Free #Horizon2020 report out @educause good inspiration #learning #education

The Horizon 2020 report (58 pages) has been released by @educause on 2 March 2020 and it is an inspiring read for those of us looking at emerging learning designs and techniques. The report covers trends in the social, economical, political, technological and of course higher education realm and new in this report is a nice contextualization of all the different trends and technologies using visual supports. Educause is Northern America based, so most of the examples and projects they refer to are also North-America based.

This report is also more consciously covering multiple scenarios resulting from the interactions between all the different realms of society, which makes it a nuanced reflection of where learning can go in the near future. The report also links to additional reading and complementary material, e.g. articles on micro-credentials and experiential learning, [High on the higher ed agenda: alternative learning and ongoing increase of online education. High on the economic agenda: climate change and the green economy]

Download it now! Why, because it has tons of interesting links with a great synopsis for each subject. See below to get an idea of only a handful of information.

Emphasized learning technologies and practices this year:

• Adaptive Learning Technologies 
• AI/Machine Learning Education Applications 
• Analytics for Student Success 
• Elevation of Instructional Design, Learning Engineering, and UX Design in Pedagogy 
• Open Educational Resources 
• XR (AR/VR/MR/Haptic) Technologies 

Adaptive learning technologies are still hot news, as the search for effective personalized learning is still looking for practical outcomes. One of these listed in the report is the Alchemy tool by UBC (University of British Colombia, Vancouver, Canada). It is described as "Alchemy is a multi-featured online tool that supports teaching and learning in any circumstance that benefits from flexible, scalable, and feedback-rich learning alongside growing learning analytics capabilities." which basically shows where learning/teaching is moving towards in this ever more specialized-topic driven learning world.
Another adaptive example I like is from Deakin University, called the professional literacy suite, where I especially like their focus on digital skills in the first year. Which fits with the demand on data and AI savviness, communication skills.... teach those early on in higher ed curricula.

AI and Machine learning in practice still focus a lot on chatbots (which is basically turn a FAQ into feedback offered by bots). The most interesting option mentioned is the Responsible Operations positioning paper by the worldwide library cooperative (38 pages, great insights) that looks at how AI and ML are embedded in society, and how this changes all parts of society and which research agenda might address these challenges.
And the University of Oklahoma has set up PAIR (a global directory of AI projects in Higher education) ... nice!

The Analytics for Student Success are fairly similar, but the report on Ethics in Learning Analytics (16 pages) by the International Council for open and distance education is a good reference document to keep at close hand.

Elevation of learning design - pedagogy is always of interest to me. In a way, the learning design changes feel as small changes, but with big impact as a growing number of teachers and learning-related professionals are picking up digital learning tools and embedding them into their curriculum to address multiple learning challenges.
Carnegie Melon has an open learning toolbox called OpenSimon (part of the Simon initiative), a great spot to explore tools, techniques, research projects and so on (e.g. the Tetrad project which is an easy visualisation tool for data).

OER: we need more OER, but for those looking for new material, Mason OER metafinder is a great starting point.

XR technologies (extended reality) are increasingly on the rise as just-in-time workplace learning is higher on the agenda of our rapidly changing world. It builds upon prior realizations and needs to simulate emergency actions for students, e.g. augmented reality use for medical students by the University of Leiden, Netherlands.

It is a great read, good to get a fresh perspective on where we are all going.

Free @eLearningGuild research on #generations in the #workplace @JaneBozarth

The inspiring eLearning Guild keeps disseminating great reports in relation to learning. One of their key authors is Jane Bozarth (= director of research @eLearningGuild), who is always an inspiration. You also know that a report will be of interest if she writes it.

In the eLearning Guild's latest report (19 pages) the focus is on Generations in the Workplace: how they see each other and why this is worth all of our attention. Interestingly, the report starts out with a clear framing on why all of us tend to have possible stereotypes confirmed when thinking about 'the other' in terms of age and what a person in a certain age group can do (see page 2 of the report). And the report concludes that little comparable research in terms of learning outcome is being done, diversity and inclusion of age groups is a concern, training might be affected by trainers encountering different attitudes towards their training, concepts that are being used as indicators (e.g. lifelong learning) to assume a strong will to learn might be based on different interpretations of those concepts by learners. The complete list of ideas and outcomes for L&D practitioners is pasted below. 

Basically, learners of all ages are more similar than different! So, erase any potential stereotypes and move on :) Get the report, it is free (if you register with the eLearning Guild, also free) and it is a really good read.

WHAT DOES THIS MEAN FOR L&D PRACTITIONERS?

