2,887 Matching Annotations
  1. Jul 2017
    1. Reciprocity now becomes a matter of at once mutually preserving the other’s distinctness while interdependently fashioning a bigger context in which these separate identities interpenetrate…are co-regulated, and to which persons invest an affection supervening their separate identities. Reciprocity now becomes a matter of both holding and being held, a mutual protection of each partner’s opportunity to experience and exercise both sides of life’s fundamental tension.”

      Lv5 reciprocity: Maintaining individual's distinctness while together create a bigger context so that these separate identities interpenetrate.

    2. As the institutional balance breaks, the person becomes more available to and interested in a kind of sharing and intimacy with others. But intimacy in the next balance is the self’s aim, rather than its source. It involves a self that travels between systems, or exists in the dynamism between them, not in the dynamism between individuals.

      The self exists in the dynamism between systems, not between individuals

    1. Postmodernism (or “poststructuralism”), in its denial of the possibility of judgement and rejection of all “metanarratives” (grounded systems), corresponds to the stage 4.5 nihilistic gap.

      Post-modernism: stage 4.5 nihilistic gaps aka the "sadness" after seeing the truth in Plan B by Charles Inouye

    2. All ideologies are relativized as tools rather than truths. Fluidity treats rationality as a valuable tool that is not always applicable; non-rational ambiguity and paradox become non-problematic. Stage 5 can, therefore, conjure with systems, as animated characters in a magical shadow-play drama.

      All ideologies are relativized as tools rather than truths. "Conjuring" with systems, shadow-play drama.

    3. To stage 3, that sounds cold and distant, but for stage 4, it means seeing the other person for who they really are. Emotions are just something people have, from time to time. Those need to be dealt with, but should not be taken too seriously. Relating to the other person’s principles, projects, and commitments means supporting what they most care about in the longer run. A romantic relationship between systematic people not only tolerates, but respects, and actively supports, their differing values and projects. That is what it means (for stage 4) to be actually in a relationship with another person, rather than losing both your selves in a warm bath of shared feelings.

      Being in relationship for Stage 4: dealing with but not taking emotions seriously. Must support differing values & projects.

    1. Legitimate peripheral participation is a more powerful motivation for accurate feedback than money. If a student’s labor contributes to the success or failure of your project, you want to be sure they are doing it right—and so you will scrutinize their work carefully, and give detailed corrective advice.

      Why does boss care about giving feedback in Legitimate Peripheral Participation? Coz it's success or failure! (FAIL = For All I Learn)

  2. Jun 2017
    1. The University of Michigan Teach-Out Series offers an opportunity for learners around the world to come together with our campus community in conversation on topics of widespread interest.

      Short, 1-week, open courses offered on edX that explore timely topics

    1. Thisstudyexaminesthecomponentsofaself-pacedonlinecoursespecificallydesignedtoincorporateweb-basedpedagogytocreateanengaginganddynamiclearningenvironment.Itcomparesstudentperformanceinaself-pacedonlinecourse,aconventionalonlinecourseandatraditionalin-classcourseandrevealsthepotentialforstudentstothriveinawidevarietyofonlinecourseformats

      This study compares performance in a face to face, online and strategically designed self-paced online course. The results showed small performance improvements for the self-paced students compared with the face to face students and larger improvements when compared to the instructor paced online course. The researchers speculate that the increased flexibility allows learners to achieve maximum performance, but this result could also be attributed to the design improvements. They discuss the design improvements made to the self-paced course, but do not share any information about the design of the face to face or instructor paced online courses. It would be interesting to see if design improvements in those formats that provided the same opportunities for interaction and feedback would change the results.

    1. It used rich media and a mix of traditional and emerging asynchronous computer-mediated communication tools to determine what forms of interaction learners in a self-paced online course value most and what impact they perceive interaction to have on their overall learning experience. This study demonstrated that depending on the specific circumstance, not all forms of interaction may be either equally valued by learners or effective.

      The results show that students most highly value interactions with the instructor and content.

