Thursday, 4 April 2019

The impact of working to your heart's content #learningDesign #MobiMOOC #inclusivity

Last week was inspiring thanks to the company I was in and the ideas that were exchanged Thank you John Traxler for organizing this wonderful workshop! My presentation was part of a multiplier event for the European MOONLITE project, looking MOOC design for refugees and migrants. A couple of days ago I realized what an impact this event had and how it affected my well-being. So why did it feel meaningful? It was the mixture of being on the road, meeting up with like-minded peers (the importance of exploring the concept of inclusivity), and suddenly realizing I was in a workshop where all presenters were female… something one rarely finds oneself in outside of the gender-circuit or designated ‘all female sessions’.
All of these factors finally got me to break out of my social media silence and see how I want to move forward.

Realizing the impact of projects that evolve out of ‘just some idea’
MobiMOOC was the eight MOOC out there and focused on mobile learning, which was also a new topic for MOOCs in April 2011. The idea of organizing MobiMOOC just came out of a wild idea, having worked on mobile learning for Sub-Saharan countries, and because I loved the experience of CCK08 the first MOOC ever.
While I was rearranging my slides for this presentation, I realized that organizing MobiMOOC resulted in quite a lot of meaningful actions and connections. To give you some idea of what was said during the talk, I am adding my slide deck here.

Being on the road
I like being on the road (though - when happens too frequently - it takes a toll on family life, creating some imbalance at home). But being on the road somehow gives me ideas, and it puts me in a mindset that feels exhilarating. Although not as exciting as Jack Kerouac’s On The Road, I do feel it has something. To me, being on the road provides ideas, and it gives a feeling of being alive. I guess my ancestors have been populated with a lot of nomads, for instance, my great grandfather who sailed the seven seas as a cook on international boats since the age of 14, and there must have been more ancestors doing the same thing. Wanderers.

Being inspired by like-minded peers
It felt so good to be in the company of inspiring peers, and to feel my heart and soul being content.
It was wonderful to meet-up with Nell Bridges (great mind, wonderful home), to finally meet up with Gabi Witthaus (I still laugh out loud with the divorce anecdote you told me), meeting Marwa Belghazi, to share ideas with Agnes Kukulska-Hulme on what I would love to be paid for (simply sharing ideas, thinking, writing them down), to meet with the always warm-hearted Daniyar Sapargaliyev who is now living in the UK with his family, trying to provide ideal surroundings for his two young sons, and of course to listen and question John Traxler who always has a different and in-depth view on academia, on life, on creating a meaningful life.

Each day I was learning and I learned from all of them, as each person I met was truly inspiring. They walk the talk of inspiring people and they work to somehow make the world a better place. How wonderful is that!

It is fascinating how you can feel what makes you tick by being surrounded by people you connect with. But most of all, each person there told me about the importance of doing something you really like. Of putting yourself out there, in whatever capacity you can (all efforts are worthwhile), and of simply being yourself.

Thursday, 28 February 2019

Liveblog @mathvermeulen #JustDoIt #vovpitstop @vovnetwerk

Liveblog Mathias Vermeulen Ode aan Angus
(Great keynote, capturing the audience first, coming to business with strong ideas)
Lang leve technologie!
Technologie is (ahem)
  • ·       Ons LMS
  • ·       Ons eLearningmodules
  • ·       Onze course vending machine

MacGyver is biggest inspiration of @mathiasVermeulen
Fabulous learning is developed by thinking ‘What would MacGyver do?”
·       Find what is out there, and use it to your own advantage and needs!
·       L&D is a party for everyone: becoming best friends with IT. HR, L&D
·       “Ik ben een bricoleur”

Zwitsers zakmes
  • ·       xAPI – LRS
  • ·       VR/AR
  • ·       Games (bury me my love – try it, text but serious game on Syria)
  • ·       Mobile
  • ·       AI and chatbots

Don’t worry be crappy (Guy Kawasaki)
Try out tools, set aside time (e.g. Friday afternoon) to test, think, come up with ideas on learning solutions.
Think ahead
  • ·       New people (we are good in this)
  • ·       More (what can we do to train our people)
  • ·       Apply (e.g. performance support when they need it: just-in-time learning)
  • ·       Solve (again, take time to learn what is out there)
  • ·       Change (produce a lean learning approach)

