Step by Step Implementation of DSRM for Personalization of Reading

This paper aims to discuss step-by-step activities of the implementation of Design Science Research Methodology (DSRM) in the development of Personalization teaching and learning materials for children. DSRM is adapted in the development of personalized teaching and learning materials due to its potential to provide specific guidelines based on specific outcomes. This paper revealed the potential of DSRM as a reliable and comprehensive methodology that leads the developer on step by step processes to perform the development. Apart from that this study provides details description on the development of personalized teaching and learning materials that is successfully developed using the DSRM methodology as guidelines

Latent Dirichlet Allocation (LDA) for improving the topic modeling of the official bulletin of the spanish state (BOE)

Since Internet was born most people can access fully free to a lot sources of information. Every day a lot of web pages are created and new content is uploaded and shared. Never in the history the humans has been more informed but also uninformed due the huge amount of information that can be access. When we are looking for something in any search engine the results are too many for reading and filtering one by one. Recommended Systems (RS) was created to help us to discriminate and filter these information according to ours preferences. This contribution analyses the RS of the official agency of publications in Spain (BOE), which is known as "Mi BOE'. The way this RS works was analysed, and all the meta-data of the published documents were analysed in order to know the coverage of the system. The results of our analysis show that more than 89% of the documents cannot be recommended, because they are not well described at the documentary level, some of their key meta-data are empty. So, this contribution proposes a method to label documents automatically based on Latent Dirichlet Allocation (LDA). The results are that using this approach the system could recommend (at a theoretical point of view) more than twice of documents that it now does, 11% vs 23% after applied this approach

Using a Summarized Lecture Material Recommendation System to Enhance Students’ Preclass Preparation in a Flipped Classroom

Research has revealed the positive effects of flipped classroom approaches on students’ learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation

EXAMINATION OF HEALTH SCIENCE UNIVERSITY STUDENTS' LEVEL OF READINESS FOR E-LEARNING

In this study, the e-readiness levels of university students studying in the field of health sciences were examined in terms of different variables. In this context, whether the level of e-readiness differs according to gender, department, class level, type of education, device ownership, working status and economic level has been examined. In addition, the relationship between e-readiness level and academic success was investigated. The research sample consists of 923 health science students studying in different departments. The results of the research show that gender, learning type, device type and income level are important factors on the e-readiness level. In addition, the e-readiness levels of the nursing department students, normal (daytime) teaching, 1st year students were found to be low in the study.  As the difficulty level of the courses increased, the level of e-readiness was found to be an important factor on academic achievement. The results obtained from this research provide important clues for academicians as well as institutions and organizations providing services in the field of health sciences who want to switch to distance education. In addition, some suggestions were made in the light of this research results. Keywords: E-readiness levels, university students, health science university

