Earticle

The aim of this study is to compile an Offshore English corpus as English for specific purposes and develop an online web concordance program for learners of Offshore English. The Offshore English Corpus(OEC) was comprised of one hundred million words. It includes four major genres such as academy, news, law, training and consulting texts to represent this field. In order to compile the OEC, we consulted with some experts of academic and industry fields in offshore industry. For building the large corpus, we used natural language processing tools such as Python tool kits and created customized Python programs to automatically extract a lot of texts from various Web sites and PDF files. Unnecessary symbols and characters are eliminated through filtering process. Along with the Python program, we used free softwares such as Wget for crawling texts from various Web sites and nPDF for extracting only texts. After building the OEC, we developed an online web concordance program with which learners of Offshore English can search for collocations, genres, and concordances. This study will be expected to contribute not only to Offshore English education but also to other ESP education fields.

Understanding an accurate trip demand by purpose is crucial for short-term regional planning but also long-term regional planning. In traditional approach, information of purpose-oriented trip demand has been derived from public survey in South Korea such as Travel Diary Survey. This type of data acquiring method maybe useful in a sense that it can capture a meaningful sample regarding to entire country, meanwhile it costs a tremendous amount of budget and time. In this research, we want to offer a novel framework for estimating purposeoriented trip demand with dynamic and effective fashions using data fusion in conjunction with Machine learning techniques. With primary results of this concept, this study showed how several state-of-the-art algorithms, including Deep neural network, UMAP, and random forest in conjunction with Genetic algorithm and Tabu-search for optimization, can contribute to this framework. Although tangible results are yet to come, we expect this framework can contribute to resilience planning such as COVID-19.

This study investigates the effect of the DDL (Data-Driven Learning) approach on the English sentence writing ability of 6th graders in elementary school. To this end, a total of seven English textbooks were used to build a corpus. Five teachers were then asked to conduct five lessons using a weak version of DDL in their 6-grade EFL classrooms. Students were asked to complete a pre- and post-test and a pre- and post-survey, and a selected number of students and four of the five teachers had in-depth interviews with the researcher. The results are as follows: First, DDL using the textbook corpus was found to be adequate for helping elementary students improve their sentence-writing ability. Second, DDL had a significant effect on upper, middle, and lower level groups of students. Third, the students felt that DDL was neither unfamiliar nor difficult. Fourth, teachers with little teaching experience found it easy to conduct their classes using the DDL approach. This study implies that DDL is an effective approach to teaching communicative functions and language forms in the elementary English classroom and can be useful for all levels of elementary students.

본 연구는 영화 개봉 전 활용 가능한 정적 데이터를 기반으로 AI 및 기계 학습(Machine Learning, ML) 기법을 활용한 영화 흥행 예측 모델을 제안하고, 이를 기존 베이스라인 모델과 비교하여 성능을 평가하였다. 2013년부터 2024년까지 수집된 KOBIS 데이터를 활용하여 배우, 감독, 배급사, 장르별 기술 통계와 상호작용 파생변수를 생성하였으며, RFECV(Recursive Feature Elimination with Cross- Validation)를 통해 주요 변수를 선별하였다. 또한, AutoML 기법을 적용하여 데이터셋에 최적화된 모델을 학습하고, 네이버 검색어 트렌드 데이터를 추가적으로 반영하여 예측 성능을 향상시켰다. 실험 결과, 최종 제안모델은 베이스라인 모델 및 기존 연구모델을 뛰어넘는 성능을 보였으며, F1-Macro 및 AHPR-Bingo를 활용한 평가 척도에서 높은 정확도를 기록하였다. 특히, 상호작용 파생변수는 모델 성능 개선에 기여하는 주요 변수로 나타났으며, 정적 데이터를 활용한 예측 모델이 영화 산업 내 의사결정 과정에서 유의미한 도구로 작용할 수 있음을 입증하였다. 본 연구는 정적 데이터만을 활용한 영화 흥행 예측 모델을 제안함으로써 학술적 및 실무적 기여를 제공한다. 연구 결과는 영화 제작사와 배급사가 개봉 전 전략적 의사결정을 지원하는 데 유용한 프레임워크를 제공하며, 향후 연구는 동적 데이터를 포함한 확장된 모델 개발 및 다양한 데이터셋 적용을 통해 본 연구를 보완할 수 있을 것으로 기대된다.

