My new year intentions to post more regularly here got disrupted quickly by a bad cold and a week of travel. But I’m back in the saddle. There are two major themes running through my work at the moment (and overlapping to an extent: initial teacher training and continuous professional development for teachers and trainers and the impact of new technologies, especially Artificial Intelligence of both education and employment. So, here is the first of a series of posts on those subjects (though probably not in any particular order).

I’ve been doing some research into the training of teachers in Sub Saharan Africa. The major issue is the shortage of qualified teachers, which is of such a scale that there seems little no hope of overcoming by tradition pre service teacher training institutions. Also scaling up provision through teacher training colleges is problematic due to the size of many African countries and the rural  nature of much of those countries. Part of the problem in many countries in Sub Saharan African countries is the lack of prestige in which teaching is held and the low pay for teachers. That being said, there still remains a major challenge in terms of training new teachers and in providing continuing professional development for existing teachers.

In this situation, it is little wonder that attention is focused on the use of ICT for teacher education. It is probably fair to say that despite the issues of connectivity and access to technology, Technology Enhanced Learning is seen as the only real answer for the shortage of teachers in many countries in the region. This is despite Infodev’s findings in its Knowledge Bank on the effective uses of Information and Communication Technology in education in developing countries that:

While much of the rhetoric (and rationale) for using ICTs to benefit education has focused on ICTs’ potential for bringing about changes in the teaching-learning paradigm, in practice, ICTs are most often used in education in LDCs to support existing teaching and learning practices with new (and, it should be noted, often quite expensive!) tools.

Infodev goes on to say:

While impact on student achievement is still a matter of reasonable debate, a consensus seems to argue that the introduction and use of ICTs in education can be a useful tool to help promote and enable educational reform, and that ICTs are both important motivational tools for learning and can promote greater efficiencies in education systems and practices.

Firstly, I must say my research is limited. But I have read the literature and reports and undertaken about 30 interviews with people in Africa working on various projects for developing capacity in teacher education. And it seems that possibly understandably the emergent model is blended learning combining short face to face training programmes with longer periods of online learning, whilst based in the school. Its not a bad model, especially if support for teachers while learning in the workplace (i.e. the school) is well designed and well supported. My worry is with the training for people supporting the school based learning. Essentially the projects appear to be adopting a cascade model. And although cascade models are attractive in terms of quickly scaling up learning, they can be ‘leaky’, breaking down at the weakest point in the cascade train.

I don’t think there are any immediate answers to this problem. I think we need more south-north dialogue and interchange if only that northern countries including in Europe face huge problems in providing professional development for teachers in the use of technology in the classroom. I also think we need to examine the different models more carefully,  especially in understanding the assumptions we are miking in designing new training and professional development provision. Without understanding the assumptions we cannot evaluate the success (or otherwise).

I just stumbled on a blog post by Andreas Schleicher, Director of Directorate for Education and Skills at OECD. He says one of the reasons why we get stuck in education is that our thinking is framed by so many myths and  debunks some of the most common.

• “The poor will always do badly in school.” That’s not true: the 10% most disadvantaged kids in Shanghai do better in maths than the 10% most advantaged students in large American cities.

• “Immigrants will lower the performance of a country on international comparisons.” That’s not true: there is no relationship between the share of immigrant students and the quality of an education system; and the school systems in which immigrant students settle matter a lot more than the country where they came from.

• “Smaller classes mean better results.” That’s not true: whenever high-performing education systems have to make a choice between a smaller class and a better teacher, they go for the latter. Often it is small classes that have created the Taylorist culture where teachers end up doing nothing other than teaching, and don’t have the time to support individual students, collaborate with other teaching professionals or work with parents – activities that are hallmarks of high-performing education systems.

• “More time spent learning always means better results.” That’s not true: students in Finland spend little more than around half the number of hours studying than what students in the United Arab Emirates spend; but students in Finland learn a lot in a short time, while students in the United Arab Emirates learn very little in a lot of time.

• “The results in PISA are merely a reflection of culture.” That’s not true: rapidly improving education systems did not change their culture but their education policies and practices.

