  Nowadays, learning increasingly involves the usage of search engines and web
resources. The related interdisciplinary research field search as learning aims
to understand how people learn on the web. Previous work has investigated
several feature classes to predict, for instance, the expected knowledge gain
during web search. Therein, eye-tracking features have not been extensively
studied so far. In this paper, we extend a previously used reading model from a
line-based one to one that can detect reading sequences across multiple lines.
We use publicly available study data from a web-based learning task to examine
the relationship between our feature set and the participants' test scores. Our
findings demonstrate that learners with higher knowledge gain spent
significantly more time reading, and processing more words in total. We also
find evidence that faster reading at the expense of more backward regressions
may be an indicator of better web-based learning. We make our code publicly
available at https://github.com/TIBHannover/reading_web_search.
