Call for papers #MOOC and #online learning focus

Get your papers out and use one of these calls to do it. Three options, in chronological order of the deadline for submission:

eMOOCs2015 conference

Deadline for submissions: 12 January 2015

Location of the conference: Mons, Belgium

Date of the conference: 18- 20 May 2015

Type of submissions: papers on MOOC experiences and research papers. 

Information on the call can be found at http://www.emoocs2015.eu/call-moocs 

Submit your papers here: https://easychair.org/account/signin.cgi?key=18800640.nAH0aIzfppFdI6fE

Guidelines for submissions can be found here: http://www.emoocs2015.eu/moocs_call_instructions

And layout instructions can be downloaded here: http://www.emoocs2015.eu/sites/default/files/guidelines_for_submissions.doc 

Description of the conference:

Organised by the Université catholique de Louvain and P.A.U. Education, the event aims to be an opportunity to gather European actors involved in Massive Open Online Courses (MOOCs), from policy makers to practitioners to researchers. This conference is the follow-up of the EMOOCs 2014 Summit. The conference will be held in Mons, Belgium. Mons will be the European capital city of culture in 2015. “Where technology meets culture…” has been one of Mons 2015’s main investments.
The conference will include four tracks: Institutions, Experience, Research, Worldwide, and for the first time a preconference MOOC will be organised. The conference website can be found here. 

http://www.emoocs2015.eu/ 

Paper publications for special issue, MOOC old and new focus issue from IRRODL.

A special issue is being gathered for IRRODL which is entitled: Towards a European Perspective on Massive Open Online Courses (MOOCs): The Past, the Present and the Future

Information on this special issue can be found here: http://www.irrodl.org/index.php/irrodl/announcement/view/10 

Guidelines for authors here: http://www.irrodl.org/index.php/irrodl/information/authors

Description:

Over the last months, the massive open online course (MOOC) debate has finally come of age, especially after Sebastian Thrun publicly announced that “we have a lousy product” (Chafkin, 2013), and a series of backlashes have led to the conclusion that MOOCs mostly benefit those learners with a lot of cultural capital. Before this turning point, MOOCs were portrayed as a completely new educational innovation, and its conceptual ancestors such as distance education were ignored. Furthermore, other types of MOOCS, such as the those based on the notion of connectivism, advocated by scholars such as Stephen Downes, George Siemens and Rita Kop, as well as the work around open content (Wiley & Gurrell, 2009), have been squeezed out of the collective memory.

However, these approaches are located within a certain culture that frames our thinking and acting about pedagogy. More precisely, the open education paradigm (for an overview, Deimann & Sloep, 2013) has been dominated by Anglo-American actors such as the Massachusetts Institute of Technology which started the global open educational resource (OER) movement a decade ago by opening up their teaching materials to the general public.  Meanwhile, a world-wide MOOC industry emerged and challenged many of higher education’s traditions.

To this end, we are inviting contributions that deal with the following aspects:

• Papers introducing theoretical/conceptual models from the area of open distance education (ODL) that inform the current MOOC debate (e.g., lessons learned);
• Papers aimed at connecting academic traditions and cultures from European and other regions (e.g. aca- demic charisma) to prevalent issues of the MOOC debate (e.g., drop-out, business model);
• Empirical (re)analysis of MOOC studies against the background of previous knowledge from the ODL field;
• Vision papers focusing on emerging trends such as social production of knowledge or “digital solidarity” (Stalder, 2013) that can expand the focus of the current discourse.

Timeline

Deadline for paper submission - January 20, 2015
Acceptance of papers for peer review - January 30, 2015
Final editorial decision based on peer review - April 20, 2015
Projected publication date - July 20, 2015

Ec-TEL conference 

Deadline for submission of abstracts: 16 March 2015

25 March 2015 - Submission of full version
27 May 2015 - Notification of acceptance
Submissions through easychair: https://www.easychair.org/conferences/?conf=ectel2015
Author guidelines here: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

Location: Toledo, Spain.

Dates of conference: 15 - 17 September 2015

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. Through EC-TEL, established and emerging researchers as well as practitioners, entrepreneurs, and technology developers explore new collaborations, strengthen networks, and complement their core expertise.

