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In the last years the Wikipedia’s search has been improving. It still sucks, but it is getting better. There is a smart team working with the search and the Wikidata is giving a structure to the data.
Think what Wikipedia Search could be? Think what Wikipedia Search could do?
First of all, it could answer to some relatively complex questions presented in a natural language. For instance, it could easily give an answer to the question: What are the ten largest cities on the planet with female mayors? – written just like that to the search field.
This would be useful, no doubt — but in a way trivial.
The most interesting possibilities of the Wikipedia Search are closely related to the vision and mission of the movement.
“Imagine a world in which every single human being can freely share in the sum of all knowledge.”
“The mission of the Wikimedia Foundation is to empower and engage people around the world to collect and develop educational content under a free license or in the public domain, and to disseminate it effectively and globally.”
To get the movement few steps forward to the direction of the the vision and to help us all with the mission, the Wikipedia Search could do so much more. Some software development and awesome UX-design, however, is needed.
In the first stage, the new Wikipedia Search could be expanded to be a federated search to all the Wikimedia projects. This would simply mean that we’ll make all the resources of all the Wikimedia projects easily accessible for all – from a single user interface.
With the Open Search one could do a search like: “Eiffel tower at night” and get as a result in a single page:
- photos of the Eiffel tower at night, all taken from different angles
- art works / painting of the Eiffel tower at night
- videos of the Eiffel tower at night
- poems about the Eiffel tower at night
- songs about the Eiffel tower at night
- translation of the sentence “Eiffel tower at night” to tens of languages
- a tip where to take a free cooking course close to the Eiffel tower at night
- a link to the book The Eiffel Tower, and other mythologies by Roland Barthes
. . . . and so on.
But that’s not all!
Just remember that all the content found with the Open Search would be free / libre content. You could study and use the content and benefit from using the content. You could make copies and distribute them. You could edit the content and distribute these derivative works.
This would make it possible for more and more people to take in use the great pool of free content and to use it in their own endeavors. Teachers could create learning materials. Students could do their studies and present their findings with presentations made out of the free content.
I see that this is what the Wikipedia/Wikimedia is all about. Serving people. Making it easier for people to freely share knowledge.
Finally. Guess what?
This is not my idea. It is not a new idea. I just like it. The Wikimedia Foundation started to work on it last year but it all ended-up to be a megalomaniac wiki-drama. You can read about it from the Wikipedia article Knowledge Engine.
This weekend the Wikipedia/Wikimedia community is getting together in the annual Wikimania conference. I couldn’t make it, because of family reasons. I am sure, the search will be discussed in the Wikimania. Enjoy! Break a leg!
In my previous blogs I reported on the preparations for the consortium meetingof our EU-funded Learning Layers (LL) project that took place in Bristol during the last few days. Now I am on my way back and have some spare minutes to reflect on the baggage of homework that I am taking from the meeting back to office. In general we had a very productive meeting – so many ideas sparking up that it was good to have colleagues taking notes (on the spot and at the other end of online connection). Therefore I just make some short remarks, how our talks helped us to bring our work further: In particular I was happy to see that we are finding a way to present our results as a part of a common group picture – rather than as stand-alone results of different partners or work packages teams working on their own. Below some main points on this:
- Evaluation and documenting the impact: So far more attention has been given on the use of specific evaluation instruments (focus groups, complementary interviews, impact score cards, logdata on use of LL tools) and analysing data gathered with these instruments. Now we opened up this discussion to consider, how to use complementary evidence that is being gathered alongside the fieldwork in the sectoral pilots and in the co-design work. Here we worked with a set of transversal themes (such as digital transformation, adoption of innovation and changes in (informal) learning practices). This has implications for the work of narrower ‘evaluation data’, complementary data and the impact scorecards.
- Presenting our R&D methodologies: We have already earlier agreed to report our results with a single deliverable – a website – and that one section should be dedicated to R&D methdologies. For this section some partners had prepared draft documents that shed light on different ‘local’, sectoral or technical aspects of our R&D work. In the light of these drafts we made clear progress in trying to open up certain contributions (such as co-design work) to be presented from the perspective of both pilot sectors – construction and healthcare. And we developed a better understanding how different activities carried out in the project can be presented as part of a coherent whole.
