[GET] Discovering Geometry Assessment Resources Chapter 10 Test Answers
With their unique blend of teaching, consulting, and entrepreneurial experiences, this dynamic author team brings a modern edge to teaching statistics to business students. Although sometimes defined as "an electronic version of a printed book",some...
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For example, a door knob affords its users a way to twist and push, whereas a length of string affords users a means to pull and tie. Contemporary digital environments have features such as multimedia displays with texts, pictures, diagrams, visual highlighting, sound, spoken messages, and input channels clicking, touching for entering information that can afford important learning opportunities for users.
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Box summarizes information delivery and input features and other technological Key Affordances of Learning Technologies Interactivity. The technology systematically responds to actions of the learner. For example, some serious games immerse learners in virtual works through role-playing and interaction with a gaming community. Reading a book, listening to an audiotape, and viewing a film are not interactive technologies because these do not present new information in response to the actions of the learner. The technology presents information that is contingent on the behavior, knowledge, and characteristics of the learner. Task-relevant feedback can range from responses to short-term events that last a few seconds to long-term performance extending over for instance a school semester. The technology gives students options for what to learn and how to learn so they can regulate their own learning. For example, choice is low for an instruction-oriented technology that pushes an agenda with few options for learner exploration.
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Nonlinear access. The technology allows the learner to select or receive learning activities in an order that deviates from a set order. Many commercial learning technologies offer a linear presentation in which material and major concepts are experienced in the same order by all learners. Linked representations. The technology provides quick connections between representations for a topic that emphasize different conceptual viewpoints, pedagogical strategies, and media, such as between spoken messages, texts, diagrams, videos, and interactive simulations. Such connections support cognitive flexibility and encoding variability to support learning. Open-ended learner input. The technology allows learners to express themselves through natural language, drawing pictures, and other forms of open-ended communication that encourage active learning. Communication with other people. The communication may include text-based computer-mediated communication e-mail, chat, discussion rooms , multimedia computer-mediated communication, computer-supported collaborative learning, conversational agents, tutors on demand, and crowd sourcing.
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Learning Through Repetition We pointed in Chapters 3 and 4 to types of learning that require a significant amount of practice and repetition of items e. Interactivity and feedback are two affordances that are particularly helpful for supporting these types of learning. For example, there is a mature industry that provides computer-based vocabulary instruction in which the computer displays a picture and two to four words. The learner selects the word that names the picture and receives immediate feedback correct versus incorrect. The computer could present thousands of trials with this simple procedure, following particular schedules of item presentation with interactivity and feedback.
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These training trials have been used in classrooms and labs and to support homework outside of class. The training can be accessible throughout the day if it is available on a mobile device. One drawback to this type of computer-based instruction is that some learners may lose motivation when using a repetitive format. One way to enhance motivation is to add the affordance of adaptivity. For example, the FaCT system is adaptable in that it offers the learner optimally spaced training trials rather than massed training see Chapter 4 for discussion of spacing and stops the training on a particular fact if the learner performs correctly on it three times Pavlik et al. This approach can result in more efficient learning because learners do not waste time studying facts they already know. Another approach is to gamify the learning by adding expanded feedback e. Yet another way to sustain motivation is to allow learners to select topics that interest them.
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Some topics may be very important but unappealing, so a possible downside of allowing too much choice is the risk that learners never get around to acquiring critical knowledge or skills. Deeper Learning with Models People need more than the foundations of literacy, numeracy, and other basic skills to handle the complex technologies, social systems, and subject matter typical of 21st century tasks Autor and Price, ; Carnevale and Smith, ; Griffin et al. Deeper learning involves understanding complex concepts and systems and is manifested in, for example, the use and construction of models see Chapter 3 , the ability to integrate information from multiple documents and experiences Wiley et al.
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The technology affordances of linked representations and open-ended learner input are particularly important for this type of learning, as are the interactivity, feedback, and adaptivity affordances of traditional computer-based training. The value of technology for representing a situation from multiple linked perspectives is evident in the example of helping learners understand a system, such as an electronic circuit. An intelligent technology can allow a learner quick access to perspectives, including a picture of the circuit as it appears in a device, a functional diagram of the components and connections, descriptions of the properties of each component, formulas that specify quantitative laws e.
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The quick access will allow the learner to link these elements. Open-ended learner input is also important for conceptual learning with system models. Intelligent tutoring systems can also support deep learning with models Sottilare et al. Noteworthy examples in mathematics are the Cognitive Tutors Anderson et al. Intelligent tutoring systems have been widely used and have produced impressive learning gains in the areas of digital literacy Kulik and Fletcher, and information technology Mitrovic et al.
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Intelligent tutoring system environments have also shown promise in domains that have strong verbal demands. Such tutoring tools have included open-ended learner input and the ability to communicate with other people, in addition to most of the other affordances. For example, AutoTutor Graesser, ; Graesser et al. AutoTutor is associated with learning gains in both physics VanLehn et al. The agent is a talking head that speaks, points, gestures, and exhibits facial expressions. The learning gains from natural language interactions have been strongest for underachieving college students and for tests that tap deeper inferential reason- Page Share Cite Suggested Citation:"8 Digital Technology.
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However, the research also suggests that conversational interactions with AutoTutor are not ideal for high-achieving college students, who tend to be more autonomous and self-regulated learners, or for use in simulation environments that are intended to push the student to acquire very precise models of the subject matter. AutoTutor is also not the best choice for perceptual, motor, and memory-based learning. Intelligent tutoring systems have been developed for a wide range of subject matters and proficiencies and have benefited learners in schools, universities, and the workforce.
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