This study proposes a box office prediction model utilizing AI and machine learning (ML) techniques based on static data available before a film’s release and evaluates its performance by comparing it with existing baseline models. Using KOBIS data collected from 2013 to 2024, we generated descriptive statistics and interaction-derived variables for actors, directors, distributors, and genres. Key features were selected using Recursive Feature Elimination with Cross-Validation (RFECV). Additionally, an AutoML approach was applied to train an optimized model for the dataset, and Naver search trend data was incorporated to enhance predictive performance. Experimental results demonstrated that the proposed model outperformed both baseline and existing research models, achieving high accuracy based on F1-Macro and AHPR-Bingo evaluation metrics. Notably, interaction-derived variables were identified as key contributors to model performance improvement, confirming that prediction models based on static data can serve as valuable tools in the decision-making processes of the film industry. This study makes both academic and practical contributions by presenting a box office prediction model solely based on static data. The findings provide a useful framework for film producers and distributors to support strategic decision-making before release. Future research can extend this study by incorporating dynamic data and applying the model to diverse datasets for further validation and enhancement.

The purpose of this study is to review the issues related to parallel corpus and previous studies using parallel corpus, and also to examine how to use parallel corpus in the studies of Korean language education. This study starts by summing up the concept, types, and the structure of parallel corpus, and examining problems in construction and use. Next, we review the current trends of language research and language education research using parallel corpus, and then examine the trends of studies related to Korean language education, especially focusing on language contrastive studies. Finally, we would like to conclude what to consider when trying to use parallel corpus in studies of Korean language education, and the direction of the Korean language education research using parallel corpus in the future.

본 연구는 한국 초등영어 학습자들의 효과적인 어휘⋅문법 학습을 위한 자료기반학습 모형을 개발하고 실제 수업에 적용함으로써 초등학생들의 영어 어휘⋅문법 학습 및 정의적 영역에 미치는 영향에대해 알아보았다. 영어 학습의 초기 단계에 있는 초등학생들의 수준에 적합한 자료기반학습의 적용을위하여, 한국 초등영어 학습자 코퍼스에서 추출한 문장들을 활용하여 학습 자료를 구성하였다. 자료기반학습은 일반적으로 학습자 주도의 귀납적 학습을 특성으로 하지만, 본 연구에서는 EFL 환경의 초기영어 학습자가 대상임을 고려하여 교사의 개입이 함께 이루어지도록 설계하였다. 또한 교사의 개입정도에 따라 귀납적 학생중심 모형과 연역적 교사중심 모형으로 구분하여, 본 연구의 대상인 세 초등학교의 4, 6학년에서 각각 두 학급을 실험 집단으로 선정하고 학급별로 학생중심 및 교사중심 자료기반학습을 적용한 연구 수업을 실시하였다. 연구 수업의 전, 후에는 학생들을 대상으로 한 사전⋅사후평가 및 서면 설문을 실시하였으며, 이 결과를 기반으로 자료기반학습의 효과를 인지적, 정의적 측면으로 구분하여 분석하였다. 연구 결과를 통하여, 자료기반학습은 초등학교 영어교육에서의 적용 가능성을 가지며 초등학생들의 영어 어휘⋅문법 학습에 긍정적인 영향을 줄 수 있다는 결론을 도출할 수있었다. 자료기반학습의 유형별 비교 결과에서는 4, 6학년 모두에서 학생중심 학습 방법의 효과가 교사중심 학습 방법의 효과에 비해 더 큰 것으로 나타났지만, 학생들의 영어 수준에 따라 자료기반학습의 유형에 따른 학습 효과가 다르게 나타날 수 있다는 결과 또한 확인하였다. 따라서, 학습자의 수준및 성향을 고려하여 자료기반학습 방법을 적절하게 적용한다면 초등학교 영어수업에서 효과적인 어휘및 문법 학습을 위한 교수⋅학습 방법으로서 자료기반학습을 활용할 수 있을 것이다.

