#opened16 live blog: College Affordability and Social Justice

Preston Davis (aka @LazyPhilosopher) invites us to think about the early days of Western civilisation where philosophers like Plato and Aristotle formed educational institutions on the basis of their own privilege.  This kind of system persisted into Roman times, where males with the ability to pay could attend organised schools where they would learn to become educated citizens of the empire.

Education was further formalised in the Middle Ages, but mostly organised according to the strategic aims of the church.  Formalised educational systems in the USA widened curriculum and admitted women, but still remain ‘exclusive’ in many ways.

Rawlsian theories of social justice are reflective of conversations that are starting to take place in OER around stepping back from personal bias when making decisions.  If we disregard the considerations of race, gender, class and so on, we can support a more democratic and equally distributed educational system.

The remark is made that aspects of the USA educational system are exclusive rather than inclusive.  Much of the OER movement was organised around saving money on textbook costs, but this overlooks wider patterns of disenfranchisement.  The Sanders run for USA president foregrounded the idea of access to higher education as a matter of social justice.  Should education be ‘free’?

From the discussion:

  • Class divides are reinforced by higher education.  Some scholarships are set aside for students from disadvantaged backgrounds, but does this really change structural patterns of disenfranchisement?
  • If public education was made free, would this lead to a loss of resources through inefficiencies?
  • Can we really act as if we are ‘difference-blind’?
  • Is the difference between the student who goes on to higher education and the one who doesn’t a matter of money?  Disenfranchisement has other elements, e.g. confidence, role models, self-interpretation,  Much of these are the kind of ‘differences’ stripped out of the Rawlsian model.
  • How can social justice be understood from the perspective of what is essentially privilege?
  • Low cost vs. free?

Ethical principles of learning analytics – mini critique

This is just a short blog post to capture some thoughts on the ethical principles of learning analytics as set out in official documentation provided by The Open University.  I have attended various briefings at the OU around this subject, mainly because there is a lot of complexity here with regard to the ethical significance of these technologies.  I was also a member of the advisory panel for the JISC Code of Practice for Learning Analytics.

Here are the ‘ethical principles’ with my own brief annotations (click to enlarge).  (This is just an internal critique of these principles as they are set out here, not of the wider project of learning analytics.)


The principles have been categorised in the following way:
Screen Shot 2015-12-03 at 13.03.15You can see the original list at

In essence, these are the points I would make about these principles are as follows:

  • Point 1.  It is asserted that learning analytics is an ethical practice, but this has yet to be established.  Arguably we should state that it should be thought of an ethical practice, but this is quite different in terms of ethical principle.  ‘Ought’ statements are much harder to justify.
  • Point 2. There is a confusing mix of deontological and consequentialist-utilitarian consideration here.  Unpicking it, I interpret it to mean that the university considers itself to have a responsibility to maximise the utility of the data about students that it owns.  The important points here are that a.) stakeholders are not clearly defined and could include (for instance, privately owned data brokers; b.) there is no acknowledgment of the possible tension between different forms of self-interest; c.) no criteria are given for ‘feasibility’.
  • Point 2. It’s difficult to see how feasibility should be a criterion for whether something is ethical.  After all, ethics is something that regulates the realm of the feasible, the possible, the actual.  This would be a much stronger principle if this word was replaced with ‘ethical’, or ‘justified’.
  • Point 3 infers that students should be at least partly defined by their data and the university’s interpretation of it.  This may not be that contentious to most people, though without clear parameters for the other criteria that are considered it could be taken to mean ‘mostly’ defined by the data held by the university.  It’s not clear what this means in practice except putting in some wording to ward off concerns about treating students as nothing more than a set of data points.
  • Point 4 seems right in setting out a principle of transparency in the process, purpose and use of student data.  But it doesn’t make a commitment to full transparency for all.  Why not?
  • This is brought into sharper relief in Point 5, which sets out a commitment to full transparency for data collection. Taken in conjunction with Point 4, it seems that transparency is endorsed for collection, but not use.
  • Point 6 is on the theme of student autonomy, and co-operation in these processes.  These are good things, though claims to have given informed consent are potentially undermined by the possible lack of transparency in use in Point 4.
  • A further possible undermining of student autonomy here is the lack of clarity about whether students can entirely opt out of these processes.  If not, how can they be considered ‘active agents’?
  • I’m not an expert in big data but I know a little bit about predictive modelling.  In Point 7. the idea is that modelling ‘should be’ free from bias.  Well, all modelling should be free from bias, but these effects cannot be truly eradicated.  It would make more sense as a principle to speak of ‘minimising’ bias.
  • Point 8. endorses adoption of learning analytics into the institutional culture, and vice versa.  It asserts that there values and benefits to the approach, though these are largely hypothetical.  It basically states that the institutional culture of the university must change, and that this should be ‘broadly accepted’ (whatever that might mean).

