[
Aljawarneh, S., Lara, J.A. (2021). Data science for analyzing and improving educational processes. Journal of Computing in Higher Education, 33, 545-550. https://doi.org/10.1007/s12528-021-09299-7
10.1007/s12528-021-09299-7
]Search in Google Scholar
[
Baker, R. (2019). Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes. Journal of Educational Data Mining, 11(1), 1-17. https://doi.org/10.5281/zenodo.3554745
]Search in Google Scholar
[
Bayne, S. (2015). What’s the matter with ‘technology-enhanced learning’? Learning, Media and Technology, 40(1), 5-20. https://doi.org/10.1080/17439884.2014.915851
10.1080/17439884.2014.915851
]Search in Google Scholar
[
Blossfeld, H. P., & Roßbach, H. G. (Eds.). (2019). Education as a Lifelong Process: The German National Educational Panel Study (NEPS), Cham: Springer.10.1007/978-3-658-23162-0
]Search in Google Scholar
[
Borghesi, A., Baldo, F., Lombardi, M., Milano, M. (2021). Injective Domain Knowledge in Neural Networks for Trans-precision Computing. Machine Learning, Optimization, and Data Science: 6th International Conference (pp. 587-600), LOD 2020, Cham, Springer. https://doi.org/10.1007/978-3-030-64583-0_52
10.1007/978-3-030-64583-0_52
]Search in Google Scholar
[
Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning. Amsterdam: Elsevier.10.1016/B978-155860932-7/50099-6
]Search in Google Scholar
[
Branchetti, L., Ferretti, F., Lemmo, A., Maffia, A., Martignone, F., Matteucci, M., & Mignani, S. (2015). A longitudinal analysis of the Italian national standardized mathematics tests. In Proceeding of CERME9, 1695-1701.
]Search in Google Scholar
[
Breiman L., Friedman J.H., Olshen R.A., & Stone C.J. (2017). Classification and regression trees. London, UK: Routledge.10.1201/9781315139470
]Search in Google Scholar
[
Cammozzo, A. (2011). Né rizoma, né albero: metafore autopoietiche nella rappresentazione della conoscenza. ZeL Edizioni: Romeo Galassi.
]Search in Google Scholar
[
Cramer, F. (2015). What is ‘Postdigital’? In D. M. Berry & M. Dieter (Eds.), Postdigital aesthetics: art, computation, and design. New York: Palgrave Macmillan10.1057/9781137437204_2
]Search in Google Scholar
[
Del Bonifro F., Gabbrielli M., Lisanti G., & Zingaro S.P. (2020). Student Dropout Prediction. AIED (1), 129-140.10.1007/978-3-030-52237-7_11
]Search in Google Scholar
[
Ertl, B., Hartmann, F. G., & Heine, J. H. (2020). Analyzing Large-Scale Studies: Benefits and Challenges. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.577410
10.3389/fpsyg.2020.577410
]Search in Google Scholar
[
Fischman, G. E., Topper, A. M., Silova, I., Goebel, J., & Holloway, J. L. (2019). Examining the influence of international large-scale assessments on national education policies. Journal of Education Policy 34(2), 470-499. doi: 10.1080/02680939.2018.146049310.1080/02680939.2018.1460493
]Search in Google Scholar
[
Fuller, S., & Jandrić, P. (2019). The postdigital human: making the history of the future. Postdigital Science and Education, 1(1), 190-217. https://doi.org/10.1007/s42438-018-0003-x.10.1007/s42438-018-0003-x
]Search in Google Scholar
[
Inozemtsev, V., Ivleva M. and Ivlev, V. (2017). Artificial Intelligence and the Problem of Computer Representation of Knowledge. Advances in Social Science, Education and Humanities Research, 124, 1151-1157.10.2991/iccessh-17.2017.268
]Search in Google Scholar
[
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), 101493.10.1016/j.giq.2020.101493
]Search in Google Scholar
[
Klašnja-Milićević, A., Ivanović, M., & Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25, 1066-1078. https://doi.org/10.1002/cae.21844
10.1002/cae.21844
]Search in Google Scholar
[
Knox, J. (2019). What Does the ‘Postdigital’ Mean for Education? Three Critical Perspectives on the Digital, with Implications for Educational Research and Practice. Postdigital Science and Education 1, 357-370. Doi:10.1007/s42438-019-00045-y10.1007/s42438-019-00045-y
]Search in Google Scholar
[
Kumar, V., Garg, M. L. (2018). Predictive Analytics: A Review of Trends and Techniques. International Journal of Computer Applications, 182, 31-37.10.5120/ijca2018917434
]Search in Google Scholar
[
Lacković, N. (2020). Postdigital Living and Algorithms of Desire. Postdigital Science and Education. https://doi.org/10.1007/s42438-020-00141-4
10.1007/s42438-020-00141-4
]Search in Google Scholar
[
Lerman, P. M. (1980). Fitting segmented regression models by grid search. Journal of the Royal Statistical Society: Series C (Applied Statistics), 29, 1, 77-7810.2307/2346413
]Search in Google Scholar
[
Motoda, H., & Liu, H. (2002). Feature selection, extraction, and construction. Communication of IICM (Institute of Information and Computing Machinery, Taiwan), 5, 67-72.