Time to return to the Wales Wide Web after something of a hiatus in November and December. And I am looking forward to writing regular posts here again.

New year is a traditional time for reviewing the past year and predicting the future. I have never really indulged in this game but have spent the last two days undertaking a “landscape study” as part of an evaluation contract I am working on. And one section of it is around emerging technologies and foresight. So here is that section. I lay no claim to scientific methodology or indeed to comprehensiveness – this is just my take on what is going on – or not – and what might go on. In truth, I think the main conclusion is that very little is changing in the use of ICT for learning (perhaps  more on that tomorrow).

There are at any time a plethora of innovations and emerging developments in technology with the potential to impact on education, both in terms of curriculum and skills demands but also in their potential for teaching and learning. At the same time, educational technology has a tendency towards a ‘hype’ cycle, with prominence for particular technologies and approaches rising and fading. Some technologies, such as virtual worlds fade and disappear; others retreat from prominence only to re-emerge in the future. For that reason, foresight must be considered not just in terms of emerging technologies but in likely future uses of technologies, some which have been around some time, in education.

Emerging innovations on the horizon at present include the use of Big Data for Learning Analytics in education and the use of AI for Personalised Learning (see below); and MOOCS continue to proliferate.

VLEs and PLEs

There is renewed interest in a move from VLEs to Personal Learning Environments (PLE), although this seems to be reflected more in functionality for personalising VLEs than the emergence of new PLE applications. In part, this may be because of the need for more skills and competence from learners for self-directed learning than for the managed learning environment provided by VLEs. Personal Learning Networks have tended to be reliant on social networking application such as Facebook and Twitter. These have been adversely affected by concerns over privacy and fake news as well as realisation of the echo effect such applications engender. At the same time, there appears to be a rapid increase in the use of WhatsApp to build personal networks for exchanging information and knowledge. Indeed, one area of interest in foresight studies is the appropriation of commercial and consumer technologies for educational purposes.

Multi Media

Although hardly an emerging technology, the use of multimedia in education is likely to continue to increase, especially with the ease of making video. Podcasting is also growing rapidly and is like to have increasing impact in the education sector. Yet another relatively mature technology is the provision of digital e-books which, despite declining commercial sales, offer potential savings to educational authorities and can provide enhanced access to those with disabilities.

The use of data for policy and planning

The growing power of ICT based data applications and especially big data and AI are of increasing importance in education.

One use is in education policy and planning, providing near real-time intelligence in a wide number of areas including future numbers of school age children, school attendance, attainment, financial and resource provision and for TVET and Higher Education demand and provision in different subjects as well as providing insights into outcomes through for instance post-school trajectories and employment. More controversial issues is the use of educational data for comparing school performance, and by parents in choosing schools for their children.

Learning Analytics

A further rapid growth area is Learning Analytics (LA). LA has been defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” [Reference] It is seen as assisting in informing decisions in education systems, promoting personalized learning and enabling adaptive pedagogies and practices. At least in the initial stages of development and use, Universities and schools have tended to harvest existing data drawn from Virtual Learning Environments (VLEs) and to analyse that data to both predict individual performance and undertake interventions which can for instance reduce drop-out rates. Other potential benefits include that LA can, for instance, allow teachers and trainers to assess the usefulness of learning materials, to increase their understanding of the learning environment in order to improve it, and to intervene to advise and assist learners. Perhaps more importantly, it can assist learners in monitoring and understanding their own activities and interactions and participation in individual and collaborative learning processes and help them to reflect on their learning.

Pardo and Siemens (YEAR?) point out that “LA is a moral practice and needs to focus on understanding instead of measuring.” In this understanding:

“learners are central agents and collaborators, learner identity and performance are dynamic variables, learning success and performance is complex and multidimensional, data collection and processing needs to be done with total transparency.”

Although initially LA has tended to be based on large data sets already available in universities, school based LA applications are being developed using teacher inputted data. This can allow teachers and understanding of the progress of individual pupils and possible reasons for barriers to learning.