The final point I’d make about this is that, for me, these are not generally worded as principles: rather as vision statements or something intended to guide internal decision making.  But when it comes to ethics, we really need clear principles if we are to understand whether they are being applied consistently, sensitively, and systematically.


Ethics, Openness and the Future of Education #opened14

By popular demand, here are my slides from today’s presentation at Open Education 2014.  All feedback welcome and if this subject is of interest to you then consider checking out the OERRH Ethics Manual and the section on ethics (week 2) of our Open Research course.

Ethical Use of New Technology in Education

Today Beck Pitt and I travelled up to Birmingham in the midlands of the UK to attend a BERA/Wiley workshop on technologies and ethics in educational research.  I’m mainly here on focus on the redraft of the Ethics Manual for OER Research Hub and to give some time over to thinking about the ethical challenges that can be raised by openness.  The first draft of the ethics manual was primarily to guide us at the start of the project but now we need to redraft it to reflect some of the issues we have encountered in practice.

Things kicked off with an outline of what BERA does and the suggestion that consciousness about new technologies in education often doesn’t filter down to practitioners.  The rationale behind the seminar seems to be to raise awareness in light of the fact that these issues are especially prevalent at the moment.

This blog post may be in direct contravention of the Chatham convention

This blog post may be in direct contravention of the Chatham convention

We were first told that these meetings would be taken under the ‘Chatham House Rule’ which suggests that participants are free to use information received but without identifying speakers or their affiliation… this seems to be straight into the meat of some of the issues provoked by openness:  I’m in the middle of life-blogging this as this suggestion is made.  (The session is being filmed but apparently they will edit out anything ‘contentious’.)

Anyway, on to the first speaker:

Jill Jameson, Prof. of Education and Co-Chair of the University of Greenwich
‘Ethical Leadership of Educational Technologies Research:  Primum non noncere’

The latin part of the title of this presentation means ‘do no harm’ and is a recognised ethical principle that goes back to antiquity.  Jameson wants to suggest that this is a sound principle for ethical leadership in educational technology.

After outlining a case from medical care Jameson identified a number of features of good practice for involving patients in their own therapy and feeding the whole process back into training and pedagogy.

  • No harm
  • Informed consent
  • Data-informed consultation on treatment
  • Anonymity, confidentiality
  • Sensitivity re: privacy
  • No coercion
  • ‘Worthwhileness’
  • Research-linked: treatment & PG teaching

This was contrasted with a problematic case from the NHS concerning the public release of patient data.  Arguably very few people have given informed consent to this procedure.  But at the same time the potential benefits of aggregating data are being impeded by concerns about sharing of identifiable information and the commercial use of such information.

In educational technology the prevalence of ‘big data’ has raised new possibilities in the field of learning analytics.  This raises the possibility of data-driven decision making and evidence-based practice.  It may also lead to more homogenous forms of data collection as we seek to aggregate data sets over time.

