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Ed Webb

High-Tech Cheating on Homework Abounds, and Professors Are Partly to Blame - Technology - The Chronicle of Higher Education - 0 views

  • "I call it 'technological detachment phenomenon,'" he told me recently. "As long as there's some technology between me and the action, then I'm not culpable for the action." By that logic, if someone else posted homework solutions online, what's wrong with downloading them?
  • "The feeling about homework is that it's really just busywork,"
  • professors didn't put much effort into teaching, so students don't put real effort into learning
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  • "The current system places too great a burden on individual faculty who would, under the circumstances, appear to have perverse incentives: Pursuing these matters lowers course evaluations, takes their severely limited time away from research for promotion, and unfortunately personalizes the issue when it is not personal at all, but a violation against the university."
  • In the humanities, professors have found technological tools to check for blatant copying on essays, and have caught so many culprits that the practice of running papers through plagiarism-detection services has become routine at many colleges. But that software is not suited to science-class assignments.
  • a "studio" model of teaching
  • The parents paid tuition in cash
  • The idea that students should be working in a shell is so interesting. It never even occurred to me as a student that I shouldn't work with someone else on my homework. How else do you figure it out? I guess that is peer-to-peer teaching. Copying someone else's work and presenting it as your own is clearly wrong (and, as demonstrated above, doesn't do the student any good), but learning from the resources at hand ought to be encouraged. Afterall, struggling through homework problems in intro physics is how you learn in the first place.
Ed Webb

Edupunks Unite? « eLearning Blog // Don't Waste Your Time … - 0 views

  • the universal trend is that the managed and forced structure of the VLE or LMS is being recognised by the facilitators as too restrictive, the educators are too slow to realise it, and the accountants are too deaf to listen to us before they invest thousands of pounds (if not millions) and hundreds of hours in developing in favour of one solution that is an immovable lump hanging around the Institution's neck.
Ed Webb

Views: Vertigo Years - Inside Higher Ed - 0 views

  • Capturing the spirit of the times, one prominent advocate styled the three-year degree as the "higher ed equivalent of a fuel-efficient car" compared to the "gas guzzling four-year course." A metaphor from the food industry might be more apt. Slow education, as in slow cooking, is enthusiastically replaced by Fast Ed or McEd, with comparable results. Higher education is certainly in need of efficiency. Our current business model, which has yielded steadily increasing costs, needs change and, perhaps, radically so. Let us not be fooled by adapting across the system solutions that appear corrective but may be destructive of the virtue and distinction of American higher education and its ambition -- education for the workforce and for participation and leadership in a democracy.
  •  
    President Durden and Dean Weissman resist the 3-year Bachelor's degree
Ed Webb

Social Media is Killing the LMS Star - A Bootleg of Bryan Alexander's Lost Presentation - Open Education Conference - 0 views

