References
-
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
- 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
-
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
- Blossfeld, H. P., & Roßbach, H. G. (Eds.). (2019). Education as a Lifelong Process: The German National Educational Panel Study (NEPS), Cham: Springer.
-
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
- Brachman, R. J., & Levesque, H. J. (2004). Knowledge Representation and Reasoning. Amsterdam: Elsevier.
- 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.
- Breiman L., Friedman J.H., Olshen R.A., & Stone C.J. (2017). Classification and regression trees. London, UK: Routledge.
- Cammozzo, A. (2011). Né rizoma, né albero: metafore autopoietiche nella rappresentazione della conoscenza. ZeL Edizioni: Romeo Galassi.
- Cramer, F. (2015). What is ‘Postdigital’? In D. M. Berry & M. Dieter (Eds.), Postdigital aesthetics: art, computation, and design. New York: Palgrave Macmillan
- Del Bonifro F., Gabbrielli M., Lisanti G., & Zingaro S.P. (2020). Student Dropout Prediction. AIED (1), 129-140.
-
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
- 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: 10.1080/02680939.2018.1460493
- 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.
- 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.
- 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.
-
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
- 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-y
- Kumar, V., Garg, M. L. (2018). Predictive Analytics: A Review of Trends and Techniques. International Journal of Computer Applications, 182, 31-37.
-
Lacković, N. (2020). Postdigital Living and Algorithms of Desire. Postdigital Science and Education. https://doi.org/10.1007/s42438-020-00141-4
- Lerman, P. M. (1980). Fitting segmented regression models by grid search. Journal of the Royal Statistical Society: Series C (Applied Statistics), 29, 1, 77-78
- Motoda, H., & Liu, H. (2002). Feature selection, extraction, and construction. Communication of IICM (Institute of Information and Computing Machinery, Taiwan), 5, 67-72.
- OECD (2018). Equity in Education: Breaking down barriers to Social Mobility, Paris: PISA, OECD Publishing.
- 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.
- Panciroli, C. (2021). Il postdigitale. Società, cultura e didattica. Scholé. Rivista di educazione e studi culturali, LIX(2), 157-166.
- Panero, M. (2019). Un’analisi longitudinale dei dati INVALSI di matematica di una stessa coorte alla scuola primaria. Working Paper, 41, 1-19.
- Pietsch, W. (2015). Aspects of theory-ladenness in data-intensive science. Philosophy of Science, 82(5), 905-916.
- 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.
- 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
-
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
- 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
- Rivoltella, P.C., & Rossi, P. G. (2019). Il corpo e la macchina. Tecnologia, cultura, educazione. Brescia: Morcelliana.
- Shi-Nash, A., Hardoon, D.R. (2016). Data analytics and predictive analytics in the era of big data. Palo Alto, Hwaiyu Geng.
- Sondhi, P. (2009). Feature construction methods: a survey. sifaka. cs. uiuc. edu, 69, 70-71.
-
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
- 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.
- 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.
-
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
- 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
- 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.