Living in Exponential Times and the Personalization of Our Data Streams

Living in Exponential Times and the Personalization of Our Data Streams

Debbie Samuels-Peretz (Wheelock College, USA)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/978-1-4666-5888-2.ch232

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We Live in Exponential Times

Welcome to the 21st century. Have a question? Need information? Go online and chances are you will find an answer within a few seconds; and it will cost you nothing. Many years ago, when books were rare, knowledge resided in very few hands. If you wanted information you would go to your priest or perhaps the elders of your tribe. Years later, the wealthy could afford handwritten books, becoming holders of knowledge too. The printing press and public libraries changed everything. Knowledge became more affordable and accessible. However none of this comes close to the access and affordability of knowledge today. At our fingertips we have access to the ideas of scholars and experts from around the world. We can hear the opinions of hundreds and thousands of people like and unlike ourselves on topics as diverse as world events or the best products to buy.

What does it mean that anyone can publish with the click of a button? With access to so much information, how to do we know who to give our limited attention to? Where do we go to find the information we want and need? As Fisch, McLeod and Bronman (2008) declared in their viral video, Shift Happens, we are living in exponential times. This article will explore information overload in the 21st century and how it is affecting our society, with particular attention to the benefits and dangers of knowledge curation, and the personalization of our data streams. Finally, questions will be raised regarding implication of these issues for education and society.

Key Terms in this Chapter

Customization: The individualizing of information based on user preferences.

Personalization: The filtering of information based on user preferences.

Filter Bubble: The consequence of getting only personalized information in your data stream which excludes divergent opinions and information sources.

Collaborative Filtering: Filtering model based on a profile of user preferences similar to a particular user.

Information Overload: The result of exponentially growing amounts of information available on the web.

Data Streams: Data streamed to the user via search results, social media, recommendation systems, etc.

Information Streams: Information streamed to the user via search results, social media, recommendation systems etc.

Knowledge Curation: The selection of a subset of information based on particular criteria that is distributed to users.

Content-Based Recommendation: Filtering model based on matching content to a profile of previously preferred content.

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