Gamification

Educational games have been around for some time. The gamification of educational materials and programmes is still in its infancy and likely to continue to advance.  Another educational technology due for a revival is the development and use of e-Portfolios, as lifelong learning becomes more of a reality and employers seek evidence of job seekers current skills and competence.

Bite sized Learning

A further response to the changing demands in the workplace and the need for new skills and competence is “bite–sized” learning through very short learning modules. A linked development is micro-credentialing be it through Digital Badges or other forms of accreditation.

Learning Spaces

As ICT is increasingly adopted within education there will be a growing trend for redesigning learning spaces to reflect the different ways in which education is organised and new pedagogic approaches to learning with ICT. This includes the development of “makerspaces”. A makerspace is a collaborative work space inside a school, library or separate public/private facility for making, learning, exploring and sharing. Makerspaces typically provide access to a variety of maker equipment including 3D printers, laser cutters, computer numerical control (CNC) machines, soldering irons and even sewing machines.

Augmented and Virtual Reality

Despite the hype around Augmented Reality (AR) and Virtual Reality (VR), the present impact on education appears limited although immersive environments are being used for training in TVET and augmented reality applications are being used in some occupational training. In the medium-term mixed reality may become more widely used in education.

Wearables

Similarly, there is some experimentation in the use of wearable devices for instance in drama and the arts but widespread use may be some time away.

Block Chain

The block chain has been developed for storing crypto currencies and is attracting interest form educational technologists. Block chain is basically a secure ledger allowing the secure recording of a chain of data transactions. It has been suggested as a solution to the verification and storage of qualifications and credentials in education and even for recording the development and adoption of Open Educational Resources. Despite this, usage in education is presently very limited and there are quite serious technical barriers to its development and wider use.

The growing power of ICT based data applications and especially big data and AI (see section 10, below) are of increasing importance in education.

The use of data for policy and planning

One use is in education policy and planning, providing near real-time intelligence in a wide number of areas including future numbers of school age children, school attendance, attainment, financial and resource provision and for TVET and Higher Education demand and provision in different subjects as well as providing insights into outcomes through for instance post-school trajectories and employment. More controversial issues is the use of educational data for comparing school performance, and by parents in choosing schools for their children.

Learning Analytics

A rapid growth area is Learning Analytics (LA). LA has been defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” [Reference] It is seen as assisting in informing decisions in education systems, promoting personalized learning and enabling adaptive pedagogies and practices. At least in the initial stages of development and use, Universities and schools have tended to harvest existing data drawn from Virtual Learning Environments (VLEs) and to analyse that data to both predict individual performance and undertake interventions which can for instance reduce drop-out rates. Other potential benefits include that LA can, for instance, allow teachers and trainers to assess the usefulness of learning materials, to increase their understanding of the learning environment in order to improve it, and to intervene to advise and assist learners. Perhaps more importantly, it can assist learners in monitoring and understanding their own activities and interactions and participation in individual and collaborative learning processes and help them to reflect on their learning.

Pardo and Siemens point out that “LA is a moral practice and needs to focus on understanding instead of measuring.” In this understanding:

“learners are central agents and collaborators, learner identity and performance are dynamic variables, learning success and performance is complex and multidimensional, data collection and processing needs to be done with total transparency.”

Although initially LA has tended to be based on large data sets already available in universities, school based LA applications are being developed using teacher in putted data. This can allow teachers and understanding of the progress of individual pupils and possible reasons for barriers to learning.

Artificial Intelligence

In research undertaken for this report, a number of interviewees raised the importance of Artificial Intelligence in education (although a number also believed it to be over hyped).

A recent report from the EU Joint Research Council (2018) says that:

“in the next years AI will change learning, teaching, and education. The speed of technological change will be very fast, and it will create high pressure to transform educational practices, institutions, and policies.”

It goes on to say AI will have:

“profound impacts on future labour markets, competence requirements, as well as in learning and teaching practices. As educational systems tend to adapt to the requirements of the industrial age, AI could make some functions of education obsolete and emphasize others. It may also enable new ways of teaching and learning.”

