Data driven #education session #OEB18 @oebconference #data @m_a_s_c

From the session on data driven education, with great EU links and projects.

Carlos Delgado Kloos: using analytics in education

Opportunities

Khan academy system is a proven system, with one of the best visualisations of how the students are advancing. With a lot of stats and graphs. Carlos used this approach for their 0 courses (courses on basic knowledge that students must know before moving on in higher ed).

Based on the Khan stats, they built a high level analytics system.

Predictions in MOOCs (see paper of Kloos), focusing on drop-out.

Monitoring in SPOCs (small private online courses)

Measurement of Real Workload of the students, the tool adapts the workload to the reality.

FlipApp (to gamify flipped classroom), remember and to notify the students that they need to see the videos before class, or they will not be able to follow. (Inge: sent to Barbara).

Creation of Educational Material using Google classroom. Google classroom sometimes knows what the answer of a quiz will be, which can save time for the teacher.

Learning analytics to improve teacher content delivery.

Use of IRT (Item Response Theory) to see which quizzes are more useful and effective, interesting to select quizzes.

Coursera define skills, match it to the jobs and based on that recommend courses.

Industry 4.0 (big data, AI…) for industry, can be transferred to Education 4.0 (learning analytics based on machine learning). (Education3.0 is using the cloud, where both learners and teachers go to).

Machine learning infers the rules from getting answers which are data analysed (in comparison to computer learning, which is just the opposite, based on rules, giving answers).

Dangers:

Correlations: correlations are not necessary correct conclusions. (see spurious correlations for fun links).

Bias: e.g. decisions for giving credit based on redlining and weblining.

Decisions for recruitment: eg. Amazon recruits that the automation of their recruiting system resulted in a biase leading to recruiting more men than women.

Decisions in trials: eg. Compas is used by judges to calculate repeat offenders, but color of skin was a clear bias in this program.

Decisions in academia: Cathy O’Neil: weapons of Math Destruction – how big data increases inequalityand threatens democracy”.

Chinese social credit system which gives minor points if you do something that is seen as not being ‘proper’. Also combined with facial recognition, and monitoring attention in class (Hangzhou number 11 high school).

Monitoring (gaggle, …)

Challenges

Luca challenge: responsible use of AI.

GDPR Art 22: automated individual decision-making, including profiling.

Sheilaproject.eu : identifying policies to adopt learning analytics. Bit.ly/sheilaMOOC is the course on the project.

Atoms and bits comparison. As with atoms you can use it for the better, or for the worse (like atomic bombs).

Maren Scheffel on Getting the trust into trusted learning analytics @m_a_s_c

(Welten Institute of Open University, Netherlands)

Learning analytics: Siemens (2011) definition still the norm. But nowadays it is a lot about analytics, but only little about learning.

Trust: currently we believe that something is reliable, the truth, or ability. Multiple definitions of trust, it is multidimensional and multidisciplinary construct. Luhmanndefined trust as a way to cope with risk, complexity, and a lack of system understanding. For Luhmann the concept of trust compensates for insufficient capabilities for fully understanding the complexity of the world (Luhmann, 1979, trust and …)

 For these reasons we must be transparent, reliable, and be integer to attract the trust of learners. There should not be a black box, but it should be a transparent box with algorithms (transparent indicators, open algorithms, full access to data, knowing who accesses your data).

Policies: see https://sheilaproject.eu   

User involvement and co-creation: see the competen-SEA project see http://competen-sea.eu capacity building projects for remote areas or sensitive learner groups. One of the outcomes was to co-design to create MOOCs (and trust) getting all the stakeholders together in order to come to an end product. MOOCs for people, by people.  Twitter #competenSEA

Session on #AI, #machineLearning and #learninganalytics #AIED #OEB18

This was a wonderful AI session, with knowledgeable speakers, which is always a pleasure. Some of the speakers showed their AI solutions, and described their process; others focused on the opportunities and challenges. Some great links as well.

