Data Visualization for Learning Analytics Indicators in Programming Teaching (Short Paper)

Authors Ranieri Alves dos Santos , Dalner Barbi , Vinicius Faria Culmant Ramos , Fernando Alvaro Ostuni Gauthier



PDF
Thumbnail PDF

File

OASIcs.ICPEC.2023.10.pdf
  • Filesize: 0.66 MB
  • 7 pages

Document Identifiers

Author Details

Ranieri Alves dos Santos
  • Graduate Program in Knowledge Engineering and Management, UFSC, Florianópolis, Brazil
Dalner Barbi
  • Graduate Program in Knowledge Engineering and Management, UFSC, Florianópolis, Brazil
Vinicius Faria Culmant Ramos
  • Graduate Program in Knowledge Engineering and Management, UFSC, Florianópolis, Brazil
Fernando Alvaro Ostuni Gauthier
  • Graduate Program in Knowledge Engineering and Management, UFSC, Florianópolis, Brazil

Cite AsGet BibTex

Ranieri Alves dos Santos, Dalner Barbi, Vinicius Faria Culmant Ramos, and Fernando Alvaro Ostuni Gauthier. Data Visualization for Learning Analytics Indicators in Programming Teaching (Short Paper). In 4th International Computer Programming Education Conference (ICPEC 2023). Open Access Series in Informatics (OASIcs), Volume 112, pp. 10:1-10:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.ICPEC.2023.10

Abstract

Learning Analytics (LA) has the potential to transform the way we learn, work and live our lives. To reach its potential, it must be clearly defined, incorporated into institutional teaching-learning strategies and processes and practices. The main goal of this study is to list indicators to be used in learning analytics in programming teaching and how to expose their views. For the development of the indicator model, this study based on a qualitative analysis, using data visualization and business intelligence tools, in projects focused on Learning Analytics. As a result, four main indicators were mapped: accesses to the system, resources accessed, activities carried out and, performance in activities.

Subject Classification

ACM Subject Classification
  • Information systems → Data analytics
Keywords
  • learning analytics
  • data visualization
  • learning indicators

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Liaqat Ali, Marek Hatala, Dragan Gasevic, and Jelena Jovanovic. A qualitative evaluation of evolution of a learning analytics tool. Comput. Educ., 58(1):470-489, 2012. URL: https://doi.org/10.1016/j.compedu.2011.08.030.
  2. Jaqueline Vasconcelos Braga, Tiago Barros Pontes, Virginia Tiradentes Souto, et al. Statistical manipulations and visual anomalies: data visualization design and statistical bias recognition/manipulacoes estatisticas e anomalias visuais: design de visualizacao de dados e reconhecimento de vieses estatisticos. Brazilian Journal of Information Design, 17(2):145-163, 2020. Google Scholar
  3. Alberto Cairo. The truthful art: Data, charts, and maps for communication. New Riders, 2016. Google Scholar
  4. Doug Clow. An overview of learning analytics. Teaching in Higher Education, 18(6):683-695, 2013. Google Scholar
  5. Abdullah Essa and Hany Ayad. Student success system: risk analytics and data visualization using ensembles of predictive models. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pages 158-161. ACM, April 2012. Google Scholar
  6. HU Hoppe. Computational methods for the analysis of learning and knowledge building communities. In Handbook of Learning Analytics, chapter 2. Society for Learning Analytics Research, 2017. Google Scholar
  7. Nur Muizzatul Shafiqah Khuzairi, Zainal Choy Cob, and Tuan Hilaluddin. Towards understanding the synergetic relationship of data visualization with learning analytics: A review. In AIP Conference Proceedings, volume 2644(1), page 030030. AIP Publishing LLC, November 2022. Google Scholar
  8. Simon Knight and Simon Buckingham Shum. Theory and learning analytics. In Handbook of Learning Analytics, pages 17-22. Society for Learning Analytics Research, 2017. Google Scholar
  9. Vitomir Kovanović, Srećko Joksimović, Dragan Gašević, Marek Hatala, and George Siemens. Content analytics: The definition, scope, and an overview of published research. In Handbook of Learning Analytics and Educational Data Mining, pages 77-92. Society for Learning Analytics Research, 2017. Google Scholar
  10. Lucas A B Macarini, Cristian Cechinel, Marilde F Batista Machado, Vanessa Faria Culmant Ramos, and Rodrigo Munoz. Predicting students success in blended learning—evaluating different interactions inside learning management systems. Applied Sciences, 9(24):5523, 2019. Google Scholar
  11. Katerina Mangaroska and Michail Giannakos. Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4):516-534, 2018. Google Scholar
  12. Basilio Maraza-Quispe, Omar Marcelo Alejandro-Oviedo, Wilson Choquehuanca-Quispe, Nilton Caytuiro-Silva, and Juan Herrera-Quispe. Towards a standardization of learning behavior indicators in virtual environments. International Journal of Advanced Computer Science and Applications, 11(11), 2020. Google Scholar
  13. Isabel Meirelles. Design for information: an introduction to the histories, theories, and best practices behind effective information visualizations. Rockport publishers, 2013. Google Scholar
  14. Rodrigo Paiva, II Bittencourt, Wagner Lemos, Andrade Vinicius, and Diego Dermeval. Visualizing learning analytics and educational data mining outputs. In International Conference on Artificial Intelligence in Education, pages 251-256. Springer, June 2018. Google Scholar
  15. Rob Phillips, Dorit Maor, Greg Preston, and Wendi Cumming-Potvin. Exploring learning analytics as indicators of study behavior. In EdMedia+ Innovate Learning, pages 2861-2867. Association for the Advancement of Computing in Education (AACE), 2012. Google Scholar
  16. Maren Scheffel, Hendrik Drachsler, Slavi Stoyanov, and Marcus Specht. Quality indicators for learning analytics. Journal of Educational Technology and Society, 17(4):117-132, 2014. Google Scholar
  17. George Siemens. Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10):1380-1400, 2013. Google Scholar
  18. Felipe Cezar Cardoso Da Silva. Data visualization: past, present and future. LIINC em Revista, 15(2):205-223, 2019. Google Scholar
  19. Dongmin Song. Learning analytics as an educational research approach. International Journal of Multiple Research Approaches, 10(1):102-111, 2018. Google Scholar
  20. Vladimir L Uskov, Jon P Bakken, Karthik S Ganapathi, Kevin Gayke, Blake Galloway, and Johra Fatima. Data cleaning and data visualization systems for learning analytics. In Smart Education and e-Learning 2020, pages 183-197. Springer, 2020. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail