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FrancoAngeli Journals

Articles/Articoli

Vol. 14 No. 1 (2023): University didactics, innovation and inclusion. Assessment and feedback

Feedback to align teacher and student in a Digital Learning Ecosystem

DOI
https://doi.org/10.3280/ess1-2023oa15761
Submitted
aprile 18, 2023
Published
2023-07-21

Abstract

In this paper, we present an example of a Digital Learning Ecosystem, set up during the first period of the pandemic emergency and then remodelled and re-proposed for hybrid didactics provided afterwards, involving five pedagogical-didactic courses of two universities in central Italy.

The central device in this Ecosystem was recursive feedback, which in contexts of didactics mediated by screens can anyhow activate discursive, adaptive, interactive and reflexive dynamics.

In order to understand if these aims were pursued, we administered an open-ended questionnaire to 274 students, which was not intended to measure their enjoyment of the method and the environment, but their perceptions regarding the effectiveness of the system on their learning processes, not only at a cognitive level, but also on at an interpersonal and intrapersonal level.

The analysis was conducted according to the Structural Topic Model, which allowed us to re-read the responses as a unique corpus of reflective writings, generated by the students after the input provided by the assigned task.

References

  1. Bischof J.M., Airoldi E.M. (2012). Summarizing topical content with word frequency in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK.
  2. Blei D.M., Lafferty J. D. (2006). Dynamic topic models, in Proceedings of the 23rd international conference on Machine learning, Pittsburgh, PA, USA, pp. 113-120.
  3. Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. J Mach Learn Res, 3: 993-1022.
  4. Bonanno A., Bozzo G., and Sapia P. (2019). Innovazione didattica nell’insegnamento della Fisica per Scienze Biologiche Didactical innovation for teaching Introductory Physics for Life Sciences. Giornale di Fisica, 60(1): 43-69.
  5. Carless D. (2019). Feedback loops and the longer-term: towards feedback spirals. Assessment & Evaluation in Higher Education, 44(5): 705-714.
  6. Carrillo C., Flores M.A. (2020). COVID-19 and teacher education: a literature review of online teaching and learning practices. European Journal of Teacher Education, 43: 466-487.
  7. Cresswell J.W. (2015). A concise introduction to Mixed Methods Research. Los Angeles: Sage.
  8. Fedeli L. (2016). Virtual body: Implications for identity, interaction and didactics. In S. Gregory, M.J.W. Lee, B. Dalgarno, and B. Tynan (eds.). Learning in Virtual Worlds. Research and Applications (pp. 67-85). Edmonton, AB: Au Press.
  9. Fishman B.J., Dede C. (2016). Teaching and Technology: New Tools for New Times. In D.H. Gitomer, C.A. Bell (Eds), Handbook of Research on Teaching (pp. 1269-1334). Washington, DC: American Educational Research Association.
  10. Garavaglia A. (2006). Ambienti di apprendimento in rete: gli spazi dell’e-learning. Azzano: San Paolo Junior.
  11. Grimmer J., Stewart B.M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3): 267-297. DOI: 10.1093/pan/mps028.
  12. Guitierrez K.D. (2008). Developing a Sociocritical Literacy in the Third Space. Reading Research Quarterly, 43(2): 148-164.
  13. Gütl C., Chang V. (2008). Ecosystem-based theoretical models for learning in environments of the 21st century. International Journal of Emerging Technologies in Learning (iJET), 3: 50-60. DOI: 10.3991/ijet.v3s3.742.
  14. Jeladze E., Pata K., and Quaicoe J.S. (2017). Factors Determining Digital Learning Ecosystem Smartness in Schools. Interactive Design Architecture(s) Journal, 35: 32-55.
  15. Jenkins H. (2006). Convergence Culture. Where Old and New Media Collide. New York: University Press. DOI: 10.18574/9780814743683.
  16. Kenneth B., Watanabe K., Wang H., Nulty P., Obeng A., Müller S., Matsuo A. (2018). Quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software. 3(30): 774. DOI: 10.21105/joss.00774.
  17. Krämer B.J. (2007). A Service Component Architecture to Federate E-Universities: A Case Study in Virtual Mobility. In B.J. Krämer, W.A. Halang (eds.), Contributions to Ubiquitous Computing (pp. 95-119). Berlin: Springer.
  18. Kuhn K.D. (2018). Using structural topic modelling to identify latent topics and trends in aviation incident reports. Transportation Research Part C: Emerging Technologies, 87(February): 105-122. DOI: 10.1016/j.trc.2017.12.018.
  19. Lafferty J.D., Blei D.M. (2006). Correlated Topic Models. In: Y. Weiss, B. Schölkopf, and J. C. Platt (eds.). Advances in Neural Information Processing Systems 18. MIT Press, pp. 147-154.
  20. Laici C. (2021). Il feedback come pratica trasformativa nella didattica universitaria. Milano: FrancoAngeli.
  21. Laurillard D. (2006). E-learning in higher education. In P. Ashwin (ed.), Changing higher education: The development of learning and teaching (pp. 71-84). London: Routledge.
