Annotated Bibliographies for Module 4
Driscoll, M. (2005). Psychology of learning for instruction (3rd ed., pp. 91-110). Boston, MA: Allyn and Bacon
There are several theories regarding the long-term storage and representation of information in semantic memory. Network models of long-term memory (LTM) can be equated to a mental dictionary in which concepts are represented based on their relationships to one another. Although this model does a good job of accounting for individual differences in memory and an individual’s ability to quickly recognize class and property relationships, it struggles to account for the typicality of concepts (p. 97). The feature comparison models of LTM propose that concepts are stored according to the essential features of the concepts, allowing for associations to occur based upon the existence of common features. This model does a better job of accounting for typicality of concepts and the idea that some members of a set better represent a concept than other members of the same set. Despite several strengths of this model, it does not account for the large size of collections that would be required, nor the semantic flexibility that occurs when an individual relies on context to determine a word’s meaning (p. 94).
The propositional models of LTM state that the fundamental unit of storage is a proposition, rather than a concept. This model accounts for procedural and declarative knowledge but is difficult to test. The parallel distributed processing models of LTM assert that numerous cognitive processes occur at the same time, as opposed to occurring one at a time. Additionally, this model supports the theory that knowledge is stored in the connections between nodes, and that learning occurs when these connections are strengthened (p. 95). Dual-code models of LTM address the belief that nonverbal and verbal knowledge are stored using different systems in memory. While storage systems may be different, a strong connection exists between these two systems. The integration of visual aides and imagery into teaching is supported by this model (p.99).
In addition to theories of how information is encoded by learners, theories exist to address how information is retrieved from memory. These theories address a learner’s ability to recall information with and without cues, as opposed to being able to recognize previously learned information. Encoding specificity addresses the idea that the cues used by a learner to encode new information, will act as the best cues for retrieving that information (p. 101). When accounting for the act of forgetting, researchers address the failure to encode and failure to retrieve as the sources of a leaner forgetting information.
Driscoll proposes four strategies for enhancing learning based on the models of cognitive information processing. These strategies include providing organized instruction, allowing for frequent and varied practice, teaching learners how to improve their abilities to encode new information, and improving learners’ understanding and awareness of their own thinking.
Clark, J.M. & Pavio, A. (1991). Dual-coding theory and education. In Educational Psychology Review, 3(3), 149-210.
In their paper, Clark and Pavio explain how dual-coding theory can be used to “model diverse educational phenomena” (p. 149). The theory provides support for understanding how learners learn and how teachers can further the learning of their students. Dual-coding theory proposes separate, but closely connected, systems for the storage of verbal and nonverbal information. The verbal system is composed of verbal codes that represent concrete objects and abstract ideas and processes information sequentially. The nonverbal system includes images that are similar to the events they represent, and information is processed simultaneously (p. 152). Referential connections join representations between the verbal and nonverbal networks, while associative connections join representations within each network. Several underlying assumptions are fundamental to the dual-coding theory (DCT). These assumptions address the variation in activity level of verbal and nonverbal representations at any one time, the importance of prior learning and experiences, the concreteness of the information being learned, and individual differences in ability and or tendency to use imagery.
DCT asserts that imagery, in addition to word concreteness, is essential to the comprehension of text. Studies have found individual differences in the use of imagery by readers, with only some learners spontaneously evoking imagery while reading. Evoking imagery while reading has been shown to reduce reading speed, which cause students who are prompted to read for speed to demonstrate poorer comprehension (p. 162). Additionally, DCT asserts that associative relationships connect words to each other and contribute to word meaning.
DCT offers several propositions for the classroom. Studies have shown that lessons that incorporate concrete information and strong imagery result in better comprehension by the learner than do lessons that are abstract and do not utilize imagery. Additionally, teachers who regularly use imagery are viewed as more effective by students and their students perform better when tested (p. 175). The use of associative strategies and mental outlines to create highly organized and structured lessons further enhances the ability of students to learn new material.
DCT also addresses the role that emotions and motor skills play in learning and memory. A strong connection between emotions and imagery exists. In an educational context, goal-related images are more likely to motivate students to persevere and students show more interest in texts that evoke imagery (p. 182). Students can also be trained to use imagery to reduce testing anxiety. Positive and negative emotional language can affect a student’s attitude toward school, and positive and negative self-talk can impact how likely a student is to persevere. Motor skills incorporate both visual and kinesthetic images, and the use of imagery can also promote improvements in motor skills.
DCT is presented as an integrated theory of learning, which provides multiple benefits over theories that are disjointed. DCT accounts for many factors in learning and educational psychology. There are still many unanswered questions and challenges, and the need for further research and exploration is emphasized.