However, the report also considers that “How this potential is realized depends on how we understand learning, teaching and education in the emerging knowledge society and how we implement this understanding in practice.” Most importantly, the report says, “the level of meaningful activity—which in socio-cultural theories of learning underpins advanced forms of human intelligence and learning—remains beyond the current state of the AI art.”

Although AI systems are well suited to collecting informal evidence of skills, experience, and competence from open data sources, including social media, learner portfolios, and open badges, this creates both ethical and regulatory challenges. Furthermore, there is a danger that AI could actually replicate bad pedagogic approaches to learning.

The greatest potential of many of these technologies may be for informal and non-formal learning, raising the challenge of how to bring together informal and formal learning and to recognise the learning which occurs outside the classroom.

MPs on the UK House of Commons education committee have released a report titled “Value for Money in Higher Education.” They draw attention to figures from the Office for National Statistics (ONS) that indicated 49 percent of recent graduates (within five years of achieving their degree) were in non-graduate roles in 2017.

This is a significant increase over the proportion at the start of 2009, just after the 2008 financial crash, when 41 percent of recent graduates were in that position. It is matched by a very similar rise even among the population of graduates taken as a whole—including mature students—from 31 percent to 37 percent in the same years.

The report stated: “Higher education institutions must be more transparent about the labour market returns of their courses.” It came with the warning that “too many universities are not providing value for money, and … students are not getting good outcomes from the degrees for which so many of them rack up debt.”

As the title of the report implies, much of the attention on graduate employment is due to the political controversy over the funding of Higher Education in the UK and the cost of participation in degree courses.

But there is another issue which has received less attention: how graduate (and non graduate) jobs are defined.

The Office for National Statistics explains the classification system as follows

1.The skill level groups are created by grouping jobs together based on their occupation according to the Standard Occupation Classification (SOC) 2010 lower level groups. The occupation group is not available for some workers, these have been excluded from the total.

Occupations were grouped by the skill level required according to the following guidelines:

2,1. High – This skill level is normally acquired through a degree or an equivalent period of work experience. Occupations at this level are generally termed ‘professional’ or managerial positions, and are found in corporate enterprises or governments. Examples include senior government officials, financial managers, scientists, engineers, medical doctors, teachers and accountants.

2,2. Upper-middle – This skill level equates to competence acquired through post-compulsory education but not to degree level. Occupations found at this level include a variety of technical and trades occupations, and proprietors of small business. For the latter, significant work experience may be typical. Examples of occupations at this level include catering managers, building inspectors, nurses, police officers (sergeant and below), electricians and plumbers.

2,3. Lower-middle – This skill level covers occupations that require the same competence acquired through compulsory education, but involve a longer period of work-related training and experience. Examples of occupations at this level include machine operation, driving, caring occupations, retailing, and clerical and secretarial occupations.

2,4. Low – This skill level equates to the competence acquired through compulsory education. Job-related competence involves knowledge of relevant health and safety regulations and may be acquired through a short period of training. Examples of occupations at this level include postal workers, hotel porters, cleaners and catering assistants.

The sentence “Occupations at this level are generally termed ‘professional’ or managerial positions, and are found in corporate enterprises or governments.” Arguably this ignores ongoing changes in the economy with high skilled technical jobs being created by Small and Medium Enterprises rather than large corporations. As Malcolm Todd,  Provost (Academic) of the University of Derby, points out in an article in WonkHE: “The current government methodology of using traditional Standard Occupational Codes (SOC) to declare which roles are graduate level is dated. It’s not reflective of the current employment market and is not ready for the future job market. Codes are based on traditional views of careers and highly skilled roles, not the whole requirements of a role.”

He draws attention to Teaching Assistants working with pupils that have special education needs and disabilities, and emerging jobs in the growing retail, social care and hospitality, many of which require high skills but are classified as non graduate jobs. At the same time, jobs presently classified as requiring a degree such as accountants are like to decline due to automation and the use of Artificial Intelligence.