This study aimed to examine the effects of data-driven learning (DDL) on Korean elementary school students’ English learning. A DDL model for English vocabulary and grammar learning was designed and its effectiveness on the cognitive and affective domains of Korean elementary school students was analyzed. The DDL model was applied in the form of inductive student-centered DDL (SDDL) and deductive teacher-centered DDL (TDDL), and these two methods were compared. For the experiment, target learning sentences were extracted from the SEEC Elementary English Learner Corpus, the Korean elementary school students’ English learner corpus. Students from three schools were divided into two groups, SDDL and TDDL; they learned eight lexico-grammatical elements using the target learning sentences during the experiment. The students were administered two rounds of recall tests and surveys regarding their attitudes toward DDL. Both methods were essentially inductive; however, considering the differing levels of English comprehension among elementary school students, TDDL with sentence translation and teacher explanation was more deductive than SDDL. The test results indicated that DDL could be useful in improving students’ vocabulary and grammar learning. In particular, SDDL was found to be more effective than TDDL in grades four and six. However, students with relatively lower comprehension of English preferred TDDL with sentence translation and teacher explanation. These findings suggest that teachers should consider their students’ levels of comprehension and characteristics when applying DDL in elementary school English teaching.

본 연구는 언어자료기반학습(DDL)을 적용한 어휘 학습이 수용적·생산적 어휘 지식에 미치는 영향과 학생들의 수준에 따른 효과를 탐구하고, DDL에 대한 학생들의 정의적 반응을 살펴보고자 한다. 원주시 W중학교 3학년 64명의 학생들을 대상으로, 14종의 교과서 코퍼스에서 추출한 콘코던스 예문으로 어휘에 대한 주목과 자각 능력을 높여 어휘 사용 능력을 향상시키는 DDL 연구수업을 실시하였다. DDL의 어휘 학습 효과를 살펴보기 위하여 사전·사후 각각 수용적·생산적 어휘 지식에 대한 평가를 실시하였고, 정의적 반응을 살펴보기 위해 설문조사와 심층면접을 하였다. 그 결과로 첫째, 교과서 코퍼스 활용 DDL은 중학생들의 전반적인 어휘 학습에 효과적이며, 수용적·생산적 어휘 지식을 향상시키는 데 도움이 된다. 따라서 어휘의 의미 이해는 물론 표현 능력을 신장시키기 위해 어휘에 대한 주목과 자각 능력을 높여주는 DDL을 실시할 필요가 있다. 둘째, 교과서 코퍼스 활용 DDL은 학생들의 수준에 따라 전반적인 어휘 학습에 모두 효과적이다. 높은 수준의 학생들의 생산적 어휘 지식에 유의한 향상을 가져왔으며, 낮은 수준의 학생들의 수용적·생산적 어휘 지식 모두에 긍정적인 효과를 가져왔다. 셋째, DDL에 대한 학생들의 정의적 반응은 전반적으로 긍정적이었다. 이에 새로운 어휘 학습 방법에 대한 학생들의 흥미와 관심을 바탕으로 다양한 수준의 학생들이 섞여 있는 우리나라 영어 교실 상황에 DDL을 적용할 수 있는 가능성을 발견하였다.

