Using Google Search Trends to Estimate Global Patterns in Learning

Abstract

The use of the Internet for learning provides a unique and growing opportunity to revisit the task of quantifying how much people have learned about a given subject in different regions around the world. Google alone receives over 5 billion searches a day and its publicly available data provides insight into learning process that is otherwise unobservable on a global scale. In this paper we, introduce the Computer Science Literacy-Proxy Index via Search (CSLI-s), a measure that utilizes online search data to make an educated guess around trends in computer science education. This measure uses a statistical signal processing technique to compose search volumes from a spectrum of topics into a coherent score. We intentionally explore and mitigate the biases of search data and, in the process, develop CSLI-s scores that correlate with traditional, more expensive metrics of learning. We then use search-trend data to measure patterns in subject literacy across countries and over time. To the best of our knowledge, this is the first measure of learning via Internet search-trends. The Internet is becoming a standard tool for learners and, as such, we anticipate search-trend data will have growing relevance to the learning science community.