Search engine reveals significant contribution in learners' online and self-regulated learning experience. Learners currently rely on traditional search engines to retrieve relevant learning materials from the massive repository of materials in the Web. Unfortunately, these search engines do not consider differences in their learning aptitudes when delivering search results. As a consequence, a learner has to perform exhaustive search in order to obtain learning materials that better suit their individual needs. To address this problem, we propose a framework that augments the existing Google search engine with the ability to filter its search results according to learners' educational backgrounds, their learning behaviors when using the tool, as well as the behavioral patterns of other learners with similar profiles. The recommendation of the personalized search results is then implemented using dynamic learner profiling, and Groupization algorithm. Our preliminary evaluation of the prototype shows that the tool is able to personalize Google search results and produces recommendation of links according to three different learners' profiles.