Developments in information and communication technology, for example new communication patterns like in social applications, mobile devices and ubiquitous network access, together with social and economical changes lead to a networked world. The increasing networking in different scales from global to local is having a profound effect on learning and teaching. It makes new forms of collaborative and personalized learning experiences reality. Learners shift between formal, non-formal and informal learning. They come together in different social settings and communities. Teachers roles are also subject to change.

There is a pressing need to shape learning arrangements in such a way that they exploit the potentials and meet the requirements of a networked world. To address these challenges the theme of EC-TEL 2015 is Design for Teaching and Learning in a Networked World.

#PhD sharing #online and #MOOC #research instruments

The last couple of months I have been hammering away with data. Trying to collect meaningful answers to the question: "how do experienced online learners determine what they want to learn and how?". Well, the research question sounds a bit more formal, but I like the question this way. This central research question came out of the results from a pilot study which I planned during the first closed beta courses of FutureLearn. The pilot study is in part described in my probation report, which I uploaded in academia and can be found here. The probation report is a report you need to submit to UK based universities to proof that you are PhD material, and that you have been working on research with academic rigor and good progress for approximately 10 months.

And to add to my PhD journey, I will share the first research instruments used for my main study via this blogpost, see below. More instruments or details will follow as I proceed.

sub-questions to build narrative towards answers to the central research question
In order to get answers to the central research question mentioned above, I divided the central research question into five sub-questions, which will hopefully give an idea of the elements I investigated to come to a more complete answer:
1. What are the MOOC participants learning objectives?

2. What are the actions undertaken by the learner to attain self-determined learning goals?
     a. Who do learners connect to in order to learn?
     b. Which technologies do learners use (devices, tools and resources)?
     c. Do they mediate that learning with others or other technologies in order to add it to their learning? If so,          how?

3. What makes a MOOC learner reach further to find an answer to their learning, or what is the point beyond which they think it is not worth the effort to reach an answer for their learning objective?
4. Did emergent learning happen resulting in unexpected learning outcomes?

Quick overview of the methodology

This main study gathered Self-Determined Learning (SDL) experiences from experienced, online learners while they are enrolled in a FutureLearn course. The research consisted of three phases, leading up to conclusions on SDL in FutureLearn courses.

•  Phase 1 – expectations: gathering expectations of the participants enrolled in FutureLearn course via an online survey
• Phase 2 – experiences: collecting learning logs in which the participants are asked to describe two learning episodes every other week for the duration of the course
• Phase 3 – reflections: interviewing the participants (one-on-one) taking part in the study via structured interviews looking into the differences between their expectations and actual perceptions on their SDL as they were participating in the FutureLearn course

The FutureLearn research participants were volunteers selected from those taking part in one of three specific FutureLearn courses. The selection was based on their prior online learning experience (which could be online learning in general, self-taught learning while using the web, MOOC, mobile learning... but they needed to be online and engaged in some kind of learning for over 3 years). 

The pre-course survey questions

For phase 1 just a couple of questions were asked. The aim of the questions was to get an idea of the motivation of the participants for enrolling in that particular course, as well as to allow me to double check their previous online learning experience. 

1.       What is your prior online learning experience? (Multiple choice: no prior experience, 1 year or less online learning experience, less than 3 years online learning experience, less than 5 years online experience, more than 5 years online learning)?

2.       What type of online learning do you have experience with? (Multiple answer: MOOC, online learning, distance education course, learning experience by self-organised learning to stay on top of my field of interest, learning online from my network, self-taught online learning on random subjects, other)

3.       What is your reason for registering for this particular course (Multiple answer: professional interest, personal interest, learning need, other)?

4.       What do you expect to get out of this course? (Open question)

Although you will not find questions related to demographics here, these were in fact provided to all FutureLearn participants as part of the overall pre-course FutureLearn course survey. And I certainly did not want to double up with the survey the participants already filled in (in the past I found that saving time is essential for willingness to participate). 