(Dutch) Yves Bosteels from Jan De Nul on eAcademy #vovpitstop @vovnetwerk #liveblog

Liveblog from Yves Bosteels over Kennis, Proces and Innovatie (just some pointers from his talk) Mostly in Dutch

Jan De Nul eAcademy (eLearning begonnen in 2017), combineren van opleidingen.
6500 medewerkers, internationaal, (80 – 100 lopende projecten, waarvoor opleiding aangeboden moeten worden, met een oplossing voor verschillende infrastructuur problemen, o.a. schepen).
Cornerstone on Demand (offline niet interessant voor schepen)
Online/offline LMS
Recurrente vragen van klanten
·       Training & Needs analysis
·       Show me training background
·       Show me certification
·       What other career training do you provide
·       Project-specific training (eg. Parkwind (nieuwe installatiemethode) – efficient bout placement
Schepen getest vanaf 2018
7 eModules (in 2018, gerigistreerde opleidingen, merendeel klassiek).
Iedere nieuwe werknemer krijgt onmiddellijk upcoming learning sessions, with training programs (cfr AICCM)
Impact van eAcademy precies gemeten?
  • ·       Kwisformule ingewerkt in modules (zie volgende slides)
  • ·       Dashboards voor teamlead en departementshoofden
  • ·       Hoe wordt er voor verankering en transfer gezorgd van wat geleerd wordt?

o   Testen in de module
o   Materiaal blijft in de eBib beschikbaar
o   Nadien ook aan bod laten komen in klassieke training
o   Van aanvraag tot aanlevering (3000 – 25000 eur per module, met module ca. 30 minuten – we automate parts to 2000 EUR per module, with adapted assessment)
Hoe zorgen ze ervoor dat mensen naar de eAcademy gaan?
  • ·       PR actie om animo te geven
  • ·       Mensen wel enthousiast qua materiaal

Implementatie voor eAcademy
·       PM aanstellen om dit gestructureerd en ‘serieus’ aan te pakken
·       Use case vroeg om eigen IT-inbreng om alles op schepen te kunnen implementeren
·       HR & IT
Flipped classroom approach: manage the expectations, ensure pre-contact knowledge acquisition
·       Alle schepen online krijgen
·       Interne opleidingsmatrices in eAcademy + mails met uitnodigingen
·       1370 externe cursussen naar eAcademy krijgen met een approval flow
·       Tegen eind maart: 16
·       Tegen eind 2019: 90
Recurrent materiaal
·       Bedrijfsrichtlijkenen
·       QHSSE
·       Recurrente treainingen zoals baggercursussen, DMS, IT, andere software
·       Inducties (projectsites & opslagplaatsen).
Expert academy: Finex portal (financial project and contact information): data and reports, ITA, Links, Tools, …. (test spec IT roll out)
Vraag naar soft-skills and Gamification (Check Marloes, Elizabeth interest: GC, Spain…)

Tuesday, 19 February 2019

Just sharing a few rejections: paper & funding, and solutions #academicLife #life #loveMyNetwork

Life can be hard, both personally and professionally, yet at the same time life can simply push you towards a more pleasant option along the way, seemingly using rejections to get you on to the right track. I sure hope this will be the case, but only hindsight will tell. [addition one day after writing this post: while sharing these ideas on Facebook, I got such an inspiring response from my network, I decided to add the ideas and remarks they had below, between square brackets]
Today I was informed that my co-authored paper for the eMOOC summit 2019 in Naples was rejected. Rejections rarely result in joy, and this was no exception. For some reason writing a paper is also a personal effort. You try with all your ability (and mostly under a bit of time pressure) to come up with a paper that shares your research in just a few pages. Referencing to prior great minds in your field of expertise. So, when a paper gets rejected, it simply hurts. It feels personal to some extent.
The rejection came one week after my submission to get a prestigious Marie Curie Fellowship got rejected as well, it did not get the threshold. The review did have a lot of positive points though (which did soften the blow). Granted, I wrote this submission as a plan B in order to increase my options to get back to work after I recovered from the year rehab after the cancer diagnosis. I put my heart into it, not only me but also the professor who was willing to employ me in his department if the fellowship was successful. Luckily, I was able to get back to work and on good terms, and on an inspiring project.
[It seems that rejections are common to everyone, even the highest esteemed scholars get them despite their obvious wisdom and knowledge. My friends shared some good advice and resources that help to bounce back from rejection. First off: upward and onward, as simple as it sounds, it works ... once you have managed to soften the feeling of a work being rejected. The process is to reflect, look at the feedback (or if they did not send any, ask for all the feedback, of course, anonymized), and rewrite and resubmit. Next, a great article in Medium on The Iceberg Illusion, adding the picture here as well.]