- Outlining ‘learning scenarios’: At different points of time our project had been working with different sets of ‘use cases’, ‘user stories’, ‘learning scenarios’ or ‘learning stories’. All these had been characterised by a preparatory and explorative phase of the project – presenting possibilities to work with the tools and learning arrangements that we were developing. Now it appeared that we are building learning scenarios that rely on ‘lead theories’ and on the way way have built upon them when developing tools and learning arrangements. Here we are drawing upon the transversal themes (mentioned in point 1. and on the more specific impact cards). This was reflected in a very specific set of ‘learning scenarios’ and tasks to draft them.
- Working further with the exploitation agendas: Here our colleagues Gilbert Peffer and Raymond Elferink presented a ‘generalised’ and at the same time well grounded model, how to adjust the prior partnership relations to new and renewed ones (with an exemplary start-up company for services in the centre). Alongside this example we also revisited the conclusions of the Aachen Integration Meeting on the co-management of the Open Source Software that has been developed in the context of the project. The most important point was that we found both models fully compatible with each other.
I guess this is enough for these spare minutes that I have had today. I am continuing my journey to Bremen (where I still have some meetings before I start my summer break).
More blogs to come …
This began as a comment on Heather’s post, but grew unwieldy and so ended up here. Heather’s post is reacting to this quote from an article she read recently: “There is one additional requirement for widespread OER adoption. Incentives need to discourage ‘free-riders’.”
This statement is demonstrably false. Of the 50 colleges in the US today with widespread OER adoption initiatives underway (by “widespread” here I mean that so many faculty across the institutions are adopting OER that it is – or will soon be – possible for students to earn complete degrees using only OER), literally none of them have discouraging incentives like those Annand describes. I could have ended this post here, but there’s more to say.
If you believe that open educational resources are public goods, which they appear to be since they are both non-excludable and non-rivalrous, then it can be hard to avoid bringing the rest of that conceptual framework (including the idea of the free rider problem) to your thinking about OER. If you don’t want to start from scratch as you think about ensuring the long-term sustainability of OER, the empirical and theoretical work already done on the problem of underprovision of public goods can be quite helpful.
Rajiv is right that the term “free rider” can, unfortunately, sound offensive and off-putting – especially if it is used in ways that sound like a criticism of a specific individual rather than a description of macro-level, society-wide behaviors. As we are thoughtful and careful, I think we can reap the benefits of the research already done on this problem without making people feel like we are singling them out. The open education family, as I think of us, has a deep moral and ethical responsibility to be accepting and welcoming of everyone regardless of their specific relationship to OER (e.g., whether they are contributors to OER or users of OER).
I spend quite a bit of time thinking / worrying about these problems. As I vocally and energetically advocate for universally replacing traditional materials with OER, I am acutely aware that there are essentially no OER available for 300 and 400 level courses or graduate courses. Importantly, the free rider problem does not describe a situation in which an individual uses open educational resources without contributing to their creation. It describes a situation in which so few people contribute to their creation that the OER needed by students and faculty never get created – and that accurately describes the current state of upper-level and graduate-level courses today.
If our only model for creating the OER necessary to replace traditional textbooks is to spend $250k of government or philanthropic funding for each and every course offered at each and every university, there is literally no path from here to there. We need to enable and facilitate alternative development models if our vision of universal OER adoption is to become a reality. (It’s no secret that I believe that these future models must be significantly more distributed and stigmergic than current models.)
We don’t need all users of OER to be contributors to OER for there to be a vibrant, healthy ecosystem of open content, assessments, simulations, and other resources for all courses at all levels. But no such ecosystem can ever emerge if no one (or as it stands today, an insufficient number of someones) contributes to the creation and continuous improvement of OER. Regardless of how we label this problem, we have to solve it to create the kind of educational future we want.
If only 2% – 5% of all faculty and their students (who are doing renewable assignments) were active creators and improvers of OER, that would likely be sufficient. If we could then persuade the other 95% – 98% of faculty to universally adopt OER in place of traditional resources, even without contributing any original or improved OER, I would be ecstatic. And I certainly wouldn’t be inclined to call them names.
I am very grateful to Contact North|Contact Nord for providing this professional translation.
