Applicazione dell'analisi dell'apprendimento nelle scuole europee di formazione generale: Revisione teorica

Autori

  • Aleksandra Batuchina Dottoressa in Scienze dell'Educazione, professore associato presso il Centro di Geografia Sociale e Studi Regionali dell'Università di Klaipeda.
  • Julija Melnikova Dottoressa di ricerca in Scienze dell'Educazione, ricercatore senior presso il Dipartimento di Educazione dell'Università di Klaipeda

DOI:

https://doi.org/10.61007/QdC.2023.2.119

Parole chiave:

learning analytics, scuole europee, tecnologie educative nelle scuole

Abstract

L'uso crescente della tecnologia nell'istruzione va di pari passo con le aree dell'analisi dell'apprendimento e dell'intelligenza artificiale nell'istruzione, con particolare attenzione a come i dati possono essere utilizzati per migliorare il processo di insegnamento/apprendimento. Nell'ultimo decennio, nell'Unione Europea si è discusso molto di dati e di istruzione basata sull'evidenza, di gestione scolastica e di gestione del sistema educativo. I dati vengono utilizzati per prendere decisioni sistematiche sulla politica educativa a livello nazionale o regionale, per preparare piani di miglioramento della scuola, per considerare i processi educativi di una classe o di uno specifico studente. L'intelligenza artificiale e l'analisi dell'apprendimento stanno diventando i metodi più diffusi per analizzare i dati raccolti negli ambienti di apprendimento digitali per supportare insegnanti e studenti nel loro apprendimento. Tuttavia, a livello europeo si sottolinea che non è stata trovata quasi nessuna ricerca che abbia risposto alla domanda su come la learning analytics possa essere applicata nelle scuole di istruzione generale al fine di migliorare le attività scolastiche. Lo scopo del saggio scientifico è quello di presentare una rassegna della letteratura sulla ricerca in materia di learning analytics e di esplorare esempi di applicazione della learning analytics nell'istruzione generale. Di conseguenza, questo saggio fornisce una revisione completa della letteratura che copre questi aspetti. La ricerca degli articoli è stata effettuata tramite Google Scholar, EBSCO Research Database e Scopus Preview. In generale, sono stati analizzati più di 157 articoli datati dal 2006 al 2022 sul tema della learning analytics. I risultati della ricerca rivelano che gli strumenti di analisi dell'apprendimento non devono solo includere soluzioni tecnologiche e pedagogiche efficaci, ma è importante considerare molti fattori contestuali e umani per rispondere alle domande sul perché e sul come verranno utilizzati, nonché da chi e in quale contesto.

Biografie autore

Aleksandra Batuchina, Dottoressa in Scienze dell'Educazione, professore associato presso il Centro di Geografia Sociale e Studi Regionali dell'Università di Klaipeda.

Aleksandra Batuchina, Post-doc presso il Dipartimento di Pedagogia (Università di Klaipėda, Lituania), Coach Professionista Associato Certificato (ICF), docente. Campi di interesse: educazione digitale; meaningful work; coaching e impatto del coaching; metodologia qualitativa; metodologia fenomenologica (Max van Manen).

Julija Melnikova, Dottoressa di ricerca in Scienze dell'Educazione, ricercatore senior presso il Dipartimento di Educazione dell'Università di Klaipeda

Julija Melnikova, Ricercatrice senior presso il Dipartimento di Pedagogia, Facoltà di Scienze Sociali e Umanistiche, Università di Klaipeda.

Filoni di interesse: Education Leadership and Management, Digitalisation of Education.

Riferimenti bibliografici

Abo, R., Koga, T., Horikoshi, I., Yamazki, K., Tamura, Y. (2016). Data visualization framework for learning analytics, The International Workshop on Learning Analytics and Educational Data Mining (LAEDM 2016). https://inolab.slis.tsukuba.ac.jp/global/2016/LAEDM2016.pdf.

Admiraal, W., Vermeulen, J., Bulterman-Bos, J. (2017). Learning Analytics in Secondary Education: Assessment for Learning in 7th Grade Language Teaching. ECER 2017. https://eera-ecer.de/ecer-programmes/conference/22/contribution/39935/.

Agasisti, T., Bowers, A.J. (2017). Data Analytics and Decision- Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions. In Johnes, G., Johnes, J., Agasisti, T., López-Torres, L. (Eds.) Handbook of Contemporary Education Economics (p.184-210). Cheltenham, UK: Edward Elgar Publishing. ISBN: 978-1-78536-906-3 http://www.e-elgar.com/shop/handbook-of-contemporary-education-economics.