Squirrel AI, the machine that regularly outperforms human teachers and redefines education by Wei Zhou

Squirrel AI is an AI to respond to the need for teachers in China. Based on knowledge diagnosis, looking for educational gaps. A bit like an intake at the beginning of a master education for adults.

Human versus machine competition for scoring education, and tailored learning content offerings. (collaborates with Stanford Uni). Also recognized by Unesco. (sidenote: it is clearly oriented at 'measurable, class and curriculum related content testing). 

 The ideas behind AI: adaptive learning is a booming market.

Knowledge graph + knowledge space theory: monitoring students real-time learning progress to evaluate student knowledge mastery and predict future learning skills. based on Bayesian network plus Bayesian inference and knowledge tracing and Item Response Theory. The system identifies the knowledge of the student based on the their intake or tests. Based on big data analysis the students get a tailored learning path. (personalised content recommendation using fuzzy logic, classification tree, and personalized based on logistic regression, graph theory, and genetic algorithm.). Adaptive learning based on Bayesian network, plus Bayesian inference, plus Bayesian knowledge tracing, plus IRT to precisely determine students current knowledge state and needs.

Nanoscale Knowledge Points: granularity is six time’s deeper.  Used in medical field.

Some experiments and results: the forth Human versus AI competition, which resulted in AI being quicker and more adapt to score tests of students.  Artificial Intelligence in Education conference (AIED18 conference link, look up video youtube.com, call for papers deadline 8 February 2019 for AIED19 here).

Claus Biermann on Approaches to the Use of AI in Learning

Artificial Intelligence and Learning: myths, limits and the real opportunities.  

Area9 lyceum: also adaptive  learning long-term company with new investments.

Referring to Blooms 2sigma problem.

Deep, personalized learning, biologically enabled data modeling, four-dimensional teaching approach.

How we differ: adaptive learning adapts to the individual, only shows content when it is necessary, takes into consideration what the student already knows, follows up on what the student is having trouble with.  This reduces the time of learning, and increases motivation. Impact from adaptive learning, almost 50% reduction of learning time.

Supports conscious competence concept.

AI is 60% of the platform, but the most important part is the human being, learning engineers, the team of humans who work together makes it possible.

Marie-Lou Papasian from Armenia (Jerevan).

Tumo is a learning platform where students direct their own development. After school program, 2 hours twice a week, and thousands of students come to the centre of TUMO. Armenia and Paris, and Beirut.

14 learning targets ranging from animation, to writing, to robotics, game development…

Main education is based on self learning, workshops and learning labs.

Coaches support the students and they are in all the workshops and learning labs.

Personalisation: each students choose their learning plan, their topics, their speed. That happens through the ‘Tumo path’, which is an interface which enables a personalised learning path (cfr LMS learning paths, but personalized in terms of speed and choices of the students). After the self-paced parts, the students can go to a workshop to reach their maximum potential, to learn and know they can explore and learn. These are advanced students (12 – 18 years, free of charge).

Harnessing the power of AI: the AI solves a lot of problems, as well as provide freedom to personalise the students learning experience. A virtual assistant will be written to help the coaches to help the student guided through the system.

AI guided dog: a mascot to help the students.

The coaches, assistants… are their to learn the students to take up more responsibility.

For those learners who are not that quick, a dynamic content aspect is planned to support their learning.

Wayne Holmes from the OU, UK and center for curriculum redesign, US

A report commissioned about personalized learning and digital ... (free German version here , English version might follow, will ask Wayne HOlmes).

Looking at the ways AI can impact education

A taxonomy of AI in education

Intelligent Tutoring System (as examples mentioned earlier in the panel talk)

Dialogue-based tutoring system (Pearson and Watson tutor example)

Exploratory Learning Environments (the biggest difference with the above, is that this is more based on diversification of solving a specific problem by the student)

Automatic writing evaluation (tools that will mark assignments for the teachers, also tools that will automatically give feedback to the students to improve their assignments).

