Elaborative strategies;  Integration strategies

Elaboration is a  cognitive learning strategy that involves any enhancement of information that clarifies or specifies the relationship between information to-be-learned and related information, i.e., a learner’s prior knowledge and experience or contiguously presented information. The addition can be an inference, an example, an analogy, a detail, an image, an overall summary, or any other mental construction. Essentially, elaboration is encoding the original content in a different but related way. There are primarily two kinds of elaboration: visual and verbal. For example, to learn the pair “cow-ball” a person could form a visual image of a cow kicking a ball. Alternatively, someone could create a sentence such as “The cow ran after the ball.” Elaborations can also differ in terms of source, i.e., in many instructional situations, learners are asked to generate the elaborations themselves. In contrast, relevant elaborations can be built into instructional materials in order to facilitate the learning of the materials.

Historical and current psychological views situate the influence of elaboration on learning within the context of the traditional multistore, multistage theory of memory (e.g., sensory, working and long-term memory) and cognitive processing (Atkinson and Shiffrin 1968). This view of memory suggests that information is processed through a series of memory stores, each with different purposes and characteristics (duration, capacity, etc.). Elaboration strategies facilitate the processing of information as it moves through these memory stores and eventually adds to and/or modifies an individual’s knowledge of the world. Elaboration strategies allow learners to access and to create organized units of knowledge while processing new information that reduces the overall load on the active memory store, hence increasing the efficiency of the learning process, while also increasing its effectiveness by enhancing the comprehension of incoming information. This increase in efficiency and effectiveness of the learning process consequently facilitates both the creation and/or encoding of new information and skills in people’s permanent memory store and increases the likelihood of retrieval of this information or skills when needed.

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Elaboration, however, is only one of a set of processing strategies that have been identified as supporting the processing, and eventual learning of new information and skills (Mayer 1989). Elaborative strategies are considered one of the more powerful of learning strategies and a critical component to making new information meaningful, comprehensible, and consequently, memorable.

Elaboration was first studied in the context of  paired-associate learning tasks (Rohwer 1973). Paired-associate learning is an important variety of learning that occurs in schools and, thus, has been extensively studied in educational research. Examples of school-learning tasks involving paired-associate learning include learning the names of new objects, learning definitions, learning the capital cities of countries, etc. Elaborations have consistently been found to facilitate retention of target verbal information (Levin 1988). In addition, researchers have found that elaboration can have significant positive effects on more complex outcomes, e.g., skill and concept application, problem-solving. The effectiveness of elaborations is differentially moderated by the prior knowledge or level of expertise of the learner, source of the elaboration (self- or instructionally generated), the learning outcome, the type of elaborations, and the structure of the instructional material.

Explanations for the influence of elaboration on learning assume that people’s knowledge is organized into networks of concepts, experiences, and beliefs (see  schema and  mental models). The specific explanations differ depending on the nature of the learning outcome. For retention of verbal or visual information, elaborative activities may increase the richness or the distinctiveness of the encoded memory episode. “Richness” within this context is operationalized as the number of interconnections between the target propositions and a learner’s related prior knowledge. At the time of recall, learners will typically activate only a small subset of the original propositions and attempt to reconstruct these propositions. The richer and more redundant the activated subset of propositions, the more likely the reconstruction of the original set of propositions. An alternative explanation for the positive effects of elaboration may be found in the research which focuses on the distinctiveness of encoded episodes. Research suggests that distinctive encoding can be described as the processing of differences among the items of an episode. That is, focusing on the differences among the units of a memory episode should increase the distinctiveness of the episode and increase the probability that the set of propositions will be more easily found and accessed during recall and retrieval situations. An example of a “richness” elaborative activity is one that asks a learner to identify how the to-be-remembered information relates to prior personal experiences. Alternatively, an example of a “distinctiveness” elaborative activity is one that asks a learner to identify how the different pieces of the to-be-remembered information fit together.

The positive effects of elaboration to facilitate more complex learning outcomes often focuses on its value in facilitating the development of appropriate mental models which allow increased understanding of and application of target concepts, principles and skills. In creating knowledge related to new domains or areas, people often create “mental models” which approximate important ideas and principles within these new domains. Well-elaborated mental models allow learners to identify critical aspects of new problems and tasks which in turn support superior problem-solving and application (Mayer 1989).

Current research on the effects of elaboration on remembering information is attempting to tease out how the nature of to-be-learned material (e.g., text coherence, authenticity), the nature of elaborative strategies (e.g., self-generated versus author-generated), and the prior knowledge and motivational disposition of a learner interact to influence the effectiveness of elaborative strategies (cf. Dornisch et al. 2011). An important focus within the research on more complex outcomes, e.g., problem-solving and related skills, has been on assessing the value of different elaborative strategies (e.g.,  self-explanation,  worked examples) on acquiring important complex learning outcomes. In general, prompted and spontaneous self-explanations and the presentation of worked examples have been found to improve the learning of problem-solving skills, particularly for low-knowledge learners. Given these positive effects, researchers are focusing on how best to develop materials to support self-explanation and the development of effective worked examples (cf. Kalyuga 2009).

 Cognitive Learning

 Cognitive Load Theory

 Elaboration Strategies and Human Resources Development

 Human Information Processing

 Imagery and Learning

 Learning Strategies

 Memory Structures

 Mental Models

 Schema(s)