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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]
As part of the Digital Learning Research Network, we held our first conference at Stanford last year.
The conference focused on making sense of higher education. The discussions and prsentations addressed many of the critical challenges faced by learners, educators, administrators, and others. The schedule and archive are available here.
This year, we are hosting the 2nd dLRN conference in downtown Fort Worth, October 21-22 The conference call for papers is now open. I’m interested in knowledge that exists in the gaps between domains. For dLRN15, we wanted to socialize/narrativize the scope of change that we face as a field.
The framework of changes can’t be understood through traditional research methods. The narrative builds the house. The research methods and approaches furnish it. Last year we started building the house. This year we are outfitting it through more traditional research methods. Please consider a submission (short, relatively pain free). Hope to see you in Fort Worth, in October!
We have updated our dLRN research website with the current projects and related partners…in case you’d like an overview of the type of research being conducted and that will be presented at #dLRN16. The eight projects we are working on:
1. Collaborative Reflection Activities Using Conversational Agents
2. Onboarding and Outcomes
3. Mindset and Affect in Statistical Courses
4. Online Readiness Modules and Student Success
5. Personal Learning Graphs
6. Supporting Team-Based Learning in MOOCs
7. Utilizing Datasets to Collaboratively Create Interventions
8. Using Learning Analytics to Design Tools for Supporting Academic Success in Higher Education
Long, detailed and damning investigation of Bridge International Academies (BIA) on the African continent. Graham Brown-Martin details the corporate and philanthropic connections underpinning the organization. BIA is essentially a commercial enterprise based on providing education to African children (planned to expand to 10 million children within 10 years). This article challenges the claim that BIA offers value for the service it provides, and notes "the United Nations who, in an unprecedented statement made public on 9 June 2016, expressed concerns about the UK 'funding of low-fee, private and informal schools run by for-profit business enterprises'." According to the article, buildings are substandard, teachers are underqualified and underpaid, and academic gains are not proven. At a certain point, people give up on a system that takes wealth out of an economy but puts nothing back in.[Link] [Comment]