The global expansion of web-enabled data presents many opportunities for innovation in educational technology research.  But there are also concerns and threats:

  • Privacy vs surveillance
  • Commercialisation of research data
  • Techno-centrism
  • Limits of big data
  • Learning analytics acts as a push against anonymity in education
  • Predictive modelling could become deterministic
  • Transparency of performance replaces ‘learning
  • Audit culture
  • Learning analytics as models, not reality
  • Datasets >< information and stand in need of analysis and interpretation

Simon Buckingham-Shum has put this in terms of a utopian/dystopian vision of big data:

Leadership is thus needed in ethical research regarding the use of new technologies to develop and refine urgently needed digital research ethics principles and codes of practice.  Students entrust institutions with their data and institutions need to act as caretakers.

I made the point that the principle of ‘do no harm’ is fundamentally incompatible with any leap into the unknown as far as practices are concerned.  Any consistent application of the principle leads to a risk-averse application of the precautionary principle with respect to innovation.  How can this be made compatible with experimental work on learning analytics and sharing of personal data?  Must we reconfigure the principle of ‘do no harm’ so it it becomes ‘minimise harm’?  It seems that way from this presentation… but it is worth noting that this is significantly different to the original maxim with which we were presented… different enough to undermine the basic position?

Ralf Klamma, Technical University Aachen
‘Do Mechanical Turks Dream of Big Data?’

Klamma started in earnest by showing us some slides:  Einstein sticking his tongue out; stills from Dr. Strangelove; Alan Turing; a knowledge network (citation) visualization which could be interpreted as a ‘citation cartel’.  The Cold War image of scientists working in isolation behind geopolitical boundaries has been superseded by building of new communities.  This process can be demonstrated through data mining, networking and visualization.

Historical figures of the like of Einstein and Turing are now more like nodes on a network diagram – at least, this is an increasingly natural perspective.  The ‘iron curtain’ around research communities has dropped:

  • Research communities have long tails
  • Many research communities are under public scrutiny (e.g. climate science)
  • Funding cuts may exacerbate the problem
  • Open access threatens the integrity of the academy (?!)

Klamma argues that social network analysis and machine learning can support big data research in education.  He highlights the US Department of Homeland Security, Science and Technology, Cyber Security Division publication The Menlo Report: Ethical Principles Guiding Information and Communication Technology Research as a useful resource for the ethical debates in computer science.  In the case of learning analytics there have been many examples of data leaks:

One way to approach the issue of leaks comes from the TellNET project.  By encouraging students to learn about network data and network visualisations they can be put in better control of their own (transparent) data.  Other solutions used in this project:

  • Protection of data platform: fragmentation prevents ‘leaks’
  • Non-identification of participants at workshops
  • Only teachers had access to learning analytics tools
  • Acknowledgement that no systems are 100% secure

In conclusion we were introduced to the concept of ‘datability‘ as the ethical use of big data:

  • Clear risk assessment before data collection
  • Ethcial guidelines and sharing best pracice
  • Transparency and accountability without loss of privacy
  • Academic freedom

Fiona Murphy, Earth and Environmental Science (Wiley Publishing)
‘Getting to grips with research data: a publisher perspective’

From a publisher perspective, there is much interest in the ways that research data is shared.  They are moving towards a model with greater transparency.  There are some services under development that will use DOI to link datasets and archives to improve the findability of research data.  For instance, the Geoscience Data Journal includes bi-direction linking to original data sets.  Ethical issues from a publisher point of view include how to record citations and accreditation; manage peer review and maintenance of security protocols.

Data sharing models may be open, restricted (e.g. dependent on permissions set by data owner) or linked (where the original data is not released but access can be managed centrally).

[Discussion of open licensing was conspicuously absent from this though this is perhaps to be expected from commercial publishers.]

Luciano Floridi, Prof. of Philosophy & Ethics of Information at The University of Oxford
‘Big Data, Small Patterns, and Huge Ethical Issues’

Data can be defined by three Vs: variety, velocity, and volume. (Options for a fourth have been suggested.)  Data has seen a massive explosion since 2009 and the cost of storage is consistently falling.  The only limits to this process are thermodynamics, intelligence and memory.

