Robert Mercer, Steve Bannon, Breitbart, Cambridge Analytica, Brexit, and Trump.

“The danger of not having regulation around the sort of data you can get from Facebook and elsewhere is clear. With this, a computer can actually do psychology, it can predict and potentially control human behaviour. It’s what the scientologists try to do but much more powerful. It’s how you brainwash someone. It’s incredibly dangerous.

“It’s no exaggeration to say that minds can be changed. Behaviour can be predicted and controlled. I find it incredibly scary. I really do. Because nobody has really followed through on the possible consequences of all this. People don’t know it’s happening to them. Their attitudes are being changed behind their backs.”

-- Jonathan Rust, Cambridge University Psychometric Centre

Which assessment? Analytics aren't measures. We need to be more forthcoming with faculty about their role in measuring student learning. Such as, http://www.sheeo.org/msc

It's not "mastery of content" it's mastery of the skills to engage with content.

I'm curious how platforms like Hypothesis, and more broadly the social practices afforded by open annotation, help create the conditions for new types of professionally-relevant (online) forums. I think a stance toward engagement with the political dimensions of learning is complementary to the work organizations like Hypothesis who are building tools and partnerships for a more democratic, peer-reviewed web. https://youtu.be/QCkm0lL-6lc

One means of engaging such dialogue is through the public annotathon scheduled for February 27th through March 3rd, and which will occur right here - in the margins of this pre-print turned blog post. See my post for more information about the annotathon, and how to join and use Hypothesis.

This pre-publication version of "The Learning Sciences in a New Era of U.S. Nationalism" is the featured text of an annotathon, scheduled for Monday, February 27th through Friday, March 3rd, in collaboration with The Politics of Learning Writing Collective and Cognition & Instruction. Thanks to Thomas Phillip, Susan Jurow, Shirin Vossoughi, Megan Bang, and Miguel Zavala for graciously agreeing to participate in the annotathon of their article, and to Noel Enyedy and Jamie Gravell for their assistance in organizing and promoting the event.

Questions can be addressed here via Page Notes (a type of annotation attached to an entire document/URL, and not in-line), or via Twitter (@remikalir).

Morris Pelzel on Doug Engelbart's Augmenting Human Intellect: A Conceptual Framework.

the presentation and arrangement of symbolic data is crucial, and any given arrangement may be more or less conducive to discovery. That is why Engelbart’s observation about “playing” with and “rearranging” the materials we are dealing with strikes such a resonant chord. Even if we are only dealing with text, the ease of recombining and manipulating our words and phrases enables our writing to more quickly reach a suitable form of expression.

as we are social animals, our intellects work most effectively, not in isolation but in connection with others. When thoughts and ideas are externally represented, they thereby enter to some degree a public space,

Gardner Campbell on Doug Engelbart's Augmenting Human Intellect: A Conceptual Framework.

even as modes of comprehension increase for some, modes of incomprehension increase for others. The person who sits with “Joe” as Joe demonstrates his new symbol-manipulating capacities reacts in ways that many of us may recognize

...

It takes humility, and hospitality, to spend time with new ideas ... to go deep and go long with concepts that ask us to re-examine many things we take for granted.

This and the next activity are good to explore diverse identities – or uniformity of some identity dimensions – in a group.

That's probably something like this: https://www.mnscu.edu/system/equity/documents/Danger%20of%20a%20single%20story%20circle%20handout.pdf

-> or see p. 16

Good activity to explore the multiple identities, combinations of identities, and intersectionalities of individual persons. Use?

Shared Writing: This seems particularly useful for online conversations that are asynchronous, as it is based on reading statements, commenting on them, and passing the comments around.

Like this one, particularly if quotes are well-chosen, especially in a larger group that otherwise has not done much reading/thought about questions of privilege, discrimination, and marginalized experiences.

I like this activity if the aim is to make personal connections and get to know the individuals involved in a learning group. As a result, probably best for a small group. Requires some preparation as participants have to be asked to bring an object to the meeting.