Learning network orchestrators (tools that put people in contact with people, e.g. smart learning partner, third space learner, the system allows the student to connect with the expert).

Language learning (the system can identify languages and support conversation)

ITS+ (eg.. ALP, Alt school, Lumilo. The teacher wears google glasses, and the students activity comes as a bubble visualizing what the student is doing).

So there is a lot of stuff already out there.

We assume that personalized learning will be wonderful, but what about participative or collaborative learning

Things in development

Collaborative learning (what one person is talking about might be of interest to what another person is talking about).

Student forum monitoring

Continuous assessment (supported by AI)

AI learning companions (e.g. mobile phones supporting the learning, makes connections)

AI teaching assistants (data of students sent to teachers)

AI as a research tool to further the learning sciences

The ethics of AIED

A lot of work has been done round ethics in data. But there are also the algorithms that tweak the data outcomes, how do we prevent biases, guard against mistakes, protect against unintended consequences….

But what about education: self-fulfilling teacher wishes…

So how do we merge algorithms and big data and education?

With great power comes great responsibility (Spiderman, 1962, or French revolution national convention, 1793)

ATS tool built by Facebook, but the students went on strike (look this up).

Gunay Kazimzade Future of Learning, biases, myths, etcetera (Azerbaijan / Germany)

Digitalization and its ethical impact on society.

Six interdisciplines overlap.

Criticality of AI-biased systems.

(look up papers, starting to get tired, although the presentation is really interesting)

What is the impact of AI on our children is her main research considerations. How is the interaction between children and the smart agents. And what do we have to do, to avoid biases while children are using AI agents.

At present the AI biases infiltrate our world as we know, but can we transform this towards less biases?

Call for papers #CfP from #BJET & call for co-authoring book on #Philosophy #AI #humanmachine #interdisciplinary

The call for papers below is for authors researching 'human learning and learning analytics in the age of artificial intelligence' and is an action to celebrate BJET's 50th anniversary. But first ... the call for co-authors to realize a new Rebus book on the subject of Introduction to Philosophy series.

Seeking Authors & Editors for Introduction to Philosophy Series

The Rebus Community initiative Introduction to Philosophy series has grown tremendously, and a few books are nearing the final stages! Led by Christina Hendricks (University of British Columbia), the series includes eight volumes in total, ranging across themes. We are currently seeking faculty interested in contributing to the series by authoring chapters in the following books:
Epistemology
Aesthetics
Metaphysics
Social and Political Philosophy
Philosophy of Religion

See the full list of open and completed chapters.

Authors should have a PhD in philosophy and teaching experience at the first-year level. PhD students and candidates may also be considered as authors, or can contribute to the book in other ways. If you are interested, please let us know in Rebus Projects. Include your CV, a brief summary of your experience teaching an intro to philosophy course, and the chapters you would like to write.

We’re also looking for a co-editor for the Aesthetics book, and an editor forPhilosophy of Science. If you’re interested in taking on one of these roles, read the full job posting and then comment in the activity on Rebus Projects, including some details about your experience and the area in which you are interested.

The editorial team encourages contributions from members of under-represented groups within the philosophy community. Decisions will be made by the team on a rolling basis.

Photo by Samuel Sianipar on Unsplash,  Reading source Mary Midgley, "Philosophical Plumbing" 

CfP for papers on the subject of Human learning and learning analytics in the age of artificial intelligence, a 50th anniversary edition of BJET

At the 50th anniversary of the Britisch Journal of Educational Technology (BJET) invites you to contribute your most current research to BJET as a way to celebrate BJET’s anniversary. Title of the special section: Human learning and learning analytics in the age of artificial intelligence (Critical perspectives on learning analytics and artificial intelligence in education)

Deadline for manuscript submissions: February 10th, 2019
Publication: Online as soon as copy editing complete.
Acceptance Deadline: 10th August 2019
Issue Publication: November 2019.
Guest editor: Andreja Istenič Starčič, Professor University of Primorska & University of Ljubljana; Visiting scholar University of North Texas. For all information, please contact: andreja.starcic@gmail.com

This special section focuses on human learning and learning analytics in the age of artificial intelligence across disciplines.

