Long Term VS Short term Retention

[ronin]

From the book, "In the mind's Eye":


Enhancing Retention
Distribution of Practice

We have focused thus far on amount of practice during training, and have assumed that the level of performance achieved during training is a reasonable index of the level of learning achieved. For a fixed amount of practice, however, learning (as measured by a later retention test) depends on the temporal distribution of practice, and the nature of that dependency illustrates that performance during training is an unreliable indicator of learning. In general, massing of practice on some component of the to-be-learned task produces better performance in the short term (e.g., during training) but much poorer performance in the long term than does spacing of practice. In some cases, massed practice yields long-term recall performance less than one-half the level that results from spaced practice, and two massed practices are often not appreciably better than a single study trial (see, e.g., Glenberg, 1979; Glenberg and Lehmann, 1980; Melton, 1970; and Rothkopf and Coke, 1966).

The so-called spacing effect—that practice sessions spaced in time are superior to massed practices in terms of long-term retention—is one of the most reliable phenomena in human experimental psychology. The effect is robust and appears to hold for verbal materials of all types and for motor skills (for reviews, see Crowder, 1976; Dempster, 1990; Lee and Genovese, 1988). A recent indication of how durable the advantages of spacing may be across truly long posttraining intervals was reported by Bahrick and Phelps (1987). They tested subjects' recall of English-Spanish word pairs 8 years after the original training phase. During the training phase, successive practice sessions were separated by 30 days, 1 day, or 0 days. The level of retention was highest for the 30-day spacing of study sessions, next highest for the 1-day spacing, and lowest for the 0-day spacing, with performance for those in the 30-day condition more than twice that for those in the 0-day condition.

Given the benefits of spaced practice and the fact that those benefits have been known to researchers since the beginning of controlled research on human memory (Ebbinghaus, 1913), one would expect that spaced repetition would be a major component of modern programs of training and instruction. The fact that this not seem to be the case issomething of a puzzle (see Bjork, 1979; Dempster, 1990). Part of the solution to that puzzle, of course, may be a point we have already stressed: during the training process itself, spaced practice may appear inferior to massed practice.

Another factor in the apparent neglect of scientific findings on distribution of practice by those responsible for the design of training programs is time pressure: massed sessions take less total time than do spaced sessions. A study by Baddeley and Longman (1978), carried out for the British Post Office, illustrates the point. Given a new sorting system, which required postal workers to enter postcodes into a sorting machine using a standard typewriter keyboard, a large number of postal workers needed to be taught to type in a relatively short period of time. Baddeley and Longman examined four different training schedules, ranging from 1 hour of practice per day (spaced) to 4 hours of practice per day (massed). In terms of the learning curve—a plot of mean keystrokes per minute as a function of hours of practice—spaced practice was far more efficient than massed practice. To reach any given level of performance, however, it took the 1-hour-per-day group many more days than it took the 4-hours-per-day group, and the authors report that the former group was the least satisfied because the members felt they were falling behind the groups that were getting more practice per day. Thus, spaced practice produced much more efficient learning as a function of time on task, but took more days, which could certainly be a negative factor from a management standpoint.

[ronin]

Fostering Understanding

Just as the organization or cohesiveness of the components of a task makes it easier to learn and remember, so too does the organizing influence of understanding (Horton and Mills, 1984; Wertheim, 1985; Wetzel et al., 1983). In a story, independent or vaguely related occurrences are similar to steps of a procedural task that are not logically arranged and, hence, do not signal each other. When relevant organizing information is provided before reading a fragmented story, this information supplies a coherent structure within which to interpret more effectively the exact meaning of the story (Owens et al., 1979). Moreover, when this structure is also compatible with a trainee' s general knowledge of the world, recall is enhanced (Morris et al., 1979).

There is considerable evidence suggesting that long-term retention of procedural tasks that are based on complex rules or principles can be enhanced by augmenting instruction with explanations or information designed to increase a learner's understanding of the to-be-learned tasks (Gentner, 1980, 1982; Smith and Goodman, 1984; Tourangeau and Sternberg,1982; Kieras, 1981; Sturges et al., 1981). Although researchers differ to some extent on how they define explanations, it seems useful to categorize them as linear, structural, and functional (Stevens and Steinberg, 1981; Smith and Goodman, 1984). Linear explanations tell a trainee what to do—that is, what steps to follow and in what order. Structural explanations clarify how or why different task components belong together. Functional explanations inform the trainee about the cause-and-effect relationships among task components. In general, linear and structural explanations are used for static tasks, such as assembling a piece of equipment; functional explanations are used for dynamic tasks, such as operating a piece of equipment. In an examination of some of the literature dealing with the long-term retention of conceptual information and procedures inherent in expository prose as a function of structural explanations, Konoske and Ellis (1985:13) conclude that effective structural explanations “should include spatial and component-part information . . . as well as . . . goal statements. In addition, structural information should be communicated using text, schematics, graphs and illustrations, whenever possible.”

