• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.159-172
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• 2009

The purposes of this study were to identify factors affecting the quality of universities' e-learning contents from the perspectives of learners and to find out specific solutions for improving them. To achieve these goals, research questions were established as follows: 1) What factors were influencing the quality of universities' e-learning contents, and how were learners perceived about each factor(by its importance and satisfaction)? 2) Were there any differences on the learners' perceptions about each factor(by its importance and satisfaction)? 3) What were any specific ways to enhance the quality of universities' e-learning contents? The participants were of 543 university students who took at least one e-learning course and were living in the metropolitan areas(Seoul, Incheon, Gyeonggi). The survey questionnaire was consisted of 38 items developed through the literature review. To analyze the data collected, factor analysis and paired-sample t-test were conducted. The results were as follows: Five identified factors influencing the quality of universities' e-learning contents from the perspectives of learners were instructional strategies, learning contents, usability, evaluation/feedback, and interface design, and all identified factors were statistically significant differences among the learners' perceptions of its importance and satisfaction. The analysis results of importance-satisfaction matrix by each factor showed that 1) learning contents was the factor that current status should be at least continuously maintained, 2) usability, instructional strategies, and evaluation/feedback were the factors that learners' satisfactions still need to be increased although those importances were not relatively high, and 3) interface factor was important, while learners' satisfaction toward it was not much high so that solutions to increase the satisfaction need to be immediately considered. Based on the results, several suggestions to enhance universities' e-learning contents from the learners' perspectives were also recommended.

• Language Facts and Perspectives
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• v.48
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• pp.195-227
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• 2019

Building upon the state-of-the-art deep learning techniques, the present study classifies the texts written by Korean EFL learners and English native speakers and thereby demonstrates how the two types of texts differ from each other. To this end, the current work makes use of the Yonsei English Learner Corpus (YELC) and Gacheon Learner Corpus (GLC) as the L2 data, and Corpus of Contemporary American English (COCA) as the L1 data. Utilizing the sentence classification methods, the current work implements a system to differentiate the two types of texts, the accuracy of which is about 94%. This indicates that the deep leaning-based system is capable of identifying the well-formedness and felicities of the texts written by Korean EFL learners. Nonetheless, the system-based judgments do not overlap with human judgments largely because the deep learning model exclusively focuses on sequence of words. The present study provides a further analysis to see how the two types of judgments differ with respect to grammatical errors (e.g., word order, voice, etc.) and felicity errors (e.g., semantic prosody, the position of adverbs, etc.).

• Hwankyungkyoyuk
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• v.16 no.1
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• pp.25-33
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• 2003

This study aimed to discern the educational meaning of nature experience activity in a nonformal educational program in Korea, focusing on the interaction between a teacher and learners. To achieve this objective, an ethnographic research method was employed using an environmental educational program for children in a nonformal setting. The results of study are as followings. Firstly, the teacher's belief-"young ecologist" shaped its own characteristics of the program. Secondly, the children had a understanding that they learn something about nature(e.g. learning by seeing, dlscovering, recollecting, and awakening). The analysis of teachers' ideas and learners' attitude toward nature experience shows that there exists a gap between the teachers' expectations and the actual ecological changes in the learners' ideas. However, the educational meaning of nature experience can be understood by the unique type of interaction between a teacher and learners. In conclusion, on the basis of this study, it is suggested that educators should be aware of the fact that nature experience can conttribute significantly to the education of children not just from the encounter with nature but also philosophically with regard to our connectedness with nature. Bringing nature into educational contexts can help children to take part in thoughtful perspectives of learning and to devise their own appropriate nature experience.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.