Interesting activity. Question: Is this useful in a larger group, or only in a smaller group? The calling-out portion enables people to participate without talking, which accommodates larger numbers; but the exposure can be intimidating – particularly for students, who then may just stay put. Maybe start with "easy" identity groups – sports team supporters? – that people are willing to show? Or would this undermine what the conversation should be about?

The discussion portion may get out of hand in a larger group; may need subgroup formation.

Open pedagogy? No. Public Pedagogy maybe. Is public pedagogy a thing?

Nice ¢25 word, here. Means "raised," as in embossed letters or bas relief. Pronounce "reh-LEE-vo," in case you, like me, need to pepper your conversation with unnecessary ornateness because you, like an Athenian, like to dazzle through "showy but false eloquence."

How does study this account for other cues of perception? e.g. listening and such. Isn't it biased against Visual only?

It is part of the Interference Theory. See more here: https://en.wikipedia.org/wiki/Interference_theory

I'm not picking up the meaning of this sentence. Thinking back to my younger years and different child studies, I assumed children learned by seeing, not comparison. If they are comparing, what are they comparing?

You had me already at the Coffee Cup picture over the equations! :)

YES! Why are they taken away? We can add to this list coding, programming, renewable energies, and maybe even gardening. These will be the sustainable skills of the future.

Entirely because they are able to make neural connections which solidify and anchor learning in long-term memory. Student attention spans and interest have skyrocketed in classrooms with coding, robotics, music production, invention and innovation to solve a genuine problem in our society or world. I remember not wanting to teach my students without providing these opportunities because I felt I was doing such a disservice to their futures. Why do we allow non-relevant learning to continue? When will students need derivatives in their lives? When will they need factoring on a daily basis? They shouldn't be forced to learn them unless they are part of the solution to the problems they are faced or challenged with.

This is what happens when we treat students and teachers as statistical data and numbers. If they aren't allowed to think for themselves and create relevant learning which addresses real-world problems, there isn't genuine challenge and application. I see many classrooms where content is 5-10 years old and is instantly disengaging because it's out of date. Why aren't more classrooms talking about and exploring our current political situation, possible trips to mars, renewable energy, how technology advances impact our society? I'm sure consciousness would be much stronger in these environments and half-heartedness would nearly disappear.

This is what my classroom looks like everyday, all day long. Students are in my art classes to produce, problem solve, learn from mistakes, learn from one another. They are actively engaged, the room gets messy. If an admin were to walk in, I'd hope they'd take a moment to observe and realize that what they are seeing is learning! Luckily I do have great admins so they do.

Learning with purpose! Where has this gone? Why is there no longer a greater purpose in most K-12 classrooms? It may have never been there to begin with but I believe if there is a purpose tied to social necessities, greater world good, solving cultural/global problems, many students would be more engaged and motivated to learn as well as rising stars.

Dewey spoke about this long before now and we still adhere to it, why is this? True innovations doesn't come from manuals nor does critical thinking and great problem solvers. Do we really still need manuals with the web and open source?

What kind of society is being formed now? Conformist or free thinkers? It seems we are headed in the wrong direction if we don't offer choice to teachers and students about their learning and growth.

The same could be said about standardized testing. Not that it's not important but it can't be the emphasis nor the entire focus.

Are we back in an age of educational individualism with our "personalized learning" etc? Should we be talking more about communal learning?

Interesting look at PBL and 21st century learning

Interesting look at PBL AND 21st century learning.

In terms of music space, I think of rehearsal, but rehearsal is made possible by the discipline of practice. They are same coin.

I live in the composition classroom both online and face-to-face. Are they rehearsal spaces? Yes. What other kinds of space can they be likened to?

improv? studio? prompt? daily exercises?

Breaking out of ed tech hype.

It doesn't have to be just student feedback about the operations and mechanics of the course, or as a replacement for a course survey tool. You can also use the platform as a way to engage students on the content relevant to the learning outcomes of the course. And use the platform to connect learners with people in the field of study.