    1. If it can be established that student self motivation has a direct effect on remediation, it stands to reason that by finding a way to increase a student’s self motivation, the remediation process can be improved to increase the likelihood of success for a student who requires the use of remedial courses in the specialized classroom setting. Attempting to understand the factors that create a learning environment of poor motivation is an arduous task, but attempting to improve those factors that increase motivation is imperative.

      This study involves a self-paced developmental mathematics course (N=86). The results showed that the students' perceived intrinsic value of the learning was a significant predictor of success in the course. Motivation had a greater impact on students' ability to succeed than prior knowledge (based on ACT math scores).

  3. May 2017
    1. ne critical element in the effectiveness of these networks is “working in the open.” This includes a number of simple practices commonly associated with open source software: making curriculum and tools easy for others to discover; publishing using an editable format that allows others to freely use and adapt them; using an open license like Creative Commons. It also includes a set of work practices that make it easy for people to collaborate across organizations and locations: collaborative writing in shared online documents; shared public plans on wiki or other editable documents; progress reports and insights shared in real time and posted on blogs. These simple practices are the grease that lubricates the network, allowing ideas to flow and innovations to spread. More importantly, they make it possible for people to genuinely build things together—and learn along the way. This point cannot be emphasized strongly enough: when people build things together they tend to own them emotionally and want to roll them out after they are created. If the people building together are from different institutions, then the innovations spread more quickly to more institutions.

      These are all important aspects of open pedagogy, imo. Transparent, network practices that connect, but also create space and opportunities for particiaption by those on the edges. Working in the open is an invitation to particiaption to others.

    2. Rather than selecting a single organization to lead the network, consider a spoke-and-hub or constellation model that empowers teams of organizations to act as “network hubs” for different sectors of the network. The best candidates for these hubs are intermediary organizations that act in the best interests of the network, allowing other network members to focus on their core mission and programmatic activities. Hub organizations play several roles. As conveners, they bring people together and build the field. As catalysts, they invest money and resources to get new ideas off the ground or help exciting projects to develop. As communicators, hub organizations enhance networks members’ ability to tell their story effectively and efficiently, internally and externally. As champions, hubs lift up the accomplishments of network actors, regionally, nationally, and internationally. And, as coordinators, hub organizations connect the dots, recommend priorities for the network, and connect those priorities to national resources.

      This could describe BCcampus - a hub organization that connects networks

    1. TPS Reflective Exercises

      TPS as metacognition - worth trying out. Would have to budget time for it. Could we combine it with something to capture data? connect to qualtrics or google forms

  4. Apr 2017
    1. Detection of fake news in social media based on who liked it.

      we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who "liked" them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts.

    1. Obviously, in this situation whoever controls the algorithms has great power. Decisions like what is promoted to the top of a news feed can swing elections. Small changes in UI can drive big changes in user behavior. There are no democratic checks or controls on this power, and the people who exercise it are trying to pretend it doesn’t exist

    2. On Facebook, social dynamics and the algorithms’ taste for drama reinforce each other. Facebook selects from stories that your friends have shared to find the links you’re most likely to click on. This is a potent mix, because what you read and post on Facebook is not just an expression of your interests, but part of a performative group identity.

      So without explicitly coding for this behavior, we already have a dynamic where people are pulled to the extremes. Things get worse when third parties are allowed to use these algorithms to target a specific audience.

    3. any system trying to maximize engagement will try to push users towards the fringes. You can prove this to yourself by opening YouTube in an incognito browser (so that you start with a blank slate), and clicking recommended links on any video with political content.

      ...

      This pull to the fringes doesn’t happen if you click on a cute animal story. In that case, you just get more cute animals (an experiment I also recommend trying). But the algorithms have learned that users interested in politics respond more if they’re provoked more, so they provoke. Nobody programmed the behavior into the algorithm; it made a correct observation about human nature and acted on it.

    1. areas where deep learning is currently being poorly utilized

      who is curating a list of deep learning success stories, case studies and applications?

    2. highly automated tools for training deep learning models

      such as?

    3. The best way we can help these people is by giving them the tools and knowledge to solve their own problems, using their own expertise and experience.

      Agree or disagree?

    1. Appendix A:Table of various deep learning applications

      This is a good list. Has anyone come across a comprehensive list of deep learning applications?