A Spanish version, translated by staff in the Faculty of Engineering, Universidad de Buenos Aires, is almost complete and will be available from the BCcampus open textbook site (as will all the translations). I will provide an announcement containing the url when it is available.
A Chinese version, translated by staff at the Beijing Open University, will be available in August, 2016.
A Portuguese version, being translated by ABED, the Brazilian Association of Distance Education, will be available in time for its Annual Congress in September, 2016.
A Turkish version is currently under consideration. I am awaiting more details.
Please note: under the Creative Commons license of the book, anyone is free to translate all or any part of the book, provided it is not used for commercial purposes and I am acknowledged as the author. I am sure that without this license, the book would not have become available so quickly in so many languages. However, if you do decide to translate the book, please let me know, so I can track its use and provide updates.
Drachsler, H. et al. (2016) Is Privacy a Show-stopper for Learning Analytics? A Review of Current Issues and Their Solutions Learning Analytics Review, no. 6, January 2016, ISSN: 2057-7494About LACE
One of the most interesting sessions for me at last week’s EDEN conference in Budapest was a workshop run by Sally Reynolds of ATiT in Brussels and Dai Griffiths of the University of Bolton, UK. They are both participants in a European Commission project called LACE (Learning Analytics Community Exchange).
The LACE web site states:
LACE partners are passionate about the opportunities afforded by current and future views of learning analytics (LA) and educational data mining (EDM) but we were concerned about missed opportunities and failing to realise value. The project aimed to integrate communities working on LA and EDM from schools, workplace and universities by sharing effective solutions to real problems.
There are a number of reviews and case studies of the use of learning analytics available from the web site, which, if you are interested in (or concerned) about the use of learning analytics, are well worth reading.The EDEN workshop
The EDEN workshop focused on one of the reviews concerned with issues around ethics and privacy in the use of learning analytics, and in particular the use of big data.
I am reasonably familiar with the use of ‘small’ data for learning analytics, such as the use of institutional student data regarding the students in the courses I am teaching, or the analysis of participation in online discussions, both in quantitative and qualitative terms. I am less familiar with the large-scale use of data and especially how data collected via learning management or MOOC registration systems are or could be used to guide teaching and learning.
However, the focus of the workshop was specifically on ethical and privacy issues, based on the review quoted above, but nevertheless I learned a great deal about learning analytics in general through the workshop.What is the concern?
This is best stated in the review article:
Once the Pandora’s Box of data availability has been opened, then individuals lose control of the data about them that have been harvested. They are unable to specify who has access to the data, and for what purpose, and may not be confident that the changes to the education system which result from learning analytics will be desirable. More generally, the lack of transparency in data collection and analysis exacerbates the fear of undermining privacy and personal information rights in society beyond the confines of education. The transport of data from one context to another can result in an unfair and unjustified discrimination against an individual.
In the review article, these concerns are exemplified by case studies covering schools, universities and the workplace. These concerns are summarized under the following headings:
- informed consent and transparency in data collection
- location and interpretation of data
- data management and security
- data ownership
- possibility of error
- role of knowing and obligation to act
There are in fact a number of guidelines regarding data collection and use that could be applied to learning analytics, such as the Nuremberg Code on research ethics, the OECD Privacy Framework, (both of which are general), or the JISC code of practice for learning analytics. However, the main challenge is that some proponents of learning analytics want to approach the issue in ways that are radically different from past data collection methods (like my ‘small’ data analysis). In particular they propose using random data collection then subsequently analysing it through data analysis algorithms to identify possible post-hoc applications and interpretations.
It could be argued that educational organizations have always collected data about students, such as registers of attendance, age, address and student grades. However, new technology, such as data trawling and the ability to combine data from completely different sources, as well as automated analysis, completely changes the game, raising the following questions:
- who determines what data is collected and used within a learning management system?
- who ensures the security of student (or instructor) data?
- who controls access to student data?
- who controls how the data is used?
- who owns the data?
In particular, increasingly student (and instructor) data is being accessed, stored and used not just outside an institution, but even outside a particular country, and hence subject to laws (such as the U.S. Patriot Act) that do not apply in the country from which the data was collected.Recommendations from the LACE working group
The LACE working group has developed an eight point checklist called DELICATE, ‘to support a new learner contract, as the basis for a trusted implementation of Learning Analytics.’