  • Note that this isn’t just a technological alternate history. It also describes a different set of social and cultural practices.
  • CMSes lumber along like radio, still playing into the air as they continue to gradually shift ever farther away on the margins. In comparison, Web 2.0 is like movies and tv combined, plus printed books and magazines. That’s where the sheer scale, creative ferment, and wife-ranging influence reside. This is the necessary background for discussing how to integrate learning and the digital world.
  • These virtual classes are like musical practice rooms, small chambers where one may try out the instrument in silent isolation. It is not connectivism but disconnectivism.
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  • CMSes shift from being merely retrograde to being actively regressive if we consider the broader, subtler changes in the digital teaching landscape. Web 2.0 has rapidly grown an enormous amount of content through what Yochai Benkler calls “peer-based commons production.” One effect of this has been to grow a large area for informal learning, which students (and staff) access without our benign interference. Students (and staff) also contribute to this peering world; more on this later. For now, we can observe that as teachers we grapple with this mechanism of change through many means, but the CMS in its silo’d isolation is not a useful tool.
  • those curious about teaching with social media have easy access to a growing, accessible community of experienced staff by means of those very media. A meta-community of Web 2.0 academic practitioners is now too vast to catalogue. Academics in every discipline blog about their work. Wikis record their efforts and thoughts, as do podcasts. The reverse is true of the CMS, the very architecture of which forbids such peer-to-peer information sharing. For example, the Resource Center for Cyberculture Studies (RCCS) has for many years maintained a descriptive listing of courses about digital culture across the disciplines. During the 1990s that number grew with each semester. But after the explosive growth of CMSes that number dwindled. Not the number of classes taught, but the number of classes which could even be described. According to the RCCS’ founder, David Silver (University of San Francisco), this is due to the isolation of class content in CMS containers.
  • unless we consider the CMS environment to be a sort of corporate intranet simulation, the CMS set of community skills is unusual, rarely applicable to post-graduation examples. In other words, while a CMS might help privacy concerns, it is at best a partial, not sufficient solution, and can even be inappropriate for already online students.
  • That experiential, teachable moment of selecting one’s copyright stance is eliminated by the CMS.
  • Another argument in favor of CMSes over Web 2.0 concerns the latter’s open nature. It is too open, goes the thought, constituting a “Wild West” experience of unfettered information flow and unpleasant forms of access. Campuses should run CMSes to create shielded environments, iPhone-style walled gardens that protect the learning process from the Lovecraftian chaos without.
  • social sifting, information literacy, using the wisdom of crowds, and others. Such strategies are widely discussed, easily accessed, and continually revised and honed.
  • at present, radio CMS is the Clear Channel of online learning.
  • For now, the CMS landsape is a multi-institutional dark Web, an invisible, unsearchable, un-mash-up-able archipelago of hidden learning content.
  • Can the practice of using a CMS prepare either teacher or student to think critically about this new shape for information literacy? Moreover, can we use the traditional CMS to share thoughts and practices about this topic?
  • The internet of things refers to a vastly more challenging concept, the association of digital information with the physical world. It covers such diverse instances as RFID chips attached to books or shipping pallets, connecting a product’s scanned UPC code to a Web-based database, assigning unique digital identifiers to physical locations, and the broader enterprise of augmented reality. It includes problems as varied as building search that covers both the World Wide Web and one’s mobile device, revising copyright to include digital content associated with private locations, and trying to salvage what’s left of privacy. How does this connect with our topic? Consider a recent article by Tim O’Reilly and John Battle, where they argue that the internet of things is actually growing knowledge about itself. The combination of people, networks, and objects is building descriptions about objects, largely in folksonomic form. That is, people are tagging the world, and sharing those tags. It’s worth quoting a passage in full: “It’s also possible to give structure to what appears to be unstructured data by teaching an application how to recognize the connection between the two. For example, You R Here, an iPhone app, neatly combines these two approaches. You use your iPhone camera to take a photo of a map that contains details not found on generic mapping applications such as Google maps – say a trailhead map in a park, or another hiking map. Use the phone’s GPS to set your current location on the map. Walk a distance away, and set a second point. Now your iPhone can track your position on that custom map image as easily as it can on Google maps.” (http://www.web2summit.com/web2009/public/schedule/detail/10194) What world is better placed to connect academia productively with such projects, the open social Web or the CMS?
  • imagine the CMS function of every class much like class email, a necessary feature, but not by any means the broadest technological element. Similarly the e-reserves function is of immense practical value. There may be no better way to share copyrighted academic materials with a class, at this point. These logistical functions could well play on.
Ed Webb

The powerful and mysterious brain circuitry that makes us love Google, Twitter, and texting. - By Emily Yoffe - Slate Magazine - 0 views