    1. https://connectedlearning.uci.edu/

      new Connected Learning Lab (CLL) at UC Irvine, an interdisciplinary research center dedicated to studying and mobilizing learning technologies in equitable, innovative, and learner-centered ways.

    1. Almost all exciting results based on recurrent neural networks are achieved with them.

      lstm是rnn的一种,但是一般所RNN指传统标准的RNN

    1. In turn, this child’s statement may shift the teacher’s learning and encourage her or him to recreate or extend this same experience to another area of study in the curriculum.

      I hadn't thought about this aspect of discourse: not just the various context the content but of the experience of learning itself, how a teacher responds to student work, how that work is set up in the first place.

      I think hypothes.is is particularly useful in relation to this type of context in the way it makes certain previously hidden aspects of learning visible...

    1. If we write that out as equations, we get:

      It would be easier to understand what are x and y and W here if the actual numbers were used, like 784, 10, 55000, etc. In this simple example there are 3 x and 3 y, which is misleading. In reality there are 784 x elements (for each pixel) and 55,000 such x arrays and only 10 y elements (for each digit) and then 55,000 of them.

    1. Really cool venue for publishing online, interactive articles for ML

  5. Mar 2017
    1. They then went and did a marvellous job, networking for themselves.

      networking

    2. It was lovely to smell the toast in a university classroom.  

      learning space

    3. before they stuck a concrete university here.

      boxes natural movement learning freedom

    4. I wandered out of the classroom into the nature on the campus.  I felt the warmth of the Indian Summer on my back, I sat down on the grass.  

      nature ecosystem

    5. Rather than learning from #ccourses to develop #clavier, I am beginning to understand that #clavier and #ccourses and #ds106 and the whole caboosh is actually the same thing.

      Personal learning network. Overlapping communities/networks

    6. Rather than blogging, (I was tired with blogging), I spent my time doing drawing. 

      Time Fallow Rejuvenation Learning Rest

    1. the area under the curve (often referred to as simply the AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative')

      AUC能够在CTR应用中有指导意义的原因

    1. Thanks for letting me take this feldgang. A feldgang is what farmers do all the time. Fieldwalks

      Fortunately you did.

      This is enabling me to continue my feldgang here.

      Retracing steps and seeing things from a different perpective

    2. Feel free to check your various personal learning networks, but …wander back please.

      I am wandering back

      https://youtu.be/mQ9b3JszQD8

    3. I freely admit that this is a mess of post

      learning

    1. I was in the room with him from my learning space in Clermont Ferrand. I could hear the bad acoustics of the room in which he was/we were?. We were sharing the slides on the screen in Krakow inside the slide share of our hangout on air.

      Social presence.

      Hybridization.

      Room, space. confusion

    1. Students need learning goals, and they need to set these for themselves.

      Again, assumes quite a bit of freedom on the part of the students. Institutional goals/outcomes or disciplinary goals/outcomes that have to be included?

    2. There is probably no need to release all the content at once. Doing so (to the tune of about 15 weeks worth of content) could be way overwhelming for any student. Releasing all the content at once around a very specific chunk of the course makes the most sense.

      Chunked release of information--here's everything we're doing for unit 2, for example? Or two weeks' worth because things are additive/scaffolded?

    3. For the most part, I made myself go through the modules in the order in which they were created. I assumed they were placed in that order for a specific reason. If I hit content I already knew or didn’t want to apply just yet then I skipped over it to return to later if needed.

      This is a lot of faith in the instructor (and not necessarily misplaced). It also requires a certain level of pre-existing familiarity to make those decisions about what is/is not useful and a certain intention in taking the course (i.e., there won't be an exam and the course is mostly voluntary).

    1. The words of one of my students, one of my fellow learners helped me continue. Those words, some of them, are translated here in this post Nagasaki mon amour.

      There is no going back. We must go forward.

      The key issues concern the data collection/policing of our conversations.

    1. trying to answer questions—or to even foster questions in the first place

      This strikes me as a crucial point: using this tool allowed you to shift from a traditional, considerably passive learning style (they're still engaging with the text, but alone) to a more potentially active one.

