• Research in Mathematical Education
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• v.9 no.1 s.21
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• pp.25-45
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• 2005

Background Phenomenography suggests that more variation is associated with wider ways of experiencing phenomena. In the discipline of mathematics, broadening the 'lived space' of mathematics learning might enhance students' ability to solve mathematics problems Aims The aim of the present study is to: 1. enhance secondary school students' capabilities for dealing with mathematical problems; and 2. examine if students' conception of mathematics can thereby be broadened. Sample 410 Secondary 1 students from ten schools participated in the study and the reference group consisted of 275 Secondary 1 students. Methods The students were provided with non-routine problems in their normal mathematics classes for one academic year. Their attitudes toward mathematics, their conceptions of mathematics, and their problem-solving performance were measured both at the beginning and at the end of the year. Results and conclusions Hierarchical regression analyses revealed that the problem-solving performance of students receiving non-routine problems improved more than that of other students, but the effect depended on the level of use of the non-routine problems and the academic standards of the students. Thus, use of non-routine mathematical problems that appropriately fits students' ability levels can induce changes in their lived space of mathematics learning and broaden their conceptions of mathematics and of mathematics learning.

• Journal of The Korean Association For Science Education
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• v.23 no.4
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• pp.386-400
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• 2003

This paper presents an analysis of the Developmental Approaches in Science, Health and Technology (DASH) program, a K-6 curriculum developed by the Curriculum Research & Development Group (CRDG) at the University of Hawaii employing the curriculum analysis framework created by Posner. Using this framework the analyst found that the DASH design is based on the research on learning, teaching, and assessment now driving efforts to reform science education at the elementary level. DASH embraces the constructivist idea that learning is a personal and social process and the recapitulation model that new concepts are built out of theories previously learned. DASH provides an understandable, exciting, and memorable experience in the operations of science, health, and technology, and develops their capacity to use the skills and knowledge of science, health, and technology both in and outside school. A number of studies of DASH have examined its functionality, effectiveness of pedagogy and what students learn. The innovative nature of DASH necessitated a multidimensional assessment that included both quantitative and qualitative research techniques. Ongoing development of the DASH program in the research setting of a university laboratory school permits ever deeper connections with emerging curriculum theory and curriculum practice, and allows new linkages as ideas are tested in research classrooms.

• School Mathematics
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• v.10 no.4
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• pp.557-571
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• 2008

The objective of this paper is to study De Morgan's thoughts on teaching and learning negative numbers. We studied De Morgan's point of view on negative numbers, and analyzed his didactical approaches for negative numbers. De Morgan make students explore impossible subtractions, investigate the rule of the impossible subtractions, and construct the signification of the impossible subtractions in succession. In De Morgan' approach, teaching and learning negative numbers are connected with that of linear equations, the signs of impossible subtractions are used, and the concept of negative numbers is developed gradually following the historic genesis of negative numbers. Also, we analyzed the strengths and weaknesses of the De Morgan's approaches compared with the mathematics curriculum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.173-178
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• 2022

The present study employed two different machine-learning approaches, the extreme gradient boosting (XGB) and light gradient boosting machine (LGBM), to predict a compressive deformation behavior of additively manufactured Ti-6Al-4V. Such approaches have rarely been verified in the field of metallurgy in contrast to artificial neural network and its variants. XGB and LGBM provided a good prediction for elongation to failure under an extrapolated condition of processing parameters. The predicting accuracy of these methods was better than that of response surface method. Furthermore, XGB and LGBM with optimum hyperparameters well predicted a deformation behavior of Ti-6Al-4V additively manufactured under the extrapolated condition. Although the predicting capability of two methods was comparable, LGBM was superior to XGB in light of six-fold higher rate of machine learning. It is also noted this work has verified the LGBM approach in solving the metallurgical problem for the first time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• The Journal of Korean Association of Computer Education
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• v.14 no.2
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• pp.33-45
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• 2011

According to recent needs for 'smart learning', the concept of smart learning was reviewed by device, environmental, and theoretical approaches. The principle of smart learning includes three elements: First, rich instructional resources as learning contents. Second, participatory learning environments with interactions among teachers and learners as learning methods. Third, practical and realistic contexts as learning environments. Based on those characteristics, instructional designs for smart learning can be summed up as learning objectives, learning resources, instructional environments, instruction process design, instruction method development, implementation, and evaluation. As a conclusion, it is required to systematically develop instructional designs addressing specific learning settings to facilitate smart learning.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.1
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• pp.29-51
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• 2010

A knowledge-based society values creative and independent individuals. This study depicts operational approaches to the effective utilization of school libraries as teaching/learning media center in order to support independent learning in relation to the way in which constructivist teaching-learning(CTL) improves learners' self-learning abilities. The result of this study seems to imply that self-learning based on constructivism is possible only when school libraries are managed as teaching/learning media centers and that the more variety there is in learning materials and when more direct interaction exists, there is more creativity and self-learning abilities are achieved in the learning process.

• Current Optics and Photonics
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• v.7 no.5
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• pp.545-556
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• 2023

This paper introduces volume-sharing multi-aperture imaging (VMAI), a potential approach proposed for volume reduction in space-borne imagers, with the aim of achieving high-resolution ground spatial imagery using deep learning methods, with reduced volume compared to conventional approaches. As an intermediate step in the VMAI payload development, we present a phase-1 design targeting a 1-meter ground sampling distance (GSD) at 500 km altitude. Although its optical imaging capability does not surpass conventional approaches, it remains attractive for specific applications on small satellite platforms, particularly surveillance missions. The design integrates one wide-field and three narrow-field cameras with volume sharing and no optical interference. Capturing independent images from the four cameras, the payload emulates a large circular aperture to address diffraction and synthesizes high-resolution images using deep learning. Computational simulations validated the VMAI approach, while addressing challenges like lower signal-to-noise (SNR) values resulting from aperture segmentation. Future work will focus on further reducing the volume and refining SNR management.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.