We have long known that educational outcomes are heavily influenced by social class. But little has been done to try to understand how social class affects learning. In that respect the article by Lien Pham on ‘How socioeconomic background makes a difference in education outcomes‘ is very welcome.

Pham notes that although “PISA publishes its PISA context assessment framework to supplement its regular international PISA testing of reading, maths and science”, ” these are just snapshots rather than an analysis of the impact of students’ background characteristics on their participation in these processes, or whether the educational system, schooling processes and classroom practices may favour certain groups over others” and “they do not help to shed light on how and why some students perform better than others.”

Pham says “In order to truly understand what is happening with inequality I believe we have to recognise the implicit social relationships and social structures in the schooling processes that position students in different vantage points.”

Pham goes on to look at what PISA says about students’ family backgrounds, student ethnicity and polices to improve educational inequality, adding his own comments and analysis. His overall conclusion is that reducing inequality neds more than just access to economic resources

We need to deeply understand students’ “real” opportunities within our systems of education. I believe we need to look more closely at what students can reasonably do (or not do) with those resources given their backgrounds and situations.

Resources are important, but just because a school has a wide variety of resources doesn’t mean all of its students will benefit from those equally.

I am arguing that policy attention to improve educational inequality should place student agency and diversity at the forefront, rather than focussing on resources with the assumption that all students will be able to access them in similar ways with similar outcomes.

You can read more in his paper: Capital and capabilities in education: Re-examining Australia’s 2015 PISA performance and context assessment framework

It’s the last day of the summer holidays.

On Sunday I am traveling to Hamburg for the European Conference on Educational Research (ECER). I am not a great fan of conferences – al least the formal part. I have long campaigned for the ‘flipped conference’. All too often conferences just consist of researchers reading out their bullet points from their slides. Their is little chance to interrogate the ideas, less so to have a proper discussion about the work they are presenting. All too often presentations overrun with it being accepted that the ten or so minutes scheduled for discussion at the end of three or four presentations will be eaten up. And it is interesting that people still hark back to the Personal Learning environment conferences where we did at least try to do things differently. In reality the best bit of the conferences are usually in the informal discussions which take place outside the official sessions.

Having said that I like the ECER conferences. One strength is the priority given to emerging researchers. Another is the international focus for ECER, not just in terms of attracting delegates from all over the world, but in stressing that presentation should focus on at east the European dimension of the research. A third advantage of ECER is that it covers many different areas of education through the 31 or so networks which organise the programme. This year, I am in a privileged position as I have been commissioned by the European Educational Research Association to make a series of short videos, interviewing the network conveners. The idea is that the videos provide a quick and informative way of people understanding the focus of the networks and the activities they are undertaking, including the increasing number of what EERA call ‘season schools’ (formerly summer schools but the changed nomenclature reflecting the fact that most take place outside the summer time). This week we are aiming to record 21 videos. It will be hard work but a lot of fun and for me a great learning opportunity.

Of course, one of the attractions of conferences is the chance to meet up with old colleagues and friends. I will be in Hamburg all week. If you would like to meet up just drop me a line.

The London School of Economics has published an online toolkit to promote children’s understanding of the digital environment and support them to make good decisions about privacy online. They say “the toolkit is aimed at children of secondary school age, parents and educators, and was developed with the participation of a mix of children in Years 8 and 10. It includes information and resources on: why privacy online is important, how online data is generated and used, children’s rights, privacy-related risks and protective strategies, where to seek support, suggestions and recommendations from children, and fun resources to watch and play.

With the help of experts and practitioners, we collected the best resources on online privacy and reviewed them based on a number of criteria: relevance and suitability to children, quality, free access, no need for creating an account, and no installing or downloading. A list of selected resources were presented to three child juries in March 2019 where 18 children were given the opportunity to assess the selected resources and help design the online toolkit.

The toolkit is part of an ICO-funded project led by Professor Sonia Livingstone. The project  aims to listen to children’s voices and develop tools to better empower them.

My summer morning’s work is settling into a routine. First I spend about half an hour learning Spanish on DuoLingo. Then I read the morning newsletters – OLDaily, WONKHE, The Canary and Times Higher Education (THE).

THE is probably the most boring of them. But this morning they led on an interesting and important research report. In an article entitled ‘Long commutes make students more likely to drop out’, Ana McKie says:

Students who have long commutes to their university may be more likely to drop out of their degrees, a study has found.