    1. Frankly, it didn't really make an impression on me at all until I started writing this retitled post spurred by Frances Bell's "Reflections on Community in #Rhizo14 - more questions than answers."

      nonlinear connection emergence

    1. Res ear ch often isolates particular pieces of the complex puzzle in order to study them in detail. However useful this may be, it obscures the dynamism of the actual teaching and learning work that goes on, and cannot show the emergent and contingent nature of that work

      So is one example of this the teaching of vocabulary and grammar out of context of authentic reading and conversation?

    2. There must be room in a learning environment for a variety of expressions of agency to flourish.

      Love this.

    3. However, in order to make significant progress, and to make enduring strides in terms of setting objectives, pursuing goals and moving towards lifelong learning, learners need to make choices and employ agency in more self-direct ed ways.

      This is just what Naoko is doing by allowing students to choose their topics of research within the context of a language learning course.

    4. Agency is therefore a central concept in learning, at many levels an in many manifestations. It is a more general and more profound concept than the closely related terms autonomy, motivation and investment. One might say that autonomy, motivation and investment are in a sense products (or manifestations) of a person’s agency.

      Interesting.

    5. the multilayered nature of interaction and language use, in all their complexity and as a network of interdependencies among all the elements in the setting, not only at the social level, but also at the physical and symbolic level

      Does this map to literary theory in any way?...

    6. any utterance can carry several layers of meaning

      And all those layers can be visualized through annotation: vocabulary, cultural context...

    7. “layer ed simultaneity.”

      Love that phrase.

    8. I like to use this image to illustrate that any utterance has a number of layers of meaning. It refers not only to the here and now, but also to the past and the future of the person or persons involved in the speech event, to the world around us, and to the identity that the speaker projects.

      Wow. Annotation fits quite nicely here as helping to visualize these layers in a slightly more user-friendly way than Escher.

    9. and they are dynamic and emergent, never finished or absolute.

      Come on, "not-yet-ness" (Collier).

    10. ecologically valid contexts, relationships, agency, motivation and identity.
    11. ecological perspective,

      Everything is inter-related. Language cannot be learned out of context, out of community.

    1. Learningto complete a whole task involves four levels ofinstruction: (a) the problem, (b) the tasks re-quired to solve the problem, (c) the operationsthat comprise the tasks, and (d) the actions thatcomprise the operations. Effective instructionshould engage students in all four levels of per-formance: the problem level, the task-level, theoperation-level, and the action-level.

      Steps is learning to complete a whole task. This could be an extension of our matrix.

    2. Van Merriénboer (1997) recommendedthat the first problem in a sequence should be aworked example that shows students the type ofwhole task that they will learn to complete.

      This is a great way to communicate/model a learning outcome.

    1. Adaptive Learning is data-driven and continually takes data from students and adapts their learning pathway to “change and improve over time for each student”.
    1. Consequently, our advice is simple: continue to train your networks on a single machine, until the training time becomes prohibitive.

      一定要对 数据加载时间、参数通信时间、计算时间有个明确的评估,不能为了并行而并行。能单机解决的问题就不着急上多机。

    2. odel parallelism can work well in practice, data parallelism is arguably the preferred approach for distributed systems and has been the focus of more research

      why ?

  6. Feb 2017
    1. 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

    1. this kind of assessmen

      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

    2. mastery of content that engages students in critical thinking, problem-solving, collaboration, and self-directed learnin

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

    1. professional forums

      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

    2. to prompt and engage a dialogue

      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.

    3. 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).

    1. 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,

    1. 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.

    1. A reflective writing technique that encourages personal reflection, provides opportunities for all voices to be heard, and leads to deeper, more thoughtful conversations

      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.

    2. Hatful of Quotes

      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.

    3. circLE oFobJEcts

      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.

    4. 80Identity Groups

      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.

    1. re-lievo,

      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."

    1. Just as young children learn by comparison,

      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?

  7. Jan 2017
    1. Whether you're a student, parent, or teacher, this book is your key to unlocking the aha! moments that make math click -- and learning enjoyable.

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

    1. We must conceive of work in wood and metal, of weaving, sewing, and cooking, as methods of life not as distinct studies.

      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.

