruenwald, L. (1999). A survey of data mining and knowledge discovery software tools. ACM SIGKDD Explorations Newsletter, 1(1), 20-33. Retrieved from

Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education. Vol. 8, pp. 1-12. Educause. Retrieved from ers0508/rs/ers0508w.pdf

NWEA. (2016). How realiable are MAP Test Results? Retrieved from

Papamitsiou, Z., & Economides, A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17 (4), 49–64. Retrieved from

Parentau, J., Sallam, R., Howson, C., Tapadinhas, J., Schlegel, K., & Oestreich, T. (2016, 4 de febrero). Magic Quadrant for Business Intelligence and Analytics Platforms. Recuperado de

Pechenizkiy, M. (2017). From the President of the International Educational Data Mining Society. In Lang, et al. (Eds.). Handbook of Learning Analytics. Retrieved from DOI: 10.18608/hla17

Prabhu, S., & Venatesan, N. (2007). Data Mining and Warehousing. New Delhi: New Age International.

Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146. Retrieved from

Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. (Eds.). (2011). Handbook of educational data mining. Boca Ratón, Fl: CRC Press.

Shum, S. & Crick, R. D. (2016). Learning Analytics for 21st Century Competencies. Journal of Learning Analytics, 3(2), 6-21. Retrieved from

Tempelaar, D. T., Heck, A., Cuypers, H., van der Kooij, H., & van de Vrie, E. (2013,). Formative assessment and learning analytics. In Proceedings of the third international conference on learning analytics and knowledge. P. 205-209. ACM. Doi:10.1145/2460296.2460337

Veldkamp, B. P., & Matteucci, M. (2013). Bayesian computerized adaptive testing. Ensaio: Avaliação e Políticas Públicas em Educação, 21(78), 57-82.

Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Journal of Educational Technology & Society, 15(3), 133.

Weiss, D. J. (2004). Computerized adaptive testing for effective and efficient measurement in counseling and education. Measurement and Evaluation in Counseling and Development, 37(2), 70.
How to Cite
Aristizábal, J. (2018). Using Learning Analytics to Improve Students’ Reading Skills: A case study in an American International School with EAL Students. GiST Education and Learning Research Journal, (17), 193-214.