Thursday 19 September 2019

LiveBlog #Ectel2019 Rose Luckin @Knowldgillusion Keynote #AI & #education mindset

 Rose Luckin takes the stage with a headset and immediately getting into her talk. The talk was very informative and to me it looked as though Rose is so knowledgeable about a range of topics, so I got a bit curious and envious in how her mind works [It I heard - I do not know if this is correct, will ask her ] that she only got into academic life later on in life?

Key topic: develop the right AI mindset for businesses

A perfect storm: data mass plus computing power and memory enhancements, sophisticated algorithms ... this made AI part of our lives and education.

3 routes to Impact on Education

  • using AI ED to tackle some of the big educational challenges
  • education people about AI so that they can use it safely and effectively
  • changing education so that we focus on human intelligence and prepare people for an AI world (hardest to do at the moment)

Working with select committee processes to try and take forward new developments. Debating on 4th industrial revolution and what it means that people understand AI (it is not coding, it is about the humans and their understanding of the fundamentals of machine algorithms, awareness, it is a much higher order we need to engage people with).

Need for multidisciplinary teams with equal input
As change happens, we need to change our educational systems (Singapore). Be resilient to change, be adaptive.
The above are not separate routes, it interconnects, and these interconnections increase AI and that we need to change and invest in our society using emerging ideas and realities of these three buckets.
We need to build bridges between communities: all stakeholders (parents, communities, government, coders...).
Currently separated communities need to work together to build a credible, societaly based AI solution.

Companies working with UCL EDUCATE
Not all companies are already using AI, but they want to understand more about it.
EDUCATE was from Europe, but turning into a global program from Jan 2020.
250 educational study start-ups (each start-up has to have a link with London, but they need to have some profile in London, so most UK-originated).
UCL provides training (labs, clinics, blended rooms, mentoring sessions)
It is free for the companies (years spend on figuring out the gaps between educational departments and industry. This was the case for hard sciences and industry, but not education). A lot of the reasons was because they did not know who to talk to, where to start => reason for starting with start-ups, embedding the educational mindset and to understand more about outcomes and validation of educational projects, so what it means when we say 'it works' (complexities... this results in the golden triangle: edtech developers, teachers & learners, academic researchers).

Start-ups are pushed to build a logic model, and the change being the learning that they want to take place. Opportunities they have to analyze the data, how should they demonstrate impact. We hope they will get to the last stage (see picture).
EdWards are set in place (awards to proof evidence applied and evidence aware awards).
120 companies became evidence aware, and 25 become evidence applied (last being much more difficult to achieve).

EDUCATE for schools
objective: build capacity in schools to identify and evaluate edtech that meets the needs of their teaching, learning or environment.
This approach can work in different educational programs.
Sit down, get head teacher in to pick two or three educational challenges - what they find tricky, than teachers are chosen to test it, to find out how the edtech works.
Currently this is under development:
all resources included in option 1, schools identify new or existing edtech to pilot
EDUCATE provides new resources to help schools plan their edtech pilot,
educate povides video and document resurces to walk schools through the pilot process
schools step through piloting process and recieve one hour of 1:1 video mentoring support
evaluate it (not sure I put this in correctly - this last step)

Century AI:
AI and big data powers personalised learning
Quipper: video insight, smart study planner, knowledge base
EvidenceB KidsCode : paths through materials, optimised parts through material

classic recommender systems (finding the right resources for the educator/student)

Chatterbox: refugee as expert native speaker with matching backgrounds (e.g. engineering background)
OyaLabs cloudbased monitor in the baby lounge and monitors interactions between baby and its cognitive developments for language developments
MyCognition algorithms automatically increase the number of training loops for the domains where you have the greatest need. If attention is your greatest needs you will receive more attention loops, building resilience in attention. As you progress the loops become more challenging. Looks at your attention, actions... assessment and report, which powers aquasnap and takes you to a underwater world (sea routes, fish names...) and adapted to your own cognitive status.

