August 2016 - nexa - server-nexa.polito.it

United Nations Global Pulse #bigdata

by J.C. DE MARTIN

http://www.unglobalpulse.org/about-new *UNITED NATIONS GLOBAL PULSE* Harnessing big data for development and humanitarian action Global Pulse is a flagship innovation initiative of the United Nations Secretary-General on big data. Its vision is a future in which big data is harnessed safely and responsibly as a public good. Its mission is to accelerate discovery, development and scaled adoption of big data innovation for sustainable development and humanitarian action. The initiative was established based on a recognition that digital data offers the opportunity to gain a better understanding of changes in human well-being, and to get real-time feedback on how well policy responses are working. To this end, Global Pulse is working to promote awareness of the opportunities Big Data presents for sustainable development and humanitarian action, forge public-private data sharing partnerships, generate high-impact analytical tools and approaches through its network of Pulse Labs, and drive broad adoption of useful innovations across the UN System. How We Work Global Pulse functions as a network of innovation labs where research on Big Data for Development is conceived and coordinated. Global Pulse partners with experts from UN agencies, governments, academia, and the private sector to research, develop, and mainstream approaches for applying real-time digital data to 21st century development challenges. Objectives include: * Achieve a critical mass of implemented innovations * Lower systemic barriers to adoption and scaling * Strengthen the big data innovation ecosystem These objectives are acheived through a two-track implementation strategy: _TRACK 1: INNOVATION DRIVER_ - Implement data innovation programmes through Pulse Labs to provide UN and development partners with access to the data, tools and expertise required to discover new uses of big data for development. - Develop toolkits, applications and platforms to improve data-driven decision-making and support evaluation of promising solutions.   _TRACK 2: ECOSYSTEM CATALYST  _ - Contribute to the development of regulatory frameworks and technical standards to address data sharing and privacy protection challenges. - Engage key stakeholders on a priority innovation agenda. - Provide public sector organisations with policy guidance and technical assistance to strengthen their capacity for integrating real-time insights into operations.

Aug. 10, 2016

NYT Room for Debate: "Think Tanks and the Influence of Corporate Dollars"

by J.C. DE MARTIN

http://www.nytimes.com/roomfordebate/2016/08/10/think-tanks-and-the-influen…

Aug. 10, 2016

"Mobilisation for digital rights"

by J.C. DE MARTIN

*Mobilisation for digital rights** * JOE MCNAMEE 13 June 2016 /Post-ACTA, decision-making has been adapted to avoid decision-moments. Of course, individual grassroots campaigns are still hugely valuable. But we need long-term advocacy.// / In a digital society, digital rights are simply our rights. They are our right to express ourselves freely, our right to a private life, our right to our possessions. But what if digital society fails to realise that our traditional rights and freedoms cannot always be defended by traditional thinking or traditional legal instruments? What if the entire framework for the regulation of the digital aspects of our societies is being built in a vacuum where politically expedient and populist policies are being put in place before society realises what is happening? Then our digital society will not have rights, because our rights nowadays have both an offline and an online dimension. Our society's hard-fought democratic rights will have been repealed without discussion. This is the challenge of digital rights advocacy in 2016. […] Continua qui: https://www.opendemocracy.net/joe-mcnamee/mobilisation-for-digital-rights

Aug. 10, 2016

"The DNC Breach and the Hijacking of Common Sense"

by J.C. DE MARTIN

*The DNC Breach and the Hijacking of Common Sense* /Jeffrey Carr// //author, 'inside Cyber Warfare' (O'Reilly Media 2009, 2011); CEO, Taia Global, Inc.; Founder, Suits and Spooks conference/ June 19, 2016 This article is about the DNC breach and its attribution to the Russian government. But first, imagine that the DNC breach wasn’t a network breach but a shooting (no one was injured). No one knows who the shooter was but he left behind his weapon, a Kalishnikov AKM. The unknown shooter used a Russian-made weapon. Does that mean that the shooter is Russian? Or that the shooter works for the company, Kalishnikov Concern? Or even more likely in the crazy world of cyber investigations, that the designer of the AKM is also the shooter? Police would certainly explore the possibility that the shooter may have been Russian but they wouldn’t exclude other suspects. And no investigator in his right mind would arrest the CEO of Remington Arms, Sig Sauer, Kalishnikov Concern or any other arms manufacturer because a gun they made was used in a crime. In the physical world of crime investigation, common sense dictates that the perpetrator of a crime may use any weapon and not just one made in the country of his birth, and that the developer or manufacturer of the weapon most likely isn’t the criminal. And yet, those seemingly crazy assumptions are made every day by cybersecurity companies involved in incident response and threat intelligence. The malware was written in Russian? It was a Russian who attacked you. Chinese characters in the code? You’ve been hacked by the Peoples Liberation Army. […] Continua qui: https://medium.com/@jeffreycarr/the-dnc-breach-and-the-hijacking-of-common-…