To some degree, the debate is clouded by a perception that graduate level jobs should command a higher salary (an argument used by the Government to justify high university tuition fees. Yet wage growth in the UK has been low across all sectors since the onset of the recession in 2008.

But with growing skills required in a range of different jobs, maybe it is time for a new look at how graduate jobs are classified or even whether dividing employment into graduate or non graduate occupations is relevant any more.

I am very sad to report the death of my friend and colleague Jen Hughes. I first met Jenny in 1984 when I signed up for a summer school course for trainers. Jen was the tutor. My only previous experience of courses had been dry ‘talk and chalk’ and Jen’s experiential approach to teaching and training inspired me.

We became great friends and in 1988 Jen encouraged me to apply for a job at the Welsh Joint Education Committee where she was working at the time. Two years later I followed her to work for Gwent County Council, where she had been appointed Deputy Director of Education, and subsequently at Gwent Tertiary College, where we set up the Training Shop, a research and development unit. We both later left the college and in 2004 Jen joined me at Pontydysgu.

An intrepid traveller, Jen worked on educational projects throughout Europe, mainly funded by the EU, and in the wider world, working as an evaluator for the UN Development Programme in Kosovo and, she claimed, all of ‘the Stans’. She was particularly committed to access to education for girls and young women

Jen was an inspired and inspiring teacher and trainer. Over the last ten years she became convinced of the importance of integrating technology in the curriculum. She taught herself to program and produced hundreds of Open Educational Resources on using technology in the classroom and more latterly on teaching computing and computational thinking in primary schools through the series of Taccle projects. She developed and tested the resources at the local primary School, Ysgol Evan James, where for many years she served as a school governor. She facilitated staff development programmes and workshops for teachers on the use of technology throughout Wales and in Germany, Finland and other European countries.

A prodigious cook, Jen’s pies,  welsh cakes and buffets became legendary in Pontypridd, especially at the Welsh Club, Clwb y Bont, where she served for a time on the committee. She was also a great rugby fan, originally as a Newport supporter, then as ‘dinner lady’ for the London Welsh and finally becoming a stalwart supporter of Pontypridd as well as the Wales national rugby team. Jen was also a great fan of the Archers radio soap series. Earlier this year we wrote a successful proposal for a paper to be presented at the Archers Academic Conference in 2019, sadly too late for her. She was also a proudly pedantic grammar expert, over the years correcting hundreds of my split infinitives and always ready to help others with their grammar.

Perhaps the most abiding memory of Jen was her enthusiasm for whatever she became involved in and her generosity of time and spirit for supporting, helping and teaching others outside her day job. Condolences from all at Pontydysgu to Alex and to Jen’s children and grandchildren. Jen will be missed by many people: she cannot and will not be forgotten.

I am ever more interested in the issue of data literacy and agree very much with Javiera Atenas from the Open Education Working Group, London who says “Learning how to use data and information is not just a subject among others, it’s an essential part of civic education.”

But it is not just learning how to use data and information. Perhaps more critical is how to understand and make critical sense out of data. Take the chart above as an example. The difference in participation in adult education are very substantial and on the face of it Nordic countries lead the way. Interesting too that Germany is well back in the middle of the pack. However I am not sure it is quite as it seems. I suspect the data is compiled from national data by Eurostat from the European Labour Force Survey. The issue may be that different countries classify participation in education in different ways.

When I get a free hour or so I wil try to follow this up. Meanwhile any comments and ideas from readers would be welcome.

I have been doing some research on the training of teachers, particularly in Sub-Saharan Africa. The figures make sober reading. A report ‘Digital Learning: Reforming Teacher Education to Promote Access, Equity and Quality in Sub-Saharan Africa’ by Bob Moon and Charmaine Villet points to the scale of the issue:

UNESCO’s Institute of Statistics (UNESCO, 2015b) has estimated that, globally, 25.8 million extra teachers will need to be recruited by 2030 to meet EFA targets (to put that in context, this is equivalent to the population of Ghana).

