• English Language & Literature Teaching
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• v.13 no.1
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• pp.235-259
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• 2007

The purpose of the present study is to develop a prototype automated scoring system for English writing. The system was developed for scoring writings of Korean middle school students. In order to develop the automated scoring system, following procedures have been applied. First, review and analysis of established automated essay scoring systems in other countries have been accomplished. By doing so, we could get the guidance for development of a new sentence-level automated scoring system for Korean EFL students. Second, knowledge base such as lexicon, grammar and WordNet for natural language processing and error corpus of English writing of Korean middle school students were established. Error corpus was established through the paper and pencil test with 589 third year middle school students. This study provided suggestions for the successful introduction of an automated scoring system in Korea. The automated scoring system developed in this study should be continuously upgraded to improve the accuracy of the scoring system. Also, it is suggested to develop an automated scoring system being able to carry out evaluation of English essay, not only sentence-level evaluation. The system needs to be upgraded for the improved precision, but, it was a successful introduction of an sentence-level automated scoring system for English writing in Korea.

• Korean Journal of Cognitive Science
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• v.26 no.2
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• pp.147-165
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• 2015

Through short-answer questions, we can reflect the depth of students' understanding and higher-order thinking skills. Scoring for short-answer questions may take long time and may be an issue on consistency of grading. To alleviate such the suffering, automated scoring systems are widely used in Europe and America, but are in the initial stage in research in Korea. In this paper, we propose a semi-automatic scoring system for short Korean free-text responses using semi-supervised learning. First of all, based on the similarity score between students' answers and model answers, the proposed system grades students' answers and the scored answers with high reliability have been included in the model answers through the thorough test. This process repeats until all answers are scored. The proposed system is used experimentally in Korean and social studies in Nationwide Scholastic Achievement Test. We have confirmed that the processing time and the consistency of grades are promisingly improved. Using the system, various assessment methods have got to be developed and comparative studies need to be performed before applying to school fields.

• Journal of KIISE
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• v.42 no.9
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• pp.1124-1132
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• 2015

In order to train a supervised machine learning algorithm, it is necessary to have non-biased labels and a sufficient amount of training data. However, it is difficult to collect the required non-biased labels and a sufficient amount of training data to develop an automatic English Composition scoring system. In addition, an English writing assessment is carried out using a multi-faceted evaluation of the overall level of the answer. Therefore, it is difficult to choose an appropriate machine learning algorithm for such work. In this paper, we show that it is possible to alleviate these problems through ensemble learning. The results of the experiment indicate that the ensemble technique exhibited an overall performance that was better than that of other algorithms.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.154-155
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• 2023

The International Convention on Standards for Training, Certification, and Watchkeeping of Seafarers (STCW) outlines the qualifications that maritime cadets must meet in order to serve as merchant marine officers. Maritime English is one of the most essential qualifications for STCW, and each national authority is implementing Maritime English education that complies with national and international regulations. In this study, an English proficiency background survey was conducted to investigate the factors related to the Maritime English skills and competencies. In line with this, maritime cadets utilized the Standard Maritime English Communication Phrases (SMCP) learning platform to track their learning progress and its efficacy. This study examined the applicability of the automatic scoring platform for Maritime English education, as well as its future potential for widespread use in the maritime education field.

• Journal of KIISE
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• v.42 no.12
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• pp.1522-1534
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• 2015

This work presents a new method that can predict, without the use of training data, whether an input essay is written on a given topic. ConceptNet is a common-sense knowledge base that is generated automatically from sentences that are extracted from a variety of document types. An essay prompt is the topic that an essay should be written about. The method that is proposed in this paper uses ConceptNet and an essay prompt to decide whether or not an input essay is off-topic. We introduce a way to find the shortest path between two nodes on ConceptNet, as well as a way to calculate the semantic similarity between two nodes. Not only an essay prompt but also a student's essay can be represented by concept nodes in ConceptNet. The semantic similarity between the concepts that represent an essay prompt and the other concepts that represent a student's essay can be used for a calculation to rank "on-topicness" ; if a low ranking is derived, an essay is regarded as off-topic. We used eight different essay prompts and a student-essay collection for the performance evaluation, whereby our proposed method shows a performance that is better than those of the previous studies. As ConceptNet enables the conduction of a simple text inference, our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required.