{"id":95,"date":"2023-10-22T08:01:05","date_gmt":"2023-10-22T08:01:05","guid":{"rendered":"https:\/\/aiopentext.itd.cnr.it\/aiforteacher\/chapter\/ai-speak-data-based-systems-part-1\/"},"modified":"2024-01-31T08:10:54","modified_gmt":"2024-01-31T08:10:54","slug":"ai-speak-data-based-systems-part-1","status":"publish","type":"chapter","link":"https:\/\/aiopentext.itd.cnr.it\/aiforteacher\/chapter\/ai-speak-data-based-systems-part-1\/","title":{"raw":"AI-Speak: Data-based systems, part 1","rendered":"AI-Speak: Data-based systems, part 1"},"content":{"raw":"
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Decisions in the classroom<\/strong><\/h3>\n<\/header>\n
\n\nAs a teacher, you have access to many kinds of data. Either tangible data such as attendance and performance records, or intangible ones such as student body-language. Consider some of the decisions you take in your professional life:What are the data that help you make these decisions? <\/em>\n\nThere are technological applications that can help you visualise or process data. Artificial intelligence systems use data to personalise learning, make predictions and decisions that might help you teach and manage the classroom: Do you have needs that technology can answer? If yes, what will be the data such a system might require to carry out the task?<\/em>\n\n<\/div>\n<\/div>\n\n[caption id=\"attachment_94\" align=\"alignleft\" width=\"264\"]\"\" Reference: Kelleher, J.D, Tierney, B, Data Science, London, 2018 and Kitchin, R, Big Data, new epistemologies and paradigm shifts, Big Data & Society, 2014[\/caption]\n

Educational systems have always generated data \u2013 students\u2019 personal data, academic records, attendance data and more. With digitalisation and AIED applications, more data is recorded and stored: mouse clicks, opened pages, timestamps and keyboard strokes1<\/sup>. With data-centric thinking becoming the norm in society, it is natural to ask how to crunch all this data to do something pertinent. Could we give more personalised feedback to the learner? Could we design better visualisation and notification tools for the teacher?2<\/sup><\/p>\n

Whatever technology is used, it has to meet a real requirement in the classroom. After the need is identified, we can look at the data available and ask what is relevant to a desired outcome. This involves uncovering factors that let educators make nuanced decisions. Can these factors be captured using available data? Is data and data-based systems the best way of addressing the need? What could be the unintended consequences of using data this way3<\/sup>?<\/p>\n

Machine learning lets us defer many of these questions to the data itself4<\/sup>. ML applications are trained on data. They work by operating on data. They find patterns and make generalisations and store these as models \u2013 data that can be used to answer future questions4<\/sup>. Their decisions and predictions, and how these affect student learning, are all data too. Thus, knowing how programmers, the machine and the user handle data is an important part of understanding how artificial intelligence works.<\/p>\n\n

About data<\/h3>\n

Data<\/strong> is generally about a real world entity - a person, an object, or an event. Each entity can be described by a number of attributes<\/strong> (features<\/strong> or variables<\/strong>)5<\/sup>. For example, name, age and class are some attributes of a student. The set of these attributes is the data we have on the student, which, while not in any way close to the real entity, does tell us something about them. Data collected, used and processed in the educational system is called educational data1<\/sup><\/strong>.<\/p>\n

A dataset<\/strong> is the data on a collection of entities arranged in rows and columns. The attendance record of a class is a dataset. In this case, each row is the record of one student. The columns could be their presence or absence during a particular day or session. Thus each column is an attribute.<\/p>\nhttps:\/\/youtu.be\/J7mpEufsjYU?list=PLkm8ZqYSzE3d6lEWsWz0fWhtSAbjwR8Z7\n\n[caption id=\"attachment_94\" align=\"alignright\" width=\"300\"]\"\" The DIKW Pyramid. Reference: Kelleher, J.D, Tierney, B, Data Science, London, 2018 and Kitchin, R, Big Data, new epistemologies and paradigm shifts, Big Data & Society, 2014[\/caption]\n

Data is created by choosing attributes and measuring them: every piece of data is the result of human decisions and choices. Thus, data creation is a subjective, partial and sometimes messy process prone to technical difficulties4,5<\/sup>. Further, what we choose to measure, and what we don't can have a big influence on expected outcomes.<\/p>\n

Data traces <\/strong>are records of student activity such as mouse clicks, data on opened pages, the timing of interactions or key presses in a digital system1<\/sup>. Metadata<\/strong> is data that describe other data5<\/sup>. Derived data<\/strong> is data calculated or inferred from other data: individual scores of each student is data. The class average is derived data. Often, derived data is more useful in getting useful insights, finding patterns and making predictions. Machine Learning applications can create derived data and link it with metadata data traces to create detailed learner models<\/a>, which help in personalising learning1<\/sup>.<\/p>\n

For any data based application to be successful, attributes should be carefully chosen and correctly measured. The patterns discovered in them should be checked to see if they make sense in the educational context. When designed and maintained correctly, data driven systems can be very valuable.<\/p>\nhttps:\/\/youtu.be\/gZcgUBVQyRM?list=PLkm8ZqYSzE3d6lEWsWz0fWhtSAbjwR8Z7\n

Check if you are (Big) Data literate<\/a><\/div>\n

This chapter aims to introduce a few basics of data and data based technology but data literacy is a very important skill to possess and merits dedicated training and continuing support and update1<\/sup>.<\/p>\n\n

Legislation you should know about<\/h3>\n

Because of the drastic drop in costs of data storage, more data and metadata are saved and retained for a longer time6<\/sup>. This can lead to privacy breaches and rights violations. Laws like the General Data Protection Regulation (GDPR)<\/strong> discourages such practices and gives EU citizens more control over their personal data. They give legally enforceable data protection regulations across all EU member states.<\/p>\n\n\n[caption id=\"attachment_94\" align=\"alignleft\" width=\"378\"]\"\" Reference: \"GDPR & ePrivacy Regulations\" by dennis_convert licenced under CC BY 2.0. To view a copy of this licence, visit https:\/\/creativecommons.org\/licenses\/by\/2.0\/?ref=openverse<\/a>[\/caption]\n

According to GDPR, personal data is any information relating to an identified or identifiable person (data subject<\/strong>). Schools, in addition to engaging with companies that handle their data, store huge\u00a0 amounts of personal information about students, parents, staff, management, and suppliers. As data controllers, they are required to store data which they process confidentially and securely and have procedures in place for the protection and proper use of all personal data1<\/sup>.<\/p>\n

Rights established by the GDPR include:<\/p>\n\n