But the above two rejections just made me realize once more that I am not a traditional academic and as such, I doubt whether I can ever be part of the whole deal. Maybe this frequency of rejection is simply normal, but at present, I just feel I need to take another leap. Just like I did three times before. Maybe I am not made to gradually move forward? Maybe my thing is just this .... jumping ahead and then working on that 'new' concept until it becomes more mainstream.
[Feedback is an essential first step, next of course is to get going and to know thy self. And to repeat to yourself that critique is not personal, and it can be based on a number of reasons that do not even have to be immediately related to the work you did. In a way, emotion wins over ratio every time, but that does not mean we cannot rationalize after the first emotions have gone.]
Ciska sometimes tells me: "don't wine because you are living off the beaten track, even if you could walk the straight and narrow, you still would roll out your own route to get to the next place". Maybe she is right, but it does not make things easier. Maybe, it is never easy for any of us. Even for those who walk the more traditional roads to achieve a professional space in society. I don't know, but each time I get such a rejection, I just feel it's because of me, and it feels personal.
Okay, time to move forward again. Working on a project which combines human resources, AI and learning... fun, I must admit.
[and this is - and has always been - an inspiring Last Lecture]

Cartoon in this blogpost is from the fabulous Nick D. Kim - the site

Monday, 14 January 2019

EU report on the impact of AI on Learning Teaching and Education #AI #education #EU #policy

The resently published report on the impact of artificial intelligence (AI) on learning, teaching and education gives a great outline on the realities of AI, the state of the art, and the challenges as well as opportunities for those of us with an expertise in learning in general, or learning in terms of learning theory. The report is part of the JRC Science for Policy documents, and it is very well written by Ilkka Tuomi (who is renowned for his expertise in Internet, data, AI and computer science). Ilkka recorded a brief overview of the report, which can be seen below. In the report-related video, he refers to current machine learning systems as datavors, he defines (and right fully so) the term of machine learning as an oxymoron and he puts current AI in very accessible parallel, namely the Artificial Instict (as current AI is mainly about behaviourist approaches and patterns).

A very interesting perspective is that Ilkka and the report stress the importance of having someone on board of AI for learning/teaching/education on board, who has expertise in learning and learning theory.

The policy challenges mentioned at the end of the report are:

  • A continuous dialogue on the appropriate and responsible uses of AI in education is therefore needed.
  • In the domain of educational policy, it is important for educators and policymakers to understand AI in the broader context of the future of learning. As AI will be used to automate productive processes, we may need to reinvent current educational institutions.
  • In general, the balance may thus shift from the instrumental role of education towards its more developmental role.
  • A general policy challenge, thus, is to increase among educators and policymakers awareness of AI technologies and their potential impact.
  • Learning sciences could have much to offer to research on AI, and such mutual interaction would enable better understanding about how to use AI for learning and in educational settings, as well as in other domains of application.
  • As there may be fundamental theoretical and practical limits in designing AI systems that can explain their behaviour and decisions, it is important to keep humans in the decision-making loop.
  • The ethics of AI is a generic challenge, but it has specific relevance for educational policies.
  • Human agency means that we can make choices about future acts, and thus become responsible for them.  AI can also limit the domain where humans can express their agency.
  • An important policy challenge is how such large datasets that are needed for the development and use of AI-based systems could be made more widely available.

