Encourage reflective and self-assessment processes through the automatic processing of personalized feedback

Titolo Rivista EDUCATION SCIENCES AND SOCIETY
Autori/Curatori Antonio Marzano
Anno di pubblicazione 2023 Fascicolo 2023/1
Lingua Inglese Numero pagine 11 P. 287-297 Dimensione file 0 KB
DOI 10.3280/ess1-2023oa15264
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Vedi l'articolo Scarica l'articolo open access

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

Technologies in training processes by radically modifying the relationship with (and between) knowledge, have determined the need to experiment with new methodological approaches to innovate didactic action, respond to subjective training needs, satisfy the ever increasing requests coming from the job market. In this paper, we want to deepen a particular action of this process, preliminary to the implementation phase of each training intervention. We refer to the needs analysis (NA) aimed at identifying training needs and requirements of the participants with respect to which to organize and modulate the contents and the didactic action. In the opinion of the author, already the NA, if accompanied by specific actions, can constitute an intentionally structured moment to enhance the effectiveness of training feedback in a diagnostic and self-assessment function and in this work we will describe an automated system designed and developed specifically for this purpose. To examine the application opportunities and to show the potential of the automated system, an experience will be presented that involved students attending the specialization course for educational support activities for pupils with disabilities held at the University of Salerno in the A.Y. 2022/2023.

Technologies in training processes by radically modifying the relationship with (and between) knowledge, have determined the need to experiment with new methodological approaches to innovate didactic action, respond to subjective training needs, satisfy the ever increasing requests coming from the job market. In this paper, we want to deepen a particular action of this process, preliminary to the implementation phase of each training intervention. We refer to the needs analysis (NA) aimed at identifying training needs and requirements of the participants with respect to which to organize and modulate the contents and the didactic action. In the opinion of the author, already the NA, if accompanied by specific actions, can constitute an intentionally structured moment to enhance the effectiveness of training feedback in a diagnostic and self-assessment function and in this work we will describe an automated system designed and developed specifically for this purpose. To examine the application opportunities and to show the potential of the automated system, an experience will be presented that involved students attending the specialization course for educational support activities for pupils with disabilities held at the University of Salerno in the A.Y. 2022/2023.

Keywords:; Teacher training; Training Needs Analysis; Customized feedback; Didactic expertise; Diagnostic evaluation

