• Korean Medical Education Review
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• v.17 no.1
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• pp.5-9
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• 2015

A teaching hospital is a place where both patient care and learning occur together. To identify the role of the teaching hospital in an effective clerkship, we first determined the features of workplace learning and the factors that affect learning in the workplace, and then we proposed a role for the teaching hospital in the clinical clerkship. Features of learning in a clerkship include learning in context, and learning from patients, supervising doctors, others in the team, and colleagues. During the clerkship, medical students learn in three-way learner-patient-teacher relationships, and students' participation in the tasks of patient care is crucial for learning. Factors that influence learning in the workplace are associated with tasks, context, and learner. Tying the three factors together, we proposed a role for the teaching hospital in the three categories: involvement in the tasks of patient care, engagement in the medical team, and engagement in the learning environment and system. Supervising doctors and team members in a teaching hospital support students' deep participation in patient care, while improving the learning environment through organizational guidelines and systems. Gathering both qualitative and quantitative data for the evaluation of a teaching hospital is important.

• Journal of Gifted/Talented Education
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• v.13 no.3
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• pp.1-17
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• 2003

Development and evaluation of distance learning for the gifted students in science and mathematics In this study, we developed and administrated the distance learning for the gifted students in science and mathematics, and analysed their responses. To do this, four types of teaching programs - lectures using program for distance learning, practice activities using simulation program, tasks solving programs based on discussions, and problem solving activities - were developed and students responses were analysed in eight area - stimulus, difficulties, structure, learning circumstances, involvement, interaction, learning outcomes, comparison with other learning -. As results, it was found that many students responded positively and thought programs helped their creativity, logical thinking, intelligent ability, and information searching ability. Students preferred practice activities based on appropriate guidances to lectures providing detailed explanations. And interaction could be stimulated by inducing discussion.

• Journal of the Korean Chemical Society
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• v.47 no.6
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• pp.625-632
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• 2003

In this study, the effects of two types of reward methods in cooperative learning were investigated upon students' achievement, learning motivation, perceptions of learning environment and perceptions of reward methods. Seventh graders (N=61) were selected from a co-ed middle school in Seoul, and were taught about 'three states of matter', 'motion of molecules' and 'change of state and thermal energy' for 14 class hours. Reward methods were classified into task-oriented reward and performance-oriented reward. The results revealed that high-level students performed better in the task-oriented reward group, and low-level students performed better in the performance-reward group for the 'application' subcategory of the achievement test. The scores of attention and relevance in learning motivation and task orientation, involvement, and order and organization in perceptions of learning environment test were significantly higher in the task-oriented reward group than those in the performance-oriented reward group.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1220-1220
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• 2022

Constructionarium Ltd is a not-for-profit organisation which delivers a residential, experiential, immersive learning opportunity to university students from across the built environment education sector. Since 2002, the Constructionarium education model has been available to students in engineering, construction management and architecture at a purpose built, 19-acre multi-disciplinary training facility in Bircham Newton, England simulating real site life and reflecting site processes, practices and health and safety requirements. The unique approach of Constructionarium puts experiential learning and sustainability at the heart of everything. In a week, students develop a practical understanding of the construction process, develop transferable skills, build a team and are exposed to the latest in sustainable technologies. Experiential learning is what differentiates a Constructionarium project from regular field trips or site visits. At Constructionarium the focus is on learning by participation rather than learning through theory or watching a demonstration. The projects cannot be replicated in a classroom or on campus. Using the hands-on construction of scaled down versions of iconic structures from around the world, students learn that it requires the involvement of the whole construction team to successfully complete their project. Skills such as communication, planning, budgeting, time management and decision making are woven into a week-long interrelationship with industry professionals, academic mentors and trades workers. Working together to enhance transferable skills brings the educational environment into the reality of completing an actual construction project handled by the students. Constructionarium has used this transformational learning model to educate thousands of students from all over the United Kingdom, Europe and Asia. Texas A&M University in the United States has sent multiple teams of students from its Department of Construction Science every operational year since 2016.

• Journal of The Korean Association For Science Education
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• v.22 no.3
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• pp.508-516
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• 2002

This study investigated the influences of group composition in cooperative computer-assisted instruction (CAI) upon students' conceptual understandings, application abilities, learning motivations, and the perceptions of involvement. Seventh graders (N=97) were selected from a co-ed middle school in Seoul, and taught about 'motion of molecules' for 5 class hours. In the two treatment groups with cooperative CAI strategy, homogeneous and heterogeneous small groups were organized by the previous science achievement. Traditional instructions were administered to the comparison group. Two-way ANCOVA results revealed that the scores of the conception test for the treatment groups were significantly higher than those for the comparison group. However, there was no difference between the homogeneous and the heterogeneous groups. The scores of the three groups did not differ significantly in the application test and the learning motivation test. However, the perceptions of involvement for the treatment groups were more positive than those for the comparison group.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.83-92
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• 2013

An emerging learning method, flipped learning, has gained much interest lately due to its process involving prior study followed by the students' classroom involvement, which direction matches that of the current educational policy that emphasizes self-directed learning. This study investigated the concept of flipped learning and explored ways to develop instructional design models that utilize it. Flipped learning is not a model that has been recently developed, as it uses the format of blended learning with the introduction of a new concept of prior learning that allows students to learn in advance through online lessons and video clips related with the classroom content to be covered. During class time, individualized supplementary or in-depth study is conducted on the basis of the students' prior learning. The main considerations for designing flipped learning are a flexible classroom environment, a shift in learning culture, intentional classroom content, and educators equipped with professional capability. The research proposes the development of instructional design models for flipped learning pursuant to such concept and considerations. Through this research, the concept of flipped learning can be comprehended; furthermore, flipped learning can be utilized more effectively in the teaching and learning environment.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of the Korean Home Economics Association
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• v.43 no.1 s.203
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• pp.53-67
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• 2005

It has been suggested that children's reasons for reading are various; reading efficacy, challenge, curiosity, involvement, importance, recognition, competition, compliance, grades, avoidance and social interactions. To extend previous studies in which only one or two motivational constructs were studied in relation to reading abilities, this study was carried out to examine 11 inter-relationships of motivational constructs and their relationships to reading abilities. Using the MRQ of Wigfield & Guthrie(1997), and the Basic Learning Skills Test of the Korea Educational Development Institute(1989), 334 elementary school children were measured for their reading motivation and abilities. The results showed that 11 motivational constructs were interrelated, which were also related with reading abilities. Out of 11 motivational constructs, importance was able to predict the reading abilities in multiple-regression analyses. From F-tests, those groups with high reading efficacy, challenge, curiosity, involvement, recognition, competition and social interactions outperformed those with low motivational constructs. The findings of this study confirm that children read for various reasons, and internal motivation and social interactions are significantly related with reading abilities. It is proposed that the internal reasons for reading are also more significant factors in explaining reading abilities than external reasons. Finally, cultural influences on reading motivation and comprehension are also discussed.