    2. It keeps them alert and active, instead of passive and receptive; it makes them more useful, more capable

      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.

    3. Consciousness of its real import is still so weak that the work is often done in a half-hearted, confused, and unrelated way

      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.

    4. There is little order of one sort where things are in process of construction; there is a certain disorder in any busy workshop; there is not silence; persons are not engaged in maintaining certain fixed physical postures; their arms are not folded; they are not holding their books thus and so. They are doing a variety of things, and there is the confusion, the bustle, that results from activity. But out of occupation, out of doing things that are to produce results, and out of doing these in a social and coöperative way, there is born a discipline of its own kind and type.

      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.

    5. with real things and materials, with the actual processes of their manipulation, and the knowledge of their social necessities and uses

      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.

    6. manual training

      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?

    7. an effort to meet the needs of the new society that is forming

      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.

    8. it destroys our democracy

      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.

    9. the progress made by the individual child

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

    1. PBL is the ongoing act of learning about different subjects simultaneously. This is achieved by guiding students to identify, through research, a real-world problem (local to global) developing its solution using evidence to support the claim, and presenting the solution through a multimedia approach based in a set of 21st-century tools.

      Interesting look at PBL and 21st century learning

    2. PBL is the ongoing act of learning about different subjects simultaneously. This is achieved by guiding students to identify, through research, a real-world problem (local to global) developing its solution using evidence to support the claim, and presenting the solution through a multimedia approach based in a set of 21st-century tools

      Interesting look at PBL AND 21st century learning.

    1. Paulo: “But we can also create space inside of the subsystem or the schooling system in order to occupy the space.” (p.203)

      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?

    1. AI criticism is also limited by the accuracy of human labellers, who must carry out a close reading of the ‘training’ texts before the AI can kick in. Experiments show that readers tend to take longer to process events that are distant in time or separated by a time shift (such as ‘a day later’).
    2. Even though AI annotation schemes are versatile and expressive, they’re not foolproof. Longer, book-length texts are prohibitively expensive to annotate, so the power of the algorithms is restricted by the quantity of data available for training them.
    3. In most cases, this analysis involves what’s known as ‘supervised’ machine learning, in which algorithms train themselves from collections of texts that a human has laboriously labelled.
    1. Asking questions via social media that are intentionally designed to elicit responses can provide a plethora of useful responses. Why wait until an end-of-year survey to find out about an issue when you can poll/question students throughout the year via social media?

      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.

  8. Dec 2016
    1. 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?
    1. 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."

    1. This is not easy. Well-designed educational technology has often lacked a learning sciences base, and many research-based education products have lacked a compelling user-centered design. How can world-class user experience (UX) design— grounded in a fail-fast culture—and educational research— grounded in rigor—peacefully coexist?

      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.

    1. Ninety-five percent of 12- to 17-year-olds already go online on a regular basis. They use social networks, and create and contribute to websites. Our work is focused on taking full advantage of the kinds of tools and technologies that have transformed every other aspect of life to power up and accelerate students’ learning. We need to do things differently, not just better.

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

    1. Skill Trees

      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.

    1. I do sometimes get a lot of value out of my math or hardware skills, but I suspect I could teach someone the actually applicable math and hardware skills I have in less than a year. Spending five years in a school and a decade in industry to pick up those skills was a circuitous route to getting where I am.

      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.

  9. Nov 2016
    1. 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?"

    1. 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.

    1. Deep neural networks use multiple layers with each layer requiring it's own weight and bias.

      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.

    1. expected to generate more questions than answers

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

    2. lifelong learning skills will serve liberal arts graduates

      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.

  10. Oct 2016
    1. On-Site Work

      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!

    1. Devices connected to the cloud allow professors to gather data on their students and then determine which ones need the most individual attention and care.
    1. 这里要求,输入的数据时成对存在,每一对都有一个公共的label,是否是同一个类别。

      Verification signal

    1. In machine learning, the term "ground truth" refers to the accuracy of the training set's classification for supervised learning techniques.

