• Journal of the Korean Society of Earth Science Education
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• v.17 no.2
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• pp.181-193
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• 2024

Summative assessment provides information on how well students have achieved learning objectives, making the development of high-quality assessment items essential for accurate evaluation. This is one of the competencies that teachers must possess. This study aims to analyze summative assessment items created by pre-service elementary teachers, examining their intentions and the difficulties encountered in the item development process. The study involved 45 second-year students enrolled in an elementary teacher training university. They were grouped into teams of three and tasked with developing ten items, documenting the purpose of each item, the answer key, and the challenges faced during item creation. The collected summative assessment items were analyzed using a two-dimensional purpose classification table that includes Klopfer's taxonomy of educational objectives. The intentions behind the summative assessments and the difficulties faced during item development were inductively organized and analyzed through qualitative data analysis. The results revealed that pre-service elementary teachers adequately reflected scientific content elements but did not evenly cover assessment domains. The most challenging aspect for them was adjusting the difficulty level. Although they considered most factors that should be taken into account during item development, these considerations were not reflected in the actual items. These findings suggest that knowledge and experience are crucial in developing summative assessment items, and systematic lectures are necessary for pre-service elementary teachers.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.437-443
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• 2024

Adhering to coding style guidelines is crucial for both companies and developers as it improves code readability and reduces the costs associated with testing and maintenance. However, teaching coding style in programming courses poses challenges. Setting up an environment for learning coding styles is hard, and there are no predefined coding style rules for beginners. From the learners' perspective, since adherence to coding styles does not affect their grades, they do not feel a strong need to learn them. This paper introduces a coding style checking system for an online evaluation system. The proposed system is implemented to check and evaluate coding styles in C, Java, and Python. Additionally, we applied 234 out of the 1,023 rules provided by the language-specific tools, which is 23.08%, allowing for the application of coding style rules according to the course progression. Moreover, we motivated learners to improve their coding style by adding quality scores to their basic scores. After introducing the coding style education system, the number of students scoring over 25 points on their initial submissions increased by 149.47%, from 18 students in the first week to 44 students in the sixth week. Learners used the coding style checking system to learn how to apply coding style rules and subsequently implemented their code in adherence to the specified coding styles.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.169-178
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• 2007

In this research we diagnosed the actual status of the 7th National science elective curriculum and suggested a way to select and organize the content of the new science elective curriculum. The first science education reform was grounded in the structuralism where the structure of discipline was valued above everything else. On the other hand, the second science education reform suggested alternative interpretations of students' opportunity to learn, putting a brake on the structuralist thinking. According to the survey result, the majority of the science elective courses are in need for revision because the contents are overcrowded, too difficult in light of students' learning readiness, failed to draw students' interest in science, and are overlapped and repeated among the 10th grade science, high school science I and II. In particular, Earth Science II and physics II are the most unfavorable courses among students. Thus, we recommended a fundamental change be made in the new curriculum in addition to the optimization of the content. In this paper, we suggested 'topic-centered content organization' for the science elective course I, i.e., Physics I, Chemistry I, Biology I and Earth Science I that is designed for both science track and non-science track students. Since curriculum provides students with an 'opportunity to learn', a curriculum study should focus on what the 'opportunity to learn' is that students ought to be offered. Based on the result of this study, we recommended one way to select and organize the content of high school elective curriculum.

• Journal of Korean Home Economics Education Association
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• v.22 no.4
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• pp.91-107
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• 2010

The purpose of this study was to investigate what middle school Home Economics(HE) teachers perceive, practice and need for self-supervision at school related to HE. Questionnaires were sent by E-mail and 150 were collected. Descriptive statistics including frequency, percentage, average, standard deviation, t-test and ANOVA analysis were reported using SPSS/win 10.1. The results of this research were as follows: First, middle school HE teachers perceived that self-supervision at school was essential since it promoted self reflection of teachers themselves and improved professional skills. Furthermore, peer-coaching was highly preferred. Second, negative responses to the supervision of principal, vice-principal, and peer teachers overwhelmed positive answers. Information exchange among peer teachers was frequent, yet, approximately 22.6% of middle school HE teachers were still avoiding sharing information process for several reasons. About half of the teachers answered that all teachers needed to participate in this process. Third, they pointed out that self-supervision at school was not implemented well because of the lack of time due to the heavy work load, negative and passive attitude for the improvement of teaching-learning activities, administration-centered supervision that did not reflect teachers' opinion, and shortage of economical, and environmental support.. HE teachers perceived that peer teachers who were doing good practices were most helpful for the supervision. Also, they preferred self-evaluation at the end of the self-supervision at school. Forth, to improve self-supervision at school, there were very high demands for reduction of administrative work, additional time, fundamental philosophy toward HE education. Fifth, the purpose and detailed plans of self-supervision were recognized as the results that were democratically derived by the HE teachers. Sixth, class inspection and informal inspection were operated once in a year, and self-training was rarely operated. Peer coaching and self-coaching were operated occasionally. Self-coaching and peer coaching were reported as the most helpful types of supervision. In addition, HE teachers answered that supervision was helpful to teaching method followed by contents, evaluation, and philosophy of education.

