Digital authentic learning: introdurre alla tone analysis per favorire un pensiero critico digitale
DOI:
https://doi.org/10.24310/thamyristhrdcc.v9i16576Keywords:
Educational technology, tone analysis, digital authentic learning, Catullus, artifcial intelligence (AI), machine learning, sentiment analysisAbstract
In an educational setting which rarely gives digital technologies the proper space and attention to allow for deep, structured and effective treatment of the subject, it is diffcult to imagine that students are being taught valuable skills. In the absence of a discipline capable of refecting the complexity of a reality that is constantly changing, it is more feasible to elicit students’ aptitudes, sparking curiosity and allowing them to discover new job sectors and felds of study. After a brief introduction to the digital authentic learning, i. e. the teaching method needed to put this proposal into effect,
this article shows how the application of tone analysis could improve critical reading, thus bringing students nearer to the new boundaries of artifcial intelligence (AI).
Downloads
Metrics
References
Barchiesi, A. & Sagrillo, F. (2012), Web intelligence & psicolinguistica. La dimensione emotiva nascosta del linguaggio on line applicata al marketing e alla comunicazione, Edizioni Franco Angeli, Milano.
Bonaiuti, G., Calvani, A., Menichetti, l. & Vivanet, G. (2018), Le tecnologie educative, Carocci editore, Roma, 206-210.
Calvani, A. (2018), “Mente e media. Quale interazione cognitiva per apprendere”, in G. Bonaiuti, A. Calvani, L. Menichetti & G. Vivanet, Le tecnologie educative, Carocci editore, Roma, 24.
Cambria, E., Schuller, B., Xia, Y. & Havasi, C. (2013), “New Avenues in Opinion Mining and Sentiment Analysis”, IEEE Intelligent Systems, 28.2, 15–21.
Ceron, A., Curini, L. & Iacus, S. M. (2014), Social media e sentiment analysis: l’evoluzione dei fenomeni sociali attraverso la rete, Springer, Berlino. Copley, F. (1957), Gaius Valerius Catullus: The Complete Poetry, University of Michigan Press, Ann Arbor.
Cornish, F. W. (1962), Catullus, Tibullus, and Pervigilium Veneris, Harvard University Press, Cambridge.
Di Blas, N., Fabbri, M. & Ferrari, L. (2018), “Il modello TPACK nella formazione delle competenze digitali dei docenti. Normative ministeriali e implicazioni pedagogiche”, Italian Journal of Educational Technology, 26.2, 24-38.
D.M. n° 254 del 16/11/2012, Indicazioni nazionali per il curricolo della scuola dell’infanzia e del primo ciclo d’istruzione, MIUR, Gazzetta Uffciale n. 30 del 5 febbraio 2013.
Feldman, R. (2013), “Techniques and applications for sentiment analysis”, Communications of the ACM, 56.4, 82-89.
Gou, l., Zhou, M. X. & Huahai, Y. (2014), “KnowMe and ShareMe: Understanding automatically discovered personality traits from social media and user sharing preferences”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 955-964.
Iannella, A (2018), “Compiti di realtà per conoscere il digitale”, in B. Limata & C. Bertocchi, Alatin Lyceum, Maieutical Labs, Torino.
Iannella, A., FIorentIno, G. & Pera, I. (2018), “Per una didattica del latino tra conoscenze disciplinari e competenze digitali”, Mondo Digitale, 17.76.
Hernández Farias, D. I. & Rosso, P. (2017), “Irony, Sarcasm, and Sentiment Analysis”, in A. F. Pozzi, E. Fersini, E. Messina & B. Liu, Sentiment Analysis in Social Networks, Elsevier, Amsterdam.
IBM CLOUD (2017), IBM Cloud Guides - Tone Analyzer, IBM [20/01/2018].
Jian, Z., Gou, l., Wang, F. & Zhou, M. X. (2014), “PEARL: An Interactive Visual Analytic Tool for Understanding Personal Emotion Style Derived from Social Media”, Proceedings of IEEE Conference on Visual Analytics Science and Technology, IEEE, 203-212.
Koehler, M. & Mishra, P. (2011), “Technological Pedagogical Content Knowledge: A framework for teacher knowledge”, The Teachers College Record, 108.6, 1017–1054.
Lombardi, M. M. (2007), “Authentic Learning for the 21st Century: An Overview”, Educause Learning Initiatives [20/01/2018].
Markham, T. (2006), “Project-based learning: A bridge just far enough”, Teacher Librarian, 39.2, 38-42.
Mellea, G. (2017), “Cos’è la Sentiment Analysis? Utilità, limiti e tools (gratis e a pagamento)” [20/01/2018].
Menichetti, L. (2018), “Tecnologie come oggetto di apprendimento”, in G. Bonaiuti, A. Calvani, L. Menichetti & G. Vivanet, Le tecnologie educative, Carocci editore, Roma, 125-177.
Michie, J. (1971), The Poems of Catullus, Vintage, New York.
Mueller, J (2003), “The Authentic Assessment Toolbox: Enhancing Student Learning through Online Faculty Development”, Journal of Online Learning and Teacher, 1, 1.
Myers, R. & Ormsby, J. (1970), Catullus: The Complete Poems for American Readers, Dutton, New York.
Pak, A. & Paroubek, P. (2010), “Twitter as a corpus for sentiment analysis and opinion mining”, Proceedings of the Seventh Conference on International Language Resources and Evaluation, LREC, 1320- 132.
Pearce, S. (2016), “Authentic learning: what, why and how?”, Teaching - Management strategies for the classroom, 10.
Portolano, A. (1995), “Per una teoria della traduzione contrastiva”, in S. Japoce & E. Staraz (a cura di), La traduzione contrastiva: teoria e prassi. Antologia della poesia latina ad uso del triennio, CEDAM, Padova.
Roncaglia, G. (2018), L’età della frammentazione. Cultura del libro e scuola digitale, Laterza, Bari.
Sloman, S. & Fernbach, P. (2018), L’illusione della conoscenza, Raffaello Cortina Editore, Milano.
Sidana, M. (2017), “Top Five Emotion / Sentiment Analysis APIs for understanding user sentiment trends”, Sifum [20/01/2018].
Sesar, C. (1974), Selected Poems of Catullus, Mason & Lipscomb, New York.
Simone, R. (2012), Presi nella rete. La mente ai tempi del web, Garzanti, Milano, cap. 7.
Downloads
Published
Issue
Section
