Ripreso da AA. VV., “Digital Disruption in Teaching and Testing. Assessments, Big Data, and the Transformation of Schooling”, Open Access version – traduzione in proprio:

GLOSSARY

In producing this Glossary, we have drawn on the writing of the authors of the chapters in this collection, along with other publicly available definitions and descriptions. The latter include the OECD, various other agencies, and other authors (see references below).

Algorithms Mathematical processes designed to solve a problem. They can be implemented computationally and configured for the purposes of achieving particular tasks, developing decision-making rules, and producing predictive and potentially actionable insights. While algorithms are initially human cre- ated, once they are created, and in use, they are able to connect with other algorithms in a self-generating process as part of machine learning and analysis of big data.

Artificial intelligence (AI) No single definition. This is because every digital tool including AI is created for its own distinct purposes and has its own unique process (Leins, 2020). However, as defined by the OECD, AI is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” subject to their purposes (OECD, 2019).

Artificial Intelligence in Education (AIEd) A specialist field in AI to enable more personalization and flexibility, and to create more engaging learning opportunities with the aim to automate and eliminate the more routine tasks of teaching such as multiple-choice testing and scoring.

Big data Generally refers to large volumes and variety of data, unstructured or structured, and too big for common software and thus usually cloud stored, that are produced continuously and at high velocity.

Bioinformatics An interdisciplinary field in which software tools and methods are developed for working and understanding large and complex sets of bio- logical data. Examples in the context of education, include eye tracking soft- ware, tracking of neural pathways, and software and hardware for tracking physical responses.

Biometrics The automated collection of biological data related to human characteristics, including facial recognition, body temperature, and perspira- tion. As a result, it is often used as a form of identification and control. Examples includes body scanners and automatic temperature checking at air- ports, and face identification scanners for use at borders.

Blockchain A decentralized database system that links existing or previous blocks of information.

Clickstream data The list of pages viewed in order by visitors to a website from a series of their mouse clicks. In an education assessment context, this refers to data collected from how test takers and users of other online programs address questions, the time they take, the mistakes they make, and how often they access them. Thus log-file and click stream data can record online actions and events in real time and produce psycho-emotional data about users and students. The data can be used for purposes of assessing personality traits, attitudes, cog- nition, and abilities.

Computer Adaptive Testing (CAT) Online tests that use branching to match test items with student ability based on responses to initial and subsequent questions. Algorithms determine a student’s pathway through a test in deter- mining the appropriate level of difficulty of the question sequence.

Data An elusive term – data can come from both analogue and digital sources. Data can be referred to as “numerical information in digital formats” (Sellar, 2017, p. 341). Data become information and evidence when systems, school leaders, and teachers infer meaning from them, use them and take action accordingly.

Data analytics The science of analysis of data using an algorithmic process to derive meaningful correlations and insights for decision-making and action.

Data dashboard A customizable information management tool designed for centralization and easy visualization of data for tracking key performance indicators. A data dashboard provides an interactive interface for tracking, measuring, and extracting applicable data in an accessible way.

Datafication Refers to the technical processes involved in the rendering of experience as data, which is then digitalized through software. This process raises philosophical and translation issues; for example, can and should all human experience be rendered as data (Zuboff, 2019)?

Data infrastructures Refer to digital storage systems that enable sharing, con- sumption, and use of data across networks of objects and of people. Used in plural form to acknowledge the variety of systems in the context of interoperability.

Data literacy Knowledge and skills to interpret, understand, and apply data so as to transfer into decision-making and inform action.

Data science When large amounts of data are curated, interpreted, and made meaningful for decision making purposes.

Digital disruption The ways in which the affordances of digital technologies have significantly affected key aspects of the functioning of society. These include modes of communication, news media, the economy, industry, workforce, health sectors, work of governments, practices of citizenship, research and development in universities, and schooling. There have also been significant digital impacts in how individuals experience their lives, make civic and community contributions, experience leisure, communicate locally and globally and construct their identities.

