Instructional designers need to understand the internal processes of learning, identify learners' cognitive difficulties with those processes, and create strategies to help learners overcome those difficulties. Generative learning theory, one conception of human learning about cognitive functioning and process, emphasizes that meaningful learning occurs from the learner's creation of understanding about the information (Wittrock, 1992). The essence of this generative learning theory is that learners need to selectively attend to events and to actually create relationships and meaning from the events. However, this learning process is not always easy nor is it unconscious for learners, specifically in computer-based learning environments. Computer-based learning environments, such as hypermedia and web-based instruction, require learners to increase control, both cognitively and metacognitively, over what and how they learn. Accordingly learners may need more support and guidance, to help them use cognitive learning strategies, monitor their understanding, and refine their learning process. Prompting learners to use generative learning strategies may increase the frequency of their using appropriate learning strategies which may improve learning when learners use those strategies. Also, providing feedback about their metacognitive processes can guide learners to assess the suitability of cognitive strategies employed and to refine the learning strategies they use. As a result, learning should be enhanced. Therefore, this study examined the effects of generative learning strategy prompts and metacognitive feedback and the mediation effects of learners' self-regulation and use of generative learning strategies on their learning. In the spring of 2008, 223 undergraduate students, enrolled in general education courses in a large land grant university in the northeastern United States, participated in this study. The participants were randomly assigned to three treatment groups: static visualized instruction with generative learning strategy tools as control group (T1), static visualized instruction with generative learning strategy tools and prompts (T2), and static visualized instruction with generative learning strategy tools and prompts with metacognitive feedback (T3). The study included a prior knowledge pre test and a post self-regulation survey measuring cognitive and metacognitive control. Two criterion tests measuring recall and comprehension served as post-tests. The study also measured the quality of learners' overt use of generative learning strategies. The primary statistical analysis method was Structural Equation Modeling (SEM) to analyze the treatments and their mediation effects. The study found that the participants who were given generative learning strategy prompts with metacognitive feedback scored significantly higher in self-regulation, the quality of generative learning strategy use, and recall and comprehension after controlling for their prior domain knowledge than the participants who were given only generative learning strategy tools. Furthermore, generative learning strategy prompts with metacognitive feedback had indirect effects on learners' recall and comprehension through learners' use of generative learning strategies, and on learners' use of generative learning strategies through learners' self-regulation. This result supported the mediation processes of self-regulation and the use of generative learning strategies. This study found positive effects from the use of metacognitive feedback in generative learning. This suggests that instructional designers or teachers should provide cognitive and metacognitive prompts while learners study. This study also answers the questions regarding what should be supported to improve students' learning and how to provide appropriate supports. Finally, this study contributes to educational research practice by demonstrating how to gather and analyze overt evidence of learners' actual interactions with the instructional interventions and by illustrating an application of Structural Equation Modeling for experimental research to understanding intervening processes of learning. This approach may stimulate future research in instructional design and development for more complex, technology-enriched learning environments. [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.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]