Aarhus universitet April 3 rd 2019

1. Open seminar Aarhus universitetApril 3rd 2019 Or – what is the value of data for everyday education, its policy and practice? The uncertain directional value of data in education Or – for what, exactly, is data relevant? Jón Torfi Jónasson School of Education, University of Iceland jtj@hi.ishttp://uni.hi.is/jtj/en/

2. For the record The importance of data I will suggest and argue that data has less directional value than is sometimes suggested (but perhaps it is often only implied). This does not suggest any undermining of the usefulness of data for understanding the world by establishing facts or seeing patterns or relationships – and thus open up a number of possible avenues – as a tool of science for seeing what is happening – assessing either the status quo, - or developments – describing the good and the bad I am suggesting a fine line that is not easy to draw, and is normally not drawn, but which, I argue, is exceedingly important in the current age of unquestioning belief by some, in the value and power of data. Data is, - has been, - and will be very important in our professional (and also often private) lives. All I am suggesting is that even though it is crucial in important contexts, it is not so in all contexts, and thus does not solve all our pressing problems – as some people seem to think. Jón Torfi Jónasson Aarhus 2019 -On data

3. The underlying questions: The rationale for the presentation • How does the research community or the testing, accountability, evaluation communities or the teaching or policy communities introduce or rationalise their emphasis on data? • What is the place of data in our educational discourse? • Are we absolutely clear about its place, its value, its use? • No we are not? But we must endeavour to establish and understand this. • So, is data perhaps not useful? • Of course it is. Absolutely crucial in some contexts! • Is the value of data overrated – overemphasized? • Yes, very definitely – in certain contexts. • Thus we must consider how we talk about data, how we appreciate its strengths and it weaknesses – how we promote a balanced enlightened discourse about data – especially for the general professional discourse. Jón Torfi Jónasson Aarhus 2019 -On data

4. The line of argument (1a) • Data is not only important, it is often crucial for a number of reasons. The importance of data can hardly be overstated when placed in the appropriate context. • Two aspects of the discourse should perhaps to be addressed first: • Terminology. How is data defined and how does this definition relate to the notions of measurement, indicator and evidence. • Data. Many different types of data; analogue, digital, numerical, verbal, visual (pictorial), … • Measurement. Data collected on a specific scale, - the validity issue • Indicator. Data collected, often by counting or measurement – validity again • Evidence. Measurement or data used as evidence – validity once more, especially construct and consequential, but some other varieties as well • Rationalisation. How the use of data is rationalised or justified in the different contexts. • General statements about the importance of data • Arguments referring to science – or understanding • Arguments referring to indicators or evidence • Arguments referring to the combination of 2) and 3), e.g. in a educational macro or micro settings Jón Torfi Jónasson Aarhus 2019 -On data

5. “In this era of quantitative enthusiasm, we use data to define problems and construct solutions to them. … Indicators seductively promise to provide guidance through a complex world.” (p. 221) There are several well-known problems with data, often very pronounced within the social arena. Here we will note three classes of problems or issues, even though more, and important ones, can be named. The problem of validity of data, when used as measurement, indicator or evidence. The data may often lack the validity we often assume it has. This may dramatically diminish the value of data, but this serious issue is not in focus in the presentation. We assume we are talking about “good” data. The problem of rationality – or irrationality in decision making, i.e., how we select, prioritize, ignore or value data as a basis for our decisions. This touches on a variety of well-known (and often ignored problems of decision making) which is discussed within the decision literature in psychology and economics. Here I mention only that of attributing causality when it is not appropriate. But there are many more. There are many potentially pernicious effects of relying on data, which perhaps can be subsumed under the category, the signifier becomes the signified (e.g. Krejsler) and thereby overtakes the stage. The data, to which attention is drawn, inadvertently becomes the principal focus of attention, marginalising other data or variables. This can happen in various ways and with various different effects. The line of argument (1b) • “How can we improve judgements and decisions, both our own and those of the institutions that we serve and serve us? The short answer is that little can be achieved without considerable investment of effort.” (p. 417) [I.e., understand how we operate.] • “Self-reinforcing and shared goal-setting reflects how QAE rather than quality, has become the goal of education.” (p. 11) Jón Torfi Jónasson Aarhus 2019 -On data