Digitization and digitalization Often seen as being synonymous but we distin- guish between them. We define digitization as putting online of non-digitized materials (e.g., taking a pen and paper test online using a USB), while we see digitalization as referring to the impact, mediation, and changes that occur when matters such as data and experience are digitalized (e.g., computer adaptive test- ing). Digitalization of assessment requires and enables different designs for assess- ment including multimodal approaches. These open up different ways of engaging young people, taking account of student differences and enabling assessment to be potentially more authentic.

Edu-business Business corporation or privately-owned company (usually for- profit) with a market in educational based products (e.g., textbooks, educa- tional software and technology, data management, and infrastructures).

Education technology (EdTech) Educational related businesses or corpora- tions that design, market, and sell educational technology products to gov- ernmental agencies and educational institutions (usually software and apps for testing, teaching, and data management tools and analysis for systems).

Extended reality (XR) Real and virtual combined environments, and human- machine interactions generated by computer technology and wearables. An umbrella concept that brings AR, VR, and MR together.

Immersive assessments (IA) Assessment that allows the test taker or student to be immersed within varying degrees of a 3D-based scenario, with the use of AR, VR, MR, and XR.

International large-scale assessments (ILSAs) International large-scale assess- ments administered to school students and that test large samples to provide an international comparative perspective on student performance to inform national educational policy and practice. Examples include the OECD’s Program for International Student Assessment (PISA), The International Association for the Evaluation of Educational Achievement (IEA)’s Trends in International Mathe- matics and Science Study (TIMSS), and Progress in International Reading Lit- eracy Study (PIRLS).

Internet of Things (IoT) Network of physical objects embedded with tech- nology such as microchips and linked via the Internet (often toys, virtual assistants).

Interoperability The capability, interactivity, and compatibility of software and computer systems used to integrate and link data.

Learning analytics Tracks students through their data traces in relation to all types of online work, including testing and curriculum work. Just as CATs enable more “personalized” testing, learning analytics potentially enable “personalized” learning through feedback to teachers and students. What is called emotional learning analytics seeks to provide analyses about students based on their non-cognitive and affective experiences collected online.

Machine learning (ML) ML and AI are prevalent in discussions of big data. ML drives AI. Here computers bring algorithms to big data analysis and surface patterns in the data. This is a “learning process” that sets computers up for dealing with increasing volumes of data. In this way the ML functions as the vehicle driving AI (see Chapter 1).

Mixed reality (MR) Sometimes called hybrid reality, MR is the merging of real and virtual worlds where physical and the digital co-exist and interact in real time.

Personalized learning Educational computer programs, software, and/or instructional approaches created to cater to the diverse learning needs, inter- ests, and contexts of individual learners.

Predictive analytics Uses advanced analysis of historical data to produce real- time insights and predict future patterns of performance and behavior.

Psycho-informatics Applies to computer science techniques, including psy- chological tracking, measurement, and analysis “of behaviors, emotions, per- sonality traits, attitudes, cognition and abilities. It makes use of behavioral data sources and analytical platforms employing techniques from data mining and machine learning to detect, characterize and classify behavioral patterns and trends” (Williamson, 2019, p. 66).

Socio-technical education data imaginary A normative imaginary concerned with appropriate and educative relationships between education data, digital technologies, and the work of policymakers and educators (see Chapter 1).

Virtual reality (VR) Immerses participants in completely virtual environments. Usually with viewing through a headset and the creation of sound and movement.

References

Leins, K. (2020, September 7). So, what is AI? Centre for AI and Digital Ethics Blog. University of Melbourne. https://law.unimelb.edu.au/centres/caide/caide-community- blog/so,-what-is-ai2.

Organisation for Economic Cooperation and Development. (OECD). (2019). Recom- mendation of the Council on Artificial Intelligence. OECD Legal Instruments. https:// legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.

Sellar, S. (2017). Making network markets in education: The development of data infra- structure in Australian schooling. Globalisation, Societies and Education, 15(3), pp. 341–351. https://doi.org/10.1080/14767724.2017.1330137.

