Mental model development, deeper levels of information processing, and elaboration are critical to learning. More so, individuals' metacomprehension accuracy is integral to making improvements to their knowledge base. In other words, without an accurate perception of their knowledge on a topic, learners may not know that knowledge gaps or misperceptions exist and, thus, would be less likely to correct them. Therefore, this study offered a dual-process approach that aimed at enhancing metacomprehension. One path aimed at advancing knowledge structure development and, thus, mental model development. The other focused on promoting a deeper level of information processing through processes like elaboration. It was predicted that this iterative approach would culminate in improved metacomprehension and increased learning. Accordingly, using the Graduated Concept Model Development (GCMD) approach, the role of learner-generated concept model development in facilitating metacomprehension and knowledge acquisition was examined. Concept maps have had many roles in the learning process as mental model assessment tools and advanced organizers. However, this study examined the process of concept model building as an effective training tool. Whereas, concept maps functioning as advanced organizers are certainly beneficial, it would seem that the benefits of having a learner examine and amend the current state of their knowledge through concept model development would prove more effective for learning. In other words, learners looking at an advanced organizer of the training material may feel assured that they have a thorough understanding of it. Only when they are forced to create a representation of the material would the gaps and misperceptions in their knowledge base likely be revealed. In short, advanced organizers seem to rely on recognition, where concept model development likely requires recalling and understanding 'how' and 'why' the interrelationships between concepts exist. Therefore, the Graduated Concept Model Development (GCMD) technique offered in this study was based on the theory that knowledge acquisition improves when learners integrate new information into existing knowledge, assign elaborated meanings to concepts, correct misperceptions, close knowledge gaps, and strengthen accurate connections between concepts by posing targeted questions against their existing knowledge structures. This study placed an emphasis on meaningful learning and suggested a process by which newly introduced concepts would be manipulated for the purpose of improving metacomprehension by strengthening accurate knowledge structures and mental model development, and through deeper and elaborated information processing. Indeed, central to improving knowledge deficiencies and misunderstandings is metacomprehension, and the constructing of concepts maps was hypothesized to improve metacomprehension accuracy and, thus, learning. This study was a one-factor between-groups design with concept map type as the independent variable, manipulated at four levels: no concept map, concept map as advanced organizer, learner-built concept map with feedback, and learner-built concept map without feedback. The dependent variables included performance (percent correct) on a declarative and integrative knowledge assessment, mental model development, and metacomprehension accuracy. Participants were 68 (34 female, 34 male, ages 18-35, mean age = 21.43) undergraduate students from a major southeastern university. Participants were randomly assigned to one of the four experimental conditions, and analysis revealed no significant differences between the groups. Upon arrival, participants were randomly assigned to one of the four experimental conditions. Participants then progressed through the three stages of the experiment. In Stage I, participants completed forms regarding informed consent, general biographical information, and task self-efficacy. In Stage II, participants completed the self-paced tutorial based on the Distributed Dynamic Decision Making (DDD) model, a simulated military command and control environment aimed at creating events to encourage team coordination and performance (for a detailed description, see Kleinman & Serfaty, 1989). The manner by which participants worked through the tutorial was determined by their assigned concept map condition. Upon finishing each module of the tutorial, participants then completed a metacomprehension prediction question. In Stage III, participants completed the computer-based knowledge assessment test, covering both declarative and integrative knowledge, followed by the metacomprehension postdiction question. Participants then completed the card sort task, as the assessment of mental model development. Finally, participants completed a general study survey and were debriefed as to the purpose of the study. The entire experiment lasted approximately 2 to 3 hours. Results indicated that the GCMD condition showed a stronger indication of metacomprehension accuracy, via prediction measures, compared with the other three conditions (control, advanced organizer, and feedback), and, specifically, significantly higher correlations than the other three conditions in declarative knowledge. Self-efficacy measures also indicated that the higher metacomprehension accuracy correlation observed in the GCMD condition was likely the result of the intervention, and not due to differences in self-efficacy in that group of participants. Likewise, the feedback and GCMD conditions led to significantly high correlations for metacomprehension accuracy based on levels of understanding on the declarative knowledge tutorial module (Module 1). The feedback condition also showed similar responses for the integrative knowledge module (Module 2). The advanced organizer, feedback, and GCMD conditions were also found to have significantly high correlation of self-reported postdiction of performance on the knowledge assessment and the actual results of the knowledge assessment results. However, results also indicated that there were no significant findings between the four conditions in mental model assessment and knowledge assessment. Nevertheless, results support the relevance of accurate mental model development in knowledge assessment outcomes. Retrospectively, two opposing factors may have complicated efforts to detect additional differences between groups. From one side, the experimental measures may not have been rigorous enough to filter out the effect from the intervention itself. Conversely, software usability issues and the resulting limitations in experimental design may have worked negatively against the two concept mapping conditions and, inadvertently, suppressed effects of the intervention. Future research in the GCMD approach will likely review cognitive workload, concept mapping software design, and the sensitivity of the measures involved.