An Estimation of the Learning Effect on Project Cost Scheduling

Mályusz, Levente; Varga, Anita

doi:10.1016/j.proeng.2017.07.239

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…In the manufacturing industry, the learning curve was evaluated for the worker’s efficiency of working, and in the medical field, for determining the proficiency of new technology or medical device, the time reduction pattern was analyzed 12 , 18 . A previous study evaluated the learning curve of an IOS and found that dental clinical experience affected the learning effect 16 .…”

Section: Discussionmentioning

confidence: 99%

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Prediction of learning curves of wired and wireless intraoral scanners

Koo,

Son,

Lee

et al. 2023

Sci Rep

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This clinical study aimed to predict the learning curve of wireless and wired intraoral scanners (IOSs) and to compare the reduction patterns of working time. Overall, 14 participants were enrolled in the study. The intraoral scanning procedure was repeated four times, each using wireless and wired IOSs (i700; MEDIT). The work time from the first to the 600th iterations was predicted using the Wright model. Regarding statistical analysis, the Mann–Whitney U-test was performed for comparison between wireless and wired IOSs and between groups with and without an IOS usage experience, and the Friedman test was performed to evaluate the time reduction (α = 0.05). There was a significant difference between wireless and wired IOSs in the first (P = 0.008) and the third (P = 0.035) iterations. Moreover, the time for 600 iterations was statistically significantly different between wireless and wired IOSs (P < 0.05); however, there was no significant difference after the sixth iteration (e.g., seventh iteration: P = 0.062). In wireless IOS, no significant difference was found between participants with and without an IOS usage experience after the 34th iteration (P = 0.053). The difference in the learning effect between wireless and wired IOSs can be overcome by initial learning; however, an IOS usage experience can affect the learning time of wireless IOSs.

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Section: Discussionmentioning

confidence: 99%

“…Learning of medical devices is conducted for stable use by inexperienced clinicians 12 . In order to evaluate the learning of dental CAD software, learning patterns were compared with learning curves for designing a dental prosthesis 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of learning curves of wired and wireless intraoral scanners

Koo,

Son,

Lee

et al. 2023

Sci Rep

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“…After learning curve theory was first proposed in the field of psychology, 9 it was then applied to other fields. In manufacturing, for example, the learning curve has been used to assess worker performance or determine the cost and efficiency of a new workflow 15 . In addition, in the field of medicine, there have also been many studies on the learning curve, especially in the field of new digital technologies such as the Da Vinci surgical system, 16 robot‐assisted kidney transplantation 17 and endoscopic ultrasonic‐assisted gastrostomy 18 .…”

Section: Discussionmentioning

confidence: 99%

“…In manufacturing, for example, the learning curve has been used to assess worker performance or determine the cost and efficiency of a new workflow. 15 In addition, in the field of medicine, there have also been many studies on the learning curve, especially in the field of new digital technologies such as the Da Vinci surgical system, 16 robot-assisted kidney transplantation 17 and endoscopic ultrasonic-assisted gastrostomy. 18 Likewise the dental field, 10,11 especially in comparisons between digital and traditional dentistry.…”

Section: Learning Curve Effectmentioning

confidence: 99%

Exploring training dental implant placement using static or dynamic devices among dental students

Wang

Zhuang

et al. 2022

Eur J Dental Education

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Background: Static computer-assisted surgery (s-CAIS) and dynamic computerassisted implant surgery (d-CAIS) are the main digital approaches in guiding dental implant placement. Purpose:The aim of this study was to explore and compare the learning curves for s-CAIS and d-CAIS by beginners. Materials and Methods:Three dental students used each dental model for drilling five positions with missing teeth. Operators performed the drilling test for five sets of dental models with an interval of 7 ± 1 days assisted by the d-CAIS system. After a sixmonth break, the same students performed the drilling test again in the same way but with the s-CAIS system. A total of thirty models were used, and 150 implants were inserted. The operation time and relative deviations were recorded and calculated.Correlations between various deviation parameters and attempts were tested with independent-samples Kruskal-Wallis tests.Results: A significant difference between the two groups was found in the operation time (p < .001). For accuracy, the difference was found in the first attempt of coronal and apical deviations but disappeared as the training went on. As the practice progressed, improvement was evident in the d-CAIS group but not in the s-CAIS group.When reaching the plateau stage of the learning curve of the d-CAIS group (after five attempts), the influence of different methods of guidance was limited between the two groups.Conclusions: A learning curve effect was found in d-CAIS but not in s-CAIS in vitro tests by beginners. The operating procedure of dynamic navigated and static templateguided implant placement was easy to master.

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