Using Data for Social Good
The big data hype has been gaining momentum over the past several years. Although a lot of it is hype and buzzwords, what is undeniable is that the use of data to improve decision-making can improve a lot of organizations. Despite this potential, most of the examples we hear about the use of data is in the corporate world, especially in finance, internet search, and advertising. In this talk, I’ll talk about the potential of using data for social good. I’ll give several examples from the Data Science for Social Good Summer Fellowship at the University of Chicago that highlight how data science can be used to solve large-scale problems with social impact in areas such as education, healthcare, sustainability, community development, and disaster relief. I’ll highlight some of the projects related to education and discuss how data scientists have the potential some of these most challenging problems.
Rayid Ghani is the Research Director at the Urban Center for Computation and Data at the University of Chicago. Previously, he was the Chief Scientist for the Obama 2012 Campaign focusing on Analytics, Technology, and Data. He has over 12 years of Applied R&D experience in Analytics across politics, retail, healthcare, manufacturing, intelligence, and financial services industries.
His interests are in using data and analytics for high impact social good problems in areas such as education, healthcare, energy, transportation, and 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.
Savoir Devenir: Developing 21st century skills to master information cultures
In the face of the coming challenges posed by “big data” and their use for OER and open education (learning analytics, metrics, MOOCs,…), learners will need to acquire 21st century skills to master the new “information cultures” and their attendant human rights (freedom of expression, privacy,…). So as to ensure that this innovative boon doesn’t 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. 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 « Forwardances », in order to harness the affordances powered by digital tools, platforms and augmented online pedagogical practices.
I will discuss the cognitive patterns of use and social relevance reflecting learners’ needs for forwardances (self actualization, playful modelling, life-streams and civic engagement). I will argue that they can be developed for digitally sustainable “information cultures”, via specific competences and e-strategies, so as to lead to the construction of the learners’ e-presence in its cognitive, social and designed dimensions. Learning analytics can then remain under human control, and ethical by learner design.
Divina Frau-Meigs is a professor at the Sorbonne Nouvelle, where she created the master’s 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 “savoir devenir dans le développement numérique durable” where she investigates the pedagogical practices for 21st century skills and their implications for public policies in education.
Geoffrey J. Gordon
What can machine learning do for open education?
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—for 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.
Dr. Geoffrey J. Gordon is an Associate Research Professor in the Department of Machine Learning at Carnegie Mellon University, and co-director of the Department’s Ph. D. program. He works on statistical machine learning, educational data, game theory, multi-robot systems, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.