You can find the presentation from this session online https://events.educause.edu/sites/default/files/library/presentations/ELI16/SESS16/ELI-16_Personalized+Learning+F2F+post.pdf

Key points:

1. Scale of data is especially good for large NNs
2. Having a combination of HPC and AI skills is important to have optimal impact (handle scale challenges and bigger/complex NN)
3. Most of the value right now comes from CNNS, FCs, RNNS. Unsupervised, GANs and others might be future but they are research topics right now.
4. E2E DL might be relevant for some cases in future like speech -> transcript, Image -> captioning, text -> image
5. Self driving cars might also move to E2E, but none of us have enough data image -> steer

Workflow:

1. Bias = Training error - Human error. Try Bigger model, run longer, New model architecture
2. Variance = Dev error - Train error. Try More data, Regularization, New model architecture.
3. Conflict between bias and variance is weaker in DL. We can have bigger model with more data.

More data:

1. Data synthesis/augmentation is becoming useful and popular: OCR (superpose alphabets on various images), Speech (Superpose various background noises), NLP(?) But does have drawbacks, if it is not representative
2. Unified data warehouse helps leverage data usage across company

Data set breakdown:

1. Dev and test should come from same distribution. As we spend a lot of time optimizing for Dev accuracy.

Progress plateaus above Human level performance:

• But there is theoretical optimal error rate (Bayes rate)

What to do when bias is high:

• Look at examples of the ones machine got it wrong
• Get labels from humans?
• Error analysis: Segment training - identify segments where training error is higher than human.
• Estimate bias/variance effect?

How do you define human level performance: Example: Error of a panel of experts

Size of data:

1. How do you define a NN as small vs medium vs large?
2. Is the reason large NN can leverage bigger data is because it would not cause overfitting unlike on smaller NNs?

The team on Google Translate has developed a neural network that can translate language pairs for which it has not been directly trained. "For example, if the neural network has been taught to translate between English and Japanese, and English and Korean, it can also translate between Japanese and Korean without first going through English."

A good blog post on why do you need to understand backpropagation links to videos from CS231n by karpathy.

In this Pearson sounds more like an edtech company than a content publisher. I wonder at what point will Pearson release a full LMS product that competes directly with BB, D2L, etc?

The tension in that last line on the cultural environments of technology vs academia is an important -and real-tension.

Hypothes.is nicely bridges the worlds of social media and formal education.

No representation of skill trees captures the concept completely, but what I hope is evident on this page is that any Badge, with its related Playlist, should be connected to other Badges and Playlists that come before (in this case, above) it, and it should be one of a few available choices (represented in this case by other Badges and Playlists on the same row), and that it leads to other Badges and Playlists (below it), and that what comes next has choices as well.

Wrong. If you just explained your skills to other people, like a textbook does, no one would understand, unless they have accumulated personal experience similar to yours -- which they would call, after the fact, "a circuitous route" to learning the textbook content.

Speech, writing, math notation, various kinds of graphs, and musical notation are all examples of cognitive technologies. They are tools that help us think, and they can become part of the way we think -- and change the way we think.

Computer interfaces can be cognitive technologies. To whatever degree an interface reflects a set of ideas or methods of working, mastering the interface provides mastery of those ideas or methods.

Experts often have ways of thinking that they rarely share with others, for various reasons. Sometimes they aren't fully aware of their thought processes. The thoughts may be difficult to convey in speech or print. The thoughts may seem sloppy compared to traditional formal explanations.

These thought processes often involve:

• minimal canonical examples - simple models
• heuristics for rapid reasoning about what might work

Nielsen considers turning such thought processes into (computer) interfaces. "Every theorem of mathematics, every significant result of science, is a challenge to our imagination as interface designers. Can we find ways of expressing these principles in an interface? What new objects and operations does a principle suggest?"

The technologies in learning the physics are:- To bring improvements in the students’ physics ability. To bring improvements in the negative reactions of students towards physics.

Every layer needs its own weights and bias. And in tensorflow, it is a good practice to put all weights inside a dictionary, which is easier for management.

I think any good course would do this. The deeper you look, the more there is to see.

Lifelong learning is essential for everyone, not just liberal arts grads. People in healthcare or engineering have to be able to adapt to changing times and technologies just like anyone else.