6. The line of argument (1c) • But issues I have very briefly mentioned are not the focus of this presentations, but here we are getting closer : • There are three very noticeable, forceful and related trends we see in data within education – and I will briefly come back to all three. • One is the enormous and growing availability of all sorts of data, numerical, digital, verbal, acoustic, visual, … . There are all sorts of real data explosions. • Second is the formal policy directives that emphasise the importance of data, ranging from descriptions of the basic functioning of education systems (ILSAs) and how they responds to its clientele, - and to the daily operations within the classroom (tests, continuous assessment). • The third is the corollary of both of these, i.e., the belief that little (nothing?) sensible can be done without data with an enormous push by vested interests. For example we have the emphasis on evidence based policy and assessment for learning and it is emphasised that actions on all levels of the education system shall be based on data. • But the question we raise is this – and here is the focus of the presentation. Assuming that we have very solid – good data, how much explicit directional value does this data really have, - on its own, e.g., for the policy maker or the teacher? Jón Torfi Jónasson Aarhus 2019 -On data

7. The line of argument (2) • The question demands a careful analysis, because it is absolutely crucial in our data clouded or dominated world to understand the value of data. Implicit in the question are a number of sub-questions and the overarching question can be re-phrased in a number of ways. • Does the data, as such, tell you that something must be done? (The answer is No, it does not – but paired with our values it certainly may. Thus the values demand a lot of work and attention – much more than should be allotted to the data itself.) • Does data at hand imply that some particular aims – or sub-aims should be set? (The answer is No, it does not. So the aims demand attention – which is not dominated by data.) Data does not define what are the ingredients of quality education. • Once some aims have been set and it has been decided that something should be done, does the data tell which route should be selected? Does the data tell you where to go next? (The answer is No, it does not - therefore problems with the notions of evidence based policy or formative assessment.) • In other words, what does the data in fact tell you? And thus, what is the explicit justification for terms like “evidence based policy / practice” or “formative assessment” – or “assessment for learning”? (I am sure it is already noted that the issue raised is taken here to apply to all levels of the educational discourse. The concern is of course not new but perhaps its considerable urgency is more pressing.) • Given the suggested answers, a new question arises, i.e., what has to be added to the setting in order to obtain an answer favourable to data? That is something that is normally implicit within the educational discourse, often felt to be unproblematic, but can be made explicit - as will be suggested, and thereby - should - become immediately problematic and we should attend to it. Jón Torfi Jónasson Aarhus 2019 -On data

8. The line of argument (3) Our current mode of thinking about the value of data seems to be moulded by the analogy with a mechanical feedback system, e.g. a temperature regulator and a host of similar devises. When the temperature goes down, the heating system is turned on – a simple feedback loop is being harnessed. Such analogy is seriously misleading. Or if you like – we assume that if we know where we stand, what the situation is like, we know where to go and even more importantly we know how to get there – we know how to react. But none of this is the case, in education, if all we have is data. Summary of the claims And thus data does not solve all our problems in our daily world of education. In particular, it very rarely tells you where to go. Its directional value ranges from being limited to being non-existing. Even when we know that we have to move and even know where to go, data may not tell which is the best way to get there. But it often appears to do so and the rhetoric and the modus operandi seem to rely on this. Main conclusion Therefore data must be given its proper place within the educational discourse. Data is important but not in all the ways that is often stated or implied. This applies to the wide spectrum of the educational policy edifice. It also applies to the testing and measurement operational mode, and that of accountability. It also applies to daily teaching. (A related discussion is necessary for the value of research for the direction of action, which has similar serious problems to address.) Jón Torfi Jónasson Aarhus 2019 -On data