  • For humans, this desire to search is not just about fulfilling our physical needs. Panksepp says that humans can get just as excited about abstract rewards as tangible ones. He says that when we get thrilled about the world of ideas, about making intellectual connections, about divining meaning, it is the seeking circuits that are firing.
  • Our internal sense of time is believed to be controlled by the dopamine system. People with hyperactivity disorder have a shortage of dopamine in their brains, which a recent study suggests may be at the root of the problem. For them even small stretches of time seem to drag.
  • When we get the object of our desire (be it a Twinkie or a sexual partner), we engage in consummatory acts that Panksepp says reduce arousal in the brain and temporarily, at least, inhibit our urge to seek.
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  • But our brains are designed to more easily be stimulated than satisfied. "The brain seems to be more stingy with mechanisms for pleasure than for desire," Berridge has said. This makes evolutionary sense. Creatures that lack motivation, that find it easy to slip into oblivious rapture, are likely to lead short (if happy) lives. So nature imbued us with an unquenchable drive to discover, to explore. Stanford University neuroscientist Brian Knutson has been putting people in MRI scanners and looking inside their brains as they play an investing game. He has consistently found that the pictures inside our skulls show that the possibility of a payoff is much more stimulating than actually getting one.
  • all our electronic communication devices—e-mail, Facebook feeds, texts, Twitter—are feeding the same drive as our searches. Since we're restless, easily bored creatures, our gadgets give us in abundance qualities the seeking/wanting system finds particularly exciting. Novelty is one. Panksepp says the dopamine system is activated by finding something unexpected or by the anticipation of something new. If the rewards come unpredictably—as e-mail, texts, updates do—we get even more carried away. No wonder we call it a "CrackBerry."
  • If humans are seeking machines, we've now created the perfect machines to allow us to seek endlessly. This perhaps should make us cautious. In Animals in Translation, Temple Grandin writes of driving two indoor cats crazy by flicking a laser pointer around the room. They wouldn't stop stalking and pouncing on this ungraspable dot of light—their dopamine system pumping. She writes that no wild cat would indulge in such useless behavior: "A cat wants to catch the mouse, not chase it in circles forever." She says "mindless chasing" makes an animal less likely to meet its real needs "because it short-circuits intelligent stalking behavior." As we chase after flickering bits of information, it's a salutary warning.
Ed Webb

Putting Syllabi Online « Easily Distracted - 0 views

  •  
    An excellent set of questions and answers. We should encourage people to do this, no?
Ed Webb

Guest Post: The Complexities of Certainty | Just Visiting - 0 views

  • Privileges abound in academia, but so do experiences of loss, instability and fear. And into this situation we were called to respond to a pandemic.
  • It is tempting to reach for certainties when everything around us is in chaos, and for a vast swath of higher ed instructors, the rapid shift from face-to-face teaching to emergency distance learning has been chaos. Small wonder, then, that people have offered -- and clung to -- advice that seeks to bring order to disorder. Many people have advised instructors to prioritize professionalism, ditching the sweatpants and putting away the visible clutter in our homes before making a Zoom call, upholding concepts like "rigor" so that our standards do not slip. To some, these appeals to universal principles are right-minded and heartening, a bulwark against confusion and disarray. But to others they have felt oppressive, even dangerously out of touch with the world in which we and our students live.
  • certainties can be dangerous; their very power is based upon reifying well-worn inequities dressed up as tradition
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  • there is no objective standard of success that we reach when we insist on rigor, which is too often deployed in defense of practices that are ableist and unkind
  • We are not just teachers, or scholars, or professionals. We are individuals thrown back in varying degrees on our own resources, worried about ourselves and our families and friends as we navigate the effects of COVID-19. Many of us are deeply anxious and afraid. Our pre-existing frailties have been magnified; we feel vulnerable, distracted and at sea. Our loved ones are sick, even dying. This is trauma. Few of us have faced such world-changing circumstances before, and as our minds absorb the impact of that reality, our brains cannot perform as capably as they usually would.
  • The most professional people I know right now are those who show up, day after day, to teach under extraordinary circumstances. Perhaps they do it with their laundry waiting to be folded, while their children interrupt, thinking constantly of their loved ones, weathering loneliness, wearing sweatpants and potentially in need of a haircut. But I know they do it while acknowledging this is not the world in which we taught two months before, and that every student is facing disruption, uncertainty and distraction. They do it creatively, making room for the unexpected, challenging their students, with the world a participant in the conversation.
Ed Webb