The present study is to examine to what extent DDL activities influence on receptive and productive aspects of vocabulary knowledge, and to what extent DDL has effects on different English levels of Korean middle school students. It also investigates students’ perceptions on DDL. Five experimental DDL lessons were developed using concordance examples extracted from the middle school English textbook corpus, and they were given to a group of 64 students (9th graders). Then pre-/post-vocabulary tests measuring receptive and productive knowledge were conducted with a follow-up survey and in-depth interviews. Three major findings are as follows: (1) DDL is effective for both receptive and productive vocabulary knowledge but has a greater effect on the productive aspect of vocabulary knowledge; (2) DDL is effective to all different English levels of students in learning vocabulary. Namely, unlike the findings from the previous research, which DDL is useful only for the advanced level of students, this study found out that DDL is effective to all English levels of middle school students; (3) the responses of the follow-up survey and in-depth interviews showed that most students were positive about the DDL, and many of the students indicated that they preferred the DDL to the traditional vocabulary learning method.

This study investigates the application of Data-Driven Learning (DDL) in English chant-making lessons for elementary students, focusing on its cognitive and affective effects. Traditional chant lessons often diminish student engagement due to their repetitive nature. To address this, chant-making activities were integrated with DDL, analyzing cognitive aspects such as collocational understanding, lexical diversity, collocation usage, and rhyme, as well as affective outcomes. The study involved 61 fifth-grade students in Jeonbuk, South Korea, who were divided into three groups: hard DDL (using a corpus program), soft DDL (using teacher-provided concordance lines), and non-DDL (using only the textbook). Data were collected through pre- and post-tests and surveys. Quantitative results reveal no statistically significant differences among groups in collocation tests, although the strong DDL group exhibited higher mean scores than the other groups. Qualitative findings show that the hard DDL group addressed broader topics and used collocations more appropriately. Additionally, the hard DDL group reported higher satisfaction with the lessons, noting increased interest, material usefulness, and applicability. While limited by the small corpus size and short duration of the study, the findings highlight DDL’s potential in elementary English education. The study emphasizes the need for long-term research, larger corpora, and user-friendly corpus tools, recommending the integration of DDL into digital English textbooks to enhance learning outcomes.

This study aims to design and implement a data-driven learning (DDL) approach that enables Korean language learners to self-correct their writing errors, and to examine its effectiveness. For this purpose, seven learners participated in the study over the course of one semester. Each learner wrote four data commentaries and four argumentative essays. An instructional method was developed that allowed learners to consult a corpus to correct the errors generated during this writing process. As a result, all learners demonstrated a successful error correction rate of over 50%, with a significant decrease in writing errors afterwards. Additionally, students demonstrated fewer errors and a higher rate of successful correction in data commentaries than argumentative essays. Survey and interview results indicated that learners' sensitivity to errors and confidence in their ability to correct them increased after the course. However, further research is recommended regarding learners' satisfaction and preference for the DDL-based error correction method, as the effectiveness of DDL-based correction varies by the writing genre and the learners' preference. Therefore, it is necessary to tailor the size and scope of corpora to suit the characteristics of individual learners.

본 연구의 목적은 종이기반 콘코던스를 활용한 자료주도학습(Data-Driven Learning)이 학문목적 한국어 학습자들의 어휘 능력에 미치는 효과를 탐구하고, 이에 대한 학습자들의 정의적 반응을 살피는 데 있다. 이를 위해 T대학에서 쓰기 관련 교양한국어 수업을 수강한 30명의 학문목적 외국인 학습자들을 대상으로, 실험 집단(15명)에서는 종이기반 콘코던스를 활용한 자료주도학습을 어휘 학습에 적용하여 실시하였고, 통제 집단(15명)에서는 어휘의 의미를 직접 설명해 주면서 정의 중심의 어휘 지도가 이루어지는 전통적 교수 방식으로 어휘 학습을 진행하였다. 종이기반 콘코던스를 활용한 자료주도학습의 어휘 학습 효과를 살펴보기 위하여 1, 2차 사후 어휘 평가 및 글쓰기 결과물 평가를 실시하였고, 정의적 태도를 살펴보고자 설문 조사 및 심층 면담을 진행하였다. 그 결과, 어휘를 학습하는 데 있어 종이기반 콘코던스를 활용한 자료주도학습이 학습자들에게 긍정적인 영향을 미친 것으로 나타났다. 아울러 어휘의 의미 이해적인 측면과 더불어 어휘 활용적인 측면까지 도움을 줄 수 있다는 것을 알 수 있었다. 이에 본 연구는 종이기반 콘코던스를 활용한 자료주도학습을 실제 한국어 어휘 학습에 적용하여 그 교육적 적용 가능성을 검토하였으며, 나아가 한국어 어휘 교육뿐만 아니라 쓰기 교육으로의 확장 가능성까지 고찰하였다는 점에서 의의가 있다.