In summary, qualitative explanations that promote understanding of a to-be-learned task are effective for enhancing retention, presumably because they enable a trainee to reach a higher level of original learning. (The role of such explanations is discussed further in the next chapter as an important part of training techniques that use the expert as a model to guide the trainee.)

[ronin]

TRANSFER OF TRAINING

As just noted, training performance (i.e., level of original learning) may or may not be an effective predictor of posttraining performance when the training and posttraining contexts are the same or quite similar. When the training and posttraining contexts differ, however, many of the most effective procedures for facilitating the kind of learning that supports transfer apparently impair performance during training. In this section we first discuss the role of level of original learning and perceived similarity between tasks as general factors in the transfer of training; we then discuss some specific procedures during training that enhance transfer to different posttraining contexts.
General Factors in Transfer
Level of Original Learning

The level of original learning is not only a major determinant of retention, it is also a major determinant of transfer. Positive transfer increases with the level of original learning as long as structurally similar responses are required in the training and transfer tasks. The greater the similarity between the tasks, in terms of both stimulus and response requirements, the greater the positive transfer between them (e.g., Ellis, 1965; Osgood, 1949; Schmidt and Young, 1987).

One might expect, for example, some positive transfer between a tennis serve and an overhand volleyball serve because of the similarities in stimuli (a tossed ball) and response requirements (an overhand throwing motion). However, when a new response is paired with a previously learned stimulus, negative transfer may initially occur (Siipola, 1941). An example of negative transfer occurred in the evolution of the butterfly stroke in swimming (described by Fischman et al., 1982). Before the 1960s, the butterfly stroke was swum using a breaststroke kick. The introduction of the dolphin kick produced some negative transfer among butterfly swimmers, probably because of the pairing of this new kick with the traditional armstroke. When there is little or no association between the stimulus-response requirements of two tasks, no transfer is expected: one would not, for example, expect significant transfer effects between the movement patterns of golf and bowling.
It seems important to know the specific relationships between level of original learning, task similarity, and positive transfer, but we found no recent studies that examined the transfer of cognitive or motor tasks as a function of the amount of learning. Several studies of complex problem solving, however, suggest that performance improves with practice of the rules defining the task (e.g., Anzai and Simon, 1979; Kotovsky et al., 1985).

When negative transfer is expected from a training task to a posttraining task—that is, when structurally dissimilar responses are required in the training and transfer tasks—the effects of level of original learning are more complicated. Research on animal learning and on human verbal learning (Mandler, 1968) found that as the level of original learning increases, transfer becomes increasingly negative but that transfer becomes positive at high levels of original learning. Mandler proposed that negative transfer due to response competition increases monotonically to an asymptote as training is extended and the level of original learning increases. Extended training, however, also produces a type of generalized learning that is consistent with the transfer task as well as the training task. Such generalized learning has positive influences that eventually become stronger than the negative influence of response competition. Such an interpretation is consistent with the “learning to learn” idea—that is, learning general problem-solving strategies that are suitable for both training and transfer tasks.

[ronin]

Perceived Similarity Between Tasks

It has been known for some time that, in general, the basis for transfer from a training task to a transfer task are the common components shared by both tasks (Thorndike, 1903). The greater the number of components common to the training and transfer tasks, the greater their similarity, which should lead to greater positive transfer. Gick and Holyoak (1987) propose that any salient similarity between training and posttraining tasks will influence a trainee's perceived similarity between the tasks, which in turn will trigger retrieval of the trainee's mental representation of the training situation during the transfer task. The greater the perceived similarity between the training and posttraining tasks, the more likely it is that a trainee will attempt to transfer what was learned during training to the posttraining task. If transfer is attempted, then the direction of transfer—whether it is positive or negative—will be determined by the similarity between the training and posttraining tasks in terms of features that are causally relevant to the goals of the tasks or to the responses required in the posttraining task.
In summary, whether or not transfer occurs from training to posttraining is a function of perceived similarity between the two contexts. Perceived similarity of the two tasks is a function of any salient shared component and of a number of other factors, such as expertise and context. To a great extent, an individual's expertise on the subject determines whether the similarities observed are surface features or structural features. Whether actual transfer is positive or negative depends on the actual amount of structural similarity. Transfer is positive when the training and transfer responses are highly similar, that is, when they contain many shared structural components and few distinctive components. Thus, the amount of transfer obtained between situations is a function of the perceived similarity; the direction of transfer is a function of the objective structural similarity. The greater the perceived similarity of the situations, the greater the amount of transfer. No transfer takes place when two situations are perceived as unrelated, regardless of the degree of response similarity. If a learner does not perceive the similarity between training and posttraining contexts, the level of performance achieved in training clearly will not predict posttraining performance.