• Journal of Korean Ophthalmic Optics Society
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• v.20 no.4
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• pp.491-499
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• 2015

Purpose: This study was to examine how decline of visual function affects visual perception by assessing visual perception after improving visual function through visual training, and observing the change in the cognitive ability of visual perception. Methods: This study analyzes the visual perceptual evaluation (TVPS_R) of 23 children below age 13($8.75{\pm}1.66$) who have visual abnormalities, and improves visual function after conducting vision training (vision therapy) of the children. Results: Convergence increased from average $3.39{\pm}2.52{\Delta}$ (prism) to $13.87{\pm}6.04{\Delta}$ in the measurement of long-distance disparate points, and from average $5.48{\pm}3.42{\Delta}$ to $18.43{\pm}7.58{\Delta}$ in the measurement of short-distance disparate points. Short-distance diplopia points increased from $25.87{\pm}7.33cm$ to $7.48{\pm}2.87cm$, and as for accommodative insufficiency, short-distance blur points increased from $19.57{\pm}7.16cm$ to $7.09{\pm}1.88cm$. In the visual perceptual evaluation performed before and after improving visual function, 6 items except visual memory showed statistically significant improvement. By order of significant improvement, response gap was highest with $17.74{\pm}16.94$(p=0.000) in visual closure, followed by $15.65{\pm}17.11$(p=0.000) in visual sequential-memory, $13.65{\pm}16.63$(p=0.001) in visual figure-ground, $12.74{\pm}18.41$(p=0.003) in visual form-constancy, $6.48{\pm}10.07$ (p=0.005) in visual discrimination, and $4.17{\pm}9.33$(p=0.043) in visual spatial-relationship. In the visual perception quotient that added up these scores, the response gap was $15.22{\pm}8.66$(p=0.000), showing a more significant result. Conclusions: Vision training enables efficient visual processing and improves visual perceptual ability. It was confirmed that improvement of visual function through visual training not only improves abnormal visual function but also affects visual perception of children such as learning, perception and recognition.

• 대한공업교육학회지
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• v.44 no.2
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• pp.104-122
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• 2019

In the era of the fourth industrial revolution, maker education is drawing attention as a method of student-led education. At a time when interest in maker education is also growing in technology education, figuring out what stage of concern(SoC) a middle school technology teacher is critical to effective implementation. This study analyzed SoC in maker education by layer sampling among 400 middle school technology teachers using Concerns-based adoption model. SoC was then obtained by measuring the origin using the SoCQ and then presenting it as a SOCQ profile. Gender, training experience with two lower variables were analyzed using t verification, working cities, teaching experience with more than three lower variables were analyzed using one-way ANOVA. Studies showed that SoC in maker education of middle school technology teachers showed the most similar characteristics to that of non-users. The difference in concern depending on gender was that male teachers were more concerned in maker education than female teachers. The difference in concern depending on the working city was that teachers working in the township were more concerned in the maker education than teachers working in the large city, and the difference in concern depending on the teaching career was higher among teachers with middle experience than those with low and high experience. There was also a higher stage of concern in maker education than in teachers without training experience. Therefore, it is necessary to provide middle school technology teachers with an introduction to the maker education and various information, teaching, learning and evaluation data to enhance overall concern and to support the use and evaluation of the maker education in the classroom by providing various teacher training and consulting on the maker education in the future. Further, through further study, we should conduct study that analyzes both Stage of Concern, Level of Use and Innovation Configuration, to put in the effort for effective settlement of maker education.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.

• Korean Journal of Optics and Photonics
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• v.28 no.5
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• pp.203-212
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• 2017

A scientific model refers to a conceptual system that can describe, explain, and predict a particular physical phenomenon. The co-construction of the scientific model is attracting attention as a new teaching and learning strategy in the field of science education and various studies. The evaluation and modification of models compared with the predicted models of data from the real world is the core of modeling strategy. However, there were only a limited data provided by the teacher in many studies of modeling comparing the students' predictions of their own models. Most of the students were not given the opportunity to evaluate the suitability of the model with the data in the real world. The purpose of this study was to develop a scientific model co-construction program that can evaluate the model by directly comparing the predicted models with the observed data from the real world. Through a collaborative discussion between teachers and researchers for 6 months, a 5-session scientific model co-construction program on the subject 'image formation by convex lenses' for second grade middle school students was developed. Eighty (80) students in 3 classes and a science teacher with 20 years of service from general public co-educational middle school in Gyeonggi-do participated in this 2-week program. After the class, students were asked about the helpfulness and difficulty of the class, and whether they would like to recommend this class to a friend. After the class, 95.8% of the students constructed the scientific model more than the model using the construction rule. Students had difficulties to identify principles or understand their friends, but the result showed that they could understand through model evaluation experiment. 92.5% of the students said that they would be more than willing to recommend this program to their friends. It is expected that the developed program will be applied to the school and contribute to the improvement of students' modeling ability and co-construction ability.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.