[논문]인공지능활용 메이커교육 프로그램 적용 영어 교수학습 모형 개발

신명희

doi:10.15207/jkcs.2021.12.3.061

인공지능활용 메이커교육 프로그램 적용 영어 교수학습 모형 개발
Development of English Teaching Model Applying Artificial Intelligence through Maker Education 원문보기

한국융합학회논문지 = Journal of the Korea Convergence Society, v.12 no.3, 2021년, pp.61 - 67

초록
AI-Helper

본 연구의 목적은 EFL학습자들을 위해 구체적 학습활동 모형을 통해 교실 수업의 한계를 극복하고 의사소통의 기회를 창출해 내고자 하였다. 연구 방법으로는 모형개발, 타당화, 적용으로 전략, 지침 등을 개발하고 도출하고자 2019년 3월부터 6월까지 실시하였다. 사전학습에서는 인공지능을 활용하여 교실 밖 자기 주도적 학습을 유도하고, 본 수업에서는 문제 해결 능력을 향상시키고 학습 내재화를 목표로 협력과 참여를 통해 결과물을 만들어내는 메이커 교육을 적용한 학습자 중심활동으로 구성하였다. 두 번의 타당성 테스트 후 수정 된 모델을 실험 그룹에 적용한 결과 창의성을 제외한 자기 주도, 관심, 문제 해결 및 참여도가 유의미했고 사후 테스트 결과는 모든 분야에서 유의미한 결과를 나타냄으로 연구 기대효과의 유용성을 확인하였다. 다만 영어 학습과 관련된 인공 지능을 수업에 쉽게 적용할 수 있는 소프트웨어의 개발과 방법에 대한 심화연구 그리고 학습활동에서 보다 체계적인 메이커 교육과의 융합활동의 제시 등 지속적인 후속 연구가 필요하다.

Abstract ▼ AI-Helper

The purpose of this study is to demonstrate how EFL learners can overcome the limitations of traditional classes and practice communication through the learning activity model. As a research method, it was conducted from March to June 2019 to develop and derive strategies and guidelines through model development, validation, and application. After two validity tests, the model was applied to the experimental group, resulting in an increase of self-direction, engagement, problem-solving, and participation. Moreover the post results showed significant results in all fields, the usefulness of this model was confirmed. However, continuous follow-up research is needed, including the development of software that can easily apply AI related to English learning to classes, and the presentation of convergence activities with more systematic maker education in learning activities.

주제어

표/그림 (8)

표 Table 1. Test of Homogeneity
표 Table 2. Research Procedures
표 Table 3. Pre-learning model using artificial intelligence
표 Table 4. Main-class model
표 Table 5. Post-class model
표 Table 6. Results of Validity
표 Table 7. Difference after experiment
표 Table 8. Pre-and Post-test of Experimental Group

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

문제 정의

Therefore, model development and provision of strategies in this study are significant. In this study, we intend to develop an interactive learning model based on a maker education program using an artificial intelligence (AI) speech recognition program. Also it can provide overall guidance in developing content that can effectively interact with English learning using artificial intelligence.
These students completed 3 hours and 3 credits of elective general education of EFL courses a week at University of D city. The subject of this study is to explore and develop basic principles for developing English teaching models designed and applied through maker education activities using artificial intelligence.
In the main class, the teaching methodology based on learners' learning activities rather than a teacher-centered approach is needed. This study presented an English education model applying artificial intelligence through the maker education case study.

제안 방법

before this class. In addition, an assessment is performed to check whether learners have acquired and understood prior learning concepts using artificial intelligence.
The questionnaire was composed of 5 questions about education satisfaction for classes. In order to measure the self-directed learning ability and the affective domain through this teaching and learning model, the previous research test tool was used to reconstruct the questionnaire into five-stage Likert questions. The components of the test paper are self-direction, interest, creativity, cooperation, problem-solving, and participation.
The experimental group conducted a pilot test with a draft design. The result was revised, supplemented, and reflected in the actual class.
The first step of this upcoming class model is a team discussion to clarify the concept of the pre-learning with relevant activities, in which the professor should promote students' interaction as a facilitator, sharing knowledge they know to develop deep learning through problem solving. The second step is team activity, which is also the core of the project's activities as maker education.
The result was revised, supplemented, and reflected in the actual class. The lessons in this study were designed and operated with flipped learning. The overall composition of the AI applying maker education program consisted of pre-learning, in-class, and post-learning.
Tinkering is a warm-up stage, making is a stage of idea creation, making, and documentation, sharing is a stage of online and offline sharing, and Improving is a stage of revision and supplementation. The on-site evaluation was done to check the applicability of the actual class, and then the response evaluation was done for participating professors and learners.
The teaching model used in this study, developed through a prior literature review, has performed two internal verification tests on experts. For the review of expert validation, 3 people responded with 5 points Likert on the validity of the instructional design strategy and detailed guidelines.
The validation tool was modified according to the purpose and contents of this study based on universality, usefulness, and validity. The questionnaire was composed of 5 questions about education satisfaction for classes.

