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Abstract This study explored how different formats of instructional visuals affect the accuracy of students' metacognitive judgments. Undergraduates (n = 133) studied a series of five biology texts and made judgments of learning. Students were assigned randomly to study the texts only (text only), study the texts with provided visuals (provided visuals group), study the texts and generate their own visuals (learner‐generated visuals group), or study the texts and observe animations of instructor‐generated visuals (instructor‐generated visuals group). After studying the texts and making judgments of learning, all students completed multiple‐choice comprehension tests on each text. The learner‐generated and instructor‐generated visuals groups exhibited significantly higher relative judgment accuracy than the text only and provided visuals groups, though this effect was relatively small. The learner‐generated visuals group also required more study time and was more likely to report the use of visual cues when making their judgments of learning. 

Abstract Prior research suggests most students do not glean valid cues from provided visuals, resulting in reduced metacomprehension accuracy. Across 4 experiments, we explored how the presence of instructional visuals affects students’ metacomprehension accuracy and cue-use for different types of metacognitive judgments. Undergraduates read texts on biology (Study 1a and b) or chemistry (Study 2 and 3) topics, made various judgments (test, explain, and draw) for each text, and completed comprehension tests. Students were randomly assigned to receive only texts (text-only condition) or texts with instructional visualizations (text-and-image condition). In Studies 1b, 2 and 3, students also reported the cues they used to make each judgment. Across the set of studies, instructional visualizations harmed relative metacomprehension accuracy. In Studies 1a and 2, this was especially the case when students were asked to judge how well they felt they could draw the processes described in the text. But in Study 3, this was especially the case when students were asked to judge how well they would do on a set of comprehension tests. In Studies 2 and 3, students who reported basing their judgments on representation-based cues demonstrated more accurate relative accuracy than students who reported using heuristic based cues. Further, across these studies, students reported using visual cues to make their draw judgments, but not their test or explain judgments. Taken together, these results indicate that instructional visualizations can hinder metacognitive judgment accuracy, particularly by influencing the types of cues students use to make judgments of their ability to draw key concepts. 

In landscape planning and design, geospatial technologies (GSTs) are used to aid in visualizing and interpreting geographic environments, identifying geospatial patterns, and making decisions around information based on maps and geospatial information. GSTs are related to the different tools and technologies used to represent the earth’s surface and have transformed the practice of landscape design and geospatial education. These technologies play an important role in promoting the development and application of STEM-relevant geospatial thinking. Curricula that incorporate GSTs have been used across educational levels, from elementary school through college, and have been shown to support the development of geospatial learning and understanding. The present work discusses the use of one type of GST, virtual globes, as a tool for developing geospatial thinking, with a specific focus on Google Earth. This review highlights outcomes of several studies using Google Earth in the context of disciplines related to landscape design, such as geography and earth science. Furthermore, the potential mechanisms underlying the effectiveness of this technology for supporting the development of geospatial knowledge, such as its role in facilitating data visualization and supporting student’s ability to think flexibly about spatial patterns and relations, are discussed. Finally, the limitations of the current research on Google Earth as a tool for supporting geospatial learning are discussed, and suggestions for future research are provided.