Digital Campus as a tool for teaching English in the era of digital education

Introduction. The digital transformation of higher education in Russia has caused the need to revise the approaches to language training of university graduates. Aim. The present research aimed to model a Digital Campus with its services and functions as a tool for language training of students in the era of digital transformation of education. Methodology and research methods. The strategic approach was employed as the leading methodological approach, which in the framework of this study is considered as a potential possibility to implement language education at the university using the “Digital Campus” as an important tool for digital transformation of universities. The research was conducted in three stages, involving 2–3 year students of Togliatti State University (113 participants) and school students of Togliatti (157 participants) from July 2022 to January 2023. Empirical research methods were used: analysis, comparison, generalisation, synthesis, modelling. Stage 1 – collection and analysis of the information presented on the websites of the universities in order to highlight the indicators of the digital development of universities. Stage 2 – a survey of schoolchildren to identify their attitude to university admission; a survey of students to determine their motivation to receive language training through digital tools. Stage 3 – modelling of a Digital Campus, its services and functions for students’ language training. Results. The authors defined the Digital Campus as an environment based on information and educational digital resources and technologies, computer equipment, telecommunication technologies and software, organisational and methodological support, connecting participants in the learning process (teachers, applicants and students, graduates, enterprises/businesses) for distant educational activities and business operations in accordance with personalised request. The data available on universities’ websites were analysed and grouped into seven indicators. The most represented are the services: additional professional education, the use of interactive forms to work with applicants, online support of student and graduate career development, and the project office. Services for the implementation of personalised educational tracks and the digital footprints accumulation during the learning process were less prominent in the course of the research. The results of the survey were the following: there was a general readiness for “applicant – university” interaction through the online environment; a small percentage of divergence of schoolchildren and student opinion regarding their expectations to learn a foreign language online was observed. The Digital Campus and its functions were modelled. Each of the campus services – a student personal account (applicants/students), a teacher personal account, and an employer personal account (enterprises/businesses) – has its own interface and access to the following functional units: “Recruitment and Project System”, “Language Courses”, “Course Designer”, “Resource Management”, “E-Learning System”, “Courses Expertise Centre”. The environment creates unified information and educational space for student language training. Scientific novelty. The approaches to understanding the digital transformation of higher education were systematised, and the indicators of higher education institutions use of digital solutions in the educational process were analytically presented. The article provides the authors’ definition of the Digital Campus. The theoretical significance of the article is in the outline and analytical presentation of existing approaches and trends in the digital transformation of higher education.Введение. Цифровая трансформация высшего образования в России повлекла за собой пересмотр подходов к языковой подготовке выпускников вузов. Цель исследования – смоделировать Цифровой кампус, прописав его сервисы и функционал, для языковой подготовки студентов в эпоху цифровой трансформации образования. Методология, методы и методики. Ведущим методологическим подходом был выбран стратегический подход, который в рамках данного исследования рассматривается в качестве потенциальная возможность реализации языкового образования в вузе с использованием «Цифрового кампуса» как важного инструмента цифровой трансформации вузов. Исследование проводилось в три этапа с привлечением студентов 2–3 курсов Тольяттинского государственного университета (113 участников) и обучающихся старших классов школ г. Тольятти (157 участника), с июля 2022 по январь 2023 года. Использованы эмпирические методы исследования: анализ, сравнение, обобщение, синтез; метод моделирования. Этап 1 – сбор и анализ информации, представленной на сайтах Опорных вузов, на предмет выделения показателей цифровой трансформации вузов. Этап 2 – анкетирование абитуриентов на выявление их отношения к поступлению в вуз, а также студентов и абитуриентов на предмет мотивированности к изучению иностранного языка через цифровые инструменты. Этап 3 – моделирование Цифрового кампуса, его сервисов и функционала для языковой подготовки студентов. Результаты. Определено авторское понимание цифрового кампуса как пространства на основе информационных и образовательных цифровых ресурсов и технологий, средств вычислительной техники, телекоммуникационных технологий и программного обеспечения, организационно-методического обеспечения, объединяющее участников процесса обучения (преподавателей, абитуриентов и студентов, выпускников, предприятия/бизнес) для удаленной образовательной деятельности и бизнес операций в соответствии с персонализированным запросом. Проанализированы и сгруппированы доступные данные, размещаемые учебными заведениями на своих веб-сайтах на предмет цифровой трансформации. Наиболее представленными являются сервисы: дополнительное профессиональное образование, применение интерактивных форм взаимодействия с абитуриентами через среду, онлайн-сопровождение карьерного роста студентов и выпускников, проектный офис. Сервисы по реализации персонализированной образовательной траектории и фиксации цифровых следов по учебному процессу менее выражены. Результаты анкетирования: общая готовность к взаимодействию «абитуриент – вуз» через онлайн среду и небольшое расхождение мнений школьников и студентов относительно изучения иностранного языка через цифровые решения. Смоделирован Цифровой кампус и его функционал. Каждый из сервисов кампуса: личный кабинет слушателя (абитуриенты/студенты), личный кабинет преподаватели, личный кабинет работодателя (предприятия/бизнес) имеет свой интерфейс и доступ к функциональным единицам: Трудоустройство и проекты, Конструктор курсов, Экспертиза программ, Интеллектуальный набор, Система электронного обучения, Управление ресурсами, Языковые курсы, что создает единое информационно-образовательное пространство для языковой подготовки студентов. Научная новизна. Систематизированы подходы к пониманию цифровой трансформации высшего образования, аналитически представлены показатели применения вузами цифровых решений, дано авторское определение Цифрового кампуса. Теоретическая значимость статьи заключается в обобщении и аналитическом представлении существующих подходов и тенденций цифровой трансформации высшего образования

Personalized question-based cybersecurity recommendation systems

En ces temps de pandémie Covid19, une énorme quantité de l’activité humaine est modifiée pour se faire à distance, notamment par des moyens électroniques. Cela rend plusieurs personnes et services vulnérables aux cyberattaques, d’où le besoin d’une éducation généralisée ou du moins accessible sur la cybersécurité. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protéger et assurer la sécurité des individus contre les pirates et les cybercriminels. En raison du rôle important joué par les systèmes de recommandation dans la vie quotidienne de l'utilisateur, il est intéressant de voir comment nous pouvons combiner les systèmes de cybersécurité et de recommandation en tant que solutions alternatives pour aider les utilisateurs à comprendre les cyberattaques auxquelles ils peuvent être confrontés. Les systèmes de recommandation sont couramment utilisés par le commerce électronique, les réseaux sociaux et les plateformes de voyage, et ils sont basés sur des techniques de systèmes de recommandation traditionnels. Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un système personnalisé, qui permet de partager les problèmes, d'interagir avec un système et de trouver des recommandations. Pour cela, ce travail propose « Cyberhelper », un système de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité. De plus, la plateforme proposée est équipée d'un algorithme hybride associé à trois différents algorithmes basés sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisée optimale en fonction du modèle utilisateur et du contexte. Les résultats expérimentaux montrent que la précision obtenue en appliquant l'algorithme proposé est bien supérieure à la précision obtenue en utilisant d'autres mécanismes de système de recommandation traditionnels. Les résultats suggèrent également qu'en adoptant l'approche proposée, chaque utilisateur peut avoir une expérience utilisateur unique, ce qui peut l'aider à comprendre l'environnement de cybersécurité.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts

