• International Journal of Contents
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• v.15 no.4
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• pp.97-106
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• 2019

EMI (English as a Medium of Instruction) classes are now accepted policy at Korean universities, yet students often struggle with required academic English writings. The present study examined an EMI class that used direct instruction and access to online assistive English writing software. From preliminary analysis, 26 students expressed interest in how an EMI academic writing class could facilitate improved English writing skills. Study participants completed a survey on self-efficacy and learning needs and assignments for an EMI academic writing class. To establish inter-rater reliability, three trained raters assessed the written essays of students prior to and after instructional intervention. Fleiss' Kappas statistics showed moderate reliability. Students' opinions on the use of online software were also analysed. Paired t-test was run on the quality of students' pre- and post-instruction assignments, and there was significant difference in the rated scores. Self-efficacy was found to have moderate positive association with improved post-essay writing scores.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.203-209
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• 2021

When carrying out computer programming, the process of checking and correcting errors in the source code is essential work for the completion of the program. Non-computer majors who are learning programming for the first time receive feedback from instructors to correct errors that occur when writing the source code. However, in a learning environment where the time for the learner to practice alone is long, such as an online learning environment, the learner starts to feel many difficulties in solving program errors by himself/herself. Therefore, training on how to check and correct errors after writing the program source code is necessary. In this paper, various types of errors that can occur in a Python program were described, the errors were classified into simple errors and complex errors according to the characteristics of the errors, and the distributions of errors by Python grammar category were analyzed. In addition, a coding learning process to refer error lists was designed to present a coding learning method that enables learners to solve program errors by themselves.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.23-28
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• 2022

Today, the online market has grown rapidly due to the development of digital platforms and the pandemic situation. Therefore, unlike the existing offline market, the distinctiveness of the online market has prompted users to check online reviews. It has been established that reviews play a significant part in influencing the user's purchase intention through precedents of several studies. However, the current review writing method makes it difficult for other users to understand the writer's emotions by expressing them through elements like tone and words. If the writer also wanted to emphasize something, it was very cumbersome to thicken the parts or change the colors to reflect their emotions. Therefore, in this paper, we propose a technique to check the user's emotions through facial expression recognition using a camera, to automatically set colors for each emotion using research on existing emotions and colors, and give colors based on the user's intention.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• The Korean Journal of Archival Studies
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• no.72
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• pp.165-203
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• 2022

Many people create records in digital space, and the amount of digital records left after individual dies has increased. The digital record left by the deceased is different from the record heritage that has physical substances. In many cases, the records of the deceased not just belong to the deceased, and many deceased did not explicitly disclose their online accounts and method of dispose of digital records during their lifetime, so this problem may lead to problems of inheritance to the bereaved family. In addition, digital records may be neglected or deleted after a person's death due to software problems, specific platform's terms of use, account deletion by bereaved family, etc. This leads to the problem that daily records, which are important clues to the social aspects at the time, are easily lost. Several studies have revealed that individuals are interested in preserving their digital records, but do not know how to do it, so they are benign neglect. For this reason, it is necessary to pay attention to personal digital records and personal digital legacy, and to prepare related policies and plans. Accordingly, this study analyzes problems related to the management of digital records after an individual's death, related to laws and systems, the status and policies of platforms and industries, the status of personal record management, etc. Various solutions were suggested, such as a need for enactment for digital personal record management act, platform's explicit policy for individual's post-mortem records, digital records management plan for archival institutions, individual's a preemptive management plan for his/her own records, and a method for writing a will related to digital account information.