Moderators of Skill Retention Interval/Performance Decrement Relationships in Eight U.S. Air Force Enlisted Specialties

Lance, Charles E.; Bennett, Winston; Teachout, Mark S.; Harville, Donald L.; Welles, Marsha L.

doi:10.1207/s15327043hup1101_4

Cited by 24 publications

(24 citation statements)

References 53 publications

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“…However, there seems to be evidence of a small amount of overlearning once the automaticity phase and thus a learning curve plateau has been reached. This can actually lead to skill decay similar to what we witnessed for the physical environment [16], [15].…”

Section: Discussionsupporting

confidence: 64%

“…The learning curve of the VE participants was quite dramatic showing a high increase in performance after only a few assemblies. A possible reason for the skill decay shown in the retest of the PE group is that when people reach a learning saturation point, there is an overlearning effect [9] where any additional learning may cause a drop in performance [16]. We noticed several times when performance for the PE participants dropped and then increased again, only to drop another time.…”

Section: Training and Learning Curvesmentioning

confidence: 94%

“…Overlearning leads to automaticity and should reduce the amount of skill decay [15]. Lance et al [16] has shown that overlearning can actually increase skill decay. They define overlearning as a learning saturation point when any additional learning causes a drop in performance or skills decay.…”

Section: Learning Transfermentioning

confidence: 99%

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Virtual Training: Learning Transfer of Assembly Tasks

Carlson

Peters

Gilbert

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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In training assembly workers in a factory, there are often barriers such as cost and lost productivity due to shutdown. The use of virtual reality (VR) training has the potential to reduce these costs. This research compares virtual bimanual haptic training versus traditional physical training and the effectiveness for learning transfer. In a mixed experimental design, participants were assigned to either virtual or physical training and trained by assembling a wooden burr puzzle as many times as possible during a twenty minute time period.After training, participants were tested using the physical puzzle and were retested again after two weeks. All participants were trained using brightly colored puzzle pieces. To examine the effect of color, testing involved the assembly of colored physical parts and natural wood colored physical pieces. Spatial ability as measured using a mental rotation test, was shown to correlate with the number of assemblies they were able to complete in the training. While physical training outperformed virtual training, after two weeks the virtually trained participants actually improved their test assembly times. The results suggest that the color of the puzzle pieces helped the virtually trained participants in remembering the assembly process. Disciplines Computer-Aided Engineering and Design | Graphics and Human Computer Interfaces | Other Mechanical EngineeringComments This is a manuscript of an article from IEEE Transactions on Visualization and Computer Graphics (2014) Abstract-In training assembly workers in a factory, there are often barriers such as cost and lost productivity due to shutdown. The use of virtual reality (VR) training has the potential to reduce these costs. This research compares virtual bimanual haptic training versus traditional physical training and the effectiveness for learning transfer. In a mixed experimental design, participants were assigned to either virtual or physical training and trained by assembling a wooden burr puzzle as many times as possible during a twenty minute time period. After training, participants were tested using the physical puzzle and were retested again after two weeks. All participants were trained using brightly colored puzzle pieces. To examine the effect of color, testing involved the assembly of colored physical parts and natural wood colored physical pieces. Spatial ability as measured using a mental rotation test, was shown to correlate with the number of assemblies they were able to complete in the training. While physical training outperformed virtual training, after two weeks the virtually trained participants actually improved their test assembly times. The results suggest that the color of the puzzle pieces helped the virtually trained participants in remembering the assembly process.

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Section: Discussionsupporting

confidence: 64%

Section: Training and Learning Curvesmentioning

confidence: 94%

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Virtual Training: Learning Transfer of Assembly Tasks

Carlson

Peters

Gilbert

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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“…Training literature includes looking at improving the delivery of training and maximising skill acquisition, Lance et al (1998) discovered. For example, learning and learning strategies (Ackerman, 1987;Ackerman & Humphries, 1991;Gallagher, 1994;Hintzman, 1990;Weiss, 1991), training program design, training techniques and training evaluation methodologies (Goldstein, 1991;Kragier, Ford & Salas, 1993;Snow & Swanston, 1992;Tannenbaum & Yukl, 1992) and the transfer of training (Baldwin & Ford, 1988;Michalak, 1981;Royer, 1979;Schmidt & Bjork, 1992).…”

Section: Skill Retentionmentioning

confidence: 99%

“…This continued training is thought to encourage automacity or proceduralisation (storing the skill into the long term memory) therefore reducing cognitive demands and allowing better long term functioning (Anderson, 1983). By definition, over learning means more practice, and so provides more opportunity to assimilate feedback (Arthur et al, 1998;Lance et al, 1998). So the more automatic the skill has become, the greater the retention.…”

Section: Overtrainingmentioning

confidence: 99%

Skill acquisition and retention in training: DSTO support to the Army ammunition study

Stothard¹,

Nicholson²

2001

PsycEXTRA Dataset

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This work was undertaken within the Land Preparedness Studies Task (ARM 01/059) within the Land Operations Division. It is in response to the request for DSTO support contained within the Terms of Reference for the Army Ammunition Study (AAS). This request was for DSTO to assist in developing a better understanding of skill degradation (retention) and acquisition, with the view that this would enable accurate prediction of training requirements and, in the longer term, develop tools to predict levels of proficiency provided by different training regimes.In summary we:• Took a systems approach to the training problem and assessed the impact of the treatment of skill acquisition and degradation of predictions of training frequency requirements.• Undertook a literature survey, concentrating on the Cognitive Psychological literature, to ascertain the current thinking on how people learn and forget.• Assessed the gaps in the literature and scoped the work needed to address these gaps.• Made some progress in developing a new approach to modelling retention.• Assessed how DSTO, or other S&T agencies, could further address the issues raised by the AAS in both the short and long terms, through the development of appropriate R&D programmes. RELEASE LIMITATION Approved for public release^DEPARTMENT OF DEFENcT This work is aimed at developing a better understanding of skill acquisition and degradation (retention), to enable accurate prediction of training requirements. It also provides a start for the development of tools to predict levels of proficiency provided by different training regimes.The representation of skill acquisition and retention, within training/resource models, is shown to greatly influence model outputs. Therefore, at the heart of any modelling effort is an understanding of how proficiency is achieved and lost (skill acquisition and retention).We review the cognitive psychology literature on learning and retention, in order to establish what is the current understanding of skill acquisition and retention. The review covers two broad areas. First, the factors affecting skill acquisition and retention, and second, the mathematical description of the rate of skill acquisition and retention.The rate of skill retention, within the field of psychology, is currently accepted to be best fit by a power function. However, there are several significant difficulties encountered when using the power function in a dynamic model. We suggest an alternative model of retention, using a System Dynamic framework, which is suitable for dynamic modelling. This model requires more development and validation to assess its robustness.Gaps found in the literature, in areas pertinent to the understanding skill retention and acquisition in the Army context, include relearning and the change in skill retention from novice to expert, and moving from individual to collective skills. The System Dynamics model potentially addresses the relearning issue. Of interest is the effect of initial over-training on long term retention, an...

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