• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.26-38
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• 2017

The architecture, engineering, and construction (AEC) industry is yet to formulate a holistic strategy to realign the evolving technological infrastructures with organisational ambitions and adaptive knowledge of the workforce. This study attempts to create an understanding of the underlying processes adopted by technology-enhanced construction organisations to disseminate and maintain knowledge within the workforce in order to keep pace with the evolving construction technologies. The study adopted expansive learning and adaptive expertise constructs to help better explain workplace learning support structures for organisational effectiveness in a turbulent situation. The two theories were tailored to empirically evaluate three case study construction organisations that have embarked on technology-enabled organisational changes. The study concluded on the creation of a facilitating workplace learning environment to enable the workforce to adapt into and resolve any inherent contradictions and cognitive ambiguities of the changing organisational conditions. This could ensure that novel and conflicting features of the emerging technologies can be adapted across the myriad multi-functional project activities in order to expand the frontiers of the technological capabilities to address the eminent issues confronting the AEC sector.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.403-414
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• 2023

In this study, we examined the adaptive practices of science teachers in their classrooms and their perspectives on the distinguishing features of these practices within science subjects. Our analysis comprised 339 cases from 128 middle and high school science teachers nationwide, and 199 cases on the characteristics of adaptive practices in science disciplines. The primary findings were as follows: First, the most significant characteristic of adaptive practice in science disciplines pertained to experimental procedures. Within the 'suggestion of additional materials/activities' category, the most frequently cited adaptive practice, teachers incorporated demonstrations to either facilitate student comprehension or enhance motivation. Additionally, 'experimental equipment manipulation or presentation of inquiry skills' emerged as the second most common adaptive practice related to experiments. Notably, over 50% of teacher responses regarding the characteristics of adaptive practices in science pertained to experiment guidance. Second, many adaptive practices involving difficulties experienced by students in learning situations were presented, particularly in areas such as numeracy and literacy. Many cases were related to the basic ability of mathematics used as a tool in science learning and understanding scientific terms in Chinese characters. Third, beyond 'experiment guidance', the characteristic adaptive practices of science subjects were related to 'connections between scientific theory and the real world', 'misconception guidance in science', 'cultivation of scientific thinking', and 'convergence approaches'. Fourth, the cases of adaptive practice presented by the science teachers differed by school level and major; therefore, it is necessary to consider school level or major in future research related to adaptive practice. Fifth, most of the adaptive action items with a small number of cases were adaptive actions executed from a macroscopic perspective, so it is necessary to pay attention to related professionalism. Finally, based on the results of this study, the implications for science education were discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.161-164
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• 2004

The yo-yo system has been introduced as an interesting plant to demonstrate the effectiveness of intelligent controllers. Having nonlinear and asymmetric characteristics, the yo-yo plant requires a controller quite different from conventional controllers such as PID. In this paper is presented an adaptive method of controlling the yo-yo system. Fuzzy logic controller based on human expertise is referred at first. Then, an adaptive fuzzy controller which has adaptation features against the variation of plant parameters is proposed. Finally, experimental results are presented.

• Educational Technology International
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• v.9 no.1
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• pp.79-96
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• 2008

The issue of connection between entities has a lengthy history in educational research, especially since it provides the necessary bridge between base and target in analogical transfer. Recently, the connection has been viewed through the application of technology to bridge between sequences in order to be cognitively useful. This study reports the effect of sequence type (AT vs. TA) and connection type (fading vs. popping) on the achievement and analogical transfer in a multimedia application. In the current research, 10th -grade and 11th -grade biology students in Korea were randomly assigned to five groups to test the effects of presentation sequence and entity connection type on analogical transfer. Consistent with previous studies, sequence type has a significant effect: analogical transfer performance was better when base representations were presented first followed by target representations rather than the reverse order. This is probably because presenting a familiar base first helps in understanding a less familiar target. However, no fully significant differences were found with the entity connection types (fading vs. popping) in analogical transfer. According to the Markman and Gentner's (2005) spatial model, analogy in a space is influenced only by the differences between concepts, not by distance in space. Thus connection types fail on the basis of this spatial model in analogical transfer test. The findings and their implications for sequence and connection research and practice are discussed. Leveraging on the analogical learning process, specific implications for scaffolding learning processes and the development of adaptive expertise are drawn.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.295-306
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• 2023