Sistemas recomendadores aplicados en Educación

En este trabajo final integrador se analizaron diferentes técnicas de recomendación y se estudió su aplicabilidad en el ámbito educativo. Así también se presenta un resumen de las métricas usualmente utilizadas para medir la performance de éstos sistemas y cuáles son las variantes o nuevas métricas a tener en cuenta cuando se aplican éstos sistemas en educación. En el trabajo experimental se utilizaron diferentes conjuntos de datos de prueba abordados en la literatura de los SRE y se compararon los resultados obtenidos con distintos algoritmos de recomendación basados en la técnica de Filtrado Colaborativo (FC).Facultad de Informátic

Sistemas recomendadores aplicados en Educación

En este trabajo final integrador se analizaron diferentes técnicas de recomendación y se estudió su aplicabilidad en el ámbito educativo. Así también se presenta un resumen de las métricas usualmente utilizadas para medir la performance de éstos sistemas y cuáles son las variantes o nuevas métricas a tener en cuenta cuando se aplican éstos sistemas en educación. En el trabajo experimental se utilizaron diferentes conjuntos de datos de prueba abordados en la literatura de los SRE y se compararon los resultados obtenidos con distintos algoritmos de recomendación basados en la técnica de Filtrado Colaborativo (FC).Facultad de Informátic