Learning log template used

In phase 2 the learning log templates were the most important research instrument used in the process of this research study. All the participants were asked to fill in the learning log template at bi-weekly intervals, and to provide two templates for each 'learning log week'. The reason for pacing the learning log frequency, was again to save time for the research participants, yet at the same time get insight in their learning process. I only asked them to start filling in the learning logs from week 2 of the course, as past research into MOOC dynamics showed that from week 2 there is a significant drop in participation from curiosity based participants and at the same time an increase of participation from active participants. 

For those interested in having a look at the learning log template, have a look at this academia upload here. The learning log template consists of open and closed questions, allowing me to find quantitative as well as qualitative data.

#OEB14 session initiating Unified Learning Theory brainstorm #education

During the last Online Educa Berlin conference, I tried to see whether an open format would work. The general idea was: all of us eLearning experts coming together to find a solution for an existing challenge (I know, I still need to work on shortening the format description :-)

Format of session: peer-to-peer experience sharing
The process was based on talking among ourselves as peers, and at the same time experts, after haven given an eLearning or learning challenge.
The challenge was that current eLearning and learning in general is completely dispersed, and disconnected from a central learning model/framework/theory. How do we - as experts, as practitioners - look upon this disconnected amalgam of different models, and specifically: would there be a way to combine all of these learning types.

Towards a Unified Learning Theory
So what did I have in mind with the suggestion that there are multiple models for different types of (e)Learning, yet no one model - or as I had it in my mind: Unified Learning Theory, an analog for the Unified Field Theory, in combination with String Theory, although I must confess that the idea I have in mind is more related to String theory (if a non-physicist can make that assumption). As an example of why I feel the Unified Learning Theory might parallel String theory approach: the magnification levels (now, I now this is still very sketchy, so feel free to share shortcomings or positive additions:

Different levels:

• Learning overall
• Educational field (e.g. mobile learning)
• Models related to those particular fields (e.g. FRAME model)
• The elements put forward in those frameworks/models (e.g. device aspect, learner aspect, social aspect)
• And breaking those elements down to their basic parts: 
• either the stimuli or patterns that influence these elements of the models (e.g. with regard to learner aspect: prior knowledge, financial options)
• or the base elements of these elements: neural pathways, social drive, ...

As you can see, and if I tried with different learning fields, it seems I cannot break all the elements down to the same base elements, which then again points more towards Unified Field theory where things co-exist one next to the other (again, seen from a simple pedagogy/techy educated mind). Or maybe the full breakdown logic should be viewed differently?

The reason I wanted to explore this peers-talking-on-a-topic format, was indeed because I have been tampering with this idea of the Unified Education Theory for some months now, but I could not connect the dots. So, more people needed to become part of this quest.

Below, I share the notes of the ideas that filled the room as an increasing amount of people joined the dialogue. (and I am grateful for all of the participant inputs!)

• The brain cannot help but learn (unless the learning gene is missing)
• learning can be attributes (conditions of learning, paths of learning)
• Models simplify learning, yet learning is very complex (note from myself: which pleads for an approach using complexity theory)
• neural pathways are influenced by elements
• there is an intention connected with learning, so how do we decide where and when to learn what?
• where do we situate incidental learning, and how much influence does this incidental learning have on our overall learning?
• What do we know about what makes learning effective in multiple learning contexts?
• The conditions for learning differ from types of learning, the conditions for learning and teaching must differ to reach the different learning goals put forward by the actual learning
• Different learning fields, result in different learning models that are needed, and different learning interactions (e.g. a discussion for language learning purposes, has a different learning outcome (writing, understanding...) then a discussion used in a math field (exchanging solutions, approaches).
• Personal learning strategies can be very varied as well. 
• Learning can have multiple forms, and not all these forms are understood, or not all the outcomes of these forms are understood: e.g. leisure learning, serendipitous learning. Although these are not taken up fully in the learning canon, they do resonate with 'the flow' that can accompany learning as put forward by Mihaly Csikszentmihalyi 
• Contextual learning, which relates to the actor/network theory is an important theory in media studies.
• The identity of the learner: as part of being, or acting competent, the same for the identity of a project. 
• The choices in learning are in part elite due to abundance or scarcity of learning options and needs. 
• Learning is based on personal competency and being able to evaluate and direct the process
• Time and personal readiness influence learning, and the ability to learn
• There are reoccurring elements in learning: motivation, frustration, self-esteem, trust in one's self, trust in others, passion and emotion accompanying learning, current state of mind, being open to learning ...

