Patrick Collison and Tyler Cowen in The Atlantic: “In 1861, the American scientist and educator William Barton Rogers published a manifesto calling for a new kind of research institution. Recognizing the “daily increasing proofs of the happy influence of scientific culture on the industry and the civilization of the nations,” and the growing importance of what he called “Industrial Arts,” he proposed a new organization dedicated to practical knowledge. He named it the Massachusetts Institute of Technology.
Rogers was one of a number of late-19th-century reformers who saw that the United States’ ability to generate progress could be substantially improved. These reformers looked to the successes of the German university models overseas and realized that a combination of focused professorial research and teaching could be a powerful engine for advance in research. Over the course of several decades, the group—Rogers, Charles Eliot, Henry Tappan, George Hale, John D. Rockefeller, and others—founded and restructured many of what are now America’s best universities, including Harvard, MIT, Stanford, Caltech, Johns Hopkins, the University of Chicago, and more. By acting on their understanding, they engaged in a kind of conscious “progress engineering.”
Progress itself is understudied. By “progress,” we mean the combination of economic, technological, scientific, cultural, and organizational advancement that has transformed our lives and raised standards of living over the past couple of centuries. For a number of reasons, there is no broad-based intellectual movement focused on understanding the dynamics of progress, or targeting the deeper goal of speeding it up. We believe that it deserves a dedicated field of study. We suggest inaugurating the discipline of “Progress Studies.”…(More)”
Paper by Philip Oreopoulos and Uros Petronijevic: “We present results from a five-year effort to design promising online and text-message interventions to improve college achievement through several distinct channels. From a sample of nearly 25,000 students across three different campuses, we find some improvement from coaching-based interventions on mental health and study time, but none of the interventions we evaluate significantly influences academic outcomes (even for those students more at risk of dropping out). We interpret the results with our survey data and a model of student effort. Students study about five to eight hours fewer each week than they plan to, though our interventions do not alter this tendency. The coaching interventions make some students realize that more effort is needed to attain good grades but, rather than working harder, they settle by adjusting grade expectations downwards. Our study time impacts are not large enough for translating into significant academic benefits. More comprehensive but expensive programs appear more promising for helping college students outside the classroom….(More)”
Paper by Victoria L. Lemieux: “This paper discusses blockchain technology as a public record keeping system, linking record keeping to power of authority, veneration (temples), and control (prisons) that configure and reconfigure social, economic, and political relations. It discusses blockchain technology as being constructed as a mechanism to counter institutions and social actors that currently hold power, but whom are nowadays often viewed with mistrust. It explores claims for blockchain as a record keeping force of resistance to those powers using an archival theoretic analytic lens. The paper evaluates claims that blockchain technology can support the creation and preservation of trustworthy records able to serve as alternative sources of evidence of rights, entitlements and actions with the potential to unseat the institutional power of the nation-state….(More)”.
Report by Amy O’Hara: “Data sharing across government agencies allows consumers, policymakers, practitioners, and researchers to answer pressing questions. Creating a data infrastructure to enable this data sharing for higher education data is challenging, however, due to legal, privacy, technical, and perception issues. To overcome these challenges, postsecondary education can learn from other domains to permit secure, responsible data access and use. Working models from both the public sector and academia show how sensitive data from multiple sources can be linked and accessed for authorized uses.
This brief describes best practices in use today and the emerging technology that could further protect future data systems and creates a new framework, the “Five Safes”, for controlling data access and use. To support decisions facing students, administrators, evaluators, and policymakers, a postsecondary infrastructure must support cycles of data discovery, request, access, analysis, review, and release. It must be cost-effective, secure, and efficient and, ideally, it will be highly automated, transparent, and adaptable. Other industries have successfully developed such infrastructures, and postsecondary education can learn from their experiences.
A functional data infrastructure relies on trust and control between the data providers, intermediaries, and users. The system should support equitable access for approved users and offer the ability to conduct independent analyses with scientific integrity for reasonable financial costs. Policymakers and developers should ensure the creation of expedient, convenient data access modes that allow for policy analyses. …
The “Five Safes” framework describes an approach for controlling data access and use. The five safes are: safe projects, safe people, safe settings, safe data, and afe outputs….(More)”.
About: “Whether we work within schools or as part of the broader ecosystem of parent-teacher associations, and philanthropic, nonprofit, and volunteer organizations, we need data to guide decisions about investing our time and resources.
This data is typically expensive to gather, often unvalidated (e.g. self-reported), and commonly available only to those who collect or report it. It can even be hard to ask for data when it’s not clear what’s available. At the same time, information – in the form of discrete research, report-card style PDFs, or static websites – is everywhere. The result is that many already resource-thin organizations that could be collaborating around strategies to help kids advance, spend a lot of time in isolation collecting and searching for data.
