Lessons from the Journey: A Query Log Analysis of Within-Session Learning

The Internet is the largest source of information in the world. Search engines help people navigate the huge space of avail-able data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investi-gate within-session and cross-session developments of exper-tise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics re-lated to expertise. Based on models of the user and their specific context, we present a method capable of automati-cally predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search