Building an AI mindset
Important for any company that wants to get into AI
What does it means to have the right data,
not just the tech team must understand the data and AI
as an individual it would be good to understand more about AI

Working with OSTC / ZISHI company: example of AI mindset collaboration. What they do: training for trader floors. They have to train everyone. They try to attract diversity in the workforce and pick them from less evident universities. ZISHI tries to use AI, AI for financial sector.
Financial sector has used AI for some time. AI used for assist in recruiting the best traders, assist in training the traders, help traders in improving performance, mentor the traders through out their careers.

Understanding OSTC's performance metrics

  • how can training behavior be measured?
  • can we profile traders by their trading behavior?
  • how do these profiles relate to performance?
  • can we then create a tool to help recruitment a tool to help traders and a tool to help managers?

The CEO of OSTC started out at the post floor of Lloyds and moved up. One's he saw the lack of training, he got into training and set up OSTC. Fundamentally what they try to do is creating AI mindset.

Much is not easy or obvious of what traders do

  • what others tell me that I do
  • what I think I do
  • what I really do
  • what family thinks you do...

Nearly half their traders left less than one year in. So something was wrong, and investment was too costly for the results in the longterm.
Modeling using machine learning techniques to profile traders and make predictions (recruitment data from tests, interviews and videos, trading history data from trading platforms, multimodal data from eye-movements and button clicks, and behavioral data.
Masses of data from the tools used in the company.

Profiling 4 types of traders, using four identified characteristics:
data visualizations, using clustering techniques.
It turns out that the behavioral patterns relate to significantly different performance (risk management, bonuses... and different cognitive abilities & traits (openness to experiences, agreeableness...) [here my mind went off... must be something related to trader-vocabulary?]

Challenges to IA mindset

  • collaboration: is everybody onboard?
  • getting rid of AI's sci-fi fantasies and fears
  • digging in rich soil will bring out stuff. Are we ready to act upon it?
  • the appetite comes with the first byte - be ethically prepared to diet
  • data is har to collect, standardize, clean, #you-name-it

Opportunities for IA mindset

  • map the organisations' data information knowledge wisdom pyramid (and who is where
  • identify data sources: what is ready to be picked, what still needs to be ripened or sown
  • what can we learn from previous (successful of failed) experiments or pilots? what hypotheses they already have? what are their blind spots?
  • metrics - how do we know what success looks like?

OSTC - lessons

  • team members across different tiers need to embrace change
  • collect as much data 
  • tech team in company not the same as data team
  • need new expertise to digitize documenten and learning content
  • develop coherent and consistent procedures in all offices across the globe despite the cultural bias
  • track the daily activities through logs and multimodal data
  • develop tools

Developing an AI mindset

  • AI is set to transform education
  • three core types of interconnected work: using AI, understanding AI, changing education because of AI
  • multi-stakeholder collaboration can help achieve these three types of work
  • EDUCATE is an example of a multi-stakeholder collaboration to help develop a research mindset in Edtech developers and educators
  • for AI companies, or companies who want to use their data and AI we also need to develop an AI mindset (or perhaps initially a data mindset)
  • Academic research partners need to be put in this mix

Barclays provided somebody (eagle) in branches, and they would help people to use technology (from simple to complex) to get people engaged about using and thinking about technology, and how they can get involved.

Wednesday 18 September 2019

#ectel2019 #mlearn2019 keynote @GeoffStead on #informal learning at scale #languages #AI

Geoff Stead (@geoffstead ) takes the stage with a headset, a black shirt and walking like a fit Californian surfer (looking great).

As chief product person of the Babbel language corporation, he talks about informal learning at scale and will offer insights. 750 people all working on 1 app, fully funded by individuals willing to pay small amounts of money to learn languages. Mostly Euro-centric coming from the organic growth of the organisation.

5000 courses => 64000 lessons (unique language pairs), focus on communicative confidence, light-hearted, diverse topics. Well over 1 million subscribers (of which I am one - Spanish).

