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This chapter outlines a framework for automated detection of student behaviors in the context of virtual learning environments. The components of the framework establish several parameters for data acquisition, preprocessing, and processing as a means to classify different types of behaviors. The authors illustrate these steps in training and evaluating a detector that differentiates between students' observations and functional behaviors while students interact with three-dimensional (3D) virtual models of dinosaur fossils. Synthetic data were generated in controlled conditions to obtain time series data from different channels (i.e., orientation from the virtual model and remote controllers) and modalities (i.e., orientation in the form of Euler angles and quaternions). Results suggest that accurate detection of interaction behaviors with 3D virtual models requires smaller moving windows to segment the log trace data as well as features that characterize orientation of virtual models in the form of quaternions. They discuss the implications for personalized instruction in virtual learning environments.

This study investigates how interactive features embedded in the illustrations of storybook apps influenced young readers’ story enjoyment and comprehension. Kindergartners and second grade...

This chapter discusses the capabilities of extended reality technology in informal learning environments, such as museums and cultural heritage sites. Recent developments in extended reality technologies have led to increased integration into these informal learning spaces and have heightened the need for a systematic investigation into the affordances of modern multimedia representations spanning tangible to virtual mediums. These affordances vary across settings, ranging from personalizing learning experiences, increasing engagement through interactive activities, and augmenting exhibits with rich multimedia content. The chapter critically appraises several affordances of extended reality technologies while expanding on these notions by outlining the cognitive theory of multimedia learning to inform practical instructional design principles. Drawing on case examples from their own research with tangible and virtual museum objects in these settings, the authors explore and discuss how extended reality technologies can serve to both collect and analyze ambient data in real time. The authors claim that ambient data will become increasingly prevalent in such settings and critical to gain insights into visitor experience and promote meaningful and engaging learning experiences. They discuss the implications of their findings for practice and suggest directions for future research.

This chapter outlines a framework for automated detection of student behaviors in the context of virtual learning environments. The components of the framework establish several parameters for data acquisition, preprocessing, and processing as a means to classify different types of behaviors. The authors illustrate these steps in training and evaluating a detector that differentiates between students' observations and functional behaviors while students interact with three-dimensional (3D) virtual models of dinosaur fossils. Synthetic data were generated in controlled conditions to obtain time series data from different channels (i.e., orientation from the virtual model and remote controllers) and modalities (i.e., orientation in the form of Euler angles and quaternions). Results suggest that accurate detection of interaction behaviors with 3D virtual models requires smaller moving windows to segment the log trace data as well as features that characterize orientation of virtual models in the form of quaternions. They discuss the implications for personalized instruction in virtual learning environments.

Purpose This paper aims to examine the impact of using digitized objects for inquiry with middle-school classrooms. Research analyzed critical thinking processes and student engagement during collaborative investigations with 3D models of authentic objects. Design/methodology/approach Digitized objects were 3D scans of fossils from the paleontology collection at the Natural History Museum of Utah implemented as 3D prints and 3D virtual models. Verbal protocol analysis examined critical thinking processes during collaborative student learning. Engagement was assessed via student feedback and a classroom observation protocol. Findings The findings demonstrated that digitized objects facilitated key critical thinking processes, particularly observation, problem finding, elaboration and evaluation. Student feedback was very positive and focused on strong interest in 3D technologies and the ability to engage in authentic exploration. Observations showed very high levels of on-task engagement. Research limitations/implications Additional research is necessary to determine if findings generalize across varied learner populations, including broader age ranges and socioeconomic samples, to activities implemented as fully online experiences and to digitized objects from varied domains. Originality/value Findings demonstrate digitized objects are effective methods to engage students in critical thinking and to promote engagement with authentic objects during classroom learning. Results demonstrate strong potential of new technologies to leverage the educational impact of digitized objects from local collections, setting the stage for expanded educational outreach by museums and libraries.

In many domains, problem solving involves the application of general domain principles to specific problem representations. In 3 classroom studies with an intelligent tutoring system, we examined the impact of (learner-generated) interactions and (tutor-provided) visual cues designed to facilitate rule–diagram mapping (where students connect domain knowledge to problem diagrams), with the goal of promoting students’ understanding of domain principles. Understanding was not supported when students failed to form a visual representation of rule–diagram mappings (Experiment 1); student interactions with diagrams promoted understanding of domain principles, but providing visual representations of rule–diagram mappings negated the benefits of interaction (Experiment 2). However, scaffolding student generation of rule–diagram mappings via diagram highlighting supported better understanding of domain rules that manifested at delayed testing, even when students already interacted with problem diagrams (Experiment 3). This work extends the literature on learning technologies, generative processing, and desirable difficulties by demonstrating the potential of visually based interaction techniques implemented during problem solving to have long-term impact on the type of knowledge that students develop during intelligent tutoring.

Computer-based instruction (CBI) has the potential to help stakeholders meet new expectations regarding the academic achievement and educational outcomes of students receiving special education services. Part of the optimism about CBI’s transformative role in special education has focused on its potential to provide flexible materials and practices surrounding academic curricula. This chapter focuses specifically on academic instruction as implemented within CBI and as it relates to the largest group of students with disabilities in public schools: students with mild to moderate, or specific, learning disabilities. We first identify some priority concerns for students in special education that may be addressed by digital tools and technology-based solutions. We then review existing research literature to determine when and how technology may support the cognitive processes and learning outcomes of students in special education. Finally, we summarize the research findings into a set of practical recommendations based upon currently available evidence.

Is the format of semantic representation for idiomatic expressions different from that for words or literal phrases? Do the semantic characteristics of idiomatic expressions influence their processing? This study examined the influence of the composi-tionality dimension on the processing of idiomatic expressions. It was hypothesized that decomposable and nondecomposable idiomatic expressions are represented differently in memory and that this difference affects the processing time of idiomatic expressions. In 2 experiments, participants read sentences containing decomposable idioms, nondecomposable idioms, or control expressions and performed a lexical decision task on figurative targets presented 0 msec, 350 msec, or 500 msec after reading. Results demonstrated a clear effect of compositionality on the activation of figurative meaning: decomposable idiomatic expressions were processed sooner than nondecomposable idiomatic ones. These results are discussed in the framework of the hybrid model (Titone & Co...

Two experiments investigated learning outcomes and comprehension processes when students learned about the heart and circulatory system using (a) text only, (b) text with simplified diagrams designed to highlight important structural relations, or (c) text with more detailed diagrams reflecting a more accurate representation. Experiment 1 found that both types of diagrams supported mental model development, but simplified diagrams best supported factual learning. Experiment 2 replicated learning effects from Experiment 1 and tested the influence of diagrams on novices' comprehension processes. Protocol analyses indicated that both types of diagrams supported inference generation and reduced comprehension errors, but simplified diagrams most strongly supported information integration during learning. Visual representations appear to be most effective when they are designed to support the cognitive processes necessary for deep comprehension.

Multimedia messages should be designed to facilitate multimedia learning processes. This chapter first explores three assumptions underlying a cognitive theory of multimedia learning: dual-channel assumption, limited-capacity assumption and active processing assumption. Three memory stores in the cognitive theory of multimedia learning are: sensory memory, working memory, long-term memory. For meaningful learning to occur in a multimedia environment, the learner must engage in five cognitive processes: selecting relevant words for processing in verbal working memory, selecting relevant images for processing in visual working memory, organizing selected words into a verbal model, organizing selected images into a pictorial model, and integrating the verbal and pictorial representations with each other and with relevant prior knowledge activated from long-term memory. The chapter also explores three demands on cognitive capacity during multimedia learning: extraneous processing, essential processing and generative processing.

The effects of content and rhetorical prompts on writing process activities and the quality of written products were examined. We also examined the usefulness of latent semantic analysis (LSA; Landauer & Dumais, 1997)-a computational technique for representing the content of documents-as a tool for assessing texts. Participants used varied combinations of prompts designed to support content and rhetorical processes. In Experiment 1, content and rhetorical processes were supported only during composition. In Experiment 2, content and rhetorical processes were supported during domain learning and writing. Time spent in 3 writing activities (planning, drafting, revising) was measured, and professional writing instructors and LSA assessed text quality. Content prompts extended time spent writing and were related to improved text quality; rhetorical prompts demonstrated some influence on planning and global text quality only when presented during domain learning. In both experiments, LSA generated consistent j...

This study explored the impact of prior domain knowledge on students’ strategies and use of digital resources during a Web-based learning task. Domain knowledge was measured using pre- and posttests of factual knowledge and knowledge application. Students utilized an age- and topic-relevant collection of 796 Web resources drawn from an existing educational digital library to revise essays that they had written prior to the online learning task. Following essay revision, participants self-reported their strategies for improving their essays. Screen-capture software was used to record all student interactions with Web-based resources and all modifications to their essays. Analyses examined the relationship between different levels of students’ prior knowledge and online learning behaviors, self-reported strategies, and learning outcomes. Findings demonstrated that higher levels of factual prior knowledge were associated with deeper learning and stronger use of digital resources, but that higher levels of deep prior knowledge were associated with less frequent use of online content and fewer deep revisions. These results suggest that factual knowledge can serve as a useful knowledge base during self-directed, online learning tasks, but deeper prior knowledge may lead novice learners to adopt suboptimal processes and behaviors.

This chapter examines text comprehension and discourse processing from a cognitive standpoint, exploring research evidence that forms the basis for what is known about how individuals learn from text and discourse. First, the history of text comprehension and discourse analysis is discussed. Next, basic processes of memory are discussed, focusing on the influence of short- and long-term memory on comprehension. The chapter then examines how aspects of the learning situation can influence comprehension outcomes, discussing factors internal to the learner (e.g., prior knowledge) as well as factors external to the individual (e.g., characteristics of the text). We explore differences between traditional, printed materials and new, online informational resources that have become increasingly common and widely used for learning. Finally, we consider several existing models of comprehension and knowledge representations, discussing the implications to our overall understanding of what comprehension means and how it can be promoted. Although there remains no exact recipe for creating comprehension in a learner, research has uncovered many of the key ingredients necessary for successful learning outcomes. Continued research in comprehension has led us to an increasingly clear understanding of how individuals learn from text, and technology-based materials are poised to create personalized learning opportunities that structure learning opportunities for individual comprehenders. Keywords: text comprehension; discourse processes; comprehension theory; inference; learning

This chapter discusses an emerging theme in supporting effective multimedia learning: developing scalable, cognitively-grounded tools that customize learning interactions for individual students. We discuss the theoretical foundation for expected benefits of customization and current approaches in educational technology that leverage a learner’s prior knowledge. We then describe the development of a customized tool for science learning, called CliCk, that uses automatic techniques to create knowledge models that can be fed into cognitively-informed pedagogical tools. CliCk leverages existing multimedia resources in educational digital libraries for two purposes: (a) to generate rich representations of domain content relevant for learner modeling that are easily scaled to new domains and disciplines, and (b) to serve as a repository of instructional resources that support customized pedagogical interactions. The potential of the CliCk system is discussed, along with initial comparisons of knowledge models created by CliCk and human experts. Finally, the chapter discusses the remaining challenges and relevant future extensions for effective customization tools in educational technology.