In May 2018, they organized a working symposium entitled The “The Human-Technology Frontier: Understanding the Human Intelligence 0.2 with Artificial Intelligence 2.0.” The symposium was sponsored by the Association of Educational Communications and Technology (AECT). Distinguished scholars, including learning scientists, psychologists, neuroscientists, computer scientists, and educators addressed some urgent questions and issues on the learner as a whole person, with healthy development of the brain, habit, behaviour, and learning in the fast-advancing technological world. The symposium inspired these special issue topics (which not limited to):

1. Learning and human intelligence: Based on what we know of the brain and what we are likely to understand in the near future, how should learning be defined/redefined?
2. Learning and innovation skills, the 4C - creativity, critical thinking, communication and collaboration: How could learning technologies support the transformative nature of learning involving all domains of learning, cognitive, psychomotor, affective-social? How could the advanced feedback and scaffolds support the transition from “combinational” to the “exploratory” and “transformational” creativity, thinking and potential consequences for communication and collaboration?
3. Towards a holistic account of a person – brain, body, habits, and environment: What would a learning and research design that embraces a whole person perspective look like?
4. Human intelligence with innovations and advances of technologies: What technologies are most likely to have a positive impact on learning in the short and long future?
5. Properties and units of measures of learning: What are the constructs of learning and beliefs about learners and learners’ needs given the multilevel technologies, collaborative networks, interaction and interface modalities, methodologies and analysis techniques we have to work with?
6. Learning perspectives: Do we face transitions in theories of learning?

In the past 50 years, BJET has been at the front offering a platform and forcing discussions in the above areas. At the 50th anniversary of BJET, we invite interdisciplinary scholars to contribute their most current research to BJET as a way to celebrate BJET’s anniversary.

Please send me the working title of your paper with a short abstract (if you include co-authors, please also provide names of all authors) to my e-mail andreja.starcic@gmail.com by November 30th, 2018.

For further information, please contact professor Andreja Istenič Starčič at andreja.starcic@gmail.com

Machine learning benefits and risks by expert Stella Lee #AI #data #learning

Machine learning has moved from a mere rave into a real strong, acknowledged learning power (not only in the news, but also on the stock market of AI, e.g. STOXX AI global indices - I was quite surprised to see this). Machine learning has the power to support personalized learning, as well as adaptive learning, which allows an instructional designer to engage learners in such a way that learning outcomes can be reached in more than one way (always a benefit!). Machine learning allows the content or information that is provided for training/learning to be delivered in such a way that it fits the learner, and that it reacts to the learner feedback (answers, speed of response, etc). To be able to tailor a fixed set of learning objectives into flexible training demands some technological options: data, algorithms that can interpret the data, access to some sort of connectivity (e.g. it might be ad hoc with a wifi and an information hub, or it might be via cloud and the internet), and money to program, iterate and optimize the learning options continuously.

This (data, interpretation, choices made by machines - algorithms) means that machine learning combines so many learning tools, data and computing power, that it inevitably comes with a high sense of philosophical and ethical decisions: what is the real learning outcome we want to achieve, what are the interpretations of our algorithms, what is the difference between manipulation towards a something people must learn and learning that still offers a critically based outcome for the learner?

Stella Lee offers a great overview of what it means to use machine learning (e.g. for personalized learning paths, for chatbox that deliver tech or coaching support, for performance enhancement). This talk is worth a look or listen. Stella Lee is one of those people who inspire me through their love for technology, by being thorough, thoughtful, and being able to turn complex learning issues into feasable learning opportunities you want to try out. She gave a talk to Google Cambridge on the subject of machine learning and AI and ... she inspired her tech-savvy audience.