Digital = scale and reach
Team of 10 people can start the magic of the web.
How can we ensure Quality?
Learner centric, otherwise what is the value of the application?

Using a learner journey to unite efforts, to enable connections between learners. Conceptual flows of individuals that is used as the mantra to move the app forward.
See picture, where they also embed some spaced learning.
They work with patterns that are turned into fake persona's, which are designed and modeled (design thinking approach). Enabling developers and strategist to understand the different demographics. These personas are linked to learner journeys. Which enables to keep a focus on the learners.

Learning from the learners
What do they do? analytics, A/B tests, behavioral segmentation (showing what you did, signposting to what you did and worked...), interviews, intercept surveys, wishboard, market surveys, UX research (ask permission to video tape parts of the learner journey and ideas), customer service, market research. Not one is representative, but hoping that with enough different angles they are hoping to get closer to the actual learning in all it's complexity.

Dev at scale
20 different teams of people, a lot of independence, but only one product. So how likely it is that the releases are synchronizable as soon as they are launched by teams? Tripping over each other, contradictions, quickly becomes chaos. So it is self-driven and autonomous, but potentially disastrous for the learners. Marketing and money was basis for scaling: stickers in planes and on poles in big cities, get people to pay a bit of money.

How do you trade off freedom versus working together
Teams organised around User Journey: Experience Groups (XGs) are clusters of teams across Product & Engineering, uniting tho enhance cross-functional collaboration around product ideas and speed up the development cycle: impressions, engagement, learning, learning media, platform and infrastructure (really interesting this!).

Product department 
Product is made up of many specialist teams. some teams are embedded within multi-function or engineering teams: didactics, product design, product management and QA, data engineering and analytics, quality and release management.

Towards "learning experience design"
Mixed multidisciplinary approach, but in larger companies most of the time they are not often set up as bridged teams in a multidisciplinary, cross-functionalness.

Babbel meetups in Berlin every 2 - 3 months, welcome to come and have a look.

LXD basics
digital learning is not content distribution, we are only a small slice of our learner's day, we never really know what is going on. Learning Experience Design, all about the multidisciplinary nature.

Learner engagement
It only works for them if they use it. What is the science of pulling learners back in?
Weekly active paying users: returners. One of the key drivers = 7 day return to learning (it is this that most of the dev teams use to validate short term impact of new features and refinements). If the people who try a new release, do they come back within 7 days to use this newly released option. This simplifies discussions on what is important.

Obsessive focus on interpreting events: Tableau, Amplitude (big fat data stream).
Mixing art and science to understand the engagement ladder (to help our learenrs focus - hooked (N Eyal) triggers motivation (Fogg), Nudge (Thaler, Flow state, spaced repetition, babbel qualitative and quantitative data....).

Gamification: treat with care, some very useful tools, often used for trivial impact.

AI to make Babbel more human
AI is a very broad umbrella term for a wide range of very specific disciplines. Babbel uses 'narrow AI' to focus on very specific problems/opportunities. NLP, CL, ASR...
Making interfaces more human (hybrid human-AI). Using NLP to give the automated feedback more human (eg "I understand what you meant").
Making guidance more useful: content recommendations, based on other, related topics and level. Still very much in beta. Optimising for speed, and identifying opportunities.

Rose Luckin's golden triangle is used.
Tutorbot corpus (Kate McCurdy, Dragan Gasevic...)

Tuesday 17 September 2019

#mLearn2019 workshop Urban safety and #smart civic #education

liveblog from mLearn2019, so consisting of bits and pieces and notes written during the workshop.

Part 1 by Wim de Jong (OU Netherlands)
Smart solutions for urban problems (design solutions), governance for safety (prevention of crime, policing....) and systemic challenges (eg.polution...).

Can technology foster the fears it tries to combat? (perception and condition of city safety)
How can we counterbalance the bias in current perceptions of safety? (Question from Daniel Spikol).