Andrade e Silva, M., Camanho, A. (2017). Using data analytics to benchmark schools: The case of Portugal. In B. Vanthienen, K. Witte (eds.). Data analytics applications in education. New York: Auerbach Publications.

Baker, R., Hawn, A. (2021). Algorithmic bias in education. Pre- print. https://doi.org/10.35542/osf.io/pbmvz; https://edarxiv.org/pbmvz/[2021-05-12].

Bousbia, N., Belamri, I. (2014). Which contribution does EDM provide to computer-based learning environments? In A. Pena- Ayala (ed.). Educational data mining: Applications and trends. Switzerland: Springer, 3–28.

Bowers, A. J., Sprott, R., Taff, S. A. (2012). Do we know who will drop out? A review of the predictors of dropping out of high school:

Precision, sensitivity, and specificity. The High School Journal, 96(2), 77-100. https://doi.org/10.1353/hsj.2013.0000.

Burch, P., Good, A. (2015). More important than the contract is the relationship. Phi Delta Kappan, 96(5), 35-39. doi: 10.1177/0031721715569467.

Campbell, J. P., DeBlois, P. B., & Oblinger, D. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40– 57.

Charlton, P., Mavrikis, M., Katsifli, D. (2013). The potential of learning analytics and big data. Ariadne, 71. http://www.ariadne.ac.uk/issue71/charltonet-al#sthash.wainfh00.dpuf.

Chen, X., Zou, D., Cheng, G., Xie, H. (2020b). Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education, 151, 103855.

Clarke, J., Nelson, K. (2013). Perspectives on learning analytics: Issues and challenges. Observations from Shane Dawson and Phil Long. The International Journal of the First Year in Higher Education, 4(1), 1–8.

Czerkawski, B., Lyman, C. (2015). Exploring Issues about Computational Thinking in Higher Education. TechTrends, 59, 57-65. https://doi.org/10.1007/s11528-015-0840-3.

Davis, S. K., Edwards, R. L., Miller, M., Aragon, J. (2018). Considering context and comparing methodological approaches in implementing learning analytics at the University of Victoria. Proceedings 8th international conference on Learning Analytics & Knowledge (LAK18), 1–4.

Dehler, J., Bodemer, D., Buder, J., Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068–1078.

Dehler, J., Bodemer, D., Buder, J., Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068–1078.

Essa, A. (2016). A possible future for next generation adaptive learning systems. Smart Learning Environments, 3(16), 1-24. https://doi.org/10.1186/s40561-016-0038-y.

Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304–317.

Ferguson, R., Clow, D., Griffiths, D., Brasher, A. (2019). Moving forward with learning analytics: Expert views. Journal of Learning Analytics, 6(3), 43–59. http://dx.doi.org/10.18608/jla.2019.63.8.

Ferguson, R., Cooper, A., Drachsler, H., Kismihók, G., Boyer, A., Tammets, K., Monés, A. M. (2015). Learning analytics: European perspectives. Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 69–72. http://oro.open.ac.uk/42346/1/LAK15%20Panel.pdf.

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130–160.

Greller, W., Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57. http://doi.org/http://hdl.handle.net/1820/4506.

Gummer, E., Mandinach, E. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1–22. Guo, J., Huang, X., Wang, B. (2017). MyCOS Intelligent Teaching Assistant, 392–393.

Har Carmel, Y. (2016). Regulating “Big Data education” in Europe: lessons learned from the US. Internet Policy Review, 5(1). Doi: 10.14763/2016.1.402.

Hesse, F. W., Dehler, J., Bodemer, D., Buder, J., (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068–1078.

Hollman, A. K., Hollman, T. J., Shimerdla, F., Bice, M. R., Adkins, M. (2019). Information technology pathways in education: Interventions with middle school students. Computers & Education, 135, 49–60.

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3.

Hylen, J. (2015). The State of Art of Learning Analytics in Danish Schools, http://www.laceproject.eu/blog/the-state-of-art-of-learning-analytics-in-danish-schools/.

Ifenthaler, D., Mah, D., Yau, J. (2019). Utilising learning analytics for study success.Reflections on current empirical findings. In D. Ifenthaler, J. Yau, D. Mah (eds.). Utilizing learning analytics to support study success. Springer, Cham.

Ifenthaler, D., Widanapathirana, C. (2015). Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines. Technology, Knowledge and Learning, 19/1–2, 221–240. http://dx.doi.org/10.1007/s10758-014-9226-4.

Jimerson, S., Childs, J. (2017). Signal and Symbol: How State and Local Policies Address Data-Informed Practice. Educational Policy, v31 n5 p584-614 Jul 2017.