In her talk she also goes deeper into the subject of 'explainable AI' which offers AI that can be interpreted easily by people (including relative laymen, which is the case for most learners). Explainable AI is an alternative to the more common black box of AI (useful article), where the data interpretation is left to a select few. Stella Lee's solution for increasing explainable AI is granularity. This simple concept of granularity, or considering what data or indicators to show, and which to keep behind the curtains enables a quicker interpretation of the data by the learner or other stakeholders. Of course this does not solve all transparency, but it enables a path towards interpretation or description towards explainable AI. That way you show the willingness to enter into dialogue with the learners, and to consider their feedback on the machine learning processes. As always engaging the learners is key for trust, advancement and clear interpretation (Stella says it way better than my brief statement here!).

Have a look at her talk on machine learning bias, risks and mitigation below (30 minute talk followed by a 15 min Q&A), or take a quick look at the accompanying article here.

One of the main risks is of course some sort of censorship, or interpretation done by the machine which results in an unbalanced, sometimes discriminatory result. In January I organised some thoughts on AI and education in another blogpost here. And I also gave a talk on the benefits and risks of AI last year, where I argued for increased ethics in AI for education (slides here).

Machine learning is a complex type of learning, it involves a lot of data interpretation, algorithms to get meaningful reactions coming from the data, and of course feedback loops to provide adaptive, personal learning tracks to a number of learners.
Situating it, I would call it costly, useful rather for formal than informal learning (at this point in time), and somewhere between individual and social learning, as the data comes from the many, but the adapted use is for the one. It does not leave much room for self-directed learning,  unless this is built into the machine learning algorithms (first ask learner for learning outcomes, then make choices based on data). 

3 Call for speakers/papers and Digital Learning Innovation Award (10.000$ faculty award!)

Learning Solutions conference organised by the ELearning Guild

Conference dates: 26 - 28 March 2019

deadline call for speakers: 27 July 2018

Submission portal: https://www.elearningguild.com/content/5527/learning-solutions-2019-conference--expo--call-for-proposals-form/ 

Venue: Orlando, Florida, USA

Learning Solutions 2018 is for training and learning professionals focused on the design, development, management, and/or distribution of technology-based learning, performance support, or blended solutions incorporating traditional training. The program supports the entire learning team, so regardless of your specific role, you’ll find the tools, technologies, ideas, strategies, and best practices for success. This event attracts people from around the world who want to keep up with the evolving needs of their learners.

Learning Solutions’ dedication to sharing proven examples in learning could help your team gain a stronger sense of what’s available and how you can put these tools and techniques into practice.
The Learning Solutions program is created by learning professionals. The program team comes from the fields of instructional design, eLearning development, and L&D leadership. They pride themselves on staying current with what matters most.
Experts will discuss the strategies and tools currently working in learning, and how they could impact your organization.
The practical, focused sessions will show you how to solve your team’s challenges and use today’s technology for new possibilities.

------------------------------------------------------------------------------------------------------

Future of Information and Communication Conference (FICC) 2019

Conference dates: 14-15 March 2019, San Francisco, USA
Deadline: 15 July 2018

Technically co-sponsored by IEEE
Please consider to submit your papers/posters/demo proposals for the Future of Information and Communication Conference (FICC) 2019 to be held from 14-15 March 2019 in San Francisco, United States.

FICC 2019 aims to provide a forum for researchers from both academia and industry to share their latest research contributions and exchange knowledge with the common goal of shaping the future of Information and Communication.
The conference programme will include paper presentations, poster sessions and project demonstrations, along with prominent keynote speakers and industrial workshops.

Important Dates
Paper Submission Due : 15 July 2018
Acceptance Notification : 01 August 2018
Author Registration : 15 August 2018
Camera Ready Submission : 15 September 2018
Conference Dates : 14-15 March 2019

Complete details are available on the conference website : http://saiconference.com/FICC

--------------------------------------------------------------------------------------------------------------------------------

Online Learning Consortium: Digital Learning Innovation Award (DLIAward) 

The portal is open for submissions until 31 July 2018.