9. Coming back to data – different data or purposes (apart from scientific data) – various types of influences? • Indicators, such as those produced by our Statistical bureaus and international agencies – Education at a glance or the WIDE base or the various UENSCO bases https://en.unesco.org/themes/education/databasessee also https://ourworldindata.org/quality-of-education Also related efforts, avariety of well being scales, OECD (How is life? 2017), PISA 2015 (Volume III, Student well-being) – UNESCO- World-Bank, OECD, … Lundahl, Lisbeth; Arnesen; Anne-Lise  & Jónasson, Jón Torfi. (2018). Justice and marketization of education in three Nordic countries: can existing large-scale datasets support comparisons? Nordic Journal of Studies in Educational Policy Volume 4, 2018 - Issue 3: Comparative perspectives on Nordic education policy. Pp 120-132 Published online: 11 Dec 2018 https://doi.org/10.1080/20020317.2018.1542908 • International scales – each a whole world of its own see e.g. the Northern lights conferences PISA, new scales: problem solving (2012), collaboration (2015), global competence (2018), creative thinking (2021), foreign language skills (2024), use of the internet (2027). Also the IEA’sTIMMS and PIRLS and OECD’s PIAAC • Quality assessment exercises (QAEs), mostly national or municipal – demanding vast amounts of data – accountability rationale (the quality industry) • Local standardised or general tests – the systemic (entrance gates), professional (subject demanded) and teaching motivated assessment – increasingly for teaching purposes, in particular a variety of formative tests and portfolios. Also related to the absolute and comparative progress of students for a dialogue with parents • A variety of diagnostic or monitoring devices - e.g. cognitive and bio-social data efforts • Efforts to combine all kinds of data into one pool for semi-automatic teaching – e.g. the AI enterprises • A variety of schools or belief systems emphasising the importance of data, often taking the sine qua non stance – you are not allowed to think without data and you get nowhere without data. • The enormous vested interests in terms of institutions, money and expertise that rely on this approach. Jón Torfi Jónasson Aarhus 2019 -On data

10. Don’t move without data -- but then again • Cooper, Levin, and Campbell (2009) remind us that it is “virtually impossible for a reasonable person to disagree with the idea that policy and practice should be based on the best available evidence” (p. 161). [Well, which is the best? Or: Evidence-Based Education Policy: What Evidence? What Basis? Whose Policy?] • “the evidence against the usefulness of all our testing is robust and compelling” (p. 305). And “the perverse consequences of heavy testing in … Philadelphia .. disregard for subjects (specifically writing and science) that had no bearing on determining a school’s Adequate Yearly Progress. (p. 304). Abrams, S. E. (2016). Education and the commercial mindset. Cambridge, Massachusetts: Harvard University Press. [But which stakeholders really listen to this type of argument; listen to the best available evidence.] • „Despite strong empirical support and early enthusiasm for PSI (Personalized system of instruction), it is not a dominant form of teaching today“. Fox, E.J. (p. 360) in Hattie, J., & Anderman, E. M. (2013). International guide to student achievement. London: Routledge. [There is a lot of old and new evidence no one listens to – perhaps justly so.] Jón Torfi Jónasson Aarhus 2019 -On data

11. UNESCO The Global Education Monitoring Report. Accountability in education Proposedpost-2015 educationgoals: Emphasizingequity, measurability and finance. There is clearly an official – local and global - emphasis on measurement and visibility • Chapter 4: To take learning seriously, start by measuring it - (p. 91) • The learning crisis is often hidden—but measurement makes it visible • Measures for learning guide action • Measures of learning spur action • Choose learning metrics based on what the country needs • Will learning metrics narrow the vision for education? • Six tips for effective learning measurement • Spotlight 3: The multidimensionality of skills • World Bank • World Development Report 2018: Learning to Realize Education's Promise • The policy actions they emphasize to address the Learning Crisis, are e.g. • Assess learning, to make it a serious goal. • Act on evidence, to make schools work for learners. Jón Torfi Jónasson Aarhus 2019 -On data

12. Coming back to data – Biosocial Education: The Social and Biological Entanglements of Learning From Youdell 2016 paper – A biosocial education future: … explores how social justice orientated education research might engage with emerging ideas and approaches from the new biological sciences, and suggests a biosocial future for empirical education research that connects molecular biology – epigenetics, nutrigenomics and neuroscience – with sociology of education. In beginning to consider what the biosocial means for education the paper works through two pressing questions ‘what happens when we learn?’ and ‘are relationships important in the classroom?’, bringing together emerging evidence across sociology, pedagogy, molecular biology and neuroscience. Biosocial influences on learning are influenced by (p. 148). • What teachers do • What we are taught • Our relationships • How we feel • Our selves • What we eat • What we do • How we sleep • Where we live • Our school • Politicians • What cells do • Cognitive Jón Torfi Jónasson Aarhus 2019 -On data

