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It is often assumed that engaging in a one‐on‐one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) (...) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5‐step frame. In the experiments, we compared 2 kinds of human tutoring (spoken and computer mediated) with 2 kinds of natural‐language‐based computer tutoring (Why2‐Atlas and Why2‐AutoTutor) and 3 control conditions that involved studying texts. The results depended on whether the students' preparation matched the content of the instruction. When novices (students who had not taken college physics) studied content that was written for intermediates (students who had taken college physics), then tutorial dialogue was reliably more beneficial than less interactive instruction, with large effect sizes. When novices studied material written for novices or intermediates studied material written for intermediates, then tutorial dialogue was not reliably more effective than the text‐based control conditions. (shrink)

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It is often assumed that engaging in a one‐on‐one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) (...) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5‐step frame. In the experiments, we compared 2 kinds of human tutoring (spoken and computer mediated) with 2 kinds of natural‐language‐based computer tutoring (Why2‐Atlas and Why2‐AutoTutor) and 3 control conditions that involved studying texts. The results depended on whether the students' preparation matched the content of the instruction. When novices (students who had not taken college physics) studied content that was written for intermediates (students who had taken college physics), then tutorial dialogue was reliably more beneficial than less interactive instruction, with large effect sizes. When novices studied material written for novices or intermediates studied material written for intermediates, then tutorial dialogue was not reliably more effective than the text‐based control conditions. (shrink)

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Human beings respond to their immediate environments in a variety of ways, with emotion playing a cardinal role. In evolutionary theories, emotions are thought to prepare an organism for action. The interplay of acoustic environments, emotions, and evolutionary needs are currently subject to discussion in soundscape research. Universal definitions of emotion and its nature are currently missing, but there seems to be a fundamental consensus that emotions are internal, evanescent, mostly conscious, relational, manifest in different forms, and serve a purpose. (...) Research in this area is expanding, particularly in regards to the context-related, affective, and emotional processing of environmental stimuli. A number of studies present ways to determine the nature of emotions elicited by a soundscape and to measure these reliably. Yet the crucial question—which basic and complex emotions are triggered and how they relate to affective appraisal—has still not been conclusively answered. To help frame research on this topic, an overview of the theoretical background is presented that applies emotion theory to soundscape. Two latent fundamental dimensions are often found at the center of theoretical concepts of emotion: valence and arousal. These established universal dimensions can also be applied in the context of emotions that are elicited by soundscapes. Another, and perhaps more familiar, parallel is found between emotion and music. However, acoustic environments are more subtle than musical arrangements, rarely applying the compositional and artistic considerations frequently used in music. That said, the measurement of emotion in the context of soundscape studies is only of additional value if some fundamental inquiries are sufficiently answered: To what extent does the reporting act itself alter emotional responses? Are all important affective qualities consciously accessible and directly measurable by self-reports? How can emotion related to the environment be separated from affective predisposition? By means of a conceptual analysis of relevant soundscape publications, the consensus and conflicts on these fundamental questions in the light of soundscape theory are highlighted and needed research actions are framed. The overview closes with a proposed modification to an existing, standardized framework to include the meaning of emotion in the design of soundscapes. (shrink)

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Peer interaction has been found to be conducive to learning in many settings. Knowledge co-construction has been proposed as one explanatory mechanism. However, KCC is a theoretical construct that is too abstract to guide the development of instructional software that can support peer interaction. In this study, we present an extensive analysis of a corpus of peer dialogs that we collected in the domain of introductory Computer Science. We show that the notion of task initiative shifts correlates with both KCC (...) and learning. Speakers take task initiative when they contribute new content that advances problem solving and that is not invited by their partner; if initiative shifts between the partners, it indicates they both contribute to problem solving. We found that task initiative shifts occur more frequently within KCC episodes than outside. In addition, task initiative shifts within KCC episodes correlate with learning for low pre-testers, and total task initiative shifts correlate with learning for high pre-testers. As recognizing task initiative shifts does not require as much deep knowledge as recognizing KCC, task initiative shifts as an indicator of productive collaboration are potentially easier to model in instructional software that simulates a peer. (shrink)

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