• Journal of Fisheries and Marine Sciences Education
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• v.21 no.1
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• pp.16-27
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• 2009

The purpose of this study was to develop of learning strategy scale for college students. This study further classified several sub-areas and defined each concepts of learning strategy. Based upon the classification of each sub-areas, tentative test items were developed through the verification of validity by three professionals. A pilot study of the developed scale was administered to 239 college students. And the research collected major data from 1,012 college students. Data were analyzed to obtain item quality, reliability, and validity analysis. The results of this study were as follows. The scale for learning strategy was defined by eight factors and they were 'self-management strategy', 'examination-readiness strategy', 'cognitive strategy', 'memorizing strategy', 'reporting strategy', 'resource-utilization strategy', 'self-regulated strategy', 'cooperative learning strategy'. The results of the confirmatory factor analysis proved the eight factors in the learning strategy. And criterion validity evidence was also obtained from a correlation analysis of the level of academic achievement.

• The Journal of Information Systems
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• v.21 no.1
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• pp.117-152
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• 2012

The purpose of this study was to identify the negative factors affecting personnel u-Learning acceptance and to analyze the interrelation among the factors in this research model. The two independent variables avoidable convenience and reliant convenience, based on pilot test results, and learning performance and perceived interactivity, based on the relevant literature, are used to examine the research model. The research problem was tested with data collected from 577 respondents in 23 universities. This study developed and empirically analyzed a model representing the relationship by using the Structural Equation Model. The major findings of this study are, firstly, that the higher reliant convenience is negatively affecting the degree of system use and learner’s satisfaction, whereas avoidable convenience is only affecting the learner’s satisfaction. Secondly, the higher learning performance and stronger perceived interactivity affects the degree of system use as well as learner’s satisfaction. Finally, the degree of system use affects the learner’s satisfaction.

• Educational Technology International
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• v.22 no.2
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• pp.227-253
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• 2021

Perceived game realism (PGR) has recently emerged as a key concept in explaining the mental processing of digital game playing and the societal impact of digital games. However, few studies have examined its conceptualization and educational effects from an empirical viewpoint, especially in educational games. This study's participants included 292 university students in South Korea. A total of 212 questionnaires were valid and used for the analyses. They learned English expressions using a computer-based educational game and then completed questionnaires on the research variables. We investigated six factors of PGR: simulational realism (SIR), freedom of choice (FRC), perceptual pervasiveness (PEP), social realism (SOR), authenticity (AUT), and character involvement (CAI). We expected the factors to have valid effects on the university students' flow and learning satisfaction after a game-based learning (GBL) experience. Our research results demonstrated a causal relationship between SIR, FRC, CAI, and learning satisfaction. Furthermore, the indirect effects of SIR and CAI on learning satisfaction through flow were statistically significant.

• East Asian mathematical journal
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• v.38 no.4
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• pp.497-516
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• 2022

In this study, we tried to devise a method to activate meta-affect in the aspect of supporting mathematics teaching and learning according to the need to find specific strategies and teaching and learning methods to activate learners' meta-affect in mathematics subjects, which are highly influenced by psychological factors. To this end, the definitional and conceptual elements of meta-affect which are the basis of this study, were identified from previous studies. Reflecting these factors, a teaching and learning model that activates meta-affect was devised, and a meta-affect activation strategy applied in the model was constructed. The mathematics teaching and learning model that activates meta-affect developed in this study was refined by verifying its suitability and convenience in the field through expert advice and application of actual mathematics classes. The developed model is meaningful in that it proposed a variety of practical teaching and learning methods that activate the meta-affect of learners in a mathematical learning situation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.349-359
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• 2022