It is difficult to find advice for L&D practitioners that is not rooted in often-unsubstantiated data from the popular press. A few takeaways from this review of the empirical literature base:
• It seems worth noting that while much research explores values and attitudes (Do people of different generations comply with rules? Do people of different generations like technology?), little of it compares outcomes. Is one generation more productive? Is one generation more prone to making errors? There is no evidence to suggest one person inherently performs better than another by dint of any grouping by birth year.
• Work has changed. More work is remote, more work is mobile, and people are becoming increasingly accustomed to finding quick answers and help. Workers need content that is available anytime, anywhere, and is accessible via any number of devices. The content may need to be less formal than it has been in the past. Workers may need help understanding how to access that content and how to use the devices.
• A number of researchers (among them Mencl & Lester, 2014; Urick et al. 2017; Woodward, 2015) tie concerns about generations in the workplace to the larger matter of diversity and inclusion. L&D workers involved in efforts in these areas may need to incorporate ideas about generations into that broader context.
• In their 2015 review of the literature, Woodward et al. found that younger generations placed greater emphasis on lifelong learning and personal development at work than older generations. Those in L&D might consider that similarities can take a number of forms. For instance, Mencl & Lester 2014 note that the Baby Boomer interested in “career advancement” and the Millennial interested in “lifelong learning” may be talking about very similar things.
• An interesting take comes from a 2018 Learning Solutions article by Joe Mayer, who reframes the conversation from “managing differences” to “avoiding generational bias”. Among his suggestions are using design thinking to take an empathetic view of the learner, to conduct extensive user-testing of your user base, to choose appropriate vehicles for content rather than try to accommodate some perceived generational preference for a particular medium or instructional approach, and to consider tenure rather than age.
• Finally, we should take care to check our own biases. In a lab experiment in which undergraduates taught a technical task (using Google chat) to learners of varying ages, researchers found that “ostensibly older trainees evoked negative expectancies when training for a technological task, which ultimately manifested in poorer training interactions and trainer evaluations of trainee performance” (McCausland, King, Bartholomew, Feyre, Ahmad & Finkelstein, 2015) p. 693. In short: Trainer stereotypes of learners based on age created a self-fulfilling prophecy. Older trainees received lower-quality training, which could ultimately affect job performance.

(p. 14, eLearning Guild, 2020) 

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)

Sources
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)
Bibblio
teachpitch

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...

Workflow
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.

#CfP Call for papers on #education, open #learning, #AI and #teaching

This call for papers offers a mix for research papers and call for speakers, enabling more research-based or more experienced based proposals to be written. The calls are organized in order of deadline.

ECTEL2019 conference

Deadline for submitting the mandatory abstract: 29 April 2019 (200 words using Springer template)
Deadline for submitting a full paper: 13 May 2019 (6 - 14 pages using Springer template)
When: 16 - 19 September 2019
Where: TU Delft, Netherlands
More information: http://www.ec-tel.eu/
Description (from website)
The European Conference on Technology Enhanced Learning (EC-TEL) is a unique opportunity for researchers, practitioners, educational developers and policy makers to address current challenges and advances in the field. This year’s theme of “Transforming learning with meaningful technologies” addresses how emerging and future learning technologies can be used in a meaningful way to enhance human-machine interrelationships, to contribute to efficient and effective education, and to assess the added value of such technologies.
The conference calls for papers focusing on this theme and addressing many topics: intermediation between learning systems, learners and educators; guidelines and methodologies to enhance learning experience through technologies; bridges between technology and learning; assessment of technologies’ educational added value; promotion of coherence and unity of technology and learning; and improvement of complementarity between technology and learning. We encourage participants to extend the debate around the role of and challenges for cutting-edge 21st century technologies and advances such as artificial intelligence and robots, augmented reality and ubiquitous computing technologies and at the same time connecting them to different pedagogical approaches, types of learning settings, and application domains that can benefit from such technologies.

Online Educa Berlin  

Deadline for submission call for proposals: 30 April 2019
When: 27 - 29 November 2019
Where: Berlin, Germany
Link to 'submit your proposal': https://secretariat.oeb.global/oeb_proposals/
More information: https://oeb.global/
Description (from website)
OEB Global, incorporating Learning Technologies, brings you to the forefront of learning technology developments. Get insights on opportunities and challenges that are changing the world of learning

• Find out how to choose and use various technologies
• Discover proven practice, approaches, strategies from leading institutions and organisations
• Participate in pre-conference activities and 120+ break-out sessions with 300+ expert speakers from 70+ countries from across different disciplines, sharing their knowledge, skills and passion
• Follow case studies presenting critical success factors and discuss innovative approaches with peers
• Meet with 2,500+ learning professionals from the education, workplace learning and government sectors and forge essential international contacts and partnerships
• Explore the exhibition at the heart of the event, where leading international e-learning manufacturers, suppliers, and service providers give hands-on demonstrations of innovative products and tools
• Join us as we analyse new technologies and trends within ICT-enhanced learning and training

OEB Global has pushed boundaries, challenged preconceptions and catalysed new ideas for shaping the future of digital learning for 24 years.

