{"id":372,"date":"2013-12-01T18:46:11","date_gmt":"2013-12-01T18:46:11","guid":{"rendered":"http:\/\/conference.ocwconsortium.org\/2014\/?page_id=372"},"modified":"2014-02-04T14:32:31","modified_gmt":"2014-02-04T13:32:31","slug":"keynotes","status":"publish","type":"page","link":"https:\/\/conference.oeglobal.org\/2014\/keynotes\/","title":{"rendered":"Keynotes"},"content":{"rendered":"

\u00a0<\/h2>\n

\"Rayid<\/a><\/h2>\n

Rayid Ghani<\/h2>\n

Using Data for Social Good\u00a0<\/h3>\n

The big data hype has been gaining momentum over the past\u00a0several years. Although a lot of it is hype and buzzwords, what is\u00a0undeniable is that the use of data to improve decision-making can\u00a0improve a lot of organizations. Despite this potential, most of the\u00a0examples we hear about the use of data is in the corporate world,\u00a0especially in finance, internet search, and advertising. In this talk,\u00a0I\u2019ll talk about the potential of using data for social good. I\u2019ll give\u00a0several examples from the Data Science for Social Good Summer Fellowship\u00a0at the University of Chicago that highlight how data science can be used\u00a0to solve large-scale problems with social impact in areas such as\u00a0education, healthcare, sustainability, community development, and\u00a0disaster relief. I\u2019ll highlight some of the projects related to\u00a0education and discuss how data scientists have the potential some of\u00a0these most challenging problems.\u00a0<\/p>\n

Rayid Ghani<\/strong> is the\u00a0Research Director at the\u00a0Urban Center for Computation and Data\u00a0at the University of Chicago. Previously, he was the Chief Scientist for the\u00a0Obama 2012 Campaign focusing on Analytics, Technology, and Data. He has over 12 years of Applied R&D\u00a0experience in Analytics across politics, retail, healthcare, manufacturing, intelligence, and financial\u00a0services industries.<\/p>\n

His interests are in\u00a0using data and analytics for high impact social good problems in areas such as education, healthcare, energy, transportation, \u00a0and public safety. He is a renowned researcher in data mining and machine learning, and he will share with us what the open education community can learn from machine learning and big data.\u00a0<\/p>\n

 <\/p>\n

\"Prof.Divina Frau-Meigs<\/h2>\n

Savoir Devenir: Developing 21st century skills to master information\u00a0cultures\u00a0<\/h3>\n

In the face of the coming challenges posed by \u201cbig data\u201d and their use for OER and open education (learning analytics, metrics, MOOCs,\u2026), learners will need to acquire 21st<\/sup> century skills to master the new \u201cinformation cultures\u201d and their attendant human rights (freedom of expression, privacy,\u2026). So as to ensure that this innovative boon doesn\u2019t bring a great disservice to open education, institutions as well as individuals will need to balance measurements with un-measurable serendipitous learning activities that promote creativity and knowledge construction.\u00a0 This balancing act requires adding a new domain to the pre-digital four pillars of education as defined by Rapport Delors for UNESCO (Learning to learn, Learning to do, Learning to live together and Learning to be). This fifth pillar deals with learning to become (savoir devenir) or \u00ab\u00a0Forwardances\u00a0\u00bb, in order to harness the affordances powered by digital tools, platforms and augmented online pedagogical practices.<\/p>\n

I will discuss the cognitive patterns of use and social relevance reflecting learners\u2019 needs for forwardances (self actualization, playful modelling, life-streams and civic engagement). I will argue that they can be developed for digitally sustainable \u201cinformation cultures\u201d, via specific competences and e-strategies, so as to lead to the construction of the learners\u2019 e-presence in its cognitive, social and designed dimensions. Learning analytics can then remain under human control, and ethical by learner design.\u00a0 \u00a0<\/p>\n

Divina Frau-Meigs<\/strong> is a professor at the Sorbonne Nouvelle, where she created the master\u2019s programme AIGEME, with two tracks (the engineering of media education and the engineering of e-learning). As an expert for UNESCO, the European Union and the Council of Europe she has developed programmes for Media and Information Literacy, buttressed on human rights and Open Educational Resources (OER). She currently holds the UNESCO chair \u201csavoir devenir dans le d\u00e9veloppement num\u00e9rique durable\u201d where she investigates the pedagogical practices for 21st<\/sup> century skills and their implications for public policies in education.<\/p>\n

\"GeoffreyGeoffrey J. Gordon<\/h2>\n

What can machine learning do for open education?<\/h3>\n

One of the big promises of open and massively online education is easy data collection: we can record everything from students’ habits in reading and viewing lectures, to their participation in discussion groups, to their timing and performance on exercises. So, open education is a natural fit for machine learning\u2014for example, we can use ML to predict future student performance, to select and sequence learning activities, and even to help grade some types of assignments. But there’s a lot more left to do: I’ll argue that even-bigger gains can come from ML that’s focused on understanding educational content and how students learn it, and on communicating this understanding to human educators. To achieve such understanding and communication, we need to take advantage of ML techniques including representation learning, structured learning, and exploration\/experimentation.<\/p>\n

Dr. Geoffrey J. Gordon<\/strong>\u00a0is an Associate Research Professor in the Department of\u00a0Machine Learning at Carnegie Mellon University, and co-director of the\u00a0Department’s Ph. D. program. He works on statistical machine learning,\u00a0educational data, game theory, multi-robot systems, and planning in\u00a0probabilistic, adversarial, and general-sum domains. His previous\u00a0appointments include Visiting Professor at the Stanford Computer\u00a0Science Department and Principal Scientist at Burning Glass\u00a0Technologies in San Diego. Dr. Gordon received his B.A. in Computer\u00a0Science from Cornell University in 1991, and his Ph.D. in Computer\u00a0Science from Carnegie Mellon University in 1999.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"

\u00a0 Rayid Ghani Using Data for Social Good\u00a0 The big data hype has been gaining momentum over the past\u00a0several years. Although a lot of it is hype and buzzwords, what is\u00a0undeniable is that the use of data to improve decision-making can\u00a0improve a lot of organizations. Despite this potential, most of the\u00a0examples we hear about the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_links_to":"","_links_to_target":""},"acf":[],"_links":{"self":[{"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/pages\/372"}],"collection":[{"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/comments?post=372"}],"version-history":[{"count":17,"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/pages\/372\/revisions"}],"predecessor-version":[{"id":727,"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/pages\/372\/revisions\/727"}],"wp:attachment":[{"href":"https:\/\/conference.oeglobal.org\/2014\/wp-json\/wp\/v2\/media?parent=372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}