For more on DELICATE see:
Drachsler, H. and Greller, W. (2016) Privacy and Learning Analytics – its a DELICATE issue Heerlen NL: The Open University of the NetherlandsIssues raised in the workshop
First it was pointed out that by today’s standards, most institutional data doesn’t qualify as ‘big data’. In education, what would constitute big data would for example be student information from the whole education system. The strategy would be to collect data about or from all students, then apply analysis that may well result in by-passing or even replacing institutions with alternative services. MOOC platforms are possibly the closest that come to this model, hence their potential for disruption. Nevertheless, even within an institution, it is important to develop policies and practices that take into account ethics and privacy when collecting and using data.
As in many workshops, we were divided into small groups to discuss some of these issues, with a small set of questions to guide the discussion. In my small group of five conference participants, none of the participants was in an institution that had a policy regarding ethics and privacy in the use of learning analytics (or if it existed, they were unaware of it).
There was a concern on our table that increasing amounts of student data around learning was accessible to external organizations (such as LMS software companies and social media organizations such as Facebook). In particular, there was a concern that in reality, many technology decisions, such as choice of an institutional learning platform, were influenced strongly by the CIO, who may not take into sufficient account ethical and privacy concerns when negotiating agreements, or even by students themselves, who are often unaware of the implications of data collection and use by technology providers.
Our table ended by suggesting that every post-secondary institution should establish a small data ethics/privacy committee that would include, if available, someone who is a specialist in data ethics and privacy, and representatives of faculty and students, as well as the CIO, to implement and oversee policy in this area.
This was an excellent workshop that tried to find solutions that combine a balance between the need to track learner behaviour and privacy and ethical issues.Over to you
Some questions for you:
- is your institution using learning analytics – or considering it
- if so, does your institution have a policy or process for monitoring data ethics and privacy issues?
- is this really a lot of fuss over nothing?
I’d love to hear from you on this.
Remixing is the act of taking previously created works or artefacts and adapting them in some way. Sometimes several works are combined or 'mashed up' to create new versions. In the digital age, where many have access to the participatory web such as social media, it is easier than ever to remix and mashup content.
Remixing is a human pastime that has existed for millennia. We see or hear something we like, and we try to make our own personal version of it. In popular culture, folk songs and stories, often with no traceable origin, have been sung or told, and then retold across the generations. Often old stories and songs are modified to meet the needs or interests of contemporary society. Parodies and satirical versions of original stories or songs are also considered to fall into this category of remixing.
One of the most widespread examples of digital remix is Wikipedia. The online encyclopaedia relies almost exclusively on members of the public to share their knowledge and update contents. That knowledge is published, and then modified and remixed to the point where it becomes more accurate. The appeal of Wikipedia and also any digital remix is that it is never complete. It is always a work in progress.
The digital age has given us many tools which can be used to easily remix the work of someone else. Garage Band for example, enables the production and reproduction of just about any musical instrument sound, and allows the user to mix these into a musical sequence. Photoshop is software that allows users to do the same thing with images. Vidding, modding, sampling and hacking are all techniques developed in the digital age to modify, remix and repurpose existing content. This article tells us why the remix culture is such an important movement because 'all cultural artefacts are open to re-appropriation' when the meaning ascribed to objects is transient.
Remixing is a creative process. It takes imagination to adapt an existing piece of art or music into something new or apply it in a completely different context. However, in formal education settings, remixing is sometimes seen as undesirable. For example, some students copy and paste content from the web into their own work, and claim that it is their own. This is clearly plagiarism, and is considered an academic offence. If instead they paraphrase the ideas of another and cite the source, it is research and is considered acceptable. There is a fine line between copying and remixing. It is the extent to which you can prove that your own influence, imagination and thought processes have been invested into the work of someone else to make it significantly different to the original piece that assumes importance.
There are many educational applications for remix. Any of the above tools can be used to promote creative thinking. Students can also be encouraged to think more critically about the origin and provenance of content, and how it can be so easily subject to change. Teachers should be aware of existing copyright law, and also how to use licences such as Creative Commons to discover and share content that is specifically created for remixing.