The DLIAward  program recognizes faculty-led teams and institutions for advancing undergraduate student success through the adoption of digital courseware. OLC is calling for submissions from accredited U.S.-based institutions in two categories:

• Institutional Award – $100,000 (up to three awarded)
• Faculty-led Team Award – $10,000 (up to 10 awarded)

We ask that only those who are serious about truly being innovative, creative, and dedicated to changing the world of digital learning apply for this award. Missed the information session? You can register to watch the archive.

All applications must be submitted through the online submission portal. Winners will be announced at the OLC Accelerate conference, Nov. 14-16 in Orlando, Florida.

Visit the website for full details regarding the award competition.

-----------------------------------------------------------------------------------------

9TH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE - LAK

Learning Analytics to Promote Inclusion and Success

4-8 March 2019, Tempe, Arizona

Deadline for submissions: 1 October 2018

Visit the conference website for more information and submission details.

https://lak19.solaresearch.org | #LAK19

The 2019 edition of the international conference on Learning Analytics & Knowledge will take place in Tempe, Arizona, USA. LAK19 is organised by the Society for Learning Analytics Research (SoLAR) and hosted by Arizona State University.

We take learning analytics to be the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. We extend invitations to researchers, practitioners, educators, leaders, administrators, government and industry professionals interested in the field of learning analytics and related disciplines.

LAK19 will place particular emphasis on exploring ways in which institutions around the globe are advancing the state of learning analytics in order to promote inclusion and success. Arizona State University, the host institution for LAK19, proclaims in its charter that ASU is ‘measured not by whom it excludes, but by whom it includes and how they succeed’.  Learning analytics play a significant role at many institutions in helping to promote these values. 

Thus the special theme of this edition of the conference will be on ways in which learning analytics can be used to promote inclusion and success. We define inclusion broadly and this definition may cover the engagement of marginalised groups, groups who have not been as successful as others at achieving educational success, learners who find their current curriculum either too challenging or not sufficiently demanding, or other forms of inclusive inquiry. It may also address issues of accessibility in terms of educational opportunities and learning analytics. Success is also defined broadly and may be viewed from the perspective of learners, educators, institutions or society more broadly.

We welcome theoretical, methodological, empirical and technical contributions to all fields related to learning analytics. Related to our special theme the following topics are of particular interest:

• Universal design for learning promotes an inclusive approach to the curriculum – how can learning analytics support curriculum design and revision from this perspective?
• How can analytics be applied in ways that support inclusion and success?
• How can the training of data scientists be made more inclusive?
• What does educational success look like, and how can it be supported?
• How can systematic biases (e.g. related to diversity) in our analytics algorithms be identified, reflected, and possibly avoided?

LAK19 will use a double-blind peer review process for the submissions. It is a LAK policy that submissions will only be considered for the category that they were originally submitted to, and there is no downgrading of papers. However, our timeline allows for rejected papers to be resubmitted in revised form as posters, demos, or individual workshop contributions. Accepted full and short research papers will be included in the ACM proceedings, as in previous years. Other accepted submissions will be published in the open access Companion Proceedings, archived on the SoLAR website.

Important Dates

All deadlines are 23:59 GMT-11

Submission deadline for main track categories (Research, Practitioners, Workshops, Tutorials and Doctoral Consortium)	1 October 2018
Notification of acceptance for Workshops and Tutorials	15 October 2018
Workshop Calls for Participation	29 October 2018
Notification of acceptance for Research, Practitioners, Doctoral Consortium	19 November 2018
Submission deadline for Posters/Demos and Workshop Papers	3 December 2018
Camera-ready papers for ACM Proceedings: Full Research Papers and Short Research Papers	17 December 2018
Notification of Acceptance for Posters/Demos and Workshop Papers	4 January 2019
Early-bird registration closes	8 January 2019
LAK19, Tempe, Arizona	4-8 March 2019

Redirect FB algorithms now and 4 lessons from #CambridgeAnalytica #digitalcitizens

Anyone interested in data and ethics has been reading a gazillion of articles the last week. So, time to recap the big results coming out of the Cambridge Analytica files: correlations have their scientific merits (argh!), humans can be profiled in just 12 likes (honestly, this is how diverse we all are?!), anything measured can be used against us (a Cobra), and teachers around the globe seem more ethical than scientists (my partner says it’s true, I say it isn’t). Well... manipulation is part of history, I guess... but still!

