This dissertation describes an experiment to investigate how learners with different levels of background in computer science learn core concepts of computer science, in particular, algorithms. We designed a study to focus on cognitive task analysis for eliciting the empirical mental elements of learning two graph algorithms. Cognitive workload methods were used while participants are exposed to an algorithm visualization (AV) tool. The experiment is designed to measure the reduction in cognitive load as students are assisted by an AV tool. The tool employed, called Map-based Educational Tools for Algorithm Learning (METAL), also, provides a framework for learning and assessment activity. This analysis was assessed for reliability and validity and administered to students majoring in computer science and to non-majors in computer science. The results of experiments indicate that there are significant differences between the learning process of students majoring in computer science and that of students who are not CS majors. Different kinds of cognitive load for each group of participants were measured. The results indicated germane cognitive load was the highest component of the learning process compared to the other kinds of cognitive load across all groups of participants. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]