Skip to main navigation menu Skip to main content Skip to site footer

Giovani, studenti e infiniti mondi

Vol. 10 No. 2 (2019): Giovani, studenti e infiniti mondi

Learning Analytics as a tool in academic learning contexts: Possible impacts on social inclusion

Submitted
novembre 14, 2019
Published
2019-12-15

Abstract

In the field of teaching and learning processes, the potential of Learning Analytics is one of the topics that is attracting most interest in the scientific community. However, it would be important to place L.A. within a historical perspective, able to focus on the scientific, cultural and social roots of this approach. This would also allow us to address a question that cannot be overlooked, namely whether Learning Analytics is one of the teaching technologies or, rather, should be understood as a new global approach to learning processes. In our opinion, L.A. are placed at the crossroads between the formal and informal dimensions of learning and are part of the behaviorist tradition, with the aim of identifying the behavioural clusters that recur most frequently and which are considered to adhere to predefined performance standards. The search for the performative standard typical of L.A., not considering the differences, the peculiarities and the specific personal abilities as of the resources, seems, moreover, to refer to the system/model of the integration that, in a homologating perspective more than inclusive, sets objectives on the basis of a presumed normality, ignoring Specific Learning Disorder (SLD) and Special Educational Needs and Disability (SEND). 

 

References

  1. Bocci F., De Angelis B., Fregola C., Olmetti Peja D., Zona U. (2016). Rizodidattica. Teorie dell’apprendimento e modelli didattici inclusivi. Lecce: Pensa Multimedia, p. 22.
  2. Borgatti S. P., Everett M. G., Johnson J. C. (2017). Analyzing Social Network. Londra: Sage Publications LTD.
  3. Buckingham Shum S. (2012). Learning analitycs. UNESCO policy brief. Retrieved from https://iite.unesco.org/pics/publications/en/files/3214711.pdf.
  4. Cardon D. (2016). Che cosa sognano gli algoritmi. Le nostre vite al tempo dei big data. Milano: Mondadori.
  5. de Waal P. (2017). Learning Analytics: i sistemi dinamici di supporto alla decisione per il miglioramento continuo dei processi di insegnamento e apprendimento. Formazione & Insegnamento, XV-2, pp. 43-51. Retrieved from https://ojs.pensamultimedia.it/index.php/siref/article/view/2336/2096.
  6. Ferguson R. (2014). Learning analytics: fattori trainanti, sviluppi e sfide. TD Tecnologie Didattiche, 22(3), pp. 138-147.
  7. Ferguson R., Buckingham S. (2012). Social learning analytics: five approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 23- 33, Vancouver, British Columbia.
  8. Galliani L. (2012). Apprendere con le tecnologie nei contesti formali, non formali e informali. In: Limone P., Media, tecnologie e scuola: per una nuova Cittadinanza Digitale. Bari: Progedit.
  9. Harrak F., Bouchet F., Luengo V. (2019). From Students’ Questions to Students’ Profiles in a Blended Learning Environment. Journal of Learning Analytics, 6 (1), pp. 54-84. Retrieved from https://learning-analytics.info/journals/index.php/JLA/article/view/6136/7146.
  10. Mattox II J. R., Van Buren M. (2016). Learning Analytics: Measurement Innovations to Support Employee Development. Londra: Kogan Page.
  11. Noble S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press.
  12. O’Neil C. (2017). Armi di distruzione matematica. Come i big data aumentano la disuguaglianza e minacciano la democrazia. Milano: Bompiani.
  13. Raca M. (2015). Camera-based estimation of student's attention in class. Doctoral thesis. Retrieved from https://infoscience.epfl.ch/record/212929/files/EPFL_TH6745.pdf.
  14. Scott J. (2017). Social Network Analysis. Londra: Sage Publications LTD.
  15. Sharma K; Caballero D; Verma H; Jermann P; Dillenbourg P. (2015). Looking AT versus Looking THROUGH: A Dual Eye-tracking study in MOOC Context. Proceedings of 11th International Conference of Computer Supported Collaborative Learning, 1. Retrieved from https://infoscience.epfl.ch/record/203805/files/CSCL15-sharma.pdf.
  16. Sharma, K., Jermann, P., & Dillenbourg, P. (2014 a). “With-me-ness”: A gaze-measure for students’ attention in MOOCs. International Conference of Learning Sciences (ICLS 2014). Retrieved from https://infoscience.epfl.ch/record/201918/files/ICLS-2014-camera-ready.pdf.
  17. Sharma, K., Jermann, P., & Dillenbourg, P. (2014 b). How Students Learn using MOOCs: An Eye-tracking Insight. EMOOCs 2014, the Second MOOC European Stakeholders Summit. Retrieved from https://infoscience.epfl.ch/record/201916/files/EMOOCS_Sharma-FinalVersion.pdf.
  18. Skinner, B. F. (1968). The technology of Teaching. Des Moines: Meredith Corporation, pp. 29-30.
  19. Wachter-Boettcher S. (2017). Technically wrong. Sexist apps, biased algorithms, and other threats of toxic tech. New York: W.W. Norton & Company.
  20. Yang S., Keller F. B., Zheng L. (2017). Social Network Analysis: Methods and Examples. Londra: Sage Publications LTD.

Metrics

Metrics Loading ...