• English Language & Literature Teaching
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• v.13 no.1
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• pp.27-46
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• 2007

Teachers can maximize students' L2 learning by knowing preferred learning styles. This paper presents the results of a survey that asked 309 English learners to identify their perceptual learning style preferences. It further compared students' favored learning styles in terms of their gender and major field of study and explored a possible link between learning styles and English achievement. Collected data using Reid's (1995) questionnaire were analyzed by descriptive statistics, MANOVA, ANOVA, correlations, multiple regressions including squared partial correlations, and Cronbach's alpha. The results indicated that Korean students favored English learning in group regardless of gender, while their preferred mode of learning was significantly different in regard to their major field of study. Certain learning styles might be profitable for English achievement. Multiple regression analyses revealed that individual mode of learning was the best predictor of students' English achievement. It furthermore showed significant relationships between visual and individual styles of learning and English performance. The findings of the study reflected students' English learning context in which English native-speaking teachers frequently used communicative pair and small group activities for speaking practices that were consonant with students' learning styles.

• Knowledge Management Research
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• v.9 no.4
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• pp.1-16
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• 2008

The purpose of the present study is to explain the effects of academic self-efficacy, self-regulated learning and online task value on academic achievement and learning transfer in corporate cyber education. 202 students who completed S corporate's cyber courses in 2007 and responded to all survey participated in this study. A hypothetical model was proposed, which was composed of academic self-efficacy, online task value and self-regulated learning factors as prediction variables, and learning transfer as well as academic achievement factors as outcome variables. The results of this study through regression analysis as follows. First, learners' academic self-efficacy, self-regulated learning and online task value predict learners' academic achievement significantly. Second, except for academic self-efficacy, learners' self-regulated learning and online task value predict on learners' learning transfer significantly. Third, academic achievement plays a role as mediating value in predicting academic achievement by online task. It implies that learners' academic self-efficacy, online task value and self-regulated learning which predict learners' academic achievement and learning transfer should be considered in developing strategies for the design and operation of cyber courses.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.519-529
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• 2020

The purpose of this study is to establish a model for predicting academic achievement of college students and to reveal the interrelationship and relative influence of each factor. For this, we surveyed the personal factors and learning strategy factors of 1,310 learners at J University, and analyzed the discriminant factors and patterns of the predictors of academic achievement through the decision tree analysis, a data mining method, and examined the relative effects of each factor. Binary logistic regression analysis was performed for viewing. As a result, the most important factor for predicting academic achievement was efficacy, and other factors such as motivation, time management, and depression were predictive of academic achievement. The patterns of factors predicting academic achievement were found to be high in efficacy and time management, and high in motivation for learning even if the efficacy was moderate. Low efficacy and learning motivation, and high depression have been shown to decrease academic achievement. Based on these results, the study suggested the efficacy and motivation to improve academic achievement of college students, strengthening time management education, and managing negative emotions.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1769-1778
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• 2018

As the value of using Big Data is increasing, various researches are being carried out utilizing big data analysis technology in the field of education as well as corporations. In this paper, we propose a method to predict learning achievement using big data cluster analysis. In the proposed method, students in Korea Children and Youth Panel Survey(KCYPS) are classified into groups with similar learning habits using the Kmeans algorithm based on the learning habits of students of the first year at middle school, and group features are extracted. Next, using the extracted features of groups, the first grade students at the middle school in the test group were classified into groups having similar learning habits using the cosine similarity, and then the neighbors were selected and the learning achievement was predicted. The method proposed in this paper has proved that the learning habits at middle school are closely related to at the university, and they make it possible to predict the learning achievement at high school and the satisfaction with university and major.