This 47 page report offers the following topics:

Introduction ...................................................................................................... 5
2 What is Artificial Intelligence? ............................................................................. 7
2.1 A three-level model of action for analysing AI and its impact ............................. 7
2.2 Three types of AI ....................................................................................... 10
2.2.1 Data-based neural AI ......................................................................... 10
2.2.2 Logic- and knowledge-based AI ........................................................... 12
2.3 Recent and future developments in AI .......................................................... 13
2.3.1 Models of learning in data-based AI ..................................................... 15
2.3.2 Towards the future............................................................................. 16
2.4 AI impact on skill and competence demand ................................................... 17
2.4.1 Skills in economic studies of AI impact ................................................. 18
2.4.2 Skill-biased and task-biased models of technology impact ....................... 20
2.4.3 AI capabilities and task substitution in the three-level model ................... 21
2.4.4 Trends and transitions ........................................................................ 22
2.4.5 Neural AI as data-biased technological change ...................................... 23
2.4.6 Education as a creator of capability platforms ........................................ 23
2.4.7 Direct AI impact on advanced digital skills demand ................................ 25
3 Impact on learning, teaching, and education ....................................................... 27
3.1 Current developments ................................................................................ 27
3.1.1 “No AI without UI” ............................................................................. 28
3.2 The impact of AI on learning ....................................................................... 28
3.2.1 Impact on cognitive development ........................................................ 30
3.3 The impact of AI on teaching ....................................................................... 31
3.3.1 AI-generated student models and new pedagogical opportunities............. 31
3.3.2 The need for future-oriented vision regarding AI .................................... 32
3.4 Re-thinking the role of education in society ................................................... 32
4 Policy challenges ............................................................................................. 34

Below is the 20 minute video of Ilkka Tuomi which explains the report in easy terms.

Friday, 4 January 2019

Call for Papers #CfP #AI #mLearning #MOOC in conferences #UNESCO @FedericaUniNa

January has started and three important calls for papers are coming up, all related to conferences. The three conferences are: eMOOCs2019 (on MOOCs), Mobile Learning week at UNESCO (focus on AI for development and mobile learning, and eLearning Africa (this year in Cote d'Ivoir), listed per deadline of the CfP.

Mobile learning week UNESCO (Paris, France): focus on AI for sustainable development
Call for proposals deadline: 11 January 2019
UNESCO Global AI Conference: Monday 4 March 2019
Policy Forum and Workshops: Tuesday 5 March 2019
Symposium: Wednesday 6 & Thursday 7 March 2019
Strategy labs & International Women’s Day: Friday 8 March 2019
Exhibits: Monday 4 to Friday 8 March 2019
More information:
UNESCO, in partnership with its confirmed partners – the International Telecommunication Union and the Profuturo Foundation – will convene a special edition of Mobile Learning Week (MLW) from 4 to 8 March 2019, at the UNESCO Headquarters building in Paris (France). The five-day event, under the theme ‘Artificial Intelligence for Sustainable development’ will start with the ‘Global Conference - Principles for AI: Towards a humanistic approach?’, followed by a one-day Policy Forum and Workshops, a two-day International Symposium and a half-day of Strategy Labs. On 8 March, towards the close of MLW, participants will be invited to join the celebration of International Women’s Day, particularly a debate on Women in AI to be held in UNESCO Headquarters. During the entire week, exhibitions and demonstrations of innovative AI applications for education and more than 20 workshops will be organized by international partners and all programme sectors of UNESCO.
eMOOCs 2019 in Napels, Italy
Deadline CfP: 14 January 2019.
Conference date:  May 20 – 22, 2019
More information
The Higher Education landscape is changing. As the information economy progresses, demand for a more highly, and differently, qualified workforce and citizens increases, and HE Institutions face the challenge of training, reskilling and upskilling people throughout their lives, rather than providing a one-time in-depth education. The corporate and NGO sectors are themselves exploring the benefits of a more qualified online approach to training, and are entering the education market in collaboration with HE Institutions, but also autonomously or via new certifying agencies. Technology is the other significant player in this fast-changing scenario. It allows for new, data-driven ways of measuring learning outcomes, new forms of curriculum definition and compilation, and alternative forms of recruitment strategy via people analytics.

At the MOOC crossroads where the three converge, we ask ourselves whether university degrees are still the major currency in the job market, or whether a broader portfolio of qualifications and micro-credentials may be emerging as an alternative. What implications does this have for educational practice? What policy decisions are required? And as online access eliminates geographical barriers to learning, but the growing MOOC market is increasingly dominated by the big American platforms, what strategic policy do European HE Institutions wish to adopt in terms of branding, language and culture?