This process is to some extent restricted by legal and ethical issues.

Epistemological Problems with Big Data: ‘big data’ has been with us for a while generally should be seen as a set of possibilities (prediction, simulation, decision-making, tailoring, deciding) rather than a problem per se.  The problem is rather that data sets have become so large and complex that they are difficult to process by hand or with standard software.

Ethical Problems with Big Data: the challenge is actually to understand the small patterns that exist within data sets.  This means that many data points are needed as ways into a particular data set so that meaning can become emergent.  Small patterns may be insignificant so working out which patterns have significance is half the battle.  Sometimes significance emerges through the combining of smaller patterns.

Thus small patterns may become significant when correlated.  To further complicate things:  small patterns may be significant through their absence (e.g. the curious incident of the dog in the night-time in Sherlock Holmes).

A specific ethical problem with big data: looking for these small patterns can require thorough and invasive exploration of large data sets.  These procedures may not respect the sensitivity of the subjects of that data.  The ethical problem with big data is sensitive patterns: this includes traditional data-related problems such as privacy, ownership and usability but now also includes the extraction and handling of these ‘patterns’.  The new issues that arise include:

  • Re-purposing of data and consent
  • Treating people not only as means, resources, types, targets, consumers, etc. (deontological)

It isn’t possible for a computer to calculate every variable around the education of an individual so we must use proxies:  indicators of type and frequency which render the uniqueness of the individual lost in order to make sense of the data.  However this results in the following:

  1. The profile becomes the profiled
  2. The profile becomes predictable
  3. The predictable becomes exploitable

Floridi advances the claim that the ethical value of data should not be higher than the ethical value of that entity but demand at most the same degree of respect.

Putting all this together:  how can privacy be protected while taking advantage of the potential of ‘big data’?.  This is an ethical tension between competing principles or ethical demands: the duties to be reconciled are 1) safeguarding individual rights and 2) improving human welfare.

  • This can be understood as a result of polarisation of a moral framework – we focus on the two duties to the individual and society and miss the privacy of groups in the middle
  • Ironically, it is the ‘social group’ level that is served by technology

Five related problems:

  • Can groups hold rights? (it seems so – e.g. national self-determination)
  • If yes, can groups hold a right to privacy?
  • When might a group qualify as a privacy holder? (corporate agency is often like this, isn’t it?)
  • How does group privacy relate to individual privacy?
  • Does respect for individual privacy require respect for the privacy of the group to which the individual belongs? (big data tends to address groups (‘types’) rather than individuals (‘tokens’))

The risks of releasing anonymised large data sets might need some unpacking:  the example given was that during the civil war in Cote d’Ivoire (2010-2011) Orange released a large metadata set which gave away strategic information about the position of groups involved in the conflict even though no individuals were identifiable.  There is a risk of overlooking group interests by focusing on the privacy of the individual.

There are legal or technological instruments which can be employed to mitigate the possibility of the misuse of big data, but there is no one clear solution at present.  Most of the discussion centred upon collective identity and the rights that might be afforded an individual according to groups they have autonomously chosen and those within which they have been categorised.  What happens, for example, if a group can take a legal action but one has to prove membership of that group in order to qualify?  The risk here is that we move into terra incognito when it comes to the preservation of privacy.

Summary of Discussion

Generally speaking, it’s not enough to simply get institutional ethical approval at the start of a project.  Institutional approvals typically focus on protection of individuals rather than groups and research activities can change significantly over the course of a project.

In addition to anonymising data there is a case for making it difficult to reconstruct the entire data set so as to stop others from misuse.  Increasingly we don’t even know who learners are (e.g. MOOC) so it’s hard to reasonably predict the potential outcomes of an intervention.