Aug. 10, 2016

LAT: "Want cheaper Internet access? Hand over your privacy "

by J.C. DE MARTIN

Los Angeles Times *Want cheaper Internet access? Hand over your privacy** * /David Lazarus// / It seems a simple enough proposition: Would you agree to receive marketing pitches in return for a discount on your high-speed Internet service? Telecom heavyweight Comcast made just such a case last week in a filing with the Federal Communications Commission. The company defended what it called “a bargained-for exchange of information for service,” which it said “is a perfectly acceptable and widely used model throughout the U.S. economy.” […] Continua qui: http://www.latimes.com/business/lazarus/la-fi-lazarus-privacy-discount-2016…

Aug. 10, 2016

Slashdot: "White House Releases Federal Source Code Policy To Help Government Agencies Go Open Source"

by Marco Ciurcina

https://yro.slashdot.org/story/16/08/08/2326222/white-house-releases-federa… White House Releases Federal Source Code Policy To Help Government Agencies Go Open Source The U.S. federal government just released a new Federal Source Code policy (PDF). For each of the next 3 years, at least 20 percent of custom-developed Federal source code is to be released as open-source software. Earlier this year, Tony Scott, Federal CIO of the U.S. government, wrote on the White House blog that the U.S. government "can save taxpayer dollars by avoiding duplicative custom software purchases and promote innovation and collaboration across Federal agencies." Today, they released the Federal Source Code policy. TechCrunch reports: "The main requirement is that any new custom source code developed 'by or for the Federal Government' has to be made available for sharing and re-use by all Federal agencies. For example, this means that the TSA can have access to custom made software that was commissioned by the FBI. Considering there is probably a great deal of overlap in applications needed by certain branches of the Federal Government, this rule alone should save the government (and taxpayers) a great deal of money. In fact, the policy states that 'ensuring Government-wide reuse rights for custom code that is developed using Federal funds has numerous benefits for American taxpayers.'"

Aug. 9, 2016

R: Paper: "Personalized Learning: The Conversations We’re Not Having"

by oreste.pollicino

Invio eseguito dallo smartphone BlackBerry 10. Da: J.C. DE MARTIN Inviato: lunedì 8 agosto 2016 18:52 A: Nexa Rispondi a: J.C. DE MARTIN Oggetto: [nexa] Paper: "Personalized Learning: The Conversations We’re Not Having" *Personalized Learning: The Conversations We’re Not Having** * Data & Society Working Paper 07.22.2016 MONICA BULGER *Abstract* The pursuit of personalized education at a mass scale drives a number of current education technology initiatives that fall under the broad umbrella of personalized learning. The personalized learning space has seen a huge infusion of capital in recent years, with notable support from the Bill & Melinda Gates Foundation and a range of other investors, philanthropists, and technologists. Federal support for personalized learning has come through Obama’s Race to the Top program and, looking ahead, will come through the recently passed Every Student Succeeds Act’s provision for competency-based assessments. The story of personalized learning is a complex one in which education technology developers are applying startup logics like risk taking and testing in the field and marketing tactics like personalized content delivery and recommender systems. Their products are designed to meet the needs of states and administrators, who seek to improve efficiency and performance through formative testing and tailored learning modules, but they are often narrated differently to the public. The promise of technologically enabled personalized learning – that through technology we can democratize access to information and instruction, scale excellence across the socioeconomic spectrum, and do so with efficiency and rigor – is a familiar one to educators. And yet there are tensions between the promises of personalized learning, the state-of-its-art, and the practical realities of its implementation. As a buzzword symbolizing the potential of data use in education, personalized learning enjoys a broad definitional reach and is aligned by proponents with educational best practices like “student-centered instruction.” At its best, personalized learning represents a pedagogy of adaptation to students’ unique combination of goals, interests, and competencies. But many so-called personalized learning platforms are more responsive than adaptive, as they provide materials appropriate to students’ proficiency levels without moving beyond a pre-determined decision tree. They do this by analyzing student data such as age, gender, grade level, and test performance against idealized models based on data sets of students with similar demographics, then offering content recommendations directly to the student or to the teacher for further intervention. The widespread adoption of personalized learning platforms may lead to unintended consequences. For example, the focus on performance may negatively affect students’ psychological wellbeing by promoting individualist notions of achievement and undermining the social dimensions of learning. With the wealth of data that such systems generate, it becomes possible to track and report students’ progression, reinforcing a laser focus on numbers and performance metrics which may undermine other key facets of learning, while forcing teachers to support algorithmic logics rather than leverage their knowledge about education. In addition, data-driven personalized learning algorithms depend upon data points but there is a lack of transparency over what data points are collected, how they are used, and who is permitted access, leading to the recent sharp uptick in proposed national and state student data privacy legislation. Full PDF here: http://www.datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf _______________________________________________ nexa mailing list nexa(a)server-nexa.polito.it https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa

Aug. 8, 2016

Re: [nexa] Guardian: "Universities are tracking their students. Is it clever or creepy?"

by J.C. DE MARTIN

Grazie per la segnalazione, Donatella, leggerò. Sempre sui dati in ambito educativo, ricordo il paper che ho appena segnalato in lista (vi è arrivato il mio email? A me no...), ovvero: The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education Data & Society Working Paper 08.08.2016 CLAIRE FONTAINE http://www.datasociety.net/pubs/ecl/Accountability_primer_2016.pdf Ciao, juan carlos On 08/08/16 15:47, Donatella Solda wrote: > Nel rapporto sui big data che abbiamo pubblicato da poco al MIUR > (http://hubmiur.pubblica.istruzione.it/web/ministero/cs280716bis > )abbiamo citato espressamente il potenziale di utilizzare i dati > derivanti dai learning environments per migliorare la didattica e, > dove necessario, anticipare l'abbandono scolastico. > > In tutto questo, la privacy è evidentemente una questione molto > rilevante - soprattutto se si considera che i ragazzi iniziano oramai > a imparare online sui vari edmodo o simili fin dalla primaria (e > quindi si avrebbe una profilazione spinta dai 6 ai 24 anni). > > La questione della riservatezza si fa ancora più rilevante, quando si > pensa che gli ambienti di apprendimento online sono sempre più > integrati con social, cloud e mille altre modernità. > > Spunti su questo sono molto graditi. > > A presto! > > Donatella > On Fri, 5 Aug 2016 at 14:01, Stefano Quintarelli > <stefano(a)quintarelli.it <mailto:stefano@quintarelli.it>> wrote: > > pero' non e' un metodo molto diverso dal metodo bardi.. > > On 05/08/2016 17:44, J.C. DE MARTIN wrote: > > Grazie, David, conosco. Un'esperienza molto particolare, > > con un obiettivo molto "stretto" (insegnare a programmare) > > e specificamente pensata per studenti che, pur dotati, > > non rendono bene in sistemi educativi tradizionali. > > > > juan carlos > > > > On 05/08/16 17:41, David Orban wrote: > >> La scuola 42 a Parigi è tutta basata sul concetto di lavoro di > gruppo > >> tra gli studenti, dove gli insegnanti sono di supporto. Tutto viene > >> gestito attraverso il tracking delle presenze e delle attività, in > >> questo caso di programmazione. I progetti, il codice, i contributi > >> vengono analizzati da sistemi automatici. > >> > >> http://www.42.fr/ > >> > >> > >> > >> David Orban > >> "What is the question that I should be asking?" > >> twitter, linkedin, etc: davidorban > >> > >> On Fri, Aug 5, 2016 at 5:24 PM, J.C. DE MARTIN > <demartin(a)polito.it <mailto:demartin@polito.it> > >> <mailto:demartin@polito.it <mailto:demartin@polito.it>>> wrote: > >> > >> *Universities are tracking their students. Is it clever or > creepy?* > >> > >> /Learning analytics are becoming increasingly popular for > >> improving learning and cutting drop-out rates – but critics > >> question the impact on privacy/ > >> > >> Chris Jutting > >> > >> Wednesday 3 August 2016 14.19 BST > >> > >> What could be more normal that heading to the university > library, > >> swiping your card and logging in to a computer? > >> > >> Most students wouldn’t think twice about it. But what they > may not > >> realise is that this mundane series of events leaves a > unique data > >> pattern that can be recorded, logged and reviewed, in a > practice > >> known as “learning analytics”. And now data analysts are using > >> this information to predict whether students will struggle with > >> their courses, or drop out. > >> > >> “We’re trying to use data to improve our understanding of how > >> students learn,” says Dr Bart Rienties, director of the > learning > >> analytics programme at the Open University. “We want to > understand > >> the story behind that data.” > >> > >> Such techniques look set to become an integral part of > university > >> life in the future, much to the delight of advocates. “Learning > >> analytics can provide a more personal learning experience, > rather > >> than a one-size-fits-all solution,” Rienties says. He was > part of > >> a new study – which analysed data from more than 113,000 > students > >> at the Open University – looking at drop-out rates and how > usage > >> patterns of online learning resources can be an early > indicator of > >> academic performance. > >> > >> […] > >> > >> Continua qui: > >> > https://www.theguardian.com/higher-education-network/2016/aug/03/learning-a… > >> > <https://www.theguardian.com/higher-education-network/2016/aug/03/learning-a…> > >> > >> _______________________________________________ > >> nexa mailing list > >> nexa(a)server-nexa.polito.it <mailto:nexa@server-nexa.polito.it> > <mailto:nexa@server-nexa.polito.it > <mailto:nexa@server-nexa.polito.it>> > >> https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa > >> <https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa> > >> > >> > > > > > > > > _______________________________________________ > > nexa mailing list > > nexa(a)server-nexa.polito.it <mailto:nexa@server-nexa.polito.it> > > https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa > > > _______________________________________________ > nexa mailing list > nexa(a)server-nexa.polito.it <mailto:nexa@server-nexa.polito.it> > https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa >

Aug. 8, 2016

Paper: "Personalized Learning: The Conversations We’re Not Having"

by J.C. DE MARTIN

*Personalized Learning: The Conversations We’re Not Having** * Data & Society Working Paper 07.22.2016 MONICA BULGER *Abstract* The pursuit of personalized education at a mass scale drives a number of current education technology initiatives that fall under the broad umbrella of personalized learning. The personalized learning space has seen a huge infusion of capital in recent years, with notable support from the Bill & Melinda Gates Foundation and a range of other investors, philanthropists, and technologists. Federal support for personalized learning has come through Obama’s Race to the Top program and, looking ahead, will come through the recently passed Every Student Succeeds Act’s provision for competency-based assessments. The story of personalized learning is a complex one in which education technology developers are applying startup logics like risk taking and testing in the field and marketing tactics like personalized content delivery and recommender systems. Their products are designed to meet the needs of states and administrators, who seek to improve efficiency and performance through formative testing and tailored learning modules, but they are often narrated differently to the public. The promise of technologically enabled personalized learning – that through technology we can democratize access to information and instruction, scale excellence across the socioeconomic spectrum, and do so with efficiency and rigor – is a familiar one to educators. And yet there are tensions between the promises of personalized learning, the state-of-its-art, and the practical realities of its implementation. As a buzzword symbolizing the potential of data use in education, personalized learning enjoys a broad definitional reach and is aligned by proponents with educational best practices like “student-centered instruction.” At its best, personalized learning represents a pedagogy of adaptation to students’ unique combination of goals, interests, and competencies. But many so-called personalized learning platforms are more responsive than adaptive, as they provide materials appropriate to students’ proficiency levels without moving beyond a pre-determined decision tree. They do this by analyzing student data such as age, gender, grade level, and test performance against idealized models based on data sets of students with similar demographics, then offering content recommendations directly to the student or to the teacher for further intervention. The widespread adoption of personalized learning platforms may lead to unintended consequences. For example, the focus on performance may negatively affect students’ psychological wellbeing by promoting individualist notions of achievement and undermining the social dimensions of learning. With the wealth of data that such systems generate, it becomes possible to track and report students’ progression, reinforcing a laser focus on numbers and performance metrics which may undermine other key facets of learning, while forcing teachers to support algorithmic logics rather than leverage their knowledge about education. In addition, data-driven personalized learning algorithms depend upon data points but there is a lack of transparency over what data points are collected, how they are used, and who is permitted access, leading to the recent sharp uptick in proposed national and state student data privacy legislation. Full PDF here: http://www.datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Aug. 8, 2016

Paper: "The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education"

by J.C. DE MARTIN

*The Myth of Accountability:** **How Data (Mis)Use is Reinforcing the** **Problems of Public Education* Data & Society Working Paper 08.08.2016 CLAIRE FONTAINE *Introduction *There is an ongoing tension in the American public education system between the values of excellence, equity, and efficiency. Accountability has emerged as a framework in education reform that promises to promote and balance all three values. Yet this frame is often contested due to disagreements over the role of incentives and penalties in achieving desirable change, and concerns that the proposed mechanisms will have significant unintended consequences that outweigh potential benefits. More fundamentally, there is widespread disagreement over how to quantify excellence and equity, if it is even possible to do so. Accountability rhetoric echoes a broader turn toward data-driven decision- making and resource allocation across sectors. As a tool of power, accountability processes shift authority and control to policymakers, bureaucrats, and test makers over professional educators. Today, measurements of school performance have become so commonplace that they are an assumed part of education debates. As new forms of data are easier to collect and analyze, drawing on and interacting with information to measure the impact of programs and to inform decision-making and policy has emerged as a key strategy to foster improvement in public schools (Coburn and Turner 2012). But when did our national obsession with educational data start? What are the historic precursors to accountability as we now know it? What set of political, economic, cultural, and social conditions led to test scores becoming the main measure of a school’s success? How do current data-driven practices compare to historical data-driven practices and how have the terms shifted over time? Who does accountability serve? What are the incentive structures, and how is accountability gamed and resisted? In short, accountability of what, to whom, for what ends, at what cost? Full PDF here: http://www.datasociety.net/pubs/ecl/Accountability_primer_2016.pdf

Aug. 8, 2016