The purpose of this study is to add and revalidate items of learning cognition and learning emotion factors for online use of the K-LSS for junior college. It is important for self-reflection and improvement of academic achievement to specifically explore and analyze the sub-factors of learning cognition, learning behavior, and learning emotion for each item that can affect the learning strategy of junior college students. The added items are two items for diagnosing the concentration of attention in the learning information processing process of the learning cognitive factor and two questions about the interpersonal anxiety factor for diagnosing the level of anxiety about others in the learning emotional factor. The study area was conducted in 5 areas nationwide, and the subjects of the study were 923 junior college students excluding 327 respondents who answered insincerity. The K-LSS_r scale is a learning strategy diagnosis scale of 52 questions composed of three sub-elements of learning cognition (18 questions), learning emotion (15 questions), and learning behavior (19 questions), and reliability for generalization in this study. As a result of the verification, Cronbach's α coefficient of the entire scale was .896, and Cronbach's α coefficient of the three factors ranged from .876 to .910. The half-segment reliability coefficient of the scale was .858 in total, and the half-segment reliability coefficients of the three factors ranged from .792 to .843. The test-retest reliability verification result for 3 weeks for 350 Junior college Students in 5 regions was .884, and the validity test for generalization also confirmed that the recruitment validity is significant.

• The Mathematical Education
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• v.47 no.2
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• pp.113-133
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• 2008

This study is to develop the methods of specifying teaching that can consider individual differences in middle school geometry education. The purpose of this study is to decide the variations causing individual differences and to find the proper learning methods considering the variations. Through literature review, this study made it clear that the matter of individual difference is just the matter of talent and examined what factors make up mathematical talents. On the basis of the result, five important variations and fourteen subordinate factors were determined. I researched into the learning methods that consider the determined subordinate factors using the 'congruence' unit of middle school textbooks and developed specific learning methods for each of the subordinate factors through specific congruence problem solving situations. This study can be summarized as follows : I researched the studies of mathematical ability conducted by several educators and psychologists. This research is divided into the early study and the developed study of mathematical ability. Through this study five specific variations were determined. And fourteen subordinate factors have been made from the determined variations. The specific learning methods based on individual differences was developed according to the fourteen subordinate factors on the basis of middle school textbooks of Korea, Gusev's textbook, problem books of Russia, and etc.

• Journal of Korean Elementary Science Education
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• v.33 no.3
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• pp.597-606
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• 2014

The purpose of this study is to survey and analyze difficulties in teaching and learning elementary school science on the chapter titled 'electric circuit'. 28 elementary school teachers who teach 5th grade science and 73 5th grade students in elementary school were taken part in this survey. The pilot questionnaire was distributed to find out both the degree and the reason of difficulties in teaching and learning. The answers are analyzed with four areas to extract elements which make class difficult; Learner factors (L), Instruction factors (I), Curriculum & textbooks factors (C), and Environment factors (E). The results are as follows. (1) It can be seen that both students and teachers feel the highest difficulty in 7th lesson 'the direction of current', while they felt little difficulty in lesson 3 'conductor and nonconductor' and lesson 8 'the safety of electricity'. (2) The most mentioned reason of difficulties in teaching and learning was Learner factors (L). (3) Teachers felt many difficulties in experimental environment. On the other hands, students didn't think experimental failures as serious trouble. (4) Students felt many difficulties in new terms and hazy concepts or expressions. (5) Teachers felt a lot of difficulties in those from Curriculum & textbooks factors.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.318-326
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• 2022

Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Korean Journal of Head & Neck Oncology
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• v.38 no.1
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• pp.17-24
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• 2022

Background/Objectives: This study analyzed the prognostic significance of clinico-pathologic factors including comprehensive nodal factors in parotid gland cancers (PGCs) patients and constructed a survival prediction model for PGCs patients using machine learning techniques. Materials & Methods: A total of 131 PGCs patients were enrolled in the study. Results: There were 19 cases (14.5%) of lymph nodes (LNs) at the lower neck level and 43 cases (32.8%) involved multiple level LNs metastases. There were 2 cases (1.5%) of metastases to the contralateral LNs. Intraparotid LNs metastasis was observed in 6 cases (4.6%) and extranodal extension (ENE) findings were observed in 35 cases (26.7%). Lymphovascular invasion (LVI) and perineural invasion findings were observed in 42 cases (32.1%) and 49 cases (37.4%), respectively. Machine learning prediction models were constructed using clinico-pathologic factors including comprehensive nodal factors and Decision Tree and Stacking model showed the highest accuracy at 74% and 70% for predicting patient's survival. Conclusion: Lower level LNs metastasis and LNR have important prognostic significance for predicting disease recurrence and survival in PGCs patients. These two factors were used as important features for constructing machine learning prediction model. Our machine learning model could predict PGCs patient's survival with a considerable level of accuracy.