Main Article Content

Abstract

The contribution describes and problematizes the use of learning analytics within a blended university course based on a socio-constructivist approach and aimed at constructing artefacts and knowledge. Specifically, the authors focus on the evaluation system adopted in the course, deliberately inspired by the principles of formative assessment: an ongoing evaluation in the form of feedback shared with the students, and which integrates the teacher's evaluation with self-evaluation and peer-evaluation. This system obviously requires the integration of qualitative procedures - from teachers and tutors - and quantitative - managed through the reporting functions of the LMS and online tools used for the course. The contribution ends with a reflection on the possibilities of technological development of learning analytics within the learning environment, such as to better support constructivist teaching: Learning Analytics that comes closest to social LA techniques providing the teacher with a richer picture of the student's behaviour and learning processes.

Keywords

Learning Analytics Blended Learning Moodle Formative Assessment

Article Details

How to Cite
Sansone, N., & Cesareni, D. (2019). Which Learning Analytics for a socio-constructivist teaching and learning blended experience?. Journal of E-Learning and Knowledge Society, 15(3), 319-329. https://doi.org/10.20368/1971-8829/1135047

References

  1. Brown G. T. L., & Harris L. R. (2013), Student self-assessment, in J. H. McMillan (ed.) The SAGE handbook of research on classroom assessment. 367-393, Thousand Oaks, CA, Sage.
  2. Cesareni D., Cacciamani S., Fujita N. (2016), Role taking and knowledge building in a blended university course, International Journal of Computer Supported Collaborative Learning. 11 (1), 9-39 DOI 10.1007 / s11412-015-9224-0.
  3. Cesareni D., Ligorio M.B., Sansone N. (2019), Fare e collaborare. L’approccio trialogico nella didattica, Milano, Franco Angeli.
  4. De Liddo A., Buckingham Shum S., Quinto I., Bachler M., & Cannavacciuolo L. (2011), Discourse-centric learning analytics. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, (pp. 23-33). Banff, Alberta, Canada
  5. Dochy F., & McDowell L. (1997), Assessment as a tool for learning. Studies in Educational Evaluation, 23, 279–298.
  6. Ferguson R., & Buckingham Shum S. (2011), Learning analytics to identify exploratory dialogue within synchronous text chat. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, (pp. 99- 103). Banff, Alberta, Canada.
  7. Ferguson R., & Buckingham Shum S. (2012), Social learning analytics: five approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, (pp. 23- 33). Vancouver, British Columbia, Canada.
  8. Gielen S., Dochy F., & Dierick S. (2003), Evaluating the consequential validity of new modes of assessment: The influence of assessment on learning, including pre-, post-, and true assessment effects, in: M. S. R. Segers, F. Dochy, & E. Cascallar (eds.), Optimising new modes of assessment: In search of qualities and standards. 35-54, Dordrecht/Boston, Kluwer Academic Publishers.
  9. Landauer T., Foltz P., & Laham D. (1998), An introduction to latent semantic analysis. Discourse Process, 25(2-3), 259-284.
  10. Ligorio M.B., & Sansone N. (2016), Manuale di didattica blended: il modello della Partecipazione Collaborativa e Costruttiva (PCC), Milano, Franco Angeli.
  11. Mercer N. (2000), Words and Minds: How We Use Language To Think Together, London, Routledge.
  12. Mercer N., & Wegerif R. (1999), Is “exploratory talk” productive talk?, Learning with computers: analysing productive interaction, New York, Routledge.
  13. Paavola S., Lakkala M., Muukkonen H., Kosone K. & Karlgren K. (2011), The roles and uses of design principles for developing the trialogical approach on learning, Research in Learning Technology, 19 (3), 233-246.
  14. Sambell K., McDowell L., & Brown S. (1997), ‘But is it fair?’ an exploratory study of student perceptions of the consequential validity of assessment Studies”, Educational Evaluation, 23, 349-371.
  15. Scardamalia M., & Bereiter C. (2006), Knowledge building: Theory, pedagogy, and technology, in: K. Sawyer (ed.), Cambridge handbook of the learning sciences. 97-118, New York, Cambridge University Press.
  16. Siemens G. (2010, August 25), What Are Learning Analytics?. [web log post] Elearnspace.org., URL: http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics.
  17. Siemens, G. (2012). Learning Analytics: Envisioning a Research Discipline and a Domain of Practice. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 2012)(pp. 04-08).New York, USA: ACM.
  18. (13) (PDF) Learning Analytics: Principles and Constraints. Available from: https://www.researchgate.net/publication/278940599_Learning_Analytics_Principles_and_Constraints [accessed Jul 30 2019].
  19. Thomason N., & Rider Y. (2008), Cognitive and pedagogical benefits of argument mapping: L.A.M.P. guides the way to better thinking, in A. Okada, S. Buckingham Shum, & T. Sherborne (eds), Knowledge Cartography: Software Tools and Mapping Techniques. 113-130), London, Springer.
  20. Zimmerman B.J. (2001), Theories of self-regulated learning and academic achievement: an overview and analysis, in: B. J. Zimmerman & D. H. Schunk (eds.), Self-regulated learning and academic achievement. 1-37 Mahwah, NJ, Lawrence Erlbaum Associates.