How much 'work' should my online course be for me and my students? - Dave's Educational Blog - 0 views

  • My recommendation for people planning their courses, is to stop thinking about ‘contact hours’. A contact hour is a constraint that is applied to the learning process because of the organizational need to have people share a space in a building. Also called a credit hour, (particularly for American universities) this has meant, from a workload perspective, that for every in class hour a student is meant to do at least 2 (in some cases 3) hours of study outside of class. Even Cliff Notes agrees with me. So… for a full load, that 30 to 45 Total Work Hours for students per course that you are designing.
  • Simple break down (not quite 90, yes i know) Watch 3 hours of video* – 5 hoursRead stuff – 20 hoursListen to me talk – 15 hoursTalk with other students in a group – 15 hoursWrite reflections about group chat – 7.5 hoursRespond to other people’s reflections – 7.5 hoursWork on a term paper – 10 hoursDo weekly quiz – 3 hoursWrite take home mid-term – 3 hoursWrite take home final – 3 hours
  • A thousand variations of this might be imagined
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  • a possible structure recommended by one of the faculty we were talking to was – read/watch, quiz, lecture, student group discussion, reflection. The reasoning here is that if you give learners (particularly new learners) a reading without some form of accountability (a quiz) they are much less likely to do it. I know that for me, when I’ve done the readings, I’m far more likely to attend class. Putting the student group discussion after the lecture gives students who can’t attend a synchronous session a chance to review the recording
  • The standardization police have been telling us for years that each student must learn the same things. Poppycock. Scaffolding doesn’t mean taking away student choice. There are numerous approaches to allowing a little or a lot of choice into your classes (learner contracts come to mind). Just remember, most students don’t want choice – at first. 12-16 years of training has told them that you the faculty member have something you want them to do and they need to find the trick of it. It will take a while until those students actually believe you want their actual opinion.
  • You can have a goal like – get them acculturated to the field – and work through your activities to get there. It’s harder, they will need your patience, but once they get their minds around it, it makes things much more interesting.
Ed Webb

CRITICAL AI: Adapting College Writing for the Age of Large Language Models such as ChatGPT: Some Next Steps for Educators - Critical AI - 1 views