In this study, we explored science teachers' perceptions of adaptive practice in terms of importance and practice. For this purpose, an adaptive practice questionnaire was developed targeting secondary science teachers, and the responses of 128 science teachers were analyzed. The results of this study were as follows: First, the science teachers responded that all 17 items regarding adaptive practice were important. In particular, items related to 'addition of examples', 'adjustment of the level/range of concepts', and 'encouragement of student participation' showed high importance and performance levels. Second, the results of the IPA concerning adaptive practice showed that the items needing intensive improvement due to high importance but low performance were 'encouraging students' self-directed learning' and 'monitoring and responding to individual differences'. The science teachers identified a lack of time and uniform curriculum as the reasons for the low level of practice despite the high importance of these items. Third, regarding the IPA of the adaptive practice items, the items located in the fourth area, an excessive area, concerned 'pace control', 'modifying activities', and 'metaphor use'. More specifically, considering that 'metaphor use' was the only item with a higher level of performance than importance, it is necessary to reconsider whether problems are caused by excessive use. Fourth, analyzing the responses regarding the reasons for the difference in importance and performance revealed that the factors related to teachers included teachers' lack of knowledge or ability, and the tendency to implement as planned. As for student factors, an excessive number of students, differences in the levels of students, and a tendency to prefer classes centered on entrance exams were identified. As environmental factors, a uniform curriculum, conditions for experiments, evaluation systems, and external demands were mentioned. Finally, the implications of the results of this study for science education were discussed.

• Science of Emotion and Sensibility
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• v.12 no.1
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• pp.55-64
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• 2009

Previous research has mainly investigated the characteristics of expertise by using typical and routine tasks. This research to overcome these limitations included non-routine task situations and observed expert and novice pilots' situation awareness (SA) performance in routine and non-routine situations. Additionally, whether the ability to aware or perceive the environmental information in limited time varies with the level of expertise was tested. To this end, $2^*2^*2$ mixed factorial design was employed, including expertise (novice/expert) as a between-subjects variable and normality of fight situation (routine/non-routine) and stimulus display time (long/short) as within-subjects variables. As results, there was no performance difference in routine situations between experts and novices, while experts significantly outperformed novices in non-routine situations. When the display time became shorter, overall SA accuracy was decreased for both experts and novices, whereas experts' performance remained significantly higher than novices in short and long conditions. When we examined the interaction between the normality of fight situation and stimulus display time, there was no difference between experts and novices in routine situations for both short and long conditions. In non-routine situations, however, experts' SA accuracy was significantly higher than novices both in short and long conditions. Overall, non-routineness of flight situation does not have any impact on SA performance of experts, while it has a critical impact on SA performance of novices.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.131-134
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• 1996

Application of machine learning to scheduling problems has focused on improving system performance based on opportunistic selection among multitudes of simple rules. This study proposes a new method of learning scheduling rules, which first establishes qualitatively meaningful criteria and quantitatively optimizes the use of them, a similar way as human scheduler accumulate their expertise. The weighting of these criteria is trained in response to the system states through simulation. To mimic human quantitative feelings, distributed fuzzy sets are used for assessing the system state. The proposed method was applied to job dispatching in a simulated FMS environment. The job-dispatching criteria used were the length of the processing time of a job and the situation of the next workstation. The results show that the proposed method can develop efficient and robust scheduling strategies, which can also provide understandable and usable know-hows to the human scheduler.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.109.2-109
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• 2001

In the PCB assembly lines, as components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. The automatic optical inspection(AOI) gradually becomes the alternative in the PCB assembly line. In Particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching, Fourier analysis, edge analysis, geometric feature recognition or optical character recognition (OCR), which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the ...

• International Journal of Contents
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• v.7 no.1
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• pp.58-64
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• 2011

This paper is concerned with simulation exercises used to train key response agencies for crisis situations. While 'multi-agency' simulations are increasingly acknowledged as a necessary and significant training tool for emergency response organisations, many current crisis simulations are still focused on the revision of existing response plans only. However, a crisis requires a rapid reaction, yet in contrast to an 'emergency', the risks for critical decision makers in crisis situations are difficult to measure, owing to their ill-structure. In other words, a crisis situation is likely to create great uncertainty, unfamiliarity and complexity, and consequently should be managed by adaptive or second order expertise and techniques, rather than routine or structured responses. In this context, the paper attempts to prove that the current practices of simulation exercises might not be good enough for uncertain, unfamiliar, and complex 'crisis' situations, in particular, by conducting case studies of two different underground fire crises in Korea (Daegu Subway Fire 2003) and the UK (King's Cross Fire 1987). Finally, it is suggested that the three abilities: 'flexibility', 'improvisation' and 'creativity' are critical in responding to a crisis situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.