Dichotomies were coming up as the peer-to-peer session went on:

• specific learning versus generic learning
• deep learning versus leisure learning
• autonomous learning versus curriculum learning
• curiosity as driver (frequently seen in children's learning, some school systems) versus intention as driver for learning (curriculum and training based learning) - but it might also be that curiosity instigated initial learning, and is followed by intentional learning
• content abundance versus flattening of the learner construct
• Perseverance and discipline versus serendipitous learning
• Professional versus personal outcomes

There retrospect, I do think all of us participating in the session felt there was a multitude of aspects related to learning. As such it might be that an attempt to bring them into a coherent (or even incoherent yet mapped out) format might be of interest to allow all of us to talk about specific learning, within the full architecture of learning. At this point, and thanks to the session I feel a Unified Learning Theory might be something to go for.

Parallel with online and f-2-f appreciation/emotion. 
A sort of PS: suddenly, as the brainstorm was happening, an emerging idea came to mind. It suddenly dawned on me that what the participants were sharing, the emotions (e.g. "I really liked the fact that this was an open session", "Can we keep the conversation going afterwards?") were similar in content to that of MOOC participants who had been engaged in open discussions on a particular subject matter. So again it seemed that you cannot take the human out of human, in which ever virtual or real world the interaction is taking place.

#oeb14 Ellen Wagner @edwsonoma on PAR and #data are changing everything

This was a very illuminating session by Ellen Wagner, as it provided real options to tackle educational challenges (on the level of institutes as well as learners) on the basis of common educational data retrieved from a varied amount of higher ed institutions. REALLY interesting.

Analytics are taking the world by storm.
parframework.org

Learning analytics, big data is at its vanguard.
Staggering revelations about big data: in just 5 years we will look at 40 zetabytes information, that is HUGE.

All these data that are floating around are not being analysed that much as we think. It takes an amazing amount of time, tech talent, and human interpretation to find value from the data.

The full effect of data can not be envisioned today, as it hits all of society.

Where are we heading?
Pushing the data in LMS, in comprehensive analytics is very complex.
Big data landscape is getting bigger every month. But virtually no company on big data is involved in education.
there is a specific reason why: money of course, but in education - despite of the fact we talk about it - we do not use it yet. We cannot process it using normal analytical tools. Most of data comes in spreadsheets, so there is a big step to figure out of the big data solutions compared to our educational analytics.

While big data raise expectations, student data drive big decisions in .edu.
Because it is new, we do not really know where we will go.

Ellen shares some US cases, to show which work is being done.

In US there is an educational problem. There is more student dept than there is house doubt, this is unsustainable, and people sometimes cannot pay it back within their lifetime
Some schools have 'open enrollment', which results in very high drop out rates unfortunately.
So colleges are now given score cards. But this means there must be standards. The metrics at present are focused on the first time freshman... but this means 85% of the contemporary learners do not fit that profile.

Public education has dropped dramatically. Performance metrics are the basis for funding, but as standards do not exist it means that it is tough to get the expectations and hit targets.

So the score card metrics need to be reviewed.

Additionally, pedagogy are not mentioned on the score cards.
In California: license of student textbooks must be tied to student performance, but this effects universities lives.

Metrics have ramped up expectations of what analytics can do. But the challenge is to build metrics that are constructive for both students and educators.

Education is helping people to grow.

Prescriptive analytics: proscribing educational treatment.

Use case: predictive analytics reporting framework (PAR)
a national, non-profit, multi-institutional collaborative focusesd on institutional effectiveness and student success
a massive data anlysis effor using predictive analytics to identify drivers related to student risk
Par uses descriptive, inferential and predictive analyses to create benchmarks, institutional predictive models and to inventory, map and measure student success interventions that have direct positive impact on behaviors correlated with success.

If you - as an educators - do not know what happens with your learners, than how can we suspect to change education for the better?

pedagogy is important, but the emotional effects of education and the feel of education must be taken into account as well.

The privacy issues related to the learner data are staggering.