13. What data are we talking about? Labile data Test data Narrative data Nutritional data Welfare data Stress or anxiety levels Hormonal changes Activity monitoring (institutional, physical, social, mental, brain, … ) Stable data Personality Social background Academic background ….. Materiality and learning – biochemical effects, related to feelings; exhaled breath analysis, voice spectra, brain scans, …. “Our understanding of the production and products of learning becomes simultaneously concerned with social structures, institutional practices, representations and meaning, subjectivities, relationships, feeling, neural networks, metabolic processes and molecular functions. “ Which data are most relevant? On what basis should we choose, once we access to most – will we take the AI route and choose all ? Irrespective of AI, we need to have this discussion. Jón Torfi Jónasson Aarhus 2019 -On data

14. Building Brains: How Pearson Plans To Automate Education With AI by Parmy Olson, Aug 29, 2018 https://www.forbes.com/sites/parmyolson/2018/08/29/pearson-education-ai/#5247fc6c1833 This is no easy task. With millions of students using its education-software, Pearson has amassed “terabytes” of data from student homework and even textbooks that have been digitized, data that Marinova is now pulling together to build software that can automatically give students feedback on their work like a teacher would. Milena „Marinova’s [senior vice president, AI products and solutions] challenge, which is a common one among large companies that want to use Big Data and AI to become more efficient and attractive to their customers, is parsing a mountain of data to build the right algorithms. How do you measure how effectively a human being has learned something, for instance? How do you get software to do it? Marinova says she’ll be drawing from “millions of samples” of homework data that Pearson has collected from student assessments and coursework exercises over the years, including the 12 million students who are enrolled in MyLab, a suite of homework assessment tools.” Intelligence Unleashed - An argument for AI in Education, Pearson 2016 https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/Intelligence-Unleashed-Publication.pdf “MACHINE LEARNING Computer systems that learn from data, enabling them to make increasingly better predictions. … One of the advantages of adaptive AI-Ed systems is that they typically gather large amounts of data, which, in a virtuous circle, can then be computed to dynamically improve the pedagogy and domain models. … A multitude of AI Ed-driven applications are already in use in our schools and universities. Many incorporate AIEd and educational data mining (EDM) techniques to ‘track’ the behaviors of students – for example, collecting data on class attendance and assignment submission in order to identify (and provide support) to students at risk of abandoning their studies. … Instead of models, many recent ITS use machine learning techniques, self-training algorithms based on large data sets, and neural networks, to enable them to make appropriate decisions about what learning content to provide to the learner. However, with this approach, it can be difficult to make the rationale for those decisions explicit. … The increasing range of data capture devices – such as biological data, voice recognition, and eye tracking – will enable AIEd systems to provide new types of evidence for currently difficult to assess skills. “ From 2017, emphasis on data, and teachers https://www.youtube.com/watch?v=_Ivky0NZcdU From 2018 https://www.youtube.com/watch?v=7YUthMwlifs Pearson, AI and data Jón Torfi Jónasson Aarhus 2019 -On data

15. Coming back to data – we are becoming totally immersed in data – how do we manage? https://codeactsineducation.wordpress.com/2018/12/18/learning-lessons-from-data-controversies/ From des 2018 Ten years ago ‘big data’ was going to change everything and solve every problem—in health, business, politics, and of course education. But, a decade later, we’re now learning some hard lessons from the rapid expansion of data analytics, algorithms, and AI across society. Education, work and Australian society in an AI world https://education.arts.unsw.edu.au/media/EDUCFile/Gonski_AIEd_Final_Aug2018_Formatted.pdf Jón Torfi Jónasson Aarhus 2019 -On data

16. Interesting reminders Lorna Earl and Karen Seashore Louis: Data use, where do we go from here. (P. 193). Educational systems around the world are caught in the data frenzy proclaiming from central offices to schools to classrooms that they are data driven organizations … Data use, however, is not a singular process and it does not have a particular “value valence” associated with it. Data, by the themselves are benign, or at least neutral. And data by themselves do not answer questions. Rather, it is the interaction between data and people that creates harm or beneficial effects. Jón Torfi Jónasson Aarhus 2019 -On data

17. Datadriveneducationalsystems? Brooking Institutution. (2018). TOWARD DATA-DRIVEN EDUCATION SYSTEMS Insights into using information to measure results and manage change. Approximately 180 leaders from 78 countries responded to the 2017 Education Snap Poll. https://www.brookings.edu/wp-content/uploads/2018/02/toward-data-driven-education-systems.pdf Sjá yfirlit: https://www.brookings.edu/blog/education-plus-development/2018/02/20/6-key-insights-into-the-data-and-information-education-leaders-want-most/ Jón Torfi Jónasson Aarhus 2019 -On data

