The number of mobile devices used in the K-12 learning space has been growing steadily. Most school-issued devices that connect to the Internet are required to have web filters installed on them. These web filters are designed to filter out inappropriate content and log student activity online. This study investigates student web query logs and classifies their web queries as either school related or non-school related to provide potentially valuable information on student learning. This process is similar to that used by the major search engines - Google, Yahoo and Bing - that generate billions of dollars in ad revenue by classifying web queries into appropriate categories to display advertisements. A new algorithm, Student Web Query Classifier, is presented for the binary classification of high school student web queries as school related or non-school related. Additionally, a proposed procedure is presented to build a corpus of school-related terms and to compare it against students' online activity. The new algorithm presented in this paper yielded 90.68% accuracy, far superior than results obtained from supervised learning algorithms Naive Bayes and Support Vector Machines.