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Socio-affective scenarios using learning analytics

PUBLISHED April 25, 2023 (DOI: https://doi.org/10.54985/peeref.2304p2992503)

4th Place Peeref Competition


Jacqueline Mayumi Akazaki1 , Leticia Rocha Machado1 , Patricia Alejandra Behar1
  1. Federal University of Rio Grande do Sul, Porto Alegre - Brazil

Conference / event

SEEL-22: KES International Conference on Smart Education and e-Learning, June 2022 (Rhodes, Greece)

Poster summary

This work aims to identify the possible Socio-affective Scenarios using Learning Analytics of recurrent students in a Virtual Learning Environment. This research with a qualitative and quantitative approach was carried out based on 13 case studies. The target audience was 285 students who participated in subjects and courses at a Brazilian public university. Data collected from the Social Map and the Affective Map were extracted in order to identify the relationship between these two aspects. As a result, 56 Socio-affective Scenarios were created using Learning Analytics to contribute to the analysis of students' learning profiles, allowing professors to develop pedagogical strategies consistent with the needs of each individual.


Virtual learning environment, Social map, Affective map, Learning analytics

Research areas

Education, Computer and Information Science


  1. Akazaki, J. M., Machado, L. R., Behar, P. A. Learning Analytics to Identify the Socio-affective Scenarios in a Virtual Learning Environment. In: Smart Education and e-Learning-Smart Pedagogy. Singapore: Springer Nature Singapore, 2022. p. 199-208. https://doi.org/10.1007/978-981-19-3112-3_19.
  2. Barvinski, C., Ferreira, G., Machado, L., Longhi, M., Behar, P. Construction of a socio-affective profile model of students in a virtual learning environment. In: Uskov, V.L., Howlett, R.J., Jain, L.C. (eds.) Smart Education and e-Learning 2019. SIST, vol. 144, pp. 159–168. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-8260-4_15
  3. Baker, R.S., Inventado, P.S. Educational data mining and learning analytics. In: Larusson, J.A., White, B. (eds.) learning analytics, pp. 61–75. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-3305-7_4
  4. Behar, P.A. Pedagogical Recommendation in Distance Education, 1st edn. Penso Editora, Porto Alegre (2019).
  5. Longhi, M.T. Mapping of affective aspects in a virtual learning environment. Doctoral Thesis (Doctorate in Informatics in Education), Interdisciplinary Center for Informatics in Education, 273p. Federal University of Rio Grande do Sul, Porto Alegre (2011).
  6. Siemens, G., Baker, R.S. de. Learning analytics and educational data mining: towards commu-nication and collaboration. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 2012), ACM Proceedings, pp. 252–254 (2012).
  7. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H. A reference model for learning analytics. Int. J. Technol. Enhanc. Learn. 4(5–6), 318–331 (2012).
  8. Yin, R.Y. Case Study: Planning and Methods. Bookman Publishing Company, Rio Grande do Sul (2015).


  1. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (No. Finance Code 001.)

Supplemental files

  1. 56 Socio-affective Scenarios   Download

Additional information

Competing interests
No competing interests were disclosed.
Data availability statement
Data sharing not applicable to this poster as no datasets were generated or analyzed during the current study.
Creative Commons license
Copyright © 2023 Akazaki et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Akazaki, J., Rocha Machado, L., Alejandra Behar, P. Socio-affective scenarios using learning analytics [not peer reviewed]. Peeref 2023 (poster).
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