Using Learning Analytics and Visualization Techniques to Evaluate the Structure of Higher Education Curricula

• Artur Mesquita Barbosa Universidade Federal do Ceará (UFC)
• Antonio Nilo de Araujo Neto Universidade Federal do Ceará (UFC)
• Emanuele Santos Universidade Federal do Ceará (UFC)
• João Paulo P. Gomes Universidade Federal do Ceará (UFC)

DOI: https://doi.org/10.5753/cbie.sbie.2017.1297

Resumo

In this paper, we propose a data mining technique that evaluates a curriculum’s structure based on academic data collected from Computer Science students from 2005 to 2016. Our approach is based on the Synthetic Control Method (SCM), which builds a linear model describing the relation between courses based on student performance information. The proposed model is compared to a linear regression model with positive coefficients. In addition to providing the relation between courses, it can also be used to predict students’ grades in a specific course based on their previous grades. The results are visualized in a user-friendly tool, which allows for contrast and comparison between the official structure and the structure found based on the data.

Palavras-chave: Learning Analytics, Visualization, Curriculum, Data Mining, Student Performance

Referências

Abadie, A., Diamond, A., and Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490): 493–505.

Abadie, A. and Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review, 93, 113-132.

Anuradha, C. and Velmurugan, T. (2015). A data mining based survey on student performance evaluation system. 5th IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2014, (December 2014): 43–47.

Badr, A., Din, E., and Elaraby, I. S. (2014). Data Mining: A prediction for Student’s Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2): 43–47.

Baradwaj, B. K. (2011). Mining Educational Data to Analyze Students’ Performance. IJACSA (International Journal of Advanced Computer Science and Applications), 2(6).

Campagni, R., Merlini, D., Sprugnoli, R., and Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13): 5508–5521.

de Brito, D. M., Júnior, I. A. d. A., Queiroga, E. V., and do Rego, T. G. (2014). Predição de desempenho de alunos do primeiro período baseado nas notas de ingresso utilizando métodos de aprendizagem de máquina. Anais do Simpósio Brasileiro de Informática na Educação, 25(1): 882–890.

Hinrichs, P. (2012). The effects of affirmative action bans on college enrollment, educational attainment, and the demographic composition of universities. The Review of Economics and Statistics, 94(3): 712–722.

Jordão, V., Gama, S., and Gonçalves, D. (2014). EduVis: Visualizing educational information. 8th Nordic Conference on Human-Computer Interaction, NordiCHI 2014, pages 1011–1014.

Kantorski, G., Flores, E. G., Schmitt, J., Hoffmann, I., and Barbosa, F. (2016). Predição da Evasão em Cursos de Graduação em Instituições Públicas. Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), 27(1): 906.

Munzner, T. (2014). Visualization Analysis & Design. AK Peters Visualization Series. CRC Press-Taylor & Francis Group, Boca Raton, FL, 1st edition.

Ogunde and Ajibade (2014). A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm. Ogunde, A. O. and Ajibade, D. A. Computer Science and Information Technology, 2(1): 21–46.

Pechenizkiy, M., Trcka, N., De Bra, P., and Toledo, P. (2012). CurriM: Curriculum Mining. Proceedings of the 5th International Conference on Educational Data Mining, (i): 1–4.

Wang, R. and Zaïane, O. R. (2015). Discovering Process in Curriculum Data to Provide Recommendation. In Proceedings of the 8th International Conference on Educational Data Mining, pages 580–581.