대상 데이터

to June 30, 2019. However, A total of 70 subjects in two groups with similar assumptions in the homogeneity test through a 25-item pre-test and t-test were selected as subjects for this study. There was no difference at the statistically significant (t =-.
The class design was applied to the actual class after twice of expert validation based on prior literature. The expert review consisted of experts with over 10 year experiences in teaching and education technology related research and education. Expert review was conducted through questionnaires and interviews.
The research period was from March, 2019 to June 2019 for 15 weeks. The first two weeks were a general orientation period for basic knowledge of artificial intelligence and maker education.
This study was conducted with 160 students in the 1st, 2nd, 3rd, and 4th grades from July 1, 2018 to June 30, 2019. However, A total of 70 subjects in two groups with similar assumptions in the homogeneity test through a 25-item pre-test and t-test were selected as subjects for this study.

성능/효과

2) Is the convergence English class model of artificial intelligence through maker education effective for learners' self-directed learning, interest, creativity, problem solving ability, and active participation ability?
In the first validation, the IRV of the teaching-learning design strategy was 0.87, and the responses from experts are consistent, but the CVI value was less than 0.8, indicating that the need for modifications to the draft design came out. The 2nd expert validation is based on the revised class design strategy after the 1st validation.

참고문헌 (15)

D. Larsen-Freeman. (2009). The Cambridge Guide to Teaching English to Speakers of Other Languages, Cambridge University Press, 34-41. DOI : 10.1017/CBO9780511667206.006
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Y. I. Kim. (2017). An Oral Health Promotion Behavior Model for Adolescents. The Korean Journal of The Korea Convergence Society, 11(2), 129-142. DOI : 10.12811/JKCS.201.11.2.129
J. K. Sim, & D. Y. Kwon. (2020). Development of Artificial Intelligence Education Content to Classify Emotion of Sentences for Elementary School. Journal of The Korean Association of Information Education, 24(3), 243-254. DOI : 10.14352/jkaie.2020.24.3.243

원문보기 상세보기
D. H. Lee. (2018) A study for the development of an English learning chatbot system based on Artificial Intelligence. Secondary English Education, 11(1), 45-68. DOI: 10.20487/kasee.11.1.201802.45

상세보기
L. Martin. (2015). The promise of the maker movement for education. Journal of Pre-College Engineering Education Research, 1(5), 30-39. DOI : 10.7771/2157-9288.1099

상세보기
G. D. Hong & H. K. Kim. (2017). Sensor-based convergence system in Ubiquitous Environment. Journal of The Korea Convergence Society, 7(1), 1-6. DOI : 10.22156/JKCS.2018.7.1.001
D. Dougherty. (2013). The maker mindset. In Honey, M. & Kanter, D. E. (Eds.), Design, Make, Play: Growing the Next Generation of STEM Innovators (7-11). New York, NY: Routledge. DOI : 10.4324/9780203108352-6
A. Zongpei . (2019). Application and Practice of Artificial Intelligence in Maker Education and Teaching. Advances in Higher Education, 3(2), 105-106. DOI : 10.18686/ahe.v3i2.1423
M. H. Shin. (2018). An Analysis of the Effects of On-Off line Convergence Learning Activities Based on Students' Learning Styles. Journal of The Korea Convergence Society, 9(2), 85-90. DOI : 10.15207/JKCS.2018.9.2.085

원문보기 상세보기
Y. B. Yoon & M. A. Park. (2020) Artificial Intelligence and Primary English Education: With Special Reference to Chatbots, Elementary Education Research Center, 31(1), 77-90, DOI : 10.20972/10.20972/Kjee.31.2.202006.1
S. A. Javier, J. Sergio, & J. Anders. (2019). Computing programs for generalized planning using a classical planner. Artificial Intelligence, 272(1), 52-85. DOI : 10.1016/j.artint.2018.10.006

상세보기
J. Underwood. (2017). Exploring AI language assistants with primary EFL students. Eurocall Research-publishing.ne, 317-321. DOI : 10.14705/rpnet.2017.eurocall2017.733
M. H. Shin (2019). Study of English Teaching Method by Convergence of Project-based Learning and Problem-based Learning for English Communication. The Korean Journal of The Korea Convergence Society, 10(2), 83-88. DOI : 10.15207/JKCS.2019.10.2.083

원문보기 상세보기
K. Peppler, R. E. Halverson. & B. K. Yasmin. (2016). Makeology: Makerspace as learning Environment New York: Routle.dge DOI : 10.4324/9781315726519

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