The EMOOCs 2019 MOOC stakeholders summit comprises the consolidated format of Research and Experience, Policy and Business tracks, as well as interactive workshops. Original contributions that share knowledge and carry forward the debate around MOOCs are very welcome.

eLearning AFrica - Abidjan - Cote d'Ivoir
Deadline CfP: February 22, 2019.
Conference date: October 23 - 25, 2019
More information
The 14th edition of eLearning Africa, the International Conference & Exhibition on ICT for Education, Training & Skills Development, which will take place in Abidjan, Côte d'Ivoire from October 23 - 25, 2019 and is co-hosted by the Government of Côte d'Ivoire. 

A unique event, Africa’s largest conference and exhibition on technology supported learning, training and skills development, eLearning Africa is a network of leading experts, professionals and investors, committed to the future of education & training in Africa.

Read more about the eLearning Africa 2019 themeThe Keys to the Future: Learnability and Employability, and become involved in shaping the conference agenda by proposing a topic, talk or session here.
Register today to profit from our Early Bird Rate

About eLearning Africa
Founded in 2005, eLearning Africa is the leading pan-African conference and exhibition on ICT for Education, Training & Skills Development. The three day event offers participants the opportunity to develop multinational and cross-industry contacts and partnerships, as well as to enhance their knowledge and skills.
Over 13 consecutive years, eLearning Africa has hosted 17,278 participants from 100+ different countries around the world, with over 80% coming from the African continent. More than 3,530 speakers have addressed the conference about every aspect of technology supported learning, training and skills development.

Tuesday, 1 January 2019

Planning for what might prove to be impossible #OPNLearn

After days if not weeks of contemplation - and reading Eleanor Roosevelt's "You Learn by Living", I have decided to go for it, no matter what this new frontier will bring me. This idea of Old Philosophers and New learning will no doubt need more time to develop and mature, but from here onward it will be a project and I will develop it as openly as possible. 

The thought of starting and being able to bring a new project to fruition is daunting. I am over 50, I have been a diabetic type 1 for seven years, and I have had breast cancer. Looking at these three facts makes me doubt whether any new project will be successful. And with success I mean being able to lean on this activity to feel confident, provide new ideas by combining old ones, and have money to support all of this happening, even growing. On the other hand ... I have been working on new technologies and innovation with success (= international awards), I was able to grow from my early years as a cleaning lady/waitress into a person with a PhD (rough road), and all along I have gathered some wonderful, intelligent, interesting and magnificent friends living across this beautiful globe. In Dutch I would say that the odds of any new project that I would start would result in ... "het kan vriezen, het kan dooien", it can go either way, but it will at least result in something. 

So here it goes. As anxiety is present and I must admit I do not like to fail at something, I need to do this. It feels as though this is the last thing I can do to attain something that might possible add to a thoughtfull, respectful world. Here goes nothing...


Saturday, 8 December 2018

#AI #MachineLearning and #philosophy session #OEB18 @oebconference @OldPhilNewLearn

At OEB2018 the last session I lead was on the subject of AI, machine learning in combination with old philosophers and new learning.  The session drew a big group of very outspoken, intelligent people making this session a wonderful source of ideas on the subject of philosophy and AI.

As promised to the participants, I am adding my notes taken during the session. There were a lot of ideas, so my apologies if I missed any. The notes follow below, afterwards embedding the slides that preceeded the session in order to indicate where the idea for the workshop came from.

Notes from the session Old Philosophers and New Learning @OldPhilNewLearn #AI #machineLearning and #Philosophy

The session started of with the choices embedded in any AI, e.g. a Tesla car running into people, will he run into a grandmother or into two kids? What is the ‘best solution’… further into the session this question got additional dimensions: we as humans do not necessarily see what is best, as we do not have all the parameters, and: we could build into the car that in case of emergency, the car needs to decide that the lives of others are more important than the lives of those in the car, and as such simply crash the car into the wall, avoiding both grandmother and kids.

The developer or creator gives parameters to the AI, with machine learning embedded, the AI will start to learn from there, based on feedback from or directed to the parameters. This is in contrast with computer-based learning, where rules are given, and they are either successful or not but they are no basis for new rules to be implemented.