This work was in schools with teachers, right? When foundations and funders of after school programs ask about how to "spread and scale" the work, it baffles me that they don't begin to answer their questions by turning to how to effectively bring the innovative after school work to teachers and students in schools. Working on the connections between in school and out of school learning is important!

Verification signal

Ground truth in machine learning

parents wanting better education because of the developing system of teaching

improve learning

its true they need to preserve the languages so more ppl learn it and so it won't die

offering more instruction=more children to learn

In other words, Google does use everyone’s information (Data as New Oil) and can use such things to target ads in Higher Education.

Just highlighting and annotating the obvious, lest we forget. There is so much of design = pathway to objectives that I want to linger over, signal boost, Mia's fundamental point: design = the learning experience. A crucial distinction.

Why isn't this in the Horizon HE report? It's more applicable to HE students who have greater opportunities and resources for experiential/self-directed learning.

learn others culture from their stand point

Set aside belief in native realism to understand essential meaning. Be in others shoes to understand perspective.

have an inside view rather than watch from the outside

involves making inferences and knowing back round knowledge

Enthrography is when you learn from the people in the culture. You don't study how they are in their culture but they teach you something

Surprising mention of “open-source platforms”, here. Doesn’t sound like these issues are absent from proprietary platforms. Maybe they mean non-institutional platforms (say, social media), where these issues are really pressing. But the wording is quite strange if that is the case.

Really? These aren’t uncontroversial, to say the least. For instance, discussion board interactions often call for careful, mixed-method work with an eye to preventing instructor effect and confirmation bias. “Time on task” is almost a codeword for distinctions between models of learning. Research in cognitive science gives very nuanced value to “time spent on task” while the Malcolm Gladwells of the world usurp some research results. A major insight behind Competency-Based Education is that it can allow for some variance in terms of “time on task”. So it’s kind of surprising that this summary puts those two things to the fore.

Ha! The Chronicle’s summary framed these categories somewhat differently. Interesting. To me, the “application” part is really about student retention. But maybe that’s a bit of a cynical reading, based on an over-emphasis in the Learning Analytics sphere towards teleological, linear, and insular models of learning. Then, the “representation” part sounds closer to UDL than to learner-driven microcredentials. Both approaches are really interesting and chances are that the report brings them together. Finally, the Chronicle made it sound as though the research implied here were less directed. The mention that it has “the potential to influence institutional practices and interventions” may be strategic, as applied research meant to influence “decision-makers” is more likely to sway them than the type of exploratory research we so badly need.

Useful breakdown. Research, predictive models, and recognition are quite distinct from one another and the approaches to data that they imply are quite different. In a way, the “personalized learning” model at the core of the second topic is close to the Big Data attitude (collect all the things and sense will come through eventually) with corresponding ethical problems. Through projects vary greatly, research has a much more solid base in both ethics and epistemology than the kind of Big Data approach used by technocentric outlets. The part about recognition, though, opens the most interesting door. Microcredentials and badges are a part of a broader picture. The data shared in those cases need not be so comprehensive and learners have a lot of agency in the matter. In fact, when then-Ashoka Charles Tsai interviewed Mozilla executive director Mark Surman about badges, the message was quite clear: badges are a way to rethink education as a learner-driven “create your own path” adventure. The contrast between the three models reveals a lot. From the abstract world of research, to the top-down models of Minority Report-style predictive educating, all the way to a form of heutagogy. Lots to chew on.

Interesting scenario. Sounds quite a bit like the role of this one person in grad school who gives you the boost you need. Usually not your director, who’s more of a name than a resource. Possibly someone with a relatively low status. It becomes something of an “informal advisor” role. “Trust” is indeed key, here. My first reaction reading this was to balk at the “trust” part, because critical thinking skills warrant other methods to gather resources. But this is a situation where trust does matter quite a bit. Not that the resources are necessarily better. But there’s much less overhead involved if rapport has been established. In fact, it’s often easy to get through a text or to start a conversation with someone using knowledge of the angle through which they’ve been recommended. “If she told me to talk to so-and-so, chances are that this person won’t take it the wrong way if we start discussing this issue.”