  • In the long run, we believe, teachers need to help students develop a critical awareness of generative machine models: how they work; why their content is often biased, false, or simplistic; and what their social, intellectual, and environmental implications might be. But that kind of preparation takes time, not least because journalism on this topic is often clickbait-driven, and “AI” discourse tends to be jargony, hype-laden, and conflated with science fiction.
  • Make explicit that the goal of writing is neither a product nor a grade but, rather, a process that empowers critical thinking
  • Students are more likely to misuse text generators if they trust them too much. The term “Artificial Intelligence” (“AI”) has become a marketing tool for hyping products. For all their impressiveness, these systems are not intelligent in the conventional sense of that term. They are elaborate statistical models that rely on mass troves of data—which has often been scraped indiscriminately from the web and used without knowledge or consent.
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  • LLMs usually cannot do a good job of explaining how a particular passage from a longer text illuminates the whole of that longer text. Moreover, ChatGPT’s outputs on comparison and contrast are often superficial. Typically the system breaks down a task of logical comparison into bite-size pieces, conveys shallow information about each of those pieces, and then formulaically “compares” and “contrasts” in a noticeably superficial or repetitive way. 
  • In-class writing, whether digital or handwritten, may have downsides for students with anxiety and disabilities
  • ChatGPT can produce outputs that take the form of  “brainstorms,” outlines, and drafts. It can also provide commentary in the style of peer review or self-analysis. Nonetheless, students would need to coordinate multiple submissions of automated work in order to complete this type of assignment with a text generator.  
  • No one should present auto-generated writing as their own on the expectation that this deception is undiscoverable. 
  • LLMs often mimic the harmful prejudices, misconceptions, and biases found in data scraped from the internet
  • Show students examples of inaccuracy, bias, logical, and stylistic problems in automated outputs. We can build students’ cognitive abilities by modeling and encouraging this kind of critique. Given that social media and the internet are full of bogus accounts using synthetic text, alerting students to the intrinsic problems of such writing could be beneficial. (See the “ChatGPT/LLM Errors Tracker,” maintained by Gary Marcus and Ernest Davis.)
  • Since ChatGPT is good at grammar and syntax but suffers from formulaic, derivative, or inaccurate content, it seems like a poor foundation for building students’ skills and may circumvent their independent thinking.
  • Good journalism on language models is surprisingly hard to find since the technology is so new and the hype is ubiquitous. Here are a few reliable short pieces.     “ChatGPT Advice Academics Can Use Now” edited by Susan Dagostino, Inside Higher Ed, January 12, 2023  “University students recruit AI to write essays for them. Now what?” by Katyanna Quach, The Register, December 27, 2022  “How to spot AI-generated text” by Melissa Heikkilä, MIT Technology Review, December 19, 2022  The Road to AI We Can Trust, Substack by Gary Marcus, a cognitive scientist and AI researcher who writes frequently and lucidly about the topic. See also Gary Marcus and Ernest Davis, “GPT-3, Bloviator: OpenAI’s Language Generator Has No Idea What It’s Talking About” (2020).
  • “On the Dangers of Stochastic Parrots” by Emily M. Bender, Timnit Gebru, et al, FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 2021. Association for Computing Machinery, doi: 10.1145/3442188. A blog post summarizing and discussing the above essay derived from a Critical AI @ Rutgers workshop on the essay: summarizes key arguments, reprises discussion, and includes links to video-recorded presentations by digital humanist Katherine Bode (ANU) and computer scientist and NLP researcher Matthew Stone (Rutgers).
Ed Webb