]Search in Google Scholar
[
OECD (2018). Equity in Education: Breaking down barriers to Social Mobility, Paris: PISA, OECD Publishing.
]Search in Google Scholar
[
Panciroli, C., Rivoltella, P.C., Gabbrielli, M. & Zawacki-Richter, O. (2020). Artificial Intelligence and education: new research perspectives. Form@ re-Open Journal per la formazione in rete, 20, 1-12.
]Search in Google Scholar
[
Panciroli, C. (2021). Il postdigitale. Società, cultura e didattica. Scholé. Rivista di educazione e studi culturali, LIX(2), 157-166.
]Search in Google Scholar
[
Panero, M. (2019). Un’analisi longitudinale dei dati INVALSI di matematica di una stessa coorte alla scuola primaria. Working Paper, 41, 1-19.
]Search in Google Scholar
[
Pietsch, W. (2015). Aspects of theory-ladenness in data-intensive science. Philosophy of Science, 82(5), 905-916.10.1086/683328
]Search in Google Scholar
[
Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning. https://doi.org/10.1186/s41039-017-0062-8.10.1186/s41039-017-0062-8
]Search in Google Scholar
[
Priyam A., Abhijeeta G., Rathee A., & Srivastava S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology, 3(2), 334-337
]Search in Google Scholar
[
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wires Data Mining and Knowledge Discovery, 10, e1355. https://doi.org/10.1002/widm.1355
10.1002/widm.1355
]Search in Google Scholar
[
Ryberg, T. (2021). Postdigital Research, Networked Learning, and Covid-19. Postdigital Science and Education 3(4), 266-271. doi: 10.1007/s42438-021-00223-x
]Open DOISearch in Google Scholar
[
Rivoltella, P.C., & Rossi, P. G. (2019). Il corpo e la macchina. Tecnologia, cultura, educazione. Brescia: Morcelliana.
]Search in Google Scholar
[
Shi-Nash, A., Hardoon, D.R. (2016). Data analytics and predictive analytics in the era of big data. Palo Alto, Hwaiyu Geng.10.1002/9781119173601.ch19
]Search in Google Scholar
[
Sondhi, P. (2009). Feature construction methods: a survey. sifaka. cs. uiuc. edu, 69, 70-71.
]Search in Google Scholar
[
Thomson, S. (2018). Achievement at school and socioeconomic background - an educational perspective. Science Learn 3, 5. https://doi.org/10.1038/s41539-018-0022-0
10.1038/s41539-018-0022-0
]Search in Google Scholar
[
Wang, L., Sy, A, Liu, L., & Piech, C. (2017). Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning. Proceedings of the 10th International Conference on Educational Data Mining, 324-329.10.1145/3051457.3053985
]Search in Google Scholar
[
Zanellati, A., Zingaro, S.P., Del Bonifro, F., Gabbrielli, M., Levrini, O. & Panciroli, C. (2021). Informing predictive models against Students Dropout. Conference DIDAMATICA 2021, Palermo.
]Search in Google Scholar
[
Zaytsev E.I., Khalabiya R.F., Stepanova I.V., & Bunina L.V. (2020). Multi-Agent System of Knowledge Representation and Processing. In: Kovalev S., Tarassov V., Snasel V., Sukhanov A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Cham: Springer. https://doi.org/10.1007/978-3-030-50097-9_14
10.1007/978-3-030-50097-9_14
]Search in Google Scholar
[
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, 1-27. doi: 10.1186/s41239-019-0171-0
]Open DOISearch in Google Scholar
[
Zingaro, S., Del Zozzo, A., Del Bonifro, F., & Gabbrielli, M. (2020). Predictive models for effective policy making against university dropout. Form@ re-Open Journal per la formazione in rete, 20(3), 165-175.
]Search in Google Scholar
, 