How can they say student-directed. It's a Facebook algorithm.

The "business" of univeristy?

xnor-net: a very efficient network

Interestingly enough, a lot of teachers either don’t know that such data might be available or perceive very little value in monitoring learners in such a way. But a lot of this can be negotiated with learners themselves.

Only partially built up.

Easier to do with a strict model for success.

Heard of SoLT? Or of the “Centre of Learning and Teaching”? Been using that order for a while, but nobody has commented upon that, to this day. There surely are some places where learning precedes learning in name and/or in practice. But the “field” is teaching-focused.

Going back to the “projects” in the Maker Movement. Not “project-based learning” with projects set through the curriculum. But the kind of “quest” that allows for learning along the way and which may switch at a moment’s notice.

Great ideas here.

Valuing the teaching profession.

传统算法的局限，在图像和语音领域，需要对不相干的钝感和对几个很小地方差异的敏感

四大金刚中的三个

深度学习的最重要的一方面就是多层特征自动学习

随机梯度下降算法，讲的很好

我擦！原来如此！！！

反向传播过程来计算一个具有多层模块权重的目标函数的梯度其实不过是求导链式规则实际应用。

数据无损压缩编码

ReLU的好

The 3D representation of a 2D image is ambiguous and multi-modal. We achieve such reasoning by learning a generative model of 3D structures, and recover this structure from 2D images via probabilistic inference.

Learning w/o Forgetting: distilled transfer learning

This is a neat idea, but do you think that this inhibits some of the students from annotating to their full potential? If I had a great idea, I might save it for myself instead of having someone else "steal" it.

This would be the optimal learning experience, for me.

How can we modernize our current education to fit the unknown needs of the future?

Or even a better question: How do we use these tools to help children learn, and acquire skills that they need?

Interesting dichotomy.

If it’s not counted, does it count?

Development as a field continues to evolve. Ideas that turn into experience generate new ideas and lessons. New ideas inform new experiences, and these are typically debated by those involved in development work.

You can read and watch the debates and discussions that took place over the past two years complementing the IDRC publication International Development: Ideas, Experience, and Prospects.

A lot hinges on those mentors. Apparently, they’re not exclusively technical, which might be an important point. The mentoring is a big part of the learning experience, surely. But there’s a certain level of complexity involved when we start discussing mentorships.

DIY, grassroots, bottom-up… but not learner-driven.

Why so coy?

R.G. Wilmot Lampros, chief product officer for Aleks, says the underlying ideas, referred to as Knowledge Space Theory, were developed by professors at the University of California at Irvine and are in the public domain. It's "there for anybody to vet," he says. But McGraw-Hill has no more plans to make its analytics algorithms public than Google would for its latest search algorithm.

"I know that there are a few results that our customers have found counterintuitive," Mr. Lampros says, but the company's own analyses of its algebra products have found they are 97 percent accurate in predicting when a student is ready to learn the next topic.

As for Ms. Thille's broader critique, he is unpersuaded. "It's a complaint about capitalism," he says. The original theoretical work behind Aleks was financed by the National Science Foundation, but after that, he says, "it would have been dead without business revenues."

MS. THILLE stops short of decrying capitalism. But she does say that letting the market alone shape the future of learning analytics would be a mistake.

Who Decides?

Though much of it may sound trite and the writeup is somewhat awkward (diverse opinions strung together haphazardly), there’s something which can help us focus on somewhat distinct attitudes towards Learning Analytics. Much of it hinges on what may or may not be measured. One might argue that learning happens outside the measurement parameters.

Time-sensitive, mission-critical, just-in-time, realtime, 24/7…

The autopsy/biopsy distinction can indeed be useful, here. Leading to insight. Especially if it’s not about which one is better. A biopsy can help prevent something in an individual patient, but it’s also a dangerous, potentially life-threatening operation. An autopsy can famously identify a “cause of death” but, more broadly, it’s been the way we’ve learnt a lot about health, not just about individual patients. So, while Teamann frames it as a severe limitation, the “autopsy” part of Learning Analytics could do a lot to bring us beyond the individual focus.