Driscoll, M. (2005). Psychology of learning for instruction (3rd ed., pp. 91-110). Boston, MA: Allyn and Bacon
There are several theories regarding the long-term storage and representation of information in semantic memory. Network models of long-term memory (LTM) can be equated to a mental dictionary in which concepts are represented based on their relationships to one another. Although this model does a good job of accounting for individual differences in memory and an individual’s ability to quickly recognize class and property relationships, it struggles to account for the typicality of concepts (p. 97). The feature comparison models of LTM propose that concepts are stored according to the essential features of the concepts, allowing for associations to occur based upon the existence of common features. This model does a better job of accounting for typicality of concepts and the idea that some members of a set better represent a concept than other members of the same set. Despite several strengths of this model, it does not account for the large size of collections that would be required, nor the semantic flexibility that occurs when an individual relies on context to determine a word’s meaning (p. 94).
The propositional models of LTM state that the fundamental unit of storage is a proposition, rather than a concept. This model accounts for procedural and declarative knowledge but is difficult to test. The parallel distributed processing models of LTM assert that numerous cognitive processes occur at the same time, as opposed to occurring one at a time. Additionally, this model supports the theory that knowledge is stored in the connections between nodes, and that learning occurs when these connections are strengthened (p. 95). Dual-code models of LTM address the belief that nonverbal and verbal knowledge are stored using different systems in memory. While storage systems may be different, a strong connection exists between these two systems. The integration of visual aides and imagery into teaching is supported by this model (p.99).
In addition to theories of how information is encoded by learners, theories exist to address how information is retrieved from memory. These theories address a learner’s ability to recall information with and without cues, as opposed to being able to recognize previously learned information. Encoding specificity addresses the idea that the cues used by a learner to encode new information, will act as the best cues for retrieving that information (p. 101). When accounting for the act of forgetting, researchers address the failure to encode and failure to retrieve as the sources of a leaner forgetting information.
Driscoll proposes four strategies for enhancing learning based on the models of cognitive information processing. These strategies include providing organized instruction, allowing for frequent and varied practice, teaching learners how to improve their abilities to encode new information, and improving learners’ understanding and awareness of their own thinking.
Clark, J.M. & Pavio, A. (1991). Dual-coding theory and education. In Educational Psychology Review, 3(3), 149-210.
In their paper, Clark and Pavio explain how dual-coding theory can be used to “model diverse educational phenomena” (p. 149). The theory provides support for understanding how learners learn and how teachers can further the learning of their students. Dual-coding theory proposes separate, but closely connected, systems for the storage of verbal and nonverbal information. The verbal system is composed of verbal codes that represent concrete objects and abstract ideas and processes information sequentially. The nonverbal system includes images that are similar to the events they represent, and information is processed simultaneously (p. 152). Referential connections join representations between the verbal and nonverbal networks, while associative connections join representations within each network. Several underlying assumptions are fundamental to the dual-coding theory (DCT). These assumptions address the variation in activity level of verbal and nonverbal representations at any one time, the importance of prior learning and experiences, the concreteness of the information being learned, and individual differences in ability and or tendency to use imagery.
DCT asserts that imagery, in addition to word concreteness, is essential to the comprehension of text. Studies have found individual differences in the use of imagery by readers, with only some learners spontaneously evoking imagery while reading. Evoking imagery while reading has been shown to reduce reading speed, which cause students who are prompted to read for speed to demonstrate poorer comprehension (p. 162). Additionally, DCT asserts that associative relationships connect words to each other and contribute to word meaning.
DCT offers several propositions for the classroom. Studies have shown that lessons that incorporate concrete information and strong imagery result in better comprehension by the learner than do lessons that are abstract and do not utilize imagery. Additionally, teachers who regularly use imagery are viewed as more effective by students and their students perform better when tested (p. 175). The use of associative strategies and mental outlines to create highly organized and structured lessons further enhances the ability of students to learn new material.
DCT also addresses the role that emotions and motor skills play in learning and memory. A strong connection between emotions and imagery exists. In an educational context, goal-related images are more likely to motivate students to persevere and students show more interest in texts that evoke imagery (p. 182). Students can also be trained to use imagery to reduce testing anxiety. Positive and negative emotional language can affect a student’s attitude toward school, and positive and negative self-talk can impact how likely a student is to persevere. Motor skills incorporate both visual and kinesthetic images, and the use of imagery can also promote improvements in motor skills.
DCT is presented as an integrated theory of learning, which provides multiple benefits over theories that are disjointed. DCT accounts for many factors in learning and educational psychology. There are still many unanswered questions and challenges, and the need for further research and exploration is emphasized.