Researchers who examined undergraduate travel time and progression rates at six London universities found that duration of commute was a significant predictor of continuation at three institutions, even after other factors such as subject choice and entry qualifications were taken into account.

THE reports that the research., commissioned by London Higher, which represents universities in the city found that “at the six institutions in the study, many students had travel times of between 10 and 20 minutes, while many others traveled for between 40 and 90 minutes. Median travel times varied between 40 and 60 minutes.”

At one university, every additional 10 minutes of commuting reduced the likelihood of progression beyond end-of-first-year assessments by 1.5 per cent. At another, the prospect of continuation declined by 0.63 per cent with each additional 10 minutes of travel.

At yet another institution, a one-minute increase in commute was associated with a 0.6 per cent reduction in the chances of a student’s continuing, although at this university it was only journeys of more than 55 minutes that were particularly problematic for younger students, and this might reflect the area these students were traveling from.

I think there are a number of implications from this study. It is highly probable that those students traveling the longest distance are either living with their parents or cannot afford the increasingly expensive accommodation in central London. Thus this is effectively a barrier to less well off students. But it is also worth noting that much work in Learning Analytics has been focused on predicting students likely to drop out. Most reports suggest it is failing to complete or to success in initial assignments that is the most reliable predicate. Yet it may be that Learning Analytics needs to take a wider look at the social, cultural, environmental and financial context of student study with a view to providing more practical support for students.

I work on the LMI for All project which provides an API and open data for Labour Market Information for mainly use in careers counseling advice and guidance and to help young people choose their future carrers or education. We already provide data on travel to work distances, based on the 2010 UK census. But I am wondering if we should also provide data on housing costs,possibly on a zonal basis around universities (although I am not sure if their is reliable data). If distances (and time) traveling to college is so important in student attainment this may be a factor students need to include in their choice of institution and course.

I’ve tended to be skeptical about Learning Analytics, seeing it of limited relevance to pedagogy and more concerned with managing learners (reducing dropouts) than having anything to say about learning. Even more, Learning Analytics research has tended to docus on higher education and formal learning, having little to say about workplace learning and vocational education and training. But things are changing, especially through the integration of AI with Learning Analytics Learning Analytics and AI for future focused learning. I’m especially interested in this since we have had a project approved under the Erasmus Plus programme on AI and vocational education and training teachers and trainers.

This presentation by Simon Buckingham Shum at the EduTECH conference in Australia in June of this year introduces some of the work at the UTS Connected Intelligence Centre, where, he says “the team has been refining (for the last 3 years) automated, personalised feedback to students on higher order transferable competencies (Graduate Attributes in university-speak, or General Capabilities in the schools sector) – namely, high performance face-to-face teamwork (exemplar: nursing simulation exercises), and critical, reflective thinking (as revealed in students’ writing).”

Simon says “Learning Analytics bring the power of data science and human-centred design to educational data, while AI makes new forms of timely assessment and feedback possible. Tech researchers, developers, educators and learners can co-design formative feedback on 21st century competencies such as critical reflective writing, teamwork, self-regulated learning, and dispositions for lifelong learning. Such tools are being coherently integrated into teaching practice and aligned with curriculum outcomes at UTS, and could be in schools.

Getting the technology’s capabilities and the user experience right is impossible without meaningful engagement with educators and students. So, this talk is organised around our emerging understanding of how to align the different elements of the whole sociotechnical infrastructure. To use the language of the framework – the ‘cogs’ can be tuned to different contexts, and must synchronise and drive in the same direction to create a coherent learning experience.”

A number of things strike me about the presentation (and the videos within the presentation).

The first is the integration of the LA Framework with more traditional educational frameworks including competences and assessment rubrics. These provide a much broader reference point for proxies for achievement and reflection than the relevant proxy used in LA (and indeed in many areas of education of achievement in examination and other assessments. The design process is intended to develop a data map to these proxies.

Secondly, rather than seeking to provide feedback to students on attainment (and likely attainment – or otherwise) or to serve as the basis for intervention by teachers, the focus is on reflection. The feedback is seen as “a  provocation to deeper discussion” and as “scaffolding reflection

Finally – and as part of the refection process – the LA is designed to provide agency for the student, who, says Simon “can push back against the machine” if they think it is wrong.

All in all, there is much content for reflection here. The slides which contain a number of references can be downloaded here (PDF).