ChatGPT Is Nothing Like a Human, Says Linguist Emily Bender - 0 views

  • Please do not conflate word form and meaning. Mind your own credulity.
  • We’ve learned to make “machines that can mindlessly generate text,” Bender told me when we met this winter. “But we haven’t learned how to stop imagining the mind behind it.”
  • A handful of companies control what PricewaterhouseCoopers called a “$15.7 trillion game changer of an industry.” Those companies employ or finance the work of a huge chunk of the academics who understand how to make LLMs. This leaves few people with the expertise and authority to say, “Wait, why are these companies blurring the distinction between what is human and what’s a language model? Is this what we want?”
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  • “We call on the field to recognize that applications that aim to believably mimic humans bring risk of extreme harms,” she co-wrote in 2021. “Work on synthetic human behavior is a bright line in ethical Al development, where downstream effects need to be understood and modeled in order to block foreseeable harm to society and different social groups.”
  • chatbots that we easily confuse with humans are not just cute or unnerving. They sit on a bright line. Obscuring that line and blurring — bullshitting — what’s human and what’s not has the power to unravel society
  • She began learning from, then amplifying, Black women’s voices critiquing AI, including those of Joy Buolamwini (she founded the Algorithmic Justice League while at MIT) and Meredith Broussard (the author of Artificial Unintelligence: How Computers Misunderstand the World). She also started publicly challenging the term artificial intelligence, a sure way, as a middle-aged woman in a male field, to get yourself branded as a scold. The idea of intelligence has a white-supremacist history. And besides, “intelligent” according to what definition? The three-stratum definition? Howard Gardner’s theory of multiple intelligences? The Stanford-Binet Intelligence Scale? Bender remains particularly fond of an alternative name for AI proposed by a former member of the Italian Parliament: “Systematic Approaches to Learning Algorithms and Machine Inferences.” Then people would be out here asking, “Is this SALAMI intelligent? Can this SALAMI write a novel? Does this SALAMI deserve human rights?”
  • Tech-makers assuming their reality accurately represents the world create many different kinds of problems. The training data for ChatGPT is believed to include most or all of Wikipedia, pages linked from Reddit, a billion words grabbed off the internet. (It can’t include, say, e-book copies of everything in the Stanford library, as books are protected by copyright law.) The humans who wrote all those words online overrepresent white people. They overrepresent men. They overrepresent wealth. What’s more, we all know what’s out there on the internet: vast swamps of racism, sexism, homophobia, Islamophobia, neo-Nazism.
  • One fired Google employee told me succeeding in tech depends on “keeping your mouth shut to everything that’s disturbing.” Otherwise, you’re a problem. “Almost every senior woman in computer science has that rep. Now when I hear, ‘Oh, she’s a problem,’ I’m like, Oh, so you’re saying she’s a senior woman?”
  • “We haven’t learned to stop imagining the mind behind it.”
  • In March 2021, Bender published “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” with three co-authors. After the paper came out, two of the co-authors, both women, lost their jobs as co-leads of Google’s Ethical AI team.
  • “On the Dangers of Stochastic Parrots” is not a write-up of original research. It’s a synthesis of LLM critiques that Bender and others have made: of the biases encoded in the models; the near impossibility of studying what’s in the training data, given the fact they can contain billions of words; the costs to the climate; the problems with building technology that freezes language in time and thus locks in the problems of the past. Google initially approved the paper, a requirement for publications by staff. Then it rescinded approval and told the Google co-authors to take their names off it. Several did, but Google AI ethicist Timnit Gebru refused. Her colleague (and Bender’s former student) Margaret Mitchell changed her name on the paper to Shmargaret Shmitchell, a move intended, she said, to “index an event and a group of authors who got erased.” Gebru lost her job in December 2020, Mitchell in February 2021. Both women believe this was retaliation and brought their stories to the press. The stochastic-parrot paper went viral, at least by academic standards. The phrase stochastic parrot entered the tech lexicon.
  • Tech execs loved it. Programmers related to it. OpenAI CEO Sam Altman was in many ways the perfect audience: a self-identified hyperrationalist so acculturated to the tech bubble that he seemed to have lost perspective on the world beyond. “I think the nuclear mutually assured destruction rollout was bad for a bunch of reasons,” he said on AngelList Confidential in November. He’s also a believer in the so-called singularity, the tech fantasy that, at some point soon, the distinction between human and machine will collapse. “We are a few years in,” Altman wrote of the cyborg merge in 2017. “It’s probably going to happen sooner than most people think. Hardware is improving at an exponential rate … and the number of smart people working on AI is increasing exponentially as well. Double exponential functions get away from you fast.” On December 4, four days after ChatGPT was released, Altman tweeted, “i am a stochastic parrot, and so r u.”
  • “This is one of the moves that turn up ridiculously frequently. People saying, ‘Well, people are just stochastic parrots,’” she said. “People want to believe so badly that these language models are actually intelligent that they’re willing to take themselves as a point of reference and devalue that to match what the language model can do.”
  • The membrane between academia and industry is permeable almost everywhere; the membrane is practically nonexistent at Stanford, a school so entangled with tech that it can be hard to tell where the university ends and the businesses begin.
  • “No wonder that men who live day in and day out with machines to which they believe themselves to have become slaves begin to believe that men are machines.”
  • what’s tenure for, after all?
  • LLMs are tools made by specific people — people who stand to accumulate huge amounts of money and power, people enamored with the idea of the singularity. The project threatens to blow up what is human in a species sense. But it’s not about humility. It’s not about all of us. It’s not about becoming a humble creation among the world’s others. It’s about some of us — let’s be honest — becoming a superspecies. This is the darkness that awaits when we lose a firm boundary around the idea that humans, all of us, are equally worthy as is.
  • The AI dream is “governed by the perfectibility thesis, and that’s where we see a fascist form of the human.”
  • “Why are you trying to trick people into thinking that it really feels sad that you lost your phone?”
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