Safe cities index (2019) here 
Sherlock app (citizens who can help and assist in crime-solving with police - Dutch)
OTT (where are the fights going on?)

Part2 Leadership in smart cities & Open innovation
New paradigm in industrial engineering. A new way to integrate a community for designing things.
Wicked problems (things are connected and affect each other): social instabilities, traffic accidents, environmental pollution, floods...)
Need for innovative solutions
requiring input and expertise of a wide array of people

the innovative ecosystem
focal entity
combination bottom-up & top-down
value capture and creation = difficult and complex
importance of partner alignment => intrinsic motivation

[While following this talk, I see how the framework shared in pictures below can be relevant when looking at #AIED and citizen jury / citizen action ].

#Ectel2019 Covadonga Rodrigo from #UNED @cova_rodrigo #gender #AI #bias

From here a couple of cases and projects (slides will follow)

Great presentation by UNED Covadonga Rodrigo: will AI be sexist? @cova_rodrigo (liveblog)
Referring to male/female recruitment of Amazon. AI had a biased in favor of men. Why?
Because the AI was trained with historical data, so more males, which made the system think male candidates were preferable.
Microsoft (2016) had the same result with their AI system: automated bots on twitter, this bot was getting sexist in the end due to AI learning.

So who is programming the AI systems: up to 90 % are men (2015), it changes gradually, but at the moment women are only 16 to 19% of the programmers. This results in differences in terms of bias. By 2023 it will probably be 27,7% (= number of software developers in the world) this is not the critical threshold of 33% that we know is critical from social sciences in order for a group to get their voices heard).

Some issues Glass ceiling, identity of what engineers are, school atmosphere, more female references in the curricula. It is not only in engineering, also in other areas.
The AI assistants are also mostly female-voice based => the female secretary, not female leads.

Ethics: curricula are biased, ethical subjects in curricula. Lack of humanistic studies in education, we need to transform this.

Mentions that she is 50+ and she was an engineer from early on, so there were women engineers, so no problem with entry of women. So we have male domination, which results in biases in terms of gender, and differences that exist in society.

Sources of sexism (slides will follow)

#ECTEL2019 Workshop #AI in #Education #liveblogpost #AIED @cova_rodrigo @paco

This is a live blog, so bits and pieces noted.

Paco Iniesto (The Open University, IET, AIED) is the workshop lead, and he is looking good and giving a strong overview.
AI is all around us: cars, games, robotics, AlphaGo (see netflix), predictive policy, dating apps, (3 min video is of interest, how they generate these images), ...

What is AI?
It isn't easy to define AI and many people have an idea, but there is no definition.
computer systems desinged to interact with the world ... (Luckin, Waynes...)

The promise of AI is not yet realized, although it has been developing for 40 years.
It's big business
AI shines a spothlight on existing educational practices
AI rehashes what we have at this point in time

Implications of AIED: algorithms and computation: what are the algorithms, what are their consequences, how to control them... accuracy and validity of assessments, are we treating students as human beings?

Lumilo augmented reality glasses for teachers (, video can be found here: This got some negative critiques from teachers and learners.

Ethical questions
Connection between effect and psychological traits of learners, but where can this lead to? (cfr Cambridge analytics).
What if we have the data for 'good', what if others use it for 'bad' ideas.
What about GDPR, who owns the data, how does this affect funding, if students opt out of the system and all their data is erased; can we use blockchain in order to keep the data connected to the learners?
Where is the data in order for the data be erased, how does this affect future employment?
Will the system be able to evaluate actual learning, if this is the case, what benefits will it bring to teaching and learning?
Does the support of learners lead to limiting the self-directed learning-to-learn of the learners
Starting from the technology to move to support the learning seems to be the other way round then it should be done,
What is the educational progress using these technologies?
What is the difference between monitoring and surveillance? (where is the barrier)
Can learners hack the system to get more or less support?
Does the teacher have enough time to support learners with difficulties? And does their help actually benefit the learning?
Consent forms of those who are not able to give consent?
marginalized people are in need of technological support, but how do we support them in a secure way?