Williamson, B. (2019). Intimate data infrastructure: Emerging comparative methods of predictive analytics and psycho-informatics. In R. Gorur, S. Sellar, & G. Steiner-Khamsi (Eds.), Comparative methodology in the era of big data and global networks. World Yearbook of Education 2019. Routledge. https://doi.org/10.4324/9781315147338–6.

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books.

Sintesi del glossario di ChatGPT

Il documento è un glossario che raccoglie termini e definizioni relativi alla tecnologia, in particolare nel contesto dell’educazione e dell’analisi dei dati. Ecco un riassunto delle principali voci trattate:

1. Algoritmi: Processi matematici progettati per risolvere problemi, implementabili computazionalmente per svolgere compiti specifici e generare informazioni predittive.
2. Intelligenza Artificiale (AI): Sistemi basati su macchine che possono fare previsioni, raccomandazioni o decisioni in base a obiettivi definiti dall’uomo, operando con diversi livelli di autonomia.
3. Intelligenza Artificiale nell’Istruzione (AIEd): Campo specializzato che mira a personalizzare l’apprendimento e automatizzare attività di insegnamento di routine.
4. Big Data: Grandi volumi di dati strutturati e non strutturati, spesso archiviati nel cloud e prodotti ad alta velocità.
5. Bioinformatica: Sviluppo di strumenti e metodi per analizzare dati biologici complessi, utilizzabili anche nell’istruzione.
6. Biometria: Raccolta automatizzata di dati biologici per l’identificazione e il controllo, come il riconoscimento facciale.
7. Blockchain: Sistema di database decentralizzato che collega blocchi di informazioni esistenti.
8. Dati Clickstream: Sequenza delle pagine web visualizzate dai visitatori, utilizzata per analizzare i comportamenti online.
9. Computer Adaptive Testing (CAT): Test online che adattano le domande in base alle risposte degli studenti.
10. Dati e Analisi dei Dati: Dati definiti come informazioni numeriche digitali; l’analisi dei dati utilizza processi algoritmici per estrarre informazioni significative.
11. Datafication: Processo tecnico di rappresentazione dell’esperienza come dati digitalizzati.
12. Infrastrutture di Dati: Sistemi di archiviazione digitale per la condivisione e l’utilizzo dei dati.
13. Data Science: Interpretazione di grandi quantità di dati per decisioni significative.
14. Rivoluzione Digitale: Impatti delle tecnologie digitali sulla società e sull’educazione.
15. Digitalizzazione e Digitalizzazione: Differenziazione tra mettere online materiali non digitalizzati e l’impatto dei cambiamenti digitali.
16. Edu-business: Aziende che vendono prodotti educativi.
17. Tecnologie per l’Istruzione (EdTech): Aziende che progettano e vendono tecnologie educative.
18. Realtà Estesa (XR): Ambienti combinati reali e virtuali generati dalla tecnologia.
19. Valutazioni Immersive (IA): Valutazioni basate su scenari 3D con AR, VR, MR e XR.
20. Valutazioni Internazionali su Larga Scala (ILSA): Test che forniscono prospettive comparative internazionali sulle prestazioni degli studenti.
21. Internet of Things (IoT): Rete di oggetti fisici connessi via Internet.
22. Interoperabilità: Compatibilità dei sistemi software per l’integrazione dei dati.
23. Analisi dell’Apprendimento: Monitoraggio degli studenti attraverso i dati delle loro attività online.
24. Machine Learning (ML): Processo di apprendimento automatico che guida l’intelligenza artificiale.
25. Realtà Mista (MR): Fusione di mondi reali e virtuali.
26. Apprendimento Personalizzato: Programmi educativi creati per soddisfare le esigenze individuali degli studenti.
27. Analisi Predittiva: Utilizzo di dati storici per prevedere modelli futuri.
28. Psicoinformatica: Analisi comportamentale e psicologica attraverso dati e tecniche di machine learning.
29. Realtà Virtuale (VR): Immersione in ambienti completamente virtuali.

Il glossario include anche riferimenti e definizioni tratte da varie fonti, tra cui l’OCSE e altri esperti del settore.