By Aldo de Jong, co-founder and partner, Claro Partners. Co-founder of Claro Partners and Startupbootcamp, Aldo de Jong is convinced that in Big Data, as in any other technology, it’s crucial to follow a human centred approach, understanding first people’s needs and then creating solutions based on those needs. Concerning privacy issues, he thinks the debate should actually be more about trust, transparency and control, since most people are willing to hand over the data they generate if they get a benefit for doing so and if those three points are properly addressed. Check De Jong’s complete reflections in the…
Recently I came across a report in which the authors alerted about a contradiction in scientific and technological research. Despite science and technology are viewed as key elements to address global problems and improve our society, there is an increasing estrangement between research and society.
The decay since 1981 in public interest in science related issues and the recent decrease in the number of students enrolled in techno-scientific careers are indicators of the increasing gap between scientific community and society. By using Google Trends to examine public attention to “science” and “technology”, we realise that searches of these two words have been declining all over the world since 2004, except in very few countries such as China. These negative trends are in contrast with the increasing published articles and participants in conferences. So, why are there indications of disconnection between science and society despite the increasing trend in academic communications?
It is a common view that typical white-collar jobs will disappear with the rise of intelligent machines. While some see this as the dark side of Analytics/Big Data/IOT, it is rather a challenge for educational systems and the future role people with have in their jobs.
«The replacement of humans in jobs -ranging from to help desks to automated cars- has been going on for many years. However, it has been taking a new form in recent years. Whereas machines where traditionally replacing manual labor, they are now also used to accomplish routine information processing and even decision making under uncertainty. The reason for this is simple: Analytic techniques fed with enough information perform better in most occasions.»
By Marco Bressan, chief data scientist, BBVA.
By Susan Grajek, Vice President for data, research and analytics, EDUCAUSE.
Big data and analytics are reshaping everything. Industry is using them to great effect, to better understand markets and customers, manage supply chains, and increase profits. Personalized medicine, fueled by analytics applied to big data, is poised to revolutionize healthcare. Higher education lags several paces behind these fields. Some institutions are demonstrating improvements in retention and degree completion, but most are still using data to monitor student outcomes and activities rather than predict or proactively intervene.
Certainly, trends related to analytics and data are influencing institutional IT strategy, more so than other types of trends EDUCAUSE tracks, including those related to teaching and learning and security and risk1. Data-driven decision-making, enterprise data management, and data integration issues are all already incorporated into or exerting a major influence on emerging IT strategy in at least half of US colleges and universities. Personalized learning, however, is only this influential at one in five institutions.
Tagged with: analytics
, big data
, data-driven decision-making
, personalized learning
Posted in Uncategorized
By Reynol Junco, associate professor, Iowa State University; and faculty associate, Berkman Center for Internet and Society1.
Over the last years, I’ve been researching how real-time behavioral data, collected unobtrusively through technology, can predict learning outcomes. As part of this line of research, I’ve recently published the paper Predicting course outcomes with digital textbook usage data in The Internet and Higher Education.
The study used data collected from student engagement with digital textbooks in order to predict course grades. Two measures of student engagement with the texts were analyzed: an engagement index that was calculated through a linear combination of the number of pages read, number of times a student opened their textbook, number of days the student used their textbook, time spent reading, number of highlights, number of bookmarks, and number of notes. The second analysis included the individual components of the engagement index.
By Manuel Armayones, associate professor and Deputy Director, Faculty of Psychology and Education Sciences, UOC.
In 1995, the American Psychology Association (APA), through its Task Force for the analysis of the concept Intelligence, considered it as the ability to “understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought”.
The question we must ask when relating this concept to that of smart cities and big data & analytics is if asking the right questions of data, so that they assist us in making decisions, can help us to better adapt to the environment, learn from experience and engage in various forms of reasoning.