In the past decade, we’ve seen solid progress in addressing part of the problem: the emergence of connected longitudinal data systems (LDS). These warehouses and linked databases contain data that can help us understand how students progress over time. No personally identifiable information (or PII) is shared, yet the data can reveal where interventions are most needed. Because these systems are typically designed for researchers and policy professionals, they are rarely accessible to the educators, parents, and partners – arts, sports, academic enrichment (e.g. STEM), mentoring, and family support programs – that play such important roles in helping young people learn and succeed…
“We need open tools for the ecosystem – parents, volunteers, non-profit organizations and the foundations and agencies that support them. These partners can realize significant benefit from the same kind of data policy makers and education leaders hold in their LDS.
That’s why we’re launching the Education Data Collaborative. Working together, we can build tools that help us use data to improve the design, efficacy, and impact of programs and interventions and find new way to work with public education systems to achieve great things for kids. …Data collaboratives, data trusts, and other kinds of multi-sector data partnerships are among the most important civic innovations to emerge in the past decade….(More)”
Colleen Flaherty at Inside Higher Ed: “What a difference preparation makes when it comes to doing research in Arctic-level air-conditioned academic libraries (or ones that are otherwise freezing — or not air-conditioned at all). Luckily, Megan L. Cook, assistant professor of English at Colby College, published a crowdsourced document called“How Cold Is that Library?” ….
Cook, who was not immediately available for comment, has said the document was group effort. Juliet Sperling, a faculty fellow in American art at Colby, credited her colleague’s “brilliance” but said the document was “generally inspired by conversations we’ve had as co-fellows” in the Andrew W. Mellon Society of Fellows in Critical Bibliography. The society brings together 60-some scholars of rare books and material texts from a variety of disciplinary or institutional approaches, she said, “so collectively, we’ve all spent quite a bit of time in libraries of various climates all over the world.” In addition to library temperatures, lighting and even humidity levels, the scholars trade research destinations’ photo policies and nearby eateries and drinking holes, among other tips. A spreadsheet opens up that resource to others, Sperling said. …(More)”.
IBM Blockchain Blog: “Blockchain technology can be a game-changer for accounting, supply chain, banking, contract law, and many other fields. But it will only be useful if lots and lots of non-technical managers and leaders trust and adopt it. And right now, just understanding what blockchain is, can be difficult to understand even for the brightest in these fields. Enter The Blockchain Game, a hands-on exercise that explains blockchain’s core principals, and serves as a launching pad for discussion of blockchain’s real-world applications.
In The Blockchain Game students act as nodes and miners on a blockchain network for storing student grades at a university. Participants record the grade and course information, and then “build the block” by calculating a unique identifier (a hash) to secure the grade ledger, and miners get rewarded for their work. As the game is played, the audience learns about hashes, private keys, and what uses are appropriate for a blockchain ledger.
Basics of the Game
A hands-on simulation centering around a blockchain for academic scores, including a discussion at the end of the simulation regarding if storing grades would be a good application for blockchain.
No computers. Participants are the computors and calculate blocks.
The game seeks to teach core concepts about a distributed ledger but can be modified to whichever use case the educator wishes to use — smart contracts, supply chain, applications and others.
Additional elements can be added if instructors want to facilitate the game on a computer….(More)”.
Blog by Jennifer Latson for Arnold Ventures: “When you buy a car, you want to know it will get you where you’re going. Before you invest in a certain model, you check its record. How does it do in crash tests? Does it have a history of breaking down? Are other owners glad they bought it?
Students choosing between college programs can’t do the same kind of homework. Much of the detailed data we demand when we buy a car isn’t available for postsecondary education — data such as how many students find jobs in the fields they studied, what they earn, how much debt they accumulate, and how quickly they repay it — yet choosing a college is a much more important financial decision.
The most promising solution to filling in the gaps, according to data advocates, is the College Transparency Act, which would create a secure, comprehensive national data network with information on college costs, graduation rates, and student career paths — and make this data publicly available. The bill, which will be discussed in Congress this year, has broad support from both Republicans and Democrats in the House and the Senate in part because it includes precautions to protect privacy and secure student data….
The data needed to answer questions about student success already exists but is scattered among various agencies and institutions: the Department of Educationfor data on student loan repayment; the Treasury Department for earnings information; and schools themselves for graduation rates.