  1. Boud D., Soler R. (2016). Sustainable assessment revisited. Assessment & Evaluation in Higher Education, 41(3): 400-413. DOI: 10.1080/02602938.2015.1018133.
  2. Bransford J.D., Brown A.L. and Cocking R.R. (1999). How people learn: Brain, mind, experience, and school. Washington: National Academy Press.
  3. Andrade H L., Brookhart S.M. (2020). Classroom assessment as the co-regulation of learning. Assessment in Education: Principles, Policy & Practice, 27(4): 350-372. DOI: 10.1080/0969594X.2019.1571992.
  4. Bell, M. (2020). The Fundamentals of Teaching, A Five-Step Model to Put the Research Evidence into Practice. London: Routledge.
  5. Brown G., Harris L. (2018). Methods in Feedback Research. In: Lipnevich A. and Smith J. (Eds.), The Cambridge Handbook of Instructional Feedback. Cambridge: Cambridge University Press. DOI: 10.1017/9781316832134.007.
  6. Calvani A. (2011). Principi dell'istruzione e strategie per insegnare. Criteri per una didattica efficace. Roma: Carocci.
  7. Calvani A. (2014). Come fare una lezione efficace. Roma: Carocci.
  8. Calvani A., Trinchero R. (2019). Dieci falsi miti e dieci regole per insegnare bene. Roma: Carocci.
  9. Calvani A., Marzano A. and Miranda S. (2021). Training of teachers in effective teaching. How to orient the observation and the change of practices in the classroom? Formazione & Insegnamento, 19(1): 599-621. DOI: 10.7346/-fei-XIX-01-21_53.
  10. Calvani A., Marzano A. and Morganti, A. (2021). La didattica in classe. Casi, problemi e soluzioni. Roma: Carocci.
  11. Crandall B., Klein G. and Hoffman, R. (2006). Working minds: A practitioner’s guide to cognitive task analysis. Cambridge: The MIT Press. DOI: 10.7551/mitpress/7304.001.0001.
  12. Earl L. (2012). Assessment as Learning: Using Classroom Assessment to Maximize Student Learning. Thousand Oaks: Corwin.
  13. Gagné R. (1995). The conditions of learning (4th ed.). New York: Holt, Rinehart & Winston.
  14. Gagné M., Briggs L.J. (1974). The principles of instructional design. New York Hold. (trad. it.: Fondamenti di progettazione didattica. Torino: SEI, 1990).
  15. Grion V., Serbati A. (2019). Valutazione sostenibile e feedback nei contesti universitari. Prospettive emergenti, ricerche e pratiche. Lecce: Pensa Multimedia.
  16. Hattie J. (2009). Visible Learning. A synthesis of over 800 meta-analyses relating to achievement. London & New York: Routledge.
  17. Hattie J. (2012). Visible learning for teachers: Maximizing impact on learning. New York: Routledge.
  18. Hattie J., Timperley H. (2007). The power of feedback. Review of Educational Research, 77(1): 81-112. DOI: 110.3102/003465430298487.
  19. Hattie J., Donoghue G.M. (2016). Learning strategies: a synthesis and conceptual model. Science of Learning, 1, 16013. DOI: 10.1038/npjscilearn.2016.13.
  20. Lipnevich A.A., Smith J.K. (2009). Effects of differential feedback on students’ examination performance. Journal of Experimental Psychology: Applied, 15(4): 319-333. DOI: 10.1037/a0017841.
  21. Marzano A. (2022). The customization of feedback to support teachers training: the COFACTOR system. Italian Journal of Educational Research, 28: 48-60. DOI: 10.7346/sird-012022-p48.
  22. Marzano A., Calvani A. (2020). Evidence based education and effective teaching: How to integrate methodological and technological knowledge into teacher training. Journal of Educational, Cultural and Psychological Studies, 22: 125-141. DOI: 10.7358/ecps-2020-022-maca.
  23. Mayer R.E. (2005). Cognitive Theory of Multimedia Learning. In: Mayer R.E., editor, The Cambridge handbook of multimedia learning. Cambridge: University Press.
  24. Menichetti L., Pellegrini M. and Gola G. (2019). Mind frames and misconceptions in teacher’e training. Formazione & Insegnamento, 17(1): 351-74. DOI: 10.7346/-fei-XVII-01-19_29.
  25. Merrill M.D. (2002). First principles of instruction. ETR&D, 50: 43-59. DOI: 10.1007/BF02505024.
  26. Miranda S. (2022). Orienting the attitudes of future teachers towards effective interventions: restructuring misconceptions and naïve didactic points of view. Journal of Educational, Cultural and Psychological Studies, 25: 141-160. DOI: 10.7358/ecps-2022-025-mira.
  27. Pereira D., Flores M.A. and Niklasson L. (2016). Assessment revisited: a review of research in assessment and evaluation in higher education. Assessment & Evaluation in Higher Education, 41(7): 1008-1032. DOI: 10.1080/02602938.2015.1055233.
  28. Reigeluth C.M. (1999). What is Instructional Design Theory and How Is it Changing? In: Reigeluth C.M., editor, Instructional-design theories and models: A new paradigm of instructional theory, Vol. 2. Mahwah: Lawrence Erlbaum Associates Publishers.
  29. Rosenshine B. (2012). Principles of instruction: Research based principles that all teachers should know. American Educator, Spring: 12-39. http://www.aft.org/pdfs/americaneducator/spring2012/Rosenshine.pdf.
  30. Shute V.J. (2008). Focus on formative feedback. Review of educational research, 78(1): 153-189. DOI: 10.3102/0034654307313795.
  31. Vegliante R., Marzano A. and Miranda S. (2022). Evidence-informed didactic mediation: from training needs to effective planning. MEDIA EDUCATION, 13(2): 141-150. DOI: 10.36253/me-13270.

  • Design and Use of Deep Confidence Network Based on Crayfish Optimization Algorithm in Automatic Assessment Method of Hearing Effectiveness Ying Cheng, in ICST Transactions on Scalable Information Systems /2024
    DOI: 10.4108/eetsis.4847

Antonio Marzano, Encourage reflective and self-assessment processes through the automatic processing of personalized feedback in "EDUCATION SCIENCES AND SOCIETY" 1/2023, pp 287-297, DOI: 10.3280/ess1-2023oa15264