International Open and Distance Learning conference

Deadline for submission: 15 July 2019
When: 14 - 16 November 2019
Where: Anadolu University, Eskişehir, Turkey.
More information: http://iodl.anadolu.edu.tr/
Description

The Anadolu University is proud to invite you to the INTERNATIONAL OPEN & DISTANCE LEARNING CONFERENCE – IODL 2019, which will be held at Anadolu University, Eskişehir, Turkey on 14-15-16 November, 2019. After the conferences in 2002, 2006 and 2010, IODL 2019 is the 4th IODL event hosted by Anadolu University Open Education System. The conference is organized by Open Education Faculty, Anadolu University.

Anadolu University, one of the world leaders in open and distance education, currently offers higher education to over one million students worldwide. Anadolu University Open Education System aims to reduce the barriers to education, especially for adult and self-learners. In the 21st century, the idea of openness is in the very core of education which is surrounded with technology in multi-cultural learning environments.

Scope
The main theme of the IODL 2019 is “Glocal ODL Opportunities and Dynamics”.
The aim of the IODL 2019 is to provide a platform for researchers and practitioners to present and discuss a broad range of topics related to open and distance learning, including but not limited to:
• Open and distance learning, Lifelong learning, Open education and globalization, Drop-out in open and distance education, Open and distance learning for refugees, Learning analytics, Financial issues in massive education, Digital division, Barriers to learning, Role of education in crisis, Education in a multicultural society, Micro credential and short learning programs, Mobile learning, Adaptive learning environments, Deep learning in ODL, AI in/for ODL, IoTs for ODL, Student Support Services in ODL, Public science, New challenges to the Higher Education Area, Evaluation and assessment in ODL, Accreditation and QA in ODL, MOOCs and OERs

Transdisciplinary AI (TransAI) conference (combining AI with other disciplines)

Deadline for submission: 1 July 2019
When: 25 - 27 September 2019
Where: Laguna Hills, California, USA
More information: https://www.transai.org/
Description
Artificial Intelligence (AI) is concerned with computing technologies that allow machines to see, hear, talk, think, learn, and solve problems even above the level of human beings. On the one hand it allows data to be analyzed by real-time models that enable unprecedented levels of accuracy and efficiency. On the other hand it enables domain specific problem solving and knowledge discovery that cannot be easily done by humans.
Transdisciplinary AI 2019 (TransAI 2019), technically sponsored by the IEEE Computer Society, is an international forum focusing on the interactions between artificial intelligence (AI) and other research disciplines. It consists of themes that each addresses the applications of AI to a specific research discipline as well as how domain specific applications may advance the research on AI.
The TransAI themes address two dimensions--technology and academic research domains so that technologies can be mapped to domain applications.

Asian conference on education

(focusing on dependence and independence, nice topic)
Deadline for submission abstract: 22 August 2019
When: 31 October - 3 November 2019
Where: Toshi Center hotel, Tokyo, Japan.
More information: https://ace.iafor.org/
Description
The 2019 conference theme for The 11th Asian Conference on Education is “Independence & Interdependence”, and invites reflections on the desirability, extent and limits of our individual independence and autonomy, of that of our students, and of the institutions and structures within which we work, teach and learn. We do not educate, and are not educated in vacuums, but in such contexts and constraints as families, groups, and societies; of nations and cultures; of identities and religions; and of political and financial realities.

Ever changing technologies offer new ways for us to be independent and autonomous learners, encouraging students to be self-directed and confident in making choices, and enabling and empowering students and teachers to be proactive and tailor content. However, myriad technologies and services make us more dependent on the very things allowing autonomy. How do we help students and teachers alike navigate and curate the vast information available? How do we encourage individual growth while also underlining the importance of belonging and of the reciprocal responsibilities and privileges of education? How do we help students build the skills and attitudes necessary for positive engagement in distributed, globalised communities that so often lead to polarisation and alienation instead? How do we educate with independence and interdependence in mind?

This conference is organised by IAFOR in association with the IAFOR Research Centre at the Osaka School of International Public Policy (OSIPP) in Osaka University, Japan.

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 http://www.lab-initio.com/ site

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?

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!