Of these, 3.2 million would be filling new posts and 22.6 million would be replacing teachers retiring or leaving the profession. There were 59 million children out of school in 2015. To have them all in school would require the recruitment of 2.7 million teachers if pupil-teacher ratios are not to exceed 40:1.According to the Institute’s forecasts, without such recruitment, 33 countries will not have enough teachers to achieve universal primary education (UPE) by 2030.

Sub-Saharan Africa faces the biggest challenge. of any major world region in this respect. For every 100 children beginning school in 2015, there will be 142 in 2030. And the figure is projected to continue growing at this rate through the middle years of the century. Of the 3.2 million posts to be filled worldwide, Sub-Saharan Africa will need 2.2 million to deal with this growth and, at a conservative estimate, 3.9 million teachers will be required to replace those leaving the profession.

Pretty clearly there is little chance of meeting these targets through scaling up traditional teacher training institutions, nor even through school based teacher training. That is why there is increasing interest in using technology – Open and Distance Learning, MOOCs, video and multi media, Open Educational Resources – in Sub-Saharan Africa. This development is often being led through different aid programmes run by UNESCO, the Commonwealth of Learning, USAid and other organisations. Early evaluations seem promising – although the real challenge will be in scaling up, mainstreaming and sustaining development projects. But researchers and developers working on initial and continuing teacher education in Europe and other richer countries could do well to look at what is happening in Africa.

Sometimes media use seems to go in circles. Over the last couple of years there has been a return to the old fashioned newsletter – even if sent the ‘modern’ way by email. Since I am contemplating crafting a couple of newsletters for projects, I have been looking for examples of good practice and design.

One of my favourites is the Marchmont Employment and Skills Observatory monthly mail called South West Skills Newsletter. The title is a bit misleading and if you are interested in labour markets, employment and vocational education and training it is worth subscribing, even if you do not live in the south west of England.

Here is a taster from this months newsletter.

One in eight young people without degrees work in graduate jobs

One in eight young people without degree-level qualifications are working in graduate jobs, according to analysis by the Office for National Statistics. In 2017, 12% of non-graduates (327,303) aged 22 to 29 were working in a graduate job – defined as a role where the tasks typically require knowledge and skills gained through higher education. Sales, HR, and retail and wholesale management most common graduate jobs for non-graduates.

This compares with 54% of graduates (1,273,336) in the same age group who had a graduate job.

Like so much labour market survey  data it is a bit hard to know what to make of this. Is it that the Office of National Statistics definition of skills is off the mark or more likely that employers know that thes eskills can be gained without going to university. And of course there is still a very large number of 46% of graduates wh9c are not working in graduate jobs.

The technology industry spends millions trying to disrupt education. And one of their fantasies is that machines can replace teachers. I don’t think they can or should. On International Teachers day it seems appropriate to point again to the importance of well trained and supported teachers having teh autonomy to shape and support learning.

And by chance I found on Twitter today this excellent bog post, writing just about the need for autonomy.

@HeyMissSmith says:

I have watched with incredulity as the idea of scripted lessons and highly controlled curriculum content has grown. The idea that knowledge can be packaged nicely and given to teachers. That you can in some way control knowledge. That it is prepackaged food a teacher microwaves for her class (as per instructions). Not so. Knowledge when it meets a class of thirty individuals plus a (hopefully) excited teacher becomes something else; it becomes an ocean of possibilities. It becomes the universe, past, present and future. A skilled and enthusiastic teacher will take knowledge, and their class reactions to it and will shape the conversations. Steer children through the endless sparks and dead-ends they create with it. They will cover much ground, but what that ground is is not apparent until the class is in front of them.

What am I saying? That we have to trust teachers with knowledge.

I’ve had this graphic hanging around for quite a while, so it may be out of date. I think the point of it is that like much data the figures are fascinating but it is quite difficult to interpret. Why do boys leave home earlier than girls? Why is there such a big difference between countries. Although obviously there will be differences between those countries where young people normally leave home to go to university and those where they usually move to another town or city. And I am sure some of it is explained by socio- economic factors. It costs money to leave home. But I am not sure this explains it all. I would be very interested in anyone else’s perspective on this data.