First meeting with Bill and Melinda Gates Foundation: NO, we do not think you can do it. So she went on a school circle to see what they wanted, could offer, needed. 700.000 student records, from multiple institutes. The data setup was upped to 8 million records.. this got the YES from Bill and Melinda. Granted, the data were nothing compared to weather data, business data.

But descriptive benchmarks can now be done, for each institutions predictive data can be given based on their student records.

something unexpected emerged: making a prediction is not enough. Finding how this can address the challenges is the most important thing.

It is difficult to get the open, transparent to work with due to educational ethica student related issues around their data.

First three years: building the data resources to get started with analytics.
Now: start the analysis.

The institutes varied: community colleges (done rarely), schools that were considered progressive, as well as 'old school', competency based universities.

We tried to collect data that was available for every student, every school: the simple things that could get hands on => common data definitions.

All data is anonymised (both learners, as well as schools. But the schools keep the encryption to link data to learners (VERY important cfr InBloom project for k12 schools, which resulted in a big emotional issue around data)

If solutions are found to improve learners education, that would be delivered to all. so every action taken by the school is questioned for impact, enabling to make the actual impactful strategies visible.

So they used structured, readily availbale data. Openly published via a cc license
https://public.datacookbook.com/public/institutions/par

One of the things that happened: they can now make comparible conclusions.

Great point on online versus f-2-f colleges (note to self: add movie)

Descriptive benchmarks (cross instittutional) and predictive insights (institutional specific), all with specific filters, e.g. isolate subgroups.

Predictive models reduce guesswork to find students at risk.

For those institutes that have open enrollment and attract all students, these predictive insights can be complimented by different other factors that you - as an institute can research: for instance the inpredictability of humanity (no paycheck in time, death, health issues)

Putting it all together
determine student probability of failure
determine which students respond to interventions
determine which interventions are most effective
allocate resources accordingly

Now also (based on John Campbell work)
inventorying and categorizing student success interventions / supports using a commmon framework
based on known predictors of risk and success
in the context of the academic life cycle

addresses "now what?" by linking predictions to action
enables cross institutional benchmarking
supports local and cross institutional

[Ellen: very hard time to turn down a dare :-)  ]

#oeb14 Ola Rosling with a #flipped keynote approach

These are the notes I took during Ola Rosling's keynote at Online Educa Berlin. Yesterday Ola Rosling made a very important remark on Big data and power, as big data is in the hands of the few, that same data is under the power influence of that few. This means that as long as big data is not made transparent and public, data use risks at being distorted by the power of those who own the big data. I liked that. So inevitably I was looking forward to this appearance at the keynote panel this morning during Online Educa Berlin.

He merges his previously acquired knowledge from google, and is now taking it to education. He works now for Gapminder… think it is related to Hans Rosling (his father, as Ola pointed out).

In his keynote approach he tried to explore a flipped keynote approach. At the end of his keynote, I did have the feeling that it was not completely flipped. There was interaction, but no discussion, not learning from each other. But a very nice experiment, and useful to work on as a concept.

Flipped keynote: got homework out before conference.

Answer the following two question to improve the conference itself

How did deaths per year from natural disasters change in the last century?

More than doubled

Remained about the same

Decreased to less than half

Answer: 12% so less than half

In the last 20 years the percent of people living in extreme poverty has …

Almost doubled

Remained about the same

Almost halved

Answer it almost halved

Women aged 30 spent how many years in school? (Men of same age spent 8 years)

7 years

5 years

3 years

Answer: 7 years

Let’s analyse the answers:  main conclusion: intuition distorts reality and our opinions.

Now the same questions were asked the chimpansees. This is because the people might be assumed by something, where chimpansees do not.

After this he looked at the fact based reality, and our assumptions based on emotions, not facts. As an example he looks at demographics of the world and its facts (amount of people per continent now and in the near future, money as it has evaluated over the years). 

#oeb14 Stephen Downes on personalised #learning

These are liveblogging notes from Stephen Downes keynote.

Preoccupations around the learner, and how they seem to be pro-active online, but rather passive in face-to-face situations. The idea of the learners as citizens and learners as consumers.

How do we equip the learners of today for the jobs today and in the future.

Stephen Downes on reclaiming personalised learning

The overall theme is that education is in for a reality check. The reality check he wants to share is that education and educational companies must face the fact that they no longer own the learners nor their learning.