18. Each of us has or knows endless examples of data that is meant to help us, even lead us. Albæk, Karsten; Asplund, Rita; Barth, Erling; Lindahl, Lena. (2015). Youth unemployment and inactivity: A comparison of school-to-work transitions and labour market outcomes in four Nordic countries. Copenhagen: Nordisk Ministerråd, 2015  urn:nbn:se:norden:org:diva-4071 Another distinct feature is the conspicuously similar share of the NEET population across the four countries despite remarkable cross‐country differences not least in the share of young people still lacking an upper secondary degree when aged 21. (p. 277). Jón Torfi Jónasson Aarhus 2019 -On data

19. Turn to the classroom – a typical situation for every class teacher in compulsory school in Iceland Jón Torfi Jónasson Aarhus 2019 -On data

20. Jón Torfi Jónasson Aarhus 2019 -On data

21. About feedback – feedback – been prominent for a while Feedback. There has been extensive research done on studying how students are affected by feedback. Kluger and DeNisi (1996)[26] reviewed over three thousand reports on feedback in schools, universities, and the workplace. Of these, only 131 of them were found to be scientifically rigorous and of those, 50 of the studies show that feedback actually has negative effects on its recipients. This is due to the fact that feedback is often "ego-involving",[17] that is the feedback focuses on the individual student rather than the quality of the student's work. Feedback is often given in the form of some numerical or letter grade and that perpetuates students being compared to their peers. The studies previously mentioned showed that the most effective feedback for students is when they are not only told in which areas they need to improve, but also how to go about improving it. https://en.wikipedia.org/wiki/Formative_assessment#Feedback Hattie og Clarke 2019, p. 1: Teachers’ definitions: The 10Cs But students emphasise guidance: “feedback helps me know where to go next” (p.1) JTJ - Q: How well does the data give that guidance Comments- give comments on the way you are doing something Clarification – answering students questions in class Criticism – when you are given constructive criticism Confirmation – when your are told you are doing it right Content development – asking about the comment Constructive reflection – giving someone positive and constructive reflection on their work Correction – showing what you did right or wrong, which helps you Cons and pros – someone telling the pros and cons of your work Commentary – they comment on my work Criterion – relative to a standard Jón Torfi Jónasson Aarhus 2019 -On data

22. Thecurrentdiscourseon formative assessment DylanWiliam --- LorrieShepard Lorrie A. Shepard (2018). Learning progressions as tools for assessment and learning “Learning progressions are one of the strongest instantiations of principles from Knowing What Students Know, requiring that assessments be based on an underlying model of learning. To support student learning, quantitative continua must also be represented substantively, describing in words and with examples what it looks like to improve in an area of learning. For formative purposes, in fact, qualitative insights are more important than scores. By definition, learning progressions require iterative cycles of development so as to build in horizontal coherence among curriculum, instruction, and assessment.” https://gregashman.wordpress.com/2018/08/11/an-interview-with-dylan-wiliam/Interview 2018 … teachers need evidence about what their students are thinking in order to make good decisions, and the quality of that evidence is often poor. The really important thing for me is that formative assessment is neutral with respect to curriculum (what we want students to learn) and pedagogy (how we get students to learn). The big idea—what psychologist David Ausubel called the most important idea in educational psychology—is that any teaching should start from what the learner already knows, and that teachers should ascertain this, and teach accordingly.  … we now know that when teachers develop their practice of formative assessment, their students learn more, even when learning is measured in terms of scores on externally mandated tests and exams. https://www.youtube.com/watch?v=fh4zN9JLdVQ LorrieShepard (2017). Notes four stages or levels which relate data and teaching: #1. Data-driven decision making #2. Strategy-focused formative assessment. #3. Socio-cognitive formative assessment. And the one she thinks is the most valuable. #4. Sociocultural formative assessment. But this requires not only rich data but also a “learning progression” which interweaves assessment, developed curriculum and a theory of learning Learning progression. Jón Torfi Jónasson Aarhus 2019 -On data

23. What might be the problem with formative assessment? Despite the rigour and richness of the socio-cultural approach Lorrie Shepard presents, coupled with the notion of learning progressions it raises the question of all kinds of additional insights into the student‘s standing that might affect the teacher‘s action. This is inferred by taking her own sensible way of thinking one or two stages further keeping in mind all the potential information suggested e.g. by Youdell and Lindlay or by Williamson. One could have much more an diverse data. Jón Torfi Jónasson Aarhus 2019 -On data