      Ground truth in machine learning

    1. are parents seeking a quality education for their children and the real-life costs of English-only education

      parents wanting better education because of the developing system of teaching

    2. We are trying to go forward

      improve learning

    3. bring languages into our schools—our Native languages and many more; it spreads our language around

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

    4. allowing it to offer dual-language instruction

      offering more instruction=more children to learn

    1. For G Suite users in primary/secondary (K-12) schools, Google does not use any user personal information (or any information associated with a Google Account) to target ads.

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

  11. Sep 2016
    1. design this class, I found I was seeking an experience of learning

      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.

    1. When educators are actively experimenting in the classroom, students in turn are more likely to confidently take creative risks themselves. It is also important that educators provide opportunities for students to take ownership of their learning and depart from teacher-defined outcomes without being penalized

      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.

    1. Regarding the major obstacles for higher education, blending formal and informal learning is considered one of the solvable challenges
    1. learn the meanings of all these things

      learn others culture from their stand point

    2. The naive realist assumes that love, snow, marriage, worship, animals, death, food, and hundreds of other things have essen-tially the same meaning to all human beings

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

    3. Discovering the insider’s view is a different spe-cies of knowledge from one that rests mainly on the outsider’s view, even when the outsider is a trained social scientist

      have an inside view rather than watch from the outside

    4. Rather than studying people, ethnography means learning from people

      involves making inferences and knowing back round knowledge

    5. ethnography means learning from people

      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

    1. Data sharing over open-source platforms can create ambiguous rules about data ownership and publication authorship, or raise concerns about data misuse by others, thus discouraging liberal sharing of data.

      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.

    2. Activities such as time spent on task and discussion board interactions are at the forefront of research.

      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.

    3. Research: Student data are used to conduct empirical studies designed primarily to advance knowledge in the field, though with the potential to influence institutional practices and interventions. Application: Student data are used to inform changes in institutional practices, programs, or policies, in order to improve student learning and support. Representation: Student data are used to report on the educational experiences and achievements of students to internal and external audiences, in ways that are more extensive and nuanced than the traditional transcript.

      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.

    1. The queue of electronic hands could take so long to get through that some students abandoned hope and lowered their hands while others got into the habit of raising their hand pre-emptively just so they’d have a spot in line if an idea came into their head later on.
    1. often private companies whose technologies power the systems universities use for predictive analytics and adaptive courseware
    2. the use of data in scholarly research about student learning; the use of data in systems like the admissions process or predictive-analytics programs that colleges use to spot students who should be referred to an academic counselor; and the ways colleges should treat nontraditional transcript data, alternative credentials, and other forms of documentation about students’ activities, such as badges, that recognize them for nonacademic skills.

      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.

    1. I wonder what would have happened if someone I trust had provided me with a list of resources and people she admired when I started out in online learning and open education four years ago.

      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.”

  12. Aug 2016
  13. Jul 2016
    1. In addition, the discontinuity between classroom theory and practical learning had implications for both the quality of learning and the learners' levels of motivation.
    1. what do we do with that information?

      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.

    2. E-texts could record how much time is spent in textbook study. All such data could be accessed by the LMS or various other applications for use in analytics for faculty and students.”
    3. Alexandra “Sasha” Milgram, is played by Winona Ryder, and she serves as the on-screen stand-in for the film audience
    4. not as a way to monitor and regulate
    5. Internet of Things as a space

      Only partially built up.

    1. Large lecture classes may go through the content too quickly for the typical student to understand. That's why so many schools follow the practice of breaking the class cohort into smaller sections led by teaching assistants.
    1. which applicants are most likely to matriculate
    2. Data collection on students should be considered a joint venture, with all parties — students, parents, instructors, administrators — on the same page about how the information is being used.
    3. "We know the day before the course starts which students are highly unlikely to succeed,"

      Easier to do with a strict model for success.

    1. focus on teaching, not learning

      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.

    2. real world, authentic purpose

      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.

    3. real learning that sticks with us over time
    1. Set project work with explicit networking goals and a phil project as part of it. Mandate that students find off campus resources which they curate and present to class (either online, on a collab blog, or in class), reward students with facetime on their blog – good posts and comments get lecturer feedback,.

      Great ideas here.

  14. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. half-spaces sepa-rated by a hyperplane19.

      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

    2. Deep learning

      四大金刚中的三个