The purpose of this study is to investigate the effects of data-driven learning(DDL) using paper-based concordance on the vocabulary proficiency of academic Korean learners and to examine their affective responses. The study was conducted on 30 foreign learners for academic purposes who took a liberal arts Korean class related to writing at T University. The experimental group(15 students) applied data-driven learning using paper-based concordance to learn vocabulary, while the control group(15 students) learned vocabulary through the traditional teaching method of definition-centered vocabulary instruction by directly explaining the meaning of vocabulary. To examine the vocabulary learning effectiveness of data-driven learning through paper-based concordance, two post-tests on vocabulary and evaluations of learners’ writing tasks were conducted. In addition, surveys and in-depth interviews were carried out to explore learners’ affective attitudes toward the learning method. The results indicated that data-driven learning through paper-based concordance had a positive impact on vocabulary learning. Specifically, it was found to enhance not only learners’ understanding of word meanings but also their ability to utilize the vocabulary. Accordingly, this study holds significance in that it examined the pedagogical applicability of data-driven learning using paper-based concordance in actual Korean vocabulary lesson, and further explored its potential extension to both vocabulary and writing education in Korean language classrooms.

목적: 본 연구는 학문 목적 한국어 쓰기 교육에서의 데이터 기반 학습 적용 방안을 살피고자 수행되었다. 방법: 데이터 기반 학습 관련 선행 연구를 바탕으로 문헌 분석을 수행하였다. 이때 주요 분석의 대상이 된 것은 제2 언어 교육 연구, 그리고 학술 및 학위 논문 장르와 관련된 학문 목적 쓰기 연구였다. 결과: 본 연구에서는 데이터 기반 학습의 개념을 밝히고, 이를 활용하기 위해 구성해야 할 말뭉치의 유형과 특성을 살폈다. 또한 학문 목적 쓰기에서의 말뭉치 분석 틀로서 이동마디(Swales, 1991)의 가능성에 주목하고, 서론과 본론, 결론의 분석에 활용 가능한 기본 틀을 제시하였다. 그리고 이를 바탕으로 학문 목적 한국어 쓰기 교육에서 적용할 수 있는 데이터 기반 학습의 모형을 구안하고, 국내 대학 부설 한국어 교육 기관에서 실행 가능한 교육 과정 운영안을 제시하였다. 결론: 본 연구는 한국어 학습자들이 실제적인 언어 자료를 통해 학문 목적 쓰기 능력을 개발할 수 있도록 하는 데이터 기반 학습 설계 및 운영의 참고 자료로 활용될 수 있을 것이다.

Purpose: This study aimed to examine specific ways to apply data-driven learning in Korean writing education for academic purposes. Methods: A literature analysis was performed based on previous studies related to data-driven learning. The analysis subject was research on second language education and writing for academic purposes related to the thesis genre. Results: “Data-driven learning” was clarified, and the types and characteristics of the corpus to be composed to utilize it were examined. Additionally, we focused on the possibility of Move (Swales, 1991) as an analysis framework that can be used for corpus analysis in writing for academic purposes and reviewed the results of previous studies used to analyze introductions, bodies, and conclusions. Next, we examined data-driven learning’s general procedures and methods and, based on this, an appropriate model for application in Korean writing education for academic purposes. Finally, a curriculum operation plan that can be applied to the teaching and learning situation of Korean language education institutions affiliated with domestic universities is presented. Conclusion: This study provides reference materials for the design and operation of data-based learning that enables learners to develop language skills related to their major through actual language data in Korean writing education for academic purposes.