“We can’t connect the dots to find out how these programs are serving certain students, and that’s because the Department of Education isn’t allowed to connect all the information these places have already collected,” says Amy Laitinen, director for higher education at New America, a think tank collaborating with IHEP to promote educational transparency. And until recently, publicly available federal postsecondary data only included full-time students who’d never enrolled in a college program before, ignoring the more than half of the higher ed population made up of students who attend school part time or who transfer from one institution to another….(More)”.
CCCBLab: “In recent years we have been witnessing a constant trickle of news on artificial intelligence, machine learning and computer vision. We are told that machines learn, see, create… and all this builds up a discourse based on novelty, on a possible future and on a series of worries and hopes. It is difficult, sometimes, to figure out in this landscape which are real developments, and which are fantasies or warnings. And, undoubtedly, this fog that surrounds it forms part of the power that we grant, both in the present and on credit, to these tools, and of the negative and positive concerns that they arouse in us. Many of these discourses may fall into the field of false debates or, at least, of the return of old debates. Thus, in the classical artistic field, associated with the discourse on creation and authorship, there is discussion regarding the entity to be awarded to the images created with these tools. (Yet wasn’t the argument against photography in art that it was an image created automatically and without human participation? And wasn’t that also an argument in favour of taking it and using it to put an end to a certain idea of art?)
Metaphors are essential in the discourse on all digital tools and the power that they have. Are expressions such as “intelligence”, “vision”, “learning”, “neural” and the entire range of similar words the most adequate for defining these types of tools? Probably not, above all if their metaphorical nature is sidestepped. We would not understand them in the same way if we called them tools of probabilistic classification or if instead of saying that an artificial intelligence “has painted” a Rembrandt, we said that it has produced a statistical reproduction of his style (something which is still surprising, and to be celebrated, of course). These names construct an entity for these tools that endows them with a supposed autonomy and independence upon which their future authority is based.
Because that is what it’s about in many discourses: constructing a characterisation that legitimises an objective or non-human capacity in data analysis….
We now find ourselves in what is, probably, the point of the first cultural reception of these tools. Of their development in fields of research and applications that have already been derived, we are moving on to their presence in the public discourse. It is in this situation and context, where we do not fully know the breadth and characteristics of these technologies (meaning fears are more abstract and diffuse and, thus, more present and powerful), when it is especially important to understand what we are talking about, to appropriate the tools and to intervene in the discourses. Before their possibilities are restricted and solidified until they seem indisputable, it is necessary to experiment with them and reflect on them; taking advantage of the fact that we can still easily perceive them as in creation, malleable and open.
In our projects The Bad Pupil. Critical pedagogy for artificial intelligences and Latent Spaces. Machinic Imaginations we have tried to approach to these tools and their imaginary. In the statement of intentions of the former, we expressed our desire, in the face of the regulatory context and the metaphor of machine learning, to defend the bad pupil as one who escapes the norm. And also how, faced with an artificial intelligence that seeks to replicate the human on inhuman scales, it is necessary to defend and construct a non-mimetic one that produces unexpected relations and images.
Fragment of De zeven werken van barmhartigheid, Meester van Alkmaar, 1504 (Rijksmuseum, Amsterdam) analysed with YOLO9000 | The Bad Pupil – Estampa
Both projects are also attempts to appropriate these tools, which means, first of all, escaping industrial barriers and their standards. In this field in which mass data are an asset within reach of big companies, employing quantitively poor datasets and non-industrial calculation potentials is not just a need but a demand….(More)”.
About: “The Public Interest Technology Universities Network is a partnership that fosters collaboration between 21 universities and colleges committed to building the nascent field of public interest technology and growing a new generation of civic-minded technologists. Through the development of curricula, research agendas, and experiential learning programs in the public interest technology space, these universities are trying innovative tactics to produce graduates with multiple fluencies at the intersection of technology and policy. By joining PIT-UN, members commit to field building on campus. Members may choose to focus on some or all of these elements, in addition to other initiatives they deem relevant to establishing public interest technology on campus:
Support curriculum and faculty development to enable interdisciplinary and cross-disciplinary education of students, so they can critically assess the ethical, political, and societal implications of new technologies, and design technologies in service of the public good.
Develop experiential learning opportunities such as clinics, fellowships, apprenticeships, and internship, with public and private sector partners in the public interest technology space.
Find ways to support graduates who pursue careers working in public interest technology, recognizing that financial considerations may make careers in this area unaffordable to many.
Create mechanisms for faculty to receive recognition for the research, curriculum development, teaching, and service work needed to build public interest technology as an arena of inquiry.
Provide institutional data that will allow us to measure the effectiveness of our interventions in helping to develop the field of public interest technology….(More)”.