The learner is no longer the customer, the learner is now the product. Maybe in the form of tuition fees, maybe in the form of big data.

When they talk about the learning process, the core concept is about being interactive, repurposing, about personal learning.

To teach is to model and demonstrate, to learn is to reflect and construct.

Learning is a form of recognition. It is something that feels natural, and that grows following patterns, this enables recognition.

Reclaiming the web, means it is important to have his own space, this is what is meant by reclaiming. The concept of bringing back to us of what is ours.

Education has been the march of the LMS – the giant silos of learning. Once you leave the LMS institution, your own content is lost. Education as a discipline where the personal content is no longer personal in many occasions. We must not only reclaiming the content, but we must reclaim the production, the process.

Stephen distinguishes between personal learning and personalized learning. Personal learning is all your own learning. Personalized you take from the shelf and tweek and then say: it is personalised. Metaphor: caramel and caramelized.

The web is not a platform, it is a bunch of personal spaces. This is the basis for the cMOOCs in 2008. The fact that these mooc were distributed added to the fact that this learning was personal. This created a network where the participants genuiinly owned their learning. But what followed took the learning away from the personal to the personalized again, where the data was no longer personal.

Learning data, learning data are important, but should not be commoditised. Not everything needs to be commercialized. Institutions need to learn this distinction: let it go. Platforms and proprietors do not own the  data. The data should belong to the person.

Why? They do not own this market no matter what they think. But a person should be able to choose from ALL sources, if you are locked into a platform what you can see is not visible. This is top down control, which is something we do not need for learning. Learning is not acquiring something, but becoming something. We do not become something by consuming prehashed content, but by resharing, creating, recreating content that is of importance to us as a personal learner.

Analytics purports to tell you who you are. All humans are put into 16 categories, There is a distinction between big data and personal data. Big data= many people using one service, personal data is the one person using many data services = deep analysis. This truly tells the story of ‘me’. And who would want that all this personal data would be given to outsiders.

Personal network versus institutional library. Personal network of connections, resources and content that you as a person aggregate.

Education as the commodity, not the student. The things we do belong to us, the person.

With that in mind that Stephen supports the idea of reclaiming personal learning

LPSS – learning and performance support systmes: It is a network of personal learning environments.

http://lpss.me – prototype PLE – it does instantiate the end to end solution of personal learning, and storing it in a space of your choice.

The design is based on putting the learner at the center connecting to services and institutes. It is not a platform, but a connector of resources and services. To be used for your own purposes and goods. The idea of Ed Net Neutrality.

No matter which provider, they should not indicate what you should do. The personal learning record – data owned by the individual, shared only with persmissions.

Look at downes eporfolio-and-bades-workshop-oeb14.hml

Relevant PLR (personal learning projects). Third parties can provide analytics services, but they do not get free unfettered access

Analytics as a service….

(inge check out owncloud)

We collectively need to decide whether our own personal data is something that needs to be commoditised or should be reclaimed as a person, as an individual.

#OEB2014 keynote debate #data corrupts #education

This is the transcript of what I plan to say during the OEB14 debate... but it will depend on my brain power and time itself (a bit of a long text). Feel free to share ideas and comments on it. Text follows the slides.

For those wanting to co-author on the potential negative effects of data on education, feel free to have a look at what I call 'the big data ramble, a plea for more human indicators' (beware that is a draft, so more chaotic than the text I bring below, but ... I want to use the chaotic draft as the basis of starting up a position paper with any of you who have an idea on the subject as co-author). 

Digital divide & towards the norm from Inge Ignatia de Waard

Introduction

I will focus on two topics: how does data risk to add to the digital divide? And why does data replicate existing societal norms?

Digital divide

One of the reasons that data is currently corrupting education, is because big data increases the digital divide. Let me give you some examples.

The have and the have not’s … institutes - Digital divide dividing our educational institutes

As big companies retain more and more data, they are now looking for educational partners to use that data. But which partners are they choosing, and how does this affect the other educational institutes?

The digital divide – or the data divide - trickles down from the big Universities all the way to the vulnerable learners. As big universities might be able to pay for data access and results, and use those data to improve the learning goals of their (cognitive, and data minded) students. The less financially strong universities will have access to less data, less tools, which will affect learning/Teaching (although I do add that this might indeed not result in loss of teaching quality).