The BERA guidelines for ethical research are up for review by the sounds of it – and a working group is going to be formed to look at this ahead of a possible meeting at the BERA annual conference.

FRRIICT: Oxford Workshop

Framework for Responsible Research & Innovation in ICT (FRIICT) is an ESRC research project led by Marina Jirotka from the University of Oxford and Bernd Stahl from De Montfort University.  Last month I attended their inaugural workshop, entitled “Identifying and addressing ethical issues in technology-related social research”.

The overall aim of the project is to:

  • develop an in-depth understanding of ICT researchers’ ethical issues and dilemmas in conducting ICT research;
  • provide of a set of recommendations and good practice to be adopted by EPSRC and the community;
  • create a self sustaining ‘ICT Observatory’ serving as a community portal and providing access to all outputs of the project.

The workshop took place in Oxford and was attended by about thirty people, mainly technology researchers or social scientists who intend to use ICT to collect research data, as well as a couple of lawyers.

The weather in Oxford was glorious but the sessions were lively enough to ensure that people didn’t get too bored by sitting inside.  Most of the two days were given over to discussion of case studies and general discussion.    The two (fabricated) case studies I worked on with groups were:

1.) Digital Sensory Room for Hospices: a therapeutic, calming sensory environment incorporating music, light, colour, smell and touch and digital communication tools which may be particularly useful for patients who have difficulty with self-expression

2.) Smartnews Inc: a smartphone news app which personalises a feed based on crowdsourcing data from relevant Twitter communities

I won’t reproduce the deliberations here, but the feeling in the group I was in was that the first of these had very little research validity (unsupported assumptions); a number of methodological problems (how to measure quality of life?); and came across as a desperate attempt by a HCI researcher to find a problem to which technology could be the solution.  The second case provokes questions about how data is shared through a service like Twitter and what kind of notion of consent might be in operation with respect to the use and storage of personal data.

The basic approach that FRRIICT seems to be following at the moment is roughly as follows:

  1. Begin with a stakeholder analysis which identifies those who might be affected by a particular intervention
  2. Sketch out the relevant rights, responsibilities and issues of that stakeholder
  3. Work out how these issues might be addressed in the context of the project
  4. Deduce whether a protocol can be derived and applied in other cases
  5. Share

Getting people with a science or technology background to think ‘ethically’ can be quite challenging.  (I tried to sketch out a tool for doing this is in my paper on ethics and mobile learning.)  Researchers typically think of ethics in terms of compliance: as long as the research ethics committee approves a project, that’s good enough for them.  For many of them, this is their only formal encounter with ethics.  But contemporary researchers working with ICT need a better awareness of how technology works, and should think about the wider social impact of technology.   Nonetheless, from a researcher’s point of view, being able to justifiably describe the consent of stakeholders as ‘informed’ is still perhaps the most important part of ‘being ethical’.  The problem, it seems to me, is that reflecting on ethics is one thing, but as soon as you want to discuss or collectively analyse these issues it strikes me that you need at least a minimal grasp of concepts and vocabulary from moral philosophy.  Arguably, everyone has an implicit sense of notions like duty, consequence and the development of moral excellence.  But moral philosophy offers ways to bring these things out and make them explicit without reducing them to pseudo-scientific decision-making tools.  Ethics is not structured like a science (or a stakeholder analysis).  Hopefully FRIICT will help us to work out the most effective forms of ethical reflection in these research contexts.

Here’s some copy from the call for papers from the next workshop (to be held in September):

As technology progressively pervades all aspects of our lives, HCI researchers are engaging with increasingly sensitive contexts. Areas under scrutiny include the provision of appropriate technology access for those approaching the end of life, the design of a social network site for parents of babies in a Neonatal Intensive Care Unit, and the design of interactive memorials in post-genocide Rwanda. The ethical and methodological considerations generated by research in sensitive contexts can go well beyond those addressed by standard ethical approval processes in Computing Science departments and research groups. Such processes need time to catch up with the innovative areas which HCI research is engaging with.