Data-driven learning (DDL), which utilizes authentic language samples from corpora, promotes independent learning by allowing students to discover language patterns. While previous studies highlight DDL’s effectiveness in language acquisition, its role in fostering learner autonomy remains underexplored. This study investigates the impact of DDL on autonomy and the acquisition of verb-preposition collocations among low-proficiency Korean middle school students. Over twelve weeks, five students engaged in hands-off (teacher-guided) and hands-on (independent) DDL activities, analyzing collocations using two corpus tools. Data were collected through pre- and post- tests, classroom observations, reflective journals, screenshots, and interviews. Findings suggest that DDL improved learners’ ability to construct verb-preposition collocations and fostered greater autonomy through self-directed problem-solving, increased learner agency and participation, and strategic engagement with corpus tools. While learners initially faced challenges in navigating corpus tools, they gradually developed effective learning strategies and greater confidence in independent exploration. These results underscore DDL’s potential to enhance both language competence and autonomous learning, particularly for low-proficiency learners.

영어 불변화사구문은 그 문법적 특이성 때문에 영어를 배우는 학습자뿐만 아니라 모국어로 하는 화자에게도 학습하는 데 있어 어려움을 겪는 대표적 구문들 중 하나이다. 이 논문은 이러한 불변화사 구문의 학습과 강의에 있어 문제시 되는 문법적 요소에 대하여 다루고자 한다. 특히 본 논문에서는 영어 코퍼스에서 추출한 자료를 기반으로 불변화사구문의 문법적 특징에 대하여 기술한 후 불변화사 구문의 학습에 있어 DDL(Data-Driven Learning) 기법을 도입하여 학습효과를 향상시키는 데 효과가 있는지 살펴보았다. 그 결과 전통적인 학습에서 보다 실제 코퍼스 자료를 기반으로 한 학습자 중심의 DDL 기법이 불변화사 구문을 이해하는데 비모국어 학습자에게 효과가 있음을 보여주었다.

English inversion constructions are not only hard for non-native speakers to learn but also difficult to teach mainly because of their intriguing grammatical and discourse properties. This paper addresses grammatical issues in learning or teaching the so-called ‘predicate inversion (PI)’ construction (e.g., Equally important in terms of forest depletion is the continuous logging of the forests). In particular, we chart the grammatical (distributional, syntactic, semantic, pragmatic) properties of the PI construction,and argue for adata-driven teaching for English grammar. To depart from the arm-chaired style of grammar teaching (relying on author-made simple sentences), our teaching method introduces a datadriven teaching. With total 25 university students in a grammar-related class, students together have analyzed the British Component of the International Corpus of English (ICE-GB), containing about one million words distributed across a variety of textual categories. We have identified total 290 PI sentences (206 from spoken and 87 from written texts). The preposed syntactic categories of the PI involve five main types:AdvP, PP, VP(ed/ing), NP, AP, and so, all of which function as the complement of the copula. In terms of discourse, we have observed, supporting Birner and Ward’s (1998) observation that these preposed phrases represent more familiar information than the postposed subject. The corpus examples gave us the three possible types: The preposed element is discourse-old whereas the postposed one is discourse-new as in Putting wire mesh over a few bricks is a good idea. Both preposed and postposed elements can also be discourse new as in But a fly in the ointment is inflation. These two elements can also be discourse old as in Racing with him on the near-side is Rinus. The dominant occurrence of the PI in the spoken texts also supports the view that the balance (or scene-setting) in information structure is the main trigger for the use of the PI construction. After being exposed to the real data and in-depth syntactic as well as informationstructure analysis of the PI construction, it is proved that the class students have had a farmore clear understanding of the construction in question and have realized that grammar does not mean to live on by itself but tightly interacts with other important grammatical components such as information structure. The study directs us toward both a datadriven and interactive grammar teaching. English Predicate Inversion: Towards Data-driven Learnin 1051