Image from Wikipedia (remixed by Steve Wheeler)
Remix culture and education by Steve Wheeler was written in Plymouth, England and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.Posted by Steve Wheeler from Learning with e's
These are all ways blockchain could be used in education (though a lot of detail would have to be added) but I'm not sure I agree with the context. Introducing the piece Donald Clark says he created a Napster like system for learning resources in 2001 but "the public sector organisations just didn’ t like innovation and stuck to their institutional silos." He predicts a similar reaction to blockchain. "The biggest obstacle to its use is cultural. Education is a slow learner and very slow adopter. Despite the obvious advantages, it will be slow to adapt this technology." Why would he expect these new systems to work within traditional institutions? I did the same sort of thing in 2001, but by not waiting for institutional approval helped create the first MOOC. It is only after an idea is demonstrated that it will change culture and be adopted by institutions. The same is true for business and enterprise software. It has nothing to do with education or the public sector, and everything to do with large organizations and culture in general. Image: Cable Green.[Link] [Comment]
Interesting article. You can probably skim the first five paragraphs, but slow down when you get to this: "Today, a broader conceptual framework for open innovation is embedded in an integrated approach to openness. It is a vital element of the open movement and should not be taken out of this context. Open innovation is transcending the boundaries of traditional knowledge production and fosters cross-fertilization of knowledge. It can serve both as a trigger for change towards openness and a cross-connector of multiple segments of the open movement."
Here are the recommendations (all quoted):
- Identify areas for growth and actively pursue development in those areas.
- Sleep, exercise, good nutrition, and stress-management help ward off the noxious effects of disrespect.
- Generate more meaning at work by shaping your activities around your motives, strengths and passions.
- Seek positive relationships. Positive relationships in and out of work help you thrive.
- Thriving in non-work activities doubles an individual’ s emotional reserves.
Sounds like a plan. Something everybody could use to more or less a degree.
Long post that introduces machine learning for designers. It requires a (free) O'Reilly login (sorry). People already expert in machine learning won't find anything new but I think it's worth the effort if you don't have background in the field.
"Conventional programming languages can be thought of as systems that are always correct about mundane things like concrete mathematical operations. Machine learning algorithms, on the other hand, can be thought of as systems that are often correct about more complicated things like identifying human faces in an image." There's a good set of recognition examples that illustrate this. It looks at biological models and deep learning, then discusses processing different types of inputs. Some of the tasks described include creating dialogue, feature discovery, designing, feedback loops, and more. It also looks at open source machine learning toolkits (TensorFlow, Torch, Caffe, cuDNN, Theano, Scikit-learn, Shogun, Spark MLlib, and Deeplearning4j) and machine Learning as a Service (MLaaS) platforms such as IBM Watson, Amazon Machine Learning, Google Prediction API, Microsoft Azure, BigML, and ClarifAI.[Link] [Comment]
Course announcement from a relatively new provider for 'applications' to "an open access course to support the development of scalable digital learning." The course is free but certification costs extra. I read this as an an announcement for Scholar, a "digital learning environment that models effective learning and knowledge development in complex settings." According to their materials, "in Scholar people focus on co-constructing knowledge (by solving problems, building a case study, developing an implementation plan) that is relevant and applicable to their work." Probably the diagram makes it most clear. What do you think, should I take the course?[Link] [Comment]
Joanne Jacobs, Jun 22, 2016
Mitch Daniels says "that outside of the extremes it’ s the luck you make not the luck of the world that determines your fate." So summarized Andrew Rotham. Or as Joanne Jacobs says, "except for 'tragically bad luck,' it rarely 'decides a life’ s outcome.'" I think that on that basis we would have to define "tragically bad luck" as "not being born rich." Jacobs also quotes Barack Obama, speaking at Harvard: "Yes, you’ ve worked hard, but you’ ve also been lucky. That’ s a pet peeve of mine: People who have been successful and don’ t realize they’ ve been lucky." I think Obama's take is more correct. As Rotham says, “ Daniels’ argument confuses what’ s possible with what’ s probable." Jacobs concludes, "for many born in poverty, economic mobility is a longshot." I have no illusion that education by itself will change this. For those not born rich, education is a necessary, but not sufficient, condition for prosperity.[Link] [Comment]