Which students will be able to pay for those universities? For if data storage and data mining is so expensive, it seems only logical that the educational fees for the data rich universities will rise.

This means that learners from less financially stable backgrounds, or parents with less financial means will have less opportunities.

(Example: oh but that is not going to happen some of you might think… but let’s look at Viktor, he is associated with a couple of large, financially rich universities: Harvard, Oxford)

The digital data-minded professors

The same is true for teachers, professors, research assistants. Learning analytics might provide insights, but these insights need to be translated back to the learners. Which means that all of us in education will have to become data savvy at some point.

Selecting the learners that fit the profiles provided by algorithms

Administrators facing tough budget decisions will look to the application of learning analytics with a profit oriented mind. In a world were profit rules, students from demographic groups that perform less can be seen as a potential loss.

If a university recognizes that a particular demographic group is more likely to quit school, the university may choose to slash recruiting on that group and exclude them for no personal reason of its own. Traditionally poorer performing demographic groups might be excluded based on economic/statistical data reasons. And this practice is already starting, for one university in Belgium is forcing out low performing students after their first year.

Example: looking back, I can tell you that I was one of those weak learners, I was scared out of my wits in my first year at the university. All of a sudden I had to learn?!!! But by some weird twists of faith, I eventually made it, and I am now part of education).

(example me, addition: Aida Opoku-Mensah : 51 million?)

Education for the purpose of future jobs, which jobs?

In 2013 during the World Innovation Summit for Education (WISE) 84% of leading educators, policy makers and governments claimed that the way learning happens today will not adequately prepare young people for the world of tomorrow. By this they referred to preparing the young for future jobs. Big data would solve this.

This seems like a very valid claim to make. One that made sense to me… until recently. Just a couple of weeks ago a new UK report came out focusing on job expectations. In that report the experts predicted that by 2030 one third of the current jobs will be lost due to automation… If there are less jobs to fill, the goal of education should turn towards multiple goals: not education resulting in professional work only, but education should also be aimed at reaching a better quality of life, becoming a truly fulfilled human being, and enabling a new society.

But with big data entering education and automation increasing, there is a strange selection criteria seeping in. If less jobs are available, not that many students need to be prepared for those jobs. So it might be more profitable to only focus on those students who are at the top of the crop.

The facts versus the actions

Until data became a reality, the teachers and trainers were the corner stone of education. Research proved this time and time again. So did we act upon that data?

At the moment government and the financial markets provide us with to contradictory messages. On the one hand we all need to accept cuts in education and training, for we need to help all of us get out of the crisis. On the other hand bigger budgets and funding than ever before are going straight into big data research and implementation (for education among others), because data will enable an improved education.

Why are bigger budgets provided to replace the proven human teaching with automated training that still needs to come of age? (sordid logic)

the drive towards the Norm

What is data other than the reproduced norm of society? The gatekeepers – those with power - build the quizzes, construct the algorithms that are needed for educational data-mining. And based on what? On the need of the day, not the need of the individual, not the need of a sustainable, qualitative society.

As all technologists and developers know, wo/man-made algorithms reproduce the norm of those who constructed the algorithms. (example hit prediction)

Not moved by the norm, but enchanted by the one

For me I am not moved by the norm, but enchanted by the one. Change does not come from the masses, it is the single mind that finds and touches the truth felt within each one of us. The masses only reproduces existing norms, existing power.

If we think of Mahatma Ghandi, Emmeline Pankhurst, Barack Obama, Rosa Luxemburg, Marcel Proust, Muḥammad ibn Mūsā al-Khwārizmī, Rosa Parks, Steve Jobs, … we know that they are individuals, they were never the norm. What is the algorithm of a great person?

The existing norm will also defend its own existence, in spite of data based proof

One of the fields with the most data gathered overtime, even before big data, even before data itself became a concept, is the weather and geological phenomena. And if we look at the last century, data is gathered that points towards climate change. Everyone agrees that climate change is now a reality.