From a philosophical point of view, the impact of AI (including its potential bias coming from the developers or the feedback received) could be analysed using Hannah Arendt’s ‘Power of the System’, in her time this referred to the power mechanisms during WWII, but the abstract lines align with the power of the AI system.

The growth of the AI based on human algorithms does not necessarily mean that the AI will think like us. It might choose to derive different conclusions, based on priority algorithms it chooses. As such current paradigms may shift.

Throughout the ages, the focus of humankind changed depending on new developments, new thoughts, new insights into philosophy. But this means that if humans put parameters into AI, those parameters (which are seen as priority parameters) will also change over time. This means that we can see from where AI starts, but not where it is heading.

How much ‘safety stops’ are built into AI?
Can we put some kind of ‘weighing’ into the AI parameters, enabling the AI to fall back on more important or less important parameters when a risk needs to be considered?

Failure as humans can results into growth based on those failures. AI also learns from ‘failures’, but the AI learns from differences in datapoints. At present the AI only receives a message ‘this is wrong’, at that moment in time – if something is wrong – humans make a wide variety of risk considerations. In the bigger picture, one can see an analogy with Darwin’s evolutionary theory where time finds what works based on evolutionary diversity. But with AI the speed of adaptation enhances immensely.

With mechanical AI it was easier to define which parameters were right or wrong. E.g. with Go or Chess you have specific boundaries, and specific rules. Within these boundaries there are multiple options, but choosing those options is a straight path of considerations. At present humans make much more considerations for one conundrum or action that occurs. This means that there is a whole array of considerations that can also imply emotions, preferences…. When looking at philosophy you can see that there is an abundance of standpoints you can take, some even directly opposing each other (Hayek versus Dewey on democracy), and this diversity sometimes gives good solutions for both, workable solutions which can be debated as being valuable outcomes although based on different priorities, and even very different takes on a concept. The choices or arguments made in philosophy (over time) also clearly point to the power of society, technology and reigning culture at that point in time. For what is good now in one place, can be considered wrong in another place, or at another point in time.

 It could benefit teachers if they were supported with AI to signal students with problems. (but of course this means that ‘care’ is one of the parameters important for society, in another society it could simply be that those students who have problems will be set aside. Either choice is valid, but it builds on other views on whether we care in a ‘supporting all’ or care in a ‘support those who can so we can move forward quicker’. It is only human emotion that makes a difference in which choice might be the ‘better’ one to choose.

AI works in the virtual world. Always. Humans make a difference between the real and the virtual world, but for the AI all is real (though virtual to us).
Asimov’s laws of robotics still apply.

Transparency is needed to enable us to see which algorithms are behind decisions, and how we – as humans – might change them if deemed necessary.

Law suits become more difficult: a group of developers can set the basis of an AI, but the machine takes it from their learning itself. The machine learns, as such the machine becomes liable if something goes wrong, but ….? (e.g. Tesla crash).

Trust in AI needs to be built over time. This also implies empathy in dialogue (e.g. sugar pill / placebo-effect in medicine, which is enhanced if the doctor or health care worker provides it with additional care and attention to the patient.
Similar, smart object dialogue took off once a feeling of attention was built into it: e.g. replies from Google home or Alexa in the realm off “Thank you” when hearing a compliment.  Currently machines fool us with faked empathy. This faked empathy also refers to the difference between feeling ‘related to’ something or being ‘attached to’ something.

Imperfections will become more important and attractive than the perfections we sometimes strive for at this moment.

AI is still defined between good and bad (ethics), and ‘improvement’ which is linked to the definition of what is ‘best’ at that time.

Societal decisions: what do we develop first – with AI? The refugee crisis or self-driving cars? This affects the parameters at the start. Compare it to some idiot savants, where high intelligence, does not necessarily implies active consciousness.

Currently some humans are already bound by AI: e.g. astronauts where the system calculates all.  

And to conclude: this session ranged from the believers in AI “I cannot wait for AI to organise our society”  to those who think it is time for the next step in evolution, in the words of Jane Bozart: “Humans had their Chance”

Thursday, 6 December 2018

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
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).
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.
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, …)
Luca challenge: responsible use of AI.
GDPR Art 22: automated individual decision-making, including profiling. : identifying policies to adopt learning analytics. 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   

User involvement and co-creation: see the competen-SEA project see 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, 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?