Un machine learning per la valutazione delle carriere universitarie

Journal title RIV Rassegna Italiana di Valutazione
Author/s Sandro Brignone
Publishing Year 2023 Issue 2022/82
Language Italian Pages 22 P. 11-32 File size 1083 KB
DOI 10.3280/RIV2022-082002
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After presenting the theme of artificial intelligence and its uses in education within the field of evaluation, the article focuses on tertiary education, where there is a high level of students at risk of not com-pleting their programs. Subsequently, an experimental project ("UniTo 2020 Data Lab Project"), carried out by the University of Turin in partnership with CSI Piedmont, is described. This research aimed to build a machine learning (ML) model capable of predicting university career outcomes of its students. The aim is to offer an effective tool for the decision makers, at various academic levels, useful to make strategic choices of intervention in cases of critical situations (outside prescribed time and non-completion of studies). The project is vast and complex and this article illustrates its principal aspects, with reference to the ML of the courses of study in the pedagogical area of the Department of Philosophy and Educational Sciences.

  1. Agostino R.M. (2020). Intelligenza artificiale e processi decisionali. La responsabilità degli amministratori di società. Mercato Concorrenza Regole, XXII(2): 371-402.
  2. Alfaro L., Rivera C., Castaneda E., Zuniga-Cueva J., Rivera-Chavez M., Fialho, F. (2020). A review of intelligent tutorial systems in computer and web based education. International Journal of Advanced Computer Science and Applications, 11(2): 755-763.
  3. AlmaLaurea (2021). XXIII Indagine Profilo dei Laureati 2020. Rapporto 2021. -- https://www.almalaurea.it/sites/almalaurea.it/files/docs/universita/profilo/profilo2021/aalmalaure_profilo_rapporto2021.pdf.
  4. ANVUR (2018). Rapporti sullo Stato del Sistema Universitario e della Ricerca. Rapporto 2018. -- https://www.anvur.it/documenti-ufficiali/rapporti-sullo-stato.
  5. Beaulac C., Rosenthal J.S. (2019). Predicting university students’ academic success and major using random forests. Research in Higher Education, 60(7): 1048-1064.
  6. Bezzi C. (2021). Manuale di ricerca valutativa. Milano: FrancoAngeli.
  7. Boden M.A., trad. it. (2019). L’intelligenza artificiale. Bologna: il Mulino.
  8. Breiman L (2001), Random forests. Machine learning, 45(1): 5-32.
  9. Chen L., Chen P., Lin Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8: 75264-75278.
  10. Del Bonifro F., Gabbrielli M., Lisanti G., Zingaro S.P. (2020). Student dropout prediction. In: International Conference on Artificial Intelligence in Education, Springer, Cham: 129-140.
  11. Gallerani F. (2021). Un modello predittivo per il contrasto della dispersione universitaria. In: Adorni G., Allegra M., Gaglio S., Gentile M., Scarabottolo N., a cura di, Atti del convegno Nazionale Didamatica “Artificial Intelligence for Education” (35° edizione), Palermo: AICA, 7-8 ottobre 2021: 10-17.
  12. Grimaldi R., a cura di (2001). Valutare l’università. Torino: Utet.
  13. Grimaldi R., a cura di (2012). Metodi formali e risorse della Rete. Manuale di ricerca empirica. Milano: FrancoAngeli.
  14. Grimaldi R., a cura di (2022). La società dei robot. Milano: Mondadori.
  15. Holmes W., Bialik M., Fadel C. (2019). Artificial intelligence in education. Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign.
  16. Kaplan J., trad. it (2017). Intelligenza artificiale: guida al futuro prossimo. Roma: Luiss University Press.
  17. Luan H., Tsai C.C. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24, 1: 250-266.
  18. Marmo R. (2020), Algoritmi di intelligenza artificiale. Milano: Hoepli.
  19. Nicoletti M.D.C., de Oliveira O.L. (2020). A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction. Higher Education Studies, 10(4): 12-24.
  20. OECD (2019). Recommendation of the Council on Artificial Intelligence. OECD Legal Instruments. -- https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.
  21. OECD (2021). Education at a Glance 2021: OECD Indicators. Paris: OECD Publishing.
  22. Palumbo M., (2015). Il processo di valutazione. Decidere, programmare, valutare. Milano: FrancoAngeli.
  23. Panciroli C., Rivoltella P.C., Gabbrielli M., Richter O.Z. (2020). Artificial Intelligence and education: new research perspectives. Form@re, 20(3): 1-12.
  24. Quintarelli S., a cura di (2020). Intelligenza artificiale. Cos’è davvero, come funziona, che effetti avrà. Torino: Bollati Boringhieri.
  25. Reid J. (1995). Managing learner support. In F. Lockwood (Ed.). Open and distance learning today. London: Routledge: 265-275.
  26. Russell S., Norvig P., trad. it. (2010), Intelligenza artificiale. Un approccio moderno. Milano-Torino: Pearson.
  27. Tahiru F. (2021). AI in Education: A Systematic Literature Review. Journal of Cases on Information Technology (JCIT), 23(1): 1-20. (vedi se lasciare o togliere)
  28. Trinchero R. (2021). Designing Intelligent Tutoring Systems With AI: Brain-Based Principles for Learning Effectiveness. In Handbook of Research on Teaching With Virtual Environments and AI, IGI Global: 539-557.
  29. Zawacki-Richter O., Marín V.I., Bond M., Gouverneur F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International Journal of Educational Technology in Higher Education, 16(1): 1-27.
  30. Zingaro S., Del Zozzo A., Del Bonifro F., Gabbrielli, M. (2020). Predictive models for effective policy making against university dropout. Form@re, 20(3): 165-175.

Sandro Brignone, Un machine learning per la valutazione delle carriere universitarie in "RIV Rassegna Italiana di Valutazione" 82/2022, pp 11-32, DOI: 10.3280/RIV2022-082002