But does it change anything? No. Having big data, does not change anything as long as the political, social and more importantly the economic systems is not willing to change and drop some of their power, some of their profit, and combine forces for a human, global solution.

With regard to education the same has happened. Unesco has been gathering data on what is needed to ensure education for all (which is by the way in part train-the-trainer, the human element of learning/Teaching), but even though the facts needed for change are known for decades, we cannot seem to meet the ‘education for all’ goals no matter how many millennial goals are stated.

So what is the hidden power behind the data, and where does that hidden power direct us towards?

Let us all think for one moment about who we are, you, me, each single one of us.

The question is simple: how much of me is the norm, how much of me is unique?

My guess is, we all were and are exceptions to a rule at some point. We are innovators, and purely because of that we situate ourselves outside of the norm (bell curve early adopters)

I have a Dream … but does data support it?

Educational data is currently build upon the idea of efficiency. But life is not made out of efficiency only, life is quality, life is creativity, life is – at best – living your dream. And if your dream situates itself in the cognitive fields, data will help you find your way and improve your cognitive abilities. But if your dream situates itself in a creative or vocational field, than data will be less likely to support that goal. For dreams are not profitable in many cases, although dreams do enrich each one of our lives

And by making decisions on what we need, what the norm should be, again the digital divide between those who have and have not will increase. Data risks corrupting education, unless we consciously step away from pure profit goals, and turn towards a global, human society.

Conclusion

As my colleagues have showed data has achieved new insights, new innovations in health, urban planning and such. But for all the good data is said to do, if we do not ensure that each one of us can actually get a job, get paid, then we will not be able to pay for healthcare, we will not be able to drive a car in a well-planned urban area. The success is of data is only as strong as the lives it can help improve and before all sustain in the long term. As long as the digital divide is not turned back, as long as data is not reproducing the norm, but ensuring a more balanced society filled with creativity and quality, educational data will have a corrupting effect.

live blognotes from Howard #hRheingold #oeb14

I was quite excited to hear Howard Rheingold. He took the stage wearing a colourful shirt (colours being his visual trademark and passion).

Live blognotes from the keynote given by Howard Rheingold

Before technology => pedagogy. Without the best pedagogy, the technologies would not be so interesting.

Concepts of importance according to Howard (and agreeing)

Learner-centred: multiple learning is now possible, if you have access to media, and you know what you are doing as a learner.

Inquiry based learning is of interest, knowing how to ask the questions in a world where so many things change so rapidly.

Collaborative learning: more learners collaborate on projects, as well as cooperative learning. Being responsible for each other’s learning. Being aware of what other’s interest and expertise is. So that the group of learners can be bigger than the sum of the individual learners.

Networked learning: learners can connect to each other across locations, regions, groups…

Howard’s passion was/is based on virtual communities.  He started virtual communities decades ago, and got more experience on this topic based on trial and error, use and experience, explicitly enabling the learner group to have a voice.

The idea is to create a conversation that is bigger due to collaboration/corporation/co-working.

It started as learners amplifying their individual voice by using blogs, commenting on discussion forum… the sense of agency, the ability to create your own networks is enabled by the web, and social media in particular.

Co-teaching: the learners teach everyone, including the teacher. Where the first meeting is face-to-face, the dialogue is kept going online.

The real change in mindset happened when he introduced the concept of co-learning. So the shift really went from the teacher lecturing, to the learners teaching themselves. It is increasingly collaborative.

What do self-learners need to know in order to effectively teach and learn from each other? (he does make explicit that only a few kept the conversation going) => peeragogy (free handbook, open to suggestions from all of us). Peeragogy.org

There is a place for teachers, but we cannot scale up the training of teachers. And do people want to pay more taxes to pay teachers => social-political issues.

More people than ever before have access to learning (remark from me: yes, but… it is not because you learn, you are able to see what type of learning will help you forward in life, or will increase critical thinking. Wisdom does have a tendency to come with age).

The web is not just corporate platforms to interact. It would not be there unless students, individuals construct information (remark from myself: yes, but are these self-made content pages taken up by search engines?)

Media should be mixed based on its usefulness.

Procedural knowledge is increasingly freely available, but understanding how all of these fit together in order to attain more knowledge needs to be researched. As such an expert facilitator helps, but more and more people connect together.