This research reports the use of a learner-friendly concordancer for Data-Driven Learning (DDL) in an English for Specific Purposes (ESP) course. It was hypothesized that DDL learners with a pedagogically modified concordancer would have higher satisfaction toward DDL than those with a research concordancer. To test this idea, a learner-friendly concordancer was developed to facilitate learners' analysis of concordance lines. This study examined the learners' perceptions with regard to their concordancers, comparing the perceptions of two groups, where one group used a learner-friendly concordancer, and the other group used a research one. While participants from both groups showed generally similar positive attitudes toward the DDL approach, the findings of fewer complaints on a learner-friendly concordancer from the treatment group are promising. The author argues for a pedagogical mediation on the use of concordancers in DDL.

As one of the approaches in corpus-based language learning, Data-Driven Learning (DDL) emphasises learners’ role, focusing on the autonomous analysis of corpus data. Based on the three features of DDL (serendipity, inductive manner, and DDL procedure),the role of learners is considered to be a key element in successful DDL application to language pedagogy. To this end, the study investigates learners’ roles in DDL from a theoretical perspective. First, the three existing metaphors of learners’ role (i.e., researcher,traveller, and spy) are critically investigated. Even though the metaphors share the three DDL features, the study highlights that they are not sufficient to describe the complete roles of learners in ESL/EFL contexts. Second, from this perspective, the researcher presents a new role, a multiplayer, which has a more flexible nature in defining and illustrating the role in DDL. In this metaphor, the role of learners can be determined by their pedagogical circumstances, rather than by a particular fixed type of DDL. Therefore,they can adjust their role and adopt a suitable type of DDL which can maximise their interlanguage development.

This study investigates the effectiveness of corpus-based Data-Driven Learning (corpus DDL) in college English class in terms of enhancing vocabulary awareness/usability and autonomy in developing lexical knowledge. For this purpose, 48 college students were trained in corpus DDL approach for two weeks. Before and after instruction, students were tested with the same vocabulary and grammar questions and were also asked about their attitude toward vocabulary learning and its methods with a survey questionnaire. As a result, first of all, corpus DDL approach significantly enhanced college students’ awareness of both vocabulary and grammar, especially of various types of collocations. In addition, corpus DDL approach fostered the college students’ language learning autonomy. Students showed a significant increase in vocabulary learning control and confidence. This study suggests two pedagogical implications for English educators. First, in learning vocabulary and grammar, it is sometimes more important for teachers to provide good resources and tools to students and to teach how to use them effectively than just delivering linguistic knowledge to the students. Second, teachers should provide more various word chunks, different types of collocations.

As a part of basic research for developing concordance materials for young EFL learners, this study explores appropriate numbers of concordance citations contained in the materials, given that secondary EFL students successfully undertake Data-Driven Learning (DDL) in the classroom. In past decades, there have been increasing demands regarding deductive DDL for language learners at low proficiency levels. However, few practical suggestions have been made how to develop the materials. This current study focuses on the importance of suitable materials for young learners and suggests guidelines for materials development in terms of citations limited to classroom activity. It followed three Korean teachers of English and 78 high school students. Students’ learning outcomes were qualitatively analyzed using DDL materials developed with different numbers of citations, and interview transcripts. Based on students’ findings and preferences, it was concluded that 12 citations were most appropriate for the classroom activity studied. Three participant teachers stated that the numbers were dependent on learning settings. The study provides practical information on appropriate numbers of citations at least required for DDL activity in the secondary classroom.