  22. Laurillard D. (2012). Teaching as Design Science. London: Routledge.
  23. Li W., McCallum A. (2006). Pachinko allocation: DAG-structured mixture models of topic correlations, in Proceedings of the 23rd international conference on Machine learning, Pittsburgh, PA, USA, 2006, pp. 577-584.
  24. Liu L., Tang L., Dong W., Yao S. , Zhou W. (2016). An overview of topic modeling and its current applications in bioinformatics, Springerplus, 5(1).
  25. Maturana H.R., Varela F. J. (1980). Autopoiesis and cognition: The realization of the living. London: Springer.
  26. Mimno D., Wallach H. M., Talley E., Leenders M., and McCallum A. (2011). Optimizing semantic coherence in topic models. Paper presented at the Conference on empirical methods in natural language processing, Edinburgh.
  27. Nicol D. (2018). Unlocking generative feedback through peer reviewing. In V. Grion, A. Serbati (eds.), Assessment of learning or assessment for learning? Towards a culture of sustainable assessment in higher education (pp. 47-59). Lecce: Pensa Multimedia.
  28. Pentucci M., Laici C. (2020). An integrated blended learning ecosystem for the development of the design skills of teachers-to-be. In L. Gómez Chova, A. López Martínez, I. Candel Torres (eds.), ICERI2020 Proceedings (pp. 2145-2154). Valencia: IATED Academy.
  29. Pereira S.P., Fernandes R.L., and Flores M.A. (2021). Teacher Education during the COVID-19 Lockdown: Insights from a Formative Intervention Approach Involving Online Feedback. Education Sciences, 11(400): 1-14.
  30. Pietsch A.-S., Lessmann S. (2018). Topic modeling for analyzing open-ended survey responses. Journal of Business Analytics, 1(2): 93-116. DOI: 10.1080/2573234X.2019.1590131.
  31. R Core Team (2021). R: A language and environment for statistical computing. https://www.R-project.org/.
  32. Rapanta C., Botturi L., Goodyear P. (2020). Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity. Postdigit Sci Educ, 2: 923-945. DOI: 10.1007/s42438-020-00155-y.
  33. Rivoltella P.C. (2021) Apprendere a distanza. Teorie e metodi. Milano: Raffaello Cortina Editore.
  34. Rivoltella P.C., Rossi P.G. (2019). Il corpo e la macchina. Tecnologia, cultura, educazione. Brescia: Scholè.
  35. Roberts M.E., Stewart B.M., and Tingley D. (2019). stm: An R Package for Structural Topic Models. Journal of Statistical Software, 91(2). DOI: 10.18637/jss.v091.i02.
  36. Roberts M.E., Stewart B.M., Tingley D., and Airoldi E.M. (2013). The Structural Topic Model and Applied Social Science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation, Cambridge, MA.
  37. Roberts M.E., Stewart B.M., Tingley D., Lucas C., Leder-Luis J., Gadarian S.K., Rand D.G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58: 1064-1082.
  38. Rossi P.G. (2016). How Digital Artifacts Affect Didactical Mediation. Pedagogia Oggi, 2: 11-26.
  39. Rossi P.G. (2017). Alignment. Education Sciences and Society, 7(2): 33-45.
  40. Rossi P.G., Pentucci M. (2021). Progettazione come azione simulata. Didattica dei processi e degli eco-sistemi. Milano: FrancoAngeli.
  41. Rossi P.G., Pentucci M., Fedeli L., Giannandrea L., and Pennazio V. (2018). From the informative feedback to the generative feedback. Education Sciences & Society, 9(2): 83-107.
  42. Sadler R. (2010). Beyond feedback: developing student capability in complex appraisal, Assessment & Evaluation in Higher Education, 35(5): 535-550.
  43. Salton G., Wong A., Yang C.S. (1975). A vector space model for automatic indexing. Commun. ACM, 18(11): 613-620. DOI: 10.1145/361219.361220.
  44. Schuman H. (1966). The random probe: A technique for evaluating the validity of closed questions. American Sociological Review, 31: 218-222.
  45. Taddy M (2013). Multinomial Inverse Regression for Text Analysis. Journal of the American Statistical Association, 108(503): 755-770.
  46. Väljataga T., Poom-Valickis K., Rumma K., and Aus K. (2020). Transforming higher education learning ecosystem: teachers’ perspective. Interactive Design Architecture(s) Journal, 46: 47-69.
  47. Varisco A.M. (2002). Costruttivismo socio-culturale. Genesi filosofiche, sviluppi psico-pedagogici, applicazioni didattiche. Roma: Carocci.
  48. Weller M., Jordan K., DeVries I., and Rolfe V. (2018). Mapping the open education landscape: citation network analysis of historical open and distance education research. Open Praxis, 10(2): 109-126.
  49. Winstone N., Carless D. (2019). Designing Effective Feedback Processes in Higher Education. A Learning-Focused Approach. London: Routledge.
  50. Winstone N.E., Nash R.A., Rowntree J., and Menezes R. (2016). What do students want most from written feedback information? Distinguishing necessities from luxuries using a budgeting methodology. Assessment & Evaluation in Higher Education, 41(8): 1237-1253.
  51. Yan X., Guo J., Lan Y., and Cheng X. (2013). A Biterm Topic Model for Short Texts. In Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, Brazil, pp. 1445-1455.

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