Sheila project:
Methods of mass destruction book

The post-it notes with ideas from three different groups addressing some of the questions mentioned in the above slide.

Monday 16 September 2019

Academia & ageism?Looking for role models & #data #academia #ageism #success @EcTel19 @mLearn19

Image result for Iris apfel
Iris Apfel overall fashion icon

As I am preparing to head out to mLearn/EcTel 2019, an issue turned up on potential ageism. Do you know of anyone who started their academic track at 50 or older and managed to gain access to a higher academic position? Please send me a message, I would love to interview them and know how they achieved that position. In case you have data regarding the below statements on age and academic positions, please inform me as well, would love to factualize my assumptions.

Academia is filled with older people!
If I look around at conferences, the biggest target population consists of older (old-ER) academics, who have achieved academic status, and doctoral students (mostly younger, present author and some of my friends excepted). So, if I walk around, it feels as though there is equal representation in terms of age.
But then I started to dig a bit deeper, while looking for successful role models within academia, who started their academic journeys later on in life. Now I wonder whether people that start their academic careers later in life, actually make it to higher positions within the academic world?

You just need a body of work …
It is a reality that you need to have some sort of body of work within a certain field to step up the professional ladder in most areas. But there seems to be a discrepancy in what is possible in the professional (read corporate) world and what is possible in the academic world. Or am I mistaken?
Forbes has this 30 under 30 people to follow. The list is comprised of people who are successful at a young age in something newsworthy. If you consider the age of 30, this means that even the ‘oldest’ ones only have 9 years maximum to reach this status of success. If you would translate this into academic tracks including bachelor, master, and phd finished, you have only about 4 to 5 years max to achieve potential success. This means you can achieve a successful status within this short amount of time: less than 5 years. Basically, if you start at 50+ you should be able to achieve success before keeling over and dying of old age :D
Now let’s get back to academia and tenure tracks for researchers starting their career later on in life.

Looking for numbers
We move towards a more data-driven world and with that, an increased belief that data is the final argument (not agreeing with this, just generalizing). So I wonder: what is the average post-doc age, and how does this average compare to average PhD age? Or better yet, how many 45+ people start a Ph.D. track, and how many 50+ people get into a post-doc function?
Just wondering, as I have the feeling that this doesn’t add up. And if this adds up, then how many of the tenure positions are held by academians starting out later in life? Can anybody get their hands on such numbers?

The reason I ask, is because some of us late academic bloomers can have a lot of citations (in open journals), written and published in a short amount of time. We also come in with transferrable skills: project leads (corporate), team skills, innovations… and I am just wondering whether these might be neglected when comparing candidates for academic positions. Could this be? Or am I wrong in assuming this? (again are there numbers?)
In a world where there is increasing pressure to combine academic with professional fields, this seems something that is missing. Because people with a prior corporate or governmental background, might be well placed in cross-over academic tenure positions? And in those countries where they urge people over 50 to stay employed or be employed, I just wonder if there are equal opportunities?

Or aren’t late bloomers part of academia status positions?
Who knows, excuses might be:
  • Yes, but they don’t have enough high profile papers,
  • Yes, but they didn’t supervise enough phd students,
  • Yes, but you need the 10 years of prior experience (not true, certainly not for 30 under 30)
  • Yes, but there is no ageism (without arguments to follow that statement),
  • Yes, but if you start late, you cannot expect to move up the academic ladder (that would be a crushing answer, would not it!).

Just looking for role models or numbers
Do you know of anyone who started their academic track at 50 or older and managed to gain access to a higher academic position? Please send me a message, I would love to interview them and know how they achieved that position.
If you have numbers regarding the above, oh!!! Please inform me as well, would love to factualize my assumptions.
In the meanwhile, writingly yours from EcTel and mLearn.