[ronin]

Enhancing Transfer

Some of the most promising methods of training for transfer to altered contexts create difficulties for a learner during training. Some of the most promising of those methods involve creating certain types of interference, introducing variability, and reducing the frequency of external feedback.
Providing Contextual Interference During Training

Research on contextual interference shows that learning that requires more cognitive processing is related to better retention and transfer. Contextual interference involves changes in the training context, including changes in the task, practice conditions, and the processing used by trainees. Contending with such changes demands cognitive processing that can, in turn, enhance the level of original learning. These changes have been referred to as “contextual variety,” which Battig (1979) believes is closely related conceptually to “transfer appropriate processing ” (Bransford et al., 1979). Battig also considers contextual variety to be a way of overcoming the boundaries in memory performance imposed by encoding specificity—in which there is a need to reinstate the original encoding context during a test—to improve performance (Tulving and Thomson, 1973).

One example of contextual interference is provided in a study conducted by Shea and Morgan (1979). They studied the learning of three similar procedural motor tasks that required adult subjects to knock down a series of barriers (with their hands) in a designated order as fast as possible without making any errors. Each of the tasks consisted of a separate pattern of barrier contacts for each trial of training. The three tasks were practiced in two ways, “blocked” and “random”: blocked practice involved performing 18 trials of one task before performing 18 trials of each of the other two tasks; random practice involved a random ordering of the three tasks over the 54 total trials. Following training, subjects transferred to either blocked or random conditions with a 10-minute and a 10-day interval. The major finding was that random practice produced poorer performance than blocked practice in training, but it produced superior performance in the posttraining context. This finding has been supported by other studies of adult learners (see Magill and Hall, 1990, for a review).

Another example, using a laboratory task, involved learning certain finger movements; the interference was having or not having the trainees also learn to articulate nonsense terms (e.g., XENF) to match the finger movements. The result was more proficient transfer performance on a different version of the finger task for those who learned the nonsense terms (Battig, 1956, 1966). This finding could be interpreted as showing that the contextual interference between word pronunciations and finger movements generated enhanced transfer. In other words, intratask interference in training produces greater intertask transfer.

[ronin]

and in summary:


CONCLUSIONS AND IMPLICATIONS FOR TRAINING

Measuring Learning and Performance The effectiveness of a training program should be measured not by the speed of acquisition of a task during training or by the level of performance reached at the end of training, but, rather, by a learner's performance in the posttraining tasks and real-world settings that are the target of training.

Two important dimensions of posttraining performance are the ability to resist forgetting and interference over periods of disuse of a given skill and the ability to generalize training to contexts and tasks thatdiffer in their surface characteristics from the training contexts or tasks. Depending on the relative priorities given to those two dimensions of posttraining performance, the optimal package of training components will differ somewhat. One general principle, however, is that tests of a learner's progress during training should, as much as possible, measure performance as it will be measured on the posttraining task(s) in the posttraining setting(s).

[ronin]

Retention Given posttraining tasks and conditions that are identical or similar to the training tasks and conditions, posttraining performance is enhanced as the level of original learning is increased. That level can be increased by putting greater demands on the learner—making the criterion of mastery more difficult, for example, or requiring supplementary (postmastery) practice after the criterion has been reached. Introducing variations in the conditions and sequencing of practice, the immediate consequence of which is to degrade performance, may be a particularly promising way to increase the level of original learning, and, hence, posttraining retention.

Skills that demand little attention or effort to perform are regarded as automatic; the more automatic a given skill, the higher the likelihood that the skill can be retained over nonuse periods without refresher training. Certain types of procedural tasks, however, tend to be easily forgotten, especially when their components have a low degree of internal organization or cohesiveness. The rate of forgetting of procedural tasks is a function of the number of steps needed to perform the task, and the steps most likely to be forgotten are those not cued by the equipment, environment, or preceding steps.

Several instructional strategies to enhance the retention and transfer of procedural tasks can be derived from the research on learning: relating the knowledge to be learned to the relevant knowledge learners already have in memory; teaching techniques (e.g., mnemonics) that learners can use to provide their own elaborations; having the training regimen require repeated use of the knowledge to be learned; and providing for and encouraging the use and elaboration of acquired knowledge and skill during nonuse periods. In general, a learner should be an active participant, not a passive observer, during the training process.

However well designed the initial training, refresher training may still be needed during posttraining periods of disuse in order to maintain a given level of knowledge and skill; refresher training can become less frequent over time. The training needs of retrainees are different from those of new trainees; relatively efficient, cost-effective techniques can be used to maintain a given level of original learning in retrainees.

[ronin]

Transfer of Training In general, the similarity of goals and cognitive processing between training and transfer tasks is a critical factor in enhancing transfer. A learner, therefore, should be challenged by means of manipulation of practice variables, such as feedback, contextual interference, and number and variability of examples. These manipulations, which may impair training performance, not only help the learner to process the learning task more deeply, but also suggest appropriate processes for transfer, particularly to related but distinct posttraining tasks.

[ronin]

FYI:
This makes the distinction in terms of training, between TMA and Sport combat arts better understood in my view.

Sport combat arts tend to favour/be about short term goals, whereas TMA tend to be about both, but as of late, tend top focus more on the long term.

[Dormir]

Just wanted to say thanks for putting this up. This kind of thing fit right in with some research I was doing this week. You the man Ronin.

[ronin]

I try my friend, I do try.