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Nowadays, learning increasingly involves the usage of search engines and webresources. The related interdisciplinary research field search as learning aimsto understand how people learn on the web. Previous work has investigatedseveral feature classes to predict, for instance, the expected knowledge gainduring web search. Therein, eye-tracking features have not been extensivelystudied so far. In this paper, we extend a previously used reading model from aline-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 examinethe relationship between our feature set and the participants' test scores. Ourfindings demonstrate that learners with higher knowledge gain spentsignificantly more time reading, and processing more words in total. We alsofind evidence that faster reading at the expense of more backward regressionsmay be an indicator of better web-based learning. We make our code publiclyavailable at https://github.com/TIBHannover/reading_web_search.

On the Influence of Reading Sequences on Knowledge Gain during Web Search