Jivet, I., Wong, J., Scheffel, M., Valle Torre, M., Specht, M., and Drachsler, H. (2021). Quantum of Choice: How Learners’ Feedback Monitoring Decisions, Goals and Self-Regulated Learning Skills Are Related. Proceedings of LAK21: 11th International Learning Analytics and Knowledge Conference, 416–427. Irvine, CA. Doi: 10.1145/3448139.3448179.

Jovanovic, J., Gasevic, D., Brooks, C., Devedzic, V., Hatala, M., Eap, T., Richards, G. (2008). LOCO-Analyst: Semantic web technologies in learning content usage analysis. International Journal of Continuing Engineering Education and Life Long Learning, 18(1), 54–76. https://doi.org/10.1504/IJCEELL.2008.016076.

Kalim, U. (2021). The Growing Role of Big Data in Education and its Implications for Educational Leadership. International Journal of Research and Innovation in Social Science (IJRISS), 5(1). ISSN 2454-6186.

Khine, M. (2018). Learning Analytics for Student Success: Future of Education in Digital Era. The European Conference on Education 2018. http://papers.iafor.org/wp-content/uploads/papers/ece2018/ECE2018_40028.pdf.

Kurvinen, E., Kaila, E., Laakso, M.-J., Salakoskis, T. (2020). Long Term Effects on Technology Enhanced Learning: The Use of Weekly Digital Lessons in Mathematics. Informatics in Education, 19, 51–75. Vilnius: Vilniaus universitetas. https://infedu.vu.lt/journal/INFEDU/article/25/info10.15388/infedu.2020.04.

Li, H., Gobert, J., Dickler, R., Moussavi, R. (2018). The impact of multiple real-time scaffolding experiences on science inquiry practices. In Nkambou R., Azevedo R., & Vassileva J. (Eds.), Intelligent Tutoring Systems: 14th International Conference, Proceedings (pp. 99-109). Springer. https://doi.org/10.1007/978-3-319-91464-0_10.

Lingard, B., Lewis, S. (2016). Globalisation of the Anglo-American approach to top-down, test-based educational accountability. In G.

T. L. Brown, L R. Harris (Eds.), Handbook of human and social conditions in assessment (1st ed., pp. 387-403).

Long, P., Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 31–40.

Macfadyen, L. P., Dawson, S., Pardo, A., Gašević, D. (2014). Embracing big data in complex educational systems: the learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17–28.

Mandinach, E., Gummer, E. (2016). Data literacy for educators: Making it count in teacher preparation and practice. New York, NY: Teacher College Press.

Mayer-Schönberger, V., Cukier, K. (2014). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston: Houghton Mifflin Harcourt.

McHugh, D. (2015). Traffic prediction and analysis using a big data and visualization approach. http://leeds.gisruk.org/abstracts/GISRUK2015_submission_20.pdf.

McKay, E. (2019). Digital literacy skill development: Prescriptive learning analytics assessment model. Australian Council for Educational Research, Research Conference 2019, 22–28. https://research.acer.edu.au/cgi/viewcontent.cgi?article=1350&context=research_conference

Meyers, E., Cahill, M., Subramaniam, M., Stripling, B. (2016). The promise and peril of learning analytics in P-12 education: An uneasy partnership? iConference 2016.https://www.ideals.illinois.edu/bitstream/handle/2142/89459/Meyer518.pdf?sequence=1&isAllowed=y.

Mouri, K., Yin, C., Uosaki, N. (2018). Learning analytics for improving learning materials using digital textbook logs. Information Engineering Express International Institute of Applied Informatics, 4(1), 23–32.

Nouri, J., Ebner, M., Ifenthaler, D., Sqr, M., Malmberg, J., Khalil, M., ... Berthelsen, U. D. (2019). Efforts in Europe for Data-Driven Improvement of Education – A review of learning analytics research in six countries.: https://online-journals.org/index.php/i-jai/article/view/11053/5818.

OECD. (2021a). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Publishing, Paris. https://doi.org/10.1787/589b283f-en.

OECD. (2021b). State of implementation of the OECD AI Principles: Insights from national AI policies. OECD Digital Economy Papers,

OECD Publishing, Paris. https://doi.org/10.1787/1cd40c44-en.

Papamitsiou, Z., Economides, A. A. (2015). Temporal learning analytics visualizations for increasing awareness during assessment. RUSC. Universities and Knowledge Society Journal, 12(3), 129–147.