24. What might be the problem with formative assessment? This may then be added to the questions raised about any potential learning progression, where a number of alternatives might in principle be suggested. The appealing rigour of the idea of a learning progression, may marginalise (silence, hide) a number of innovative alternatives: • See e.g. a number of points raised by Wiliam, D. (2018). How Can Assessment Support Learning? A Response to Wilson and Shepard, Penuel, and Pellegrino. Educational Measurement: Issues and Practice, Spring 2018, Vol. 37, No. 1, pp. 42–44 Here he notes examples of practices, that we might feel were in keeping with common sense, do not produce the best results. • The idea presented by Carol Sanford, that feedback on the whole tends to be detrimental, at least in the long run, and she suggest what she claims are superior alternatives for professional development – claiming that on the whole feedback has a negative developmental effect. • The notion of a specified learning progression may greatly overestimate the necessity of the a mastery of the base or basic concepts of any discipline – this is perhaps one of the serious curricular myths. See e.g., the ideas proposed by Tom Fox about edGe-ucation. This a genuine challenge to the notion of learning progressions. Tom Fox (2013). Evidence for Addressing the Unsolved through edGe-ucating or - Can Informing Science Promote Democratic Knowledge Production? https://doi.org/10.28945/1886 „EdGe-ucating is a process aimed to democratize intellectual breakthroughs, replacing more recent assumptions about specialized experts being the only ones who can create new knowledge. …. This article has suggested how a learning society can be defined as a culture in which citizens with little previous training can be supported and guided to work on intellectual unknowns. We can create strategies for engaging citizen’s imaginations that will restructure, replace, or at least alter the templates which educators, researchers, and most problem solvers have been applying since ancient times.” Jón Torfi Jónasson Aarhus 2019 -On data

25. Thus what should we keep in mind in a data driven culture? • Can goals (and sub-goals) be set on the basis of data? As an example, can the aims of education or upbringing be defined by data. No, they cannot. Data, on its own, does not define aims or goals or sub-goals. But they may be specified by criteria that can be translated into data. But when these have been determined, - measurements (or some other data) may define the gap between the present situation and the goal, but nothing else. So the hard question is: What is the role of data in defining goals? • Once the gap between the current state and the ideal state has been defined (by specified criteria at each end), is the route thereby explicitly defined? No, definitely not. Only in situations when the teacher has very clear and well defined curriculum and a very clear theory of learning can she feel justified in selecting a particular route. This is rarely the case, but when these factors guide the route to be taken it is due to them and not the data itself. When it becomes clear that data neither is the basis for defining goals nor the route to a goal, a number of serious questions arise: A) What is the assumed relationship between data and action both at the teaching and policy level. B) How should we rephrase conceptions like evidence based policy or formative assessment. C) To what extent does the data, nevertheless, define the discourse and, perhaps inadvertently, the problems and even aims and alternative courses of action? It is clear that available evidence often does not give much help in setting sensible goals or finding the best avenues in a micro or macro educational settings. Sometimes people may think that aproblem can be solved by collecting more data. But that help is very uncertain. Jón Torfi Jónasson Aarhus 2019 -On data

26. Thus what should we keep in mind in a data driven culture? Or – we asked, what is the value of data for everyday education, its policy and practice? It is both implicit and explicit in the argument that the value is uncertain. That is certainly not implying in any way that data is not invaluable for describing the world and understanding it as we do in all our research. But sometimes it is implied that data is a miracle cure for everything – and that is damaging for education. We have suggested that the directional value of data is, to put it mildly, very uncertain. Or – we asked, for what, exactly, in educational practice is data relevant? Thus, under the enormous pressure to collect data and make it central to our educational endeavour, we should be pressed to think very hard about its various uses, and what precedence it should be given over many other fundamentally important tasks in our educational edifice. In particular we should ponder what important tasks are not solved or helped much by data. What should we teach about data? In the modern context we should teach about much more important things underpinning our use of data, from what we are doing now. The technical curriculum should be totally transformed. Jón Torfi Jónasson Aarhus 2019 -On data

27. Thanks Jón Torfi Jónasson Aarhus 2019 -On data