• English Language & Literature Teaching
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• v.13 no.3
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• pp.227-251
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• 2007

The development and use of computer mediated communications (CMC) as a tool for teaching and learning English has grown considerably in recent years. The purpose of this study is to investigate factors related to learners' participation, achievement, and satisfaction in EFL classes using web-based bulletin boards. The total number of 77 university students participated in this study. Three domains and eight independent variables investigated in this study were a learner-related domain (attitudes toward CMC, intrinsic motivation, extrinsic motivation, attitudes toward writing), an interaction-related domain (student-student interaction, teacher-student interaction), and an environmental domain (physical support and design of the web site). In order to determine interrelation of variables correlation analysis and multiple regression analysis were used. The results of this study showed that the factors predicting a learner's participation were instrumental motivation, attitudes toward writing, and teacher-student interaction. The factors explaining a learner's achievement were learner's participation and attitudes toward writing, and the factors predicting a learner's satisfaction were integrative motivation, student-student interaction, teacher-student interaction, physical support and learner's participation.

• Educational Technology International
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• v.25 no.1
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• pp.27-65
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• 2024

The purpose of this study was to identify differences in learning patterns according to content complexity in video-based learning environments and to derive variables that have an important effect on learning achievement within particular learning contexts. To achieve our aims, we observed and collected data on learners' cognitive processes through perceived affordances, using behavioral logs and eye movements as specific indicators. These two types of reaction data were collected from 67 male and female university students who watched two learning videos classified according to their task complexity through the video learning player. The results showed that when the content complexity level was low, learners tended to navigate using other learners' digital logs, but when it was high, students tended to control the learning process and directly generate their own logs. In addition, using derived prediction models according to the degree of content complexity level, we identified the important variables influencing learning achievement in the low content complexity group as those related to video playback and annotation. In comparison, in the high content complexity group, the important variables were related to active navigation of the learning video. This study tried not only to apply the novel variables in the field of educational technology, but also attempt to provide qualitative observations on the learning process based on a quantitative approach.

• The Journal of Bigdata
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• v.5 no.2
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• pp.155-170
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• 2020

The purpose of this study was to apply machine learning techniques (Decision Tree, Random Forest, XGBoost) to data from the 4th~6th year of the Seoul Education Longitudinal Study to find the factors predicting the career maturity of middle school students in Seoul city. In order to evaluate the machine learning application result, the performance of the model according to the indicators was checked. In addition, the model was analyzed using the XGBoostExplainer package, and R and R Studio tools were used for this study. As a result, there was a slight difference in the ranking of variable importance by each model, but the rankings were high in 'Achievement goal awareness', 'Creativity', 'Self-concept', 'Relationship with parents and children', and 'Resilience'. In addition, using the XGBoostExplainer package, it was found that the factors that protect and deteriorate career maturity by panel and 'Achievement goal awareness' is the top priority factor for predicting career maturity. Based on the results of this study, it was suggested that a comparative study of machine learning and variable selection methods and a comparative study of each cohort of the Seoul Education Termination Study should be conducted.

• Journal of Convergence for Information Technology
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• v.8 no.4
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• pp.231-236
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• 2018

There has been a notable increased interest of the study of emotions in educational contexts. The purpose of this study was to analyze predicting emotional variables of self-regulated learning strategies and self-handicapping strategies with the university students. Participants were 143 students of undergraduates at A University and B University. Collected data were analyzed by correlation analysis and regression analysis, respectively. It turned out that class related emotions, learning related emotions, and test emotions predicted self-handicapping strategies negatively. However, achievement emotions didn't predict self-regulated learning strategies. The result of this study will provide the theoretical basis and practical usefulness of academic emotions.

• Journal of The Korean Association of Information Education
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• v.13 no.3
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• pp.313-323
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• 2009

The objective of this paper is an implementation of analysing and predicting functions to promote self-directed learning for student's performance assessment system in programming subjects. By adapting Rubric model, the proposed functions inform a student of the assessment criteria and level to be carried out with respects to two-way specifications such as rational ability, problem solving ability and creativity. The proposed system also provides a graphical results of each ability instead of assessment result, for better understanding and analyzing himself/herself based on to the performance assessment and the result. Moreover, the proposed system contains a method to predict future achievement result with moving average technique. Therefore, an academic achievement can be precisely determined by himself/herself to estimate self-directed learning. The teacher can provide different level of educational resources such as supplement learning, problem explains and private instructor etc., in order to maximize efficiency of education.

• The Mathematical Education
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• v.61 no.2
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• pp.257-271
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• 2022

As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.