First of all, a nice MIT research project on “How to manipulate Facebook andTwitter instead of letting them manipulate you’ (yes, it is a timely title 😊 ) mentioned in MIT’s Technology Review. The project let’s you – the user – manipulate algorithms emphorced on you by Twitter and Facebook (I like it, activism from within the system). This initiative is called GOBO (if you want to jump right in, you can login for this project here) and it is a prject from researchers at the MIT Media Lab’s Center for CivicMedia. It has an interesting parallel referring to Cambridge Analytica approach, BUT in this case it is truly scientific, and they ensure deleting ANY and EVERY data collected once they have results on how you would like to see algorithms adjusted. So take back the algorithms of Twitter and Facebook with GOBO.

I am just resurfacing after the Cambridge Analytica fraud (I call it fraud as they have been anything but ethical in their so called scientific data gathering: no informed consent, data gathered and not anonymised before using it for 3 parties, data not deleted after a project was finished….).

Correlations are used successfully? Argh!! For years, many educationalists and researchers emphasize that correlation is no replacement for causality. Causality is the basis of all strong research. It is clear that education and correlation aren’t a love story. We- as educators and researchers - know and understand the importance of context, of language use, of how personal each of our learning journeys takes form. In a sense, we should know better then to construct a test that puts everyone in the same batch, and then believe in it to state those things that we think sound nice (however tempting that type of action is... I mean, saves time on reflecting, nuancing, evaluating... and all these time-staking stuff) … but Cambridge Analytica got away with it. PISA was/is another such example. It even manages to enter the OECD report (https://www.oecd.org/education/) as core element of proof leading to rigorous outcomes. PISA test is an in correlation resulting test. A nice list of educationalists that argued against using PISA here. With the Cambridge Analytica files, the correlation monster pops up once again … AND it is now used ‘successfully’ to blind-side people and to get them to doubt their political choices just enough to swing their vote. So, correlations can be used quite viciously for some of the time.  

Forget complex human traits: humans can be profiled in just 12 likes! And all of this comes from research (great paper on how it was set up here, Schwartz , Eichstaedt, Kern, Durzynski, Ramones, Agrawal, Shah, Kosinski,Stillwell, Seligman and Ungar (2013) . Well… how difficult is becomes to state (and belief) that humanity is truly diverse! Admittedly, the Big Five Traits also distil human diversity into just 5 personality traits, but still… being profiled on 12 likes… How individual are we, if that is all it takes to cast each one of us in a box that subsequently can be manipulated from that moment onward? It becomes quite difficult to see humans as complex beings when I take that into account… but we are social, at least that is now proven once again.

Anything measured can be used against us. One of the most interesting blogposts I have read, is an older one from MikeTaylor, stating that as soon as you try to measure how well people are doing, they will switch to optimising for whatever you’re measuring, rather than putting their best efforts into actually doing good work, and this optimising is always at risk of being distorted, even corrupted (Mike refers to Goodhart’s law, Campbell’s law and the Cobra effect – great read).

And teachers around the world have more ethical sense than scientists that do not teach… well it is a discussion, my partner says that fact is well known, I say scientists who do not teach can be ethical as well…. Those darn Cambridge Analytical (and derivates) people! (good example of this is Autumm Caines , she wrote on Platform literacy refering to her encounter with Cambridge Analytica to get all her data from them all the way back in February 2017 (which was a hastle!). Yes, she got active one year before this whole event blew up into an international scandal. Autumm keeps ethics high!