Clicca qui per scaricare

Metodi e tecniche di trattamento automatico della lingua per l’estrazione di conoscenza dalla documentazione scolastica
Titolo Rivista: CADMO 
Autori/Curatori: Giulia Venturi, Felice Dell’Orletta, Simonetta Montemagni, Elettra Morini, Maria Teresa Sagri 
Anno di pubblicazione:  2020 Fascicolo: 2  Lingua: Italiano 
Numero pagine:  20 P. 49-68 Dimensione file:  301 KB
DOI:  10.3280/CAD2020-002005
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più:  clicca qui   qui 

In its daily activities, the school produces large amounts of textual data in response to different needs, ranging from planning activities, to supporting internal and external communication and self-assessment. These data represent a vast and varied information asset which needs to be profitably analysed to monitor and study ongoing phenomena in the field of education. To profile the contents conveyed by this continuously growing documenta¬tion, we propose advanced methods and techniques for knowledge extraction based on language technologies. The paper illustrates the first and promis¬ing results of the proposed methodology for monitoring educational strategies through time, space and different types of schools, starting from free texts. The methodology has been tested within two scenarios focusing on i) the analysis of the strategic choices and actions implemented by schools to achieve improvement and innovation objectives, and ii) the monitoring of the technical-professional and soft skills developed in School-Work Alternation experiences. Although preliminary, achieved results show that Natural Language Processing enabled methods and techniques can lead to effective and exhaustive school profiling.
Keywords: School system monitoring, educational data mining, big data, natural language processing, knowledge extraction.

  1. Bonin, F., Dell’Orletta, F., Montemagni, S., Venturi, G. (2010), A Contrastive Approach to Multi-word Extraction from Domain-specific Corpora. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, 17-23 May, pp. 3222-3229.
  2. Cantini, C., Chellini, C., Sagri, M.T. (2016), “Big Data Analytics National Educational System Monitoring and Decision Making”, World Journal of Social Science Research, 3 (2), pp. 219-242.
  3. Dell’Orletta, F., Greco, S., Montemagni, S., Morini, E., Sagri, M.T., Venturi, G. (2019), “Le parole del miglioramento. Come le scuole descrivono il cambiamento”, Psicologia dell’Educazione, 1, pp. 47-68,
  4. Dell’Orletta, F., Venturi, G., Cimino, A., Montemagni, S. (2014), T2K²: a System for Automatically Extracting and Organizing Knowledge from Texts. In Proceedings of the 9th Edition of the International Conference on Language Resources and Evaluation (LREC 2014), 26-31 May, Reykjavik, Iceland.
  5. Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (2010), Integrating a Bottom-Up and Top-Down Methodology for Building Semantic Resources for the Multilingual Legal Domain. In Semantic Processing of Legal Texts, Lecture Notes in Artificial Intelligence, vol. 6036. Berlin-Heidelberg: Springer, pp. 95-121.
  6. Hamilton, L., Pepper, K. (2002), “Making a Change: The Effects of the Leadership Role on School Climate”, Learning Environments Research, 5 (2), pp. 155-166.
  7. Hargreaves, A., Fullan, M. (2012), Professional Capital: Transforming Teaching in Every School. New York: Teachers College Press.
  8. Kools, M., Stoll, L. (2016), “What Makes a School a Learning Organisation?”, OECD Education Working Papers No. 137. Paris: OECD Publishing.
  9. Morini, E., Rossi, F. (2016), “Il modello INDIRE. Professionalità, strumenti e metodi per l’attivazione di un processo di miglioramento continuo nelle scuole”, Scuola democratica, Learning for Democracy, 2, pp. 487-506.
  10. OECD (2013), Innovative Learning Environments, Educational Research and Innovation. Paris: OECD Publishing.
  11. Schildkamp, K., Lai, M.K., Earl, L. (Eds) (2013), Data-based Decision Making in Education: Challenges and Opportunities. Dordrecht: Springer.
  12. Schildkamp, K., Karbautzki, L., Vanhoof, J. (2014), “Exploring Data Use Practices around Europe: Identifying Enablers and Barriers”, Studies in Educational Evaluation, 42, pp. 15-24.
  13. Senge, P. et al. (2012), Schools that Learn. New York: Crown Business.
  14. Wayman, J.C., Cho, V., Shaw. S. (2009), First-year Results from an Efficacy Study of the Acuity Data System. Austin: The University of Texas.

  1. Michele Di Giorgio, Police cultures. The construction of a collective imaginary for the guardie di Pubblica sicurezza, 1949–1960 in Journal of Modern Italian Studies /2022 pp. 224, DOI: 10.1080/1354571X.2021.2009219

Giulia Venturi, Felice Dell’Orletta, Simonetta Montemagni, Elettra Morini, Maria Teresa Sagri, in "CADMO" 2/2020, pp. 49-68, DOI:10.3280/CAD2020-002005


FrancoAngeli è membro della Publishers International Linking Association associazione indipendente e no profit per facilitare l'accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche