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The signaling principle, also known as the cueing principle, refers to the finding that people learn more deeply from a multimedia message when cues are added that guide attention to the relevant elements of the material or highlight the organization of the essential material. This chapter reviews the main findings from research on signaling in multimedia learning addressing the effects of incorporating cues into the text, the picture, or both. Text-based cues can consist of sentences that precede the learning materials and highlight their organization. Picture-based cues can consist, for instance, of arrows in which case they are extrinsic in the sense that an element is added to the picture. The chapter considers the design of cues based on eye movements and the effects of using eye movements as cues. The signaling principle may have some relation to other principles identified by the cognitive theory of multimedia learning as well.
Volunteer potato is a perennial weed that is difficult to control in crop rotations. It was our objective to build a small, low-cost robot capable of detecting volunteer potato plants in a cornfield and thus demonstrate the potential for automatic control of this weed. We used an electric toy truck as the basis for our robot. We developed a fast row-recognition algorithm based on the Hough transform and implemented it using a webcam. We developed an algorithm that detects the presence of a potato plant based on a combination of size, shape, and color of the green elements in an image and implemented it using a second webcam. The robot was able to detect potatoes while navigating autonomously through experimental and commercial cornfields. In a first experiment, 319 out of 324 images were correctly classified (98.5%) as showing, or not showing, a potato plant. In a second experiment, 126 out of 141 images were correctly classified (89.4%). Detection of a potato plant resulted in an acoustic signal, but future robots may be fitted with weed control equipment, or they may use a global positioning system to map the presence of weed plants so that regular equipment can be used for control.
Inquiry or scientific discovery learning environments are environments in which a domain is not directly offered to learners but in which learners have to induce the domain from experiences or examples. Because this is a difficult task the discovery process needs to be combined with guidance for the learner. The most effective way to provide this guidance is to integrate it in the learning environment. Guidance may be directed at one or more of the discovery learning processes, for example, hypothesis generation or monitoring, or at structuring the overall process. With adequate guidance discovery learning can be an effective learning approach in which mainly “intuitive” or “deep” conceptual knowledge can be acquired. Inquiry learning now finds new directions in collaborative inquiry and modeling environments.
Guided Discovery Learning
In the design of learning environments the emphasis in the learning process is often placed on the learning material or the teacher. In this way instruction that explains principles and rules in a domain to a learner is created. This instructive mode of teaching and learning can be contrasted with an inductive learning mode in which the emphasis in the learning process is with the learner. This scientific discovery (or inquiry) learning is characterized by the induction of principles from experiences and/or examples (Swaak & de Jong, 1996). The learner's knowledge acquisition process progresses by stating rules or hypotheses on the basis of concrete situations and by subsequently testing these hypotheses in new situations.
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