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Register for a free to start saving and receiving special member only perks. Advances in the digital technologies available to support learning are among the most dramatic developments since the publication of HPL I. These examples suggest the range of ways technologies can support learning in varied sociocultural contexts. The game in the first example was appropriate for repetitive drill and practice on numerical operations, whereas the intelligent tutoring system was needed to acquire deep mental models of aircraft devices.

The free online courses supported self-regulated learning by the individual who needed to change fields. As these examples suggest, learning technology is most useful when it is deed to meet specific needs and contexts. Since that report was published, new technologies have been developed and researchers have expanded understanding of how digital technology can most effectively be used to foster learning. In this chapter, we discuss ways to align learning technologies with goals for learning, drawing on research on new technologies that have shown promise for stimulating active learning and supporting learning in school and in the workforce.

We also discuss the use of technologies for supporting older learners and close with a discussion of access to learning opportunities. Learning technologies open up ificant possibilities for supporting learners. Researchers in the field use the term affordances to refer to opportunities that a technology makes possible related to learning and instruction Collins et al. In this section we first examine the nature of the affordances of learning technologies and then explore research on how technology can support several aspects of learning. An affordance has been defined as a feature or property of an object that makes possible a particular way of relating to the object for the person who.

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 Available smart and educated looking for the same opportunities for users.

Box summarizes information delivery and input features and other technological. We pointed in Chapters 3 and 4 to types of learning that require a ificant amount of practice and repetition of items e. We have noted that such learning. 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. 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 Available smart and educated looking for the same 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. 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. 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 3the ability to integrate information from multiple documents and experiences Wiley et al.

Deeper learning is needed for complex. 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. 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.

Intelligent tutoring system environments have also shown promise in domains that have strong verbal demands.

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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.

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. Hundreds of studies have shown the effectiveness of intelligent tutoring systems in promoting deeper learning for some populations of learners on core literacy and numeracy skills, complex STEM topics, and 21st century skills Kulik and Fletcher, However, two issues related to implementation have been noted.

First, the systems are expensive to build, so using them on a large scale can be a challenge for schools, universities, and workforce programs with limited budgets. Developers of these systems are exploring ways to develop content more quickly and cheaply, as in the U. Second, like any classroom intervention, intelligent tutoring systems need to be integrated adequately into teacher training and curricula in order to have an impact Dynarsky et al.

The ability to work effectively in teams is among the 21st century learning objectives that have been identified in a of venues because of its critical importance in the workplace National Research Council, b ; OECD, See Chapter 7 for further discussion of collaborative learning.

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Collaborative learning can be distinguished from cooperative learning Dillenbourg et al. Collaborative learning requires interdependencywherein group members work together to plan and organize t activities to complete a task or solve a problem.

The action of each person builds on the actions of others, and an action of one person may be taken up or completed by others in the group. In contrast, cooperative learning involves breaking a task into pieces: group members work separately, although they may coordinate activities that proceed in parallel. The completed pieces are assembled by the group Hesse et al.

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Many of these tools are free or very low in cost. Learning technologies have been deed to promote deeper conceptual learning as part of group collaboration. Two examples for which the developers have shown positive effects are described in Box However, the availability of communication technologies for cooperation and collaboration does not necessarily translate into learning gains. For example, Reich and colleagues studied the use of Wikis in kindergarten through twelfth grade K classrooms by extracting a random sample of Wikis from a popular site that provides free hosting for education-related Wikis.

Nearly three-quarters of the Wikis showed no evidence of student-created content, and only 1 percent featured multimedia content created collaboratively by students. Equally discouraging was their finding that content created by students, as opposed to teachers, was more common among schools serving high-income students than among schools serving less-affluent populations. Several computer technologies have been developed to train learners to acquire metacognitive Available smart and educated looking for the same self-regulated learning strategies. Two examples that have shown promise in improving these types of learning are MetaTutor and iDrive.

MetaTutor Azevedo et al. It uses conversational agents to train students on 13 strategies, such as taking notes, drawing tables or diagrams, re-reading, and making inferences that theory suggests are important for self-regulated learning Azevedo and Cromley, Initial studies have shown some positive impacts but Available smart and educated looking for the same for all learning strategies.

One reason may be that the instruction was delivered using a standard script; individualized training adapted to learners may be more effective. The student agent asks a series of deep questions about the science content e. Increases in the targeted cognitive activities have been shown Gholson and Craig, ; Rosenshine et al.

These technologies have two of the important affordances for learning described earlier. They give the learner choice, which seems to optimize motivation, and allow him to communicate with other people, which is especially productive when learners are just beginning to develop self-regulation strategies. However, such approaches have had mixed success, and it usually takes many hours of training with many examples for learners to show appreciable progress Azevedo et al.

We have pointed to the importance of stimulating active student learning rather than merely delivering information to the student through books and lectures see Chapters 5 and 7. Digital technologies offer a variety of possibilities for stimulating and engaging learners.

Games are known to capture the attention of players for hours, as the players actively participate for competition or other forms of pleasure. Social media also shares these benefits. It is possible for deers of learning technologies to capitalize on these phenomena and leverage social engagement for academic learning. Some games were not originally deed with the goal of enhancing academic learning, but case studies have found that they nevertheless provide opportunities for learning and identity formation that can spill over into other aspects of life. These findings have spawned efforts to use technologies such as digital games, social media, and online affinity groups to engage students for academic purposes Gee, Several such online games have been used at scale in both afterschool and classroom settings; examples include Atlantis, Civilization, Crystal Island, Minecraft, Sim City, and Whyville Dawley and Dede, Nevertheless, games may be more effective than alternative approaches for some specific.

More relevant for schooling are the recent reviews of serious games that target specific academic content. A large of quantitative studies Clark et al. Some researchers have suggested that video games are inherently engaging and motivating to people Prensky, ; Squire, and that research on video games can provide insights into the de of educational environments Gee, ; Squire, Malone argued, for instance, that computer games are intrinsically motivating because they can provide optimal challenge and fantasy, while stimulating curiosity.

Malone and Lepper expanded on the motivating factors of computer games by adding that such games give users a sense of control because their actions affect game outcomes. Gee identified a taxonomy of motivational factors that could be used to de video games. However, very little empirical evidence supports these claims Zusho et al. Although some studies have linked video game playing to motivation, this possible relationship has not been explored in educational settings. Further, the literature on adult populations college students and other adults suggests that users play video games for a variety of cognitive, affective, and social reasons.

For instance, such games may satisfy psychological needs for competence, autonomy, and relatedness, which are associated with intrinsic motivation see Chapter 6but the applicability of such findings to K populations is unknown Zusho et al. Further, it cannot be assumed that gaming, or technology in general, would be inherently motivating to all learners. Whether technology is motivating to people is likely to depend on the learner, the task, and the learning context. The entertainment industry has established the practice of connecting television shows or movies with social media sites, online games, and products based on favorite characters.

Educators have found opportunities in this phenomenon for linking education and training programs to popular stories, personalities, and characters Jenkins et al. For example, the U. Army has used transmedia. The U.

The games can be played on computers, smartphones, electronic tablets, or smartboards. The PBS Kids Lab Website also helps users link game content related to mathematics and literacy curricula to activities for home, school, or afterschool settings Herr-Stephenson et al. For example, deers of the Ready to Learn transmedia experiences have built videos, games, and digital device applications apps to support model learning by stimulating discussions between young children and their caretakers and helping children formulate questions and express their ideas Mihalca and Miclea,

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