Pardo, A., Dawson, S., Gašević, D., Steigler-Peters, S. (2016). The role of learning analytics in future education models. https://www.telstra.com.au/content/dam/tcom/business-enterprise/industries/pdf/tele0126_whitepaper_5_spreads_lr_notrims.pdf.

Polonetsky, J., Jerome, J. (2014). Student data: Trust, Transparency, and the role of consent. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2628877.

Pykett, J. (2012). The pedagogical state: education, citizenship, governing. In J.Pykett (Ed.), Governing through pedagogy: Re-educating citizens (pp. 1-4). London: Routledge.

Rienties, B., Herodotou, C., Olney, T., Schencks, M., Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. International Review of Research in Open and Distributed Learning, 19(5), 1.//doi.org/10.19173/irrodl.v19i5.3493.

Ritter, S., Yudelson, M., Fancsali, S. E., & Berman, S. R. (2016). How mastery learning works at scale. In Proceedings of the Third ACM Conference on Learning @ Scale (pp. 71-79). Association for Computing Machinery. https://doi.org/10.1145/2876034.2876039.

Romero, C., Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.

Sales, A., Botelho, A. F., Patikorn, T., Heffernan, N. T. (2018). Using big data to sharpen design-based inference in A/B tests. In K. E. Boyer, M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 479- 485). International educational Data Mining Society.

Sclater, N., Mullan J. (2017). Learning analytics and student success – assessing the evidence. JISC, Bristol.

Sergis, S., Sampson, D. G. (2016). School analytics: A framework for supporting school complexity leadership. In: J. Spector, D. Ifenthaler, D. Sampson, P. Isaias (eds.). Competencies in Teaching, Learning and Educational Leadership in the Digital Age, 79–122.

Sergis, S., Sampson, D., Pelliccione, L. (2017). Investigating the impact of Flipped Classroom on students’ learning experiences: A Self-Determination Theory approach. Computers in Human Behavior. 10.1016/j.chb.2017.08.011.

Settles, B., Meeder, B. (2016). A Trainable Spaced Repetition Model for Language Learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1848–1858, Berlin, Germany. Association for Computational Linguistics.

Siemens, G. (2013). Learning analytics: envisioning a research discipline and a domain of practice. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. https://dl.acm.org/doi/10.1145/2330601.2330605.

Singh, R. P. (2018). Learning analytics: Potential, protection, and privacy in the educational system. In M. K. Singh, Z. Zerihun, N. Singh (Eds.), Impact of learning analytics on curriculum design and student performance (pp. 1–18). IGI Global. https://doi.org/10.4018/978-1-5225-5369-4.ch001.

Tsai, Y. S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., Gašević, D. (2018). The SHEILA framework: Informing institutional strategies and policy processes of learning analytics. Journal of Learning Analytics, 5(3), 5–20. http://dx.doi.org/10.18608/jla.2018.53.2. ET2020.

Van Leeuwen, A., Knoop-van Campen, C. A. N., Molenaar, I., Rummel, N. (2021). How teacher characteristics relate to how teachers use dashboards. Journal of Learning Analytics, 8(2), 6–21. https://doi.org/10.18608/jla.2021.7325.

Viberg, O., Khalil, M., Baars, M. (2020). Self-Regulated Learning and Learning Analytics in Online Learning Environments: A Review of Empirical Research. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 524–533. https://doi.org/10.1145/3375462.3375483.

Wang, Y. (2016). Big Opportunities and Big Concerns of Big Data in Education. TechTrends, 60, 381–384. https://doi.org/10.1007/s11528-016-0072-1.

Wang, Y., Decker, J. R. (2014). Can virtual schools thrive in the real world? TechTrends, 58(6), 57–62.

Weber, A. S. (2015). The Big Student Big Data Grab. IJIET International Journal of Information and Education Technology, 6(1), 65–70. Doi:10.7763/ijiet.2016.v6.660.

Weller, S. (2020). Using internet video calls in qualitative (longitudinal) interviews: Some implications for rapport. International Journal of Social Research Methodology, 20, 613– 625. htts://doi.org/10.1080/13645579.2016.1269505Zeide, 2017.

Zilvinskis, J., Borden, V. M. H. (2017). An Overview of Learning Analytics. New Directions for Higher Education, 9–17. https://doi.org/10.1002/he.20239.

Downloads

Pubblicato

2023-09-01

Come citare

Batuchina, A., & Melnikova, J. (2023). Applicazione dell’analisi dell’apprendimento nelle scuole europee di formazione generale: Revisione teorica. Quaderni Di comunità. Persone, Educazione E Welfare Nella Società 5.0, (2), 201–234. https://doi.org/10.61007/QdC.2023.2.119