The aim of this workshop is to bring together researchers and practitioners with a common interest in conducting HCI research in sensitive contexts. Examples of ‘sensitive contexts’ include working with potentially vulnerable individuals such as children, adults with disabilities and cloistered nuns, and working in communities affected by a traumatic event. By sharing their experiences and reflections, participants in the workshop will generate a collective understanding of the ethical issues surrounding HCI research in sensitive contexts. We hope that participants will subsequently use this understanding to inform the design of ethical review processes in their own research groups, and incorporate awareness of ethical considerations into research design.

67 interviews (grounded research) were carried out EPSRC management and researchers, NGOs, professional organisations in a preparatory phase of the research.  The researchers found the following:

  • There is a perception that ethics is not strictly speaking a part of ICT research
  • 2/3 of respondents believed that technology is value-neutral.  The other third believes that ‘social value’ plays a part in technology research
  • Most ICT researchers only think about ethics in terms of securing private data and acquiring informed consent for experiments involving human subjects
  • Many researchers feel that their responsibility is to come up with reliable results

Insights thus far:

  • We must recalibrate ‘long term’ and ‘generic’ research: debunk the idea that basic research takes place outside of society
  • Reposition foresight methodologies and make them more approachable
  • We need to refine definitions of ICT as well as acknowledging and meeting skepticism
  • Use cases and misuse cases are typically deterministic and contrived
  • We need to develop new scenarios within ICT research which are more relevant to emerging contexts: social media, geo-tagging, ‘big data’, etc.
  • How can systems be designed in such a way that they demonstrate appreciation of ethical issues?

H808 Professional Values (7.1)

CMALT stands for Certified Membership of the Association for Learning Technology .  “CMALT is a portfolio-based professional accreditation scheme developed by ALT to enable people whose work involves learning technology to:

  • have their experience and capabilities certified by peers;
  • demonstrate that they are taking a committed and serious approach to their professional development.”

The CMALT prospectus mentions the following values in relation to professional accreditation.

  • Commitment to ongoing professional development
  • Gaining and providing recognition of skills, feedback
  • Critical reflection on practice
  • Keeping up to date with new technology
  • Willingness to learn from colleagues and those with different backgrounds
  • Effective communication and dissemination
  • Awareness of wider context
  • Understanding accessibility and assistive technologies
  • Acknowledging copyright
  • Ongoing evaluation and validation of professional skills


Possibly relevant but not mentioned:

  • Effectively meeting obligations to students
  • Staying focused on delivering results
  • Awareness of institutional ethics

 Like a lot of ethical guidance, most of these are formal in nature.  Let’s think about how they compare with education ethics more generally conceived.  The Association of American Educators presents a number of principles and maxims in their code of ethics.  I don’t have the space to discuss them all here, but here are some highlights.



PRINCIPLE I: Ethical Conduct toward Students It probably goes without saying that the first part of being ethical is to be ethically aware.  But in this case it includes the idea that educators should endeavour to “present facts without distortion, bias, or personal prejudice”.  We don’t find this in the CMALT code, perhaps because learning technologists rarely teach themselves.
PRINCIPLE II: Ethical Conduct toward Practices and Performance This is mostly about demonstrating competence and being committed to professional development.  It also includes the idea that teachers shouldn’t embezzle money or otherwise abuse their position. 
PRINCIPLE III: Ethical Conduct toward Professional Colleagues This covers confidentiality and truthfulness without acknowledging the tension between the two!
PRINCIPLE IV: Ethical Conduct toward Parents and Community Professional educators should work co-operatively, being active in school communities and respecting the values of those within them.


Overall this gives the impression that educators have a quite different set of responsibilities to learning technologists and, accordingly, a distinct set of ethical codes and principles.  There is more of a sense of duty of care and precaution in the educational ethics, while the CMALT values are more to do with innovation, future facing, and ongoing professional change.