중등 수학 교육을 중심으로

학위논문(석사) -- 서울대학교대학원 : 사범대학 교육학과(교육공학전공), 2023. 2. 임철일.In response to the demand for a perspective and strategic approach that fits the digital transformation era, policies that specify the implementation and support of technology-based education have been announced in Korea. Furthermore, due to the COVID-19 pandemic and the shift towards distance learning, the limitations of outdated devices and wireless internet constraints are being overcome. As a result, teachers and learners can more easily utilize cloud-based learning tools by using their own smart devices, and the amount of learning data available to teachers in the classroom has increased. Therefore, the environment to execute personalized classes based on learning data, which were previously difficult to implement in traditional big class settings, has been established through the use of cloud-based learning tools. However, to provide personalized support to individual learners and promote meaningful learning, it is necessary to go beyond simply incorporating technology into the education setting. When adopting new technology, teachers tend to adopt it in a way that maintains their existing teaching practices instead of changing them. Therefore, it is necessary to design a class that is different from the previous method in order to observe changes in instructors and learners and bring out positive changes by using cloud-based learning tools and learning data for personalized education at school sites. Therefore, this study aims to develop a practical and specific instruction model and strategies that guide teachers on how, when and what to do when using cloud-based learning tools to deliver data-based personalized education. To achieve this, the research questions were set to 1) develop a data-based personalized instruction model and strategy using cloud-based learning tools, and 2) examine the responses of instructors and learners to the developed instruction model and strategy. In the case of learner response, changes in mathematics learning achievement were examined in the cognitive aspect, and changes in learning attitude were examined in the affective aspect. This study was conducted according to the design and development research method as follows. Based on the empirical exploration of teachers using cloud-based learning tools at the site and an integrative literature review, the initial instruction model and instructional strategy were derived. In order to confirm the internal validity of the initial model developed, two rounds of internal validation were conducted with a total of six people, including three instructional design experts, two mathematics education experts, and one secondary education expert with experience in using cloud-based learning tools. The third version of instruction model was derived after the internal validation. An external validation was conducted with the revised model and strategy with in a math class of third graders in a middle school located in Seoul. As a method of external validation, quasi-experimental research and interviews were used. In the case of a quasi-experimental research, two class groups were set, one consisting of an experimental group using instruction models, strategies, and cloud-based learning tools, and the other consisting of a group that only used cloud-based learning tools. Before applying the instruction model and strategy, the homogeneity of the group was verified based on the results of the final exam grades in the first semester of the third graders and a pre-test on mathematics learning attitudes. In addition, after completing 8 lessons in the field trial, evaluations were carried out to assess the students' achievement in mathematics and their attitude towards learning mathematics The records of teacher and learner interviews during the field trial and the learners' reflection journals were analyzed. Based on the analysis, the instruction model and strategies were revised and improved to derive the final version. The final model consists of 5 steps: 1) learning data analysis, which affect on the all stages of the model, 2) planning, 3) execution, 4) evaluation, and 5) environment configuration supporting the previous 4 steps. In addition, 13 instructional strategies and 41 detailed guidelines were developed and offered with detailed examples and explanations. The result of the post-test comparison between the control group and the experimental group showed the following. In the case of the cognitive domain, no significant difference was found between the groups in the learning achievement test results. This shows that the instructional model and the strategies developed in this study do not have a significant effect on learning achievement. In the case of the affective domain, the results of a post-test on the attitude towards mathematics learning showed a significant difference in the area of interest and perception of value in mathematics compared to the control group in the experimental group. Also, when examining changes within the experimental group, it was confirmed that the participation during the model application period was statistically significantly higher compared to the participation before the model application period. Additionally, in the case of low-achieving learners, a large increase in post-participation was observed compared to other high-performing groups of learners. Interview results with teachers and learners showed that the developed instruction model was effectively utilized in implementing data-based personalized lessons with learning data and cloud-based learning tools. In particular, learners showed a higher degree of satisfaction compared to previous lessons. Based on the above research results, discussions and implications were derived and the need for follow-up studies was suggested. This study presents concrete steps and activities that should be taken by instructors when conducting data-based personalized learning in a school context with a cloud-based learning environment. It explores a realistic way of realizing personalized learning in school classrooms, which was difficult to implement in crowded classroom, by utilizing learning data and cloud-based learning tools. Additionally, the study highlights the importance of instructional design in utilizing cloud-based learning tools and learning data.디지털 트랜스포메이션 시대에 부합하는 시각과 전략의 실천이 요구됨에 따라 국내에서는 테크놀로지 활용한 수업의 실천과 지원을 명시하는 정책들이 발표되었다. 또한, 교사와 학생의 유의미한 스마트기기 사용을 위한 정책과 코로나 19로 인한 원격 교육 시행으로 인하여 물리적 한계였던 기기의 노후화와 무선인터넷 환경 제약이 극복되고 있다. 이로 인하여 교사와 학습자들은 개별 스마트기기를 활용하여 클라우드 기반 학습도구를 더욱 원활하게 활용할 수 있게 되었으며 학교 현장에서 교사가 활용할 수 있는 학습 데이터가 증가하였다. 따라서 기존 다인수 학급 형태로 이루어지는 학교 교육에서 실현하기 어려웠던 학습 데이터 기반의 맞춤형 수업을 클라우드 기반 학습도구를 활용하여 실행할 수 있는 환경이 조성되고 있다. 그러나 개별 학습자에게 맞춤형 지원을 제공하고 유의미한 학습을 촉진하기 위해서는 단순히 테크놀로지를 교육 현장에 투입하는 것에서 더 나아가 본질에 있어서 변화가 필요하다. 새로운 테크놀로지를 활용할 때, 교사들은 자신의 교육적 행위를 바꾸기보다는 기존의 행위를 유지하는 방식으로 채택하는 경향이 있다. 따라서 클라우드 기반 학습도구와 학습 데이터를 학교 현장의 맞춤형 수업을 위해 활용함으로써 교수자와 학습자의 변화를 관찰하고 긍정적인 변화를 끌어내기 위해서 이전의 방식과 다른 수업을 설계할 필요가 있다. 이에 본 연구에서는 교사가 클라우드 기반 학습도구를 활용하여 데이터 기반 맞춤형 수업을 진행할 때, 클라우드 기반 학습도구를 통해서 수집되는 학습 데이터를 바탕으로 맞춤형 수업이 학교 현장에서 효과적으로 이루어질 수 있도록 교사가 언제, 무엇을, 어떻게 해야 하는지를 안내하는 실제적이고 구체적인 수업 모형과 전략, 지침을 개발하고자 하였다. 이를 달성하기 위하여 1) 클라우드 기반 학습도구를 활용한 데이터 기반 맞춤형 수업 모형과 전략을 개발하고 2) 개발된 수업 모형과 전략에 대한 교수자와 학습자의 반응을 검토하는 것으로 연구 문제를 설정하였다. 학습자 반응의 경우, 인지적 측면에서 수학 학습 성취도의 변화를 살펴보았고 정의적 측면에서는 학습 태도의 변화를 살펴보았다. 본 연구는 설계 개발 연구 방법에 따라 다음과 같은 절차로 진행되었다. 현장에서 클라우드 기반 학습도구를 활용한 교사들과의 경험적 탐색 및 통합적 문헌 검토를 바탕으로 초기 수업 모형과 교수전략을 도출하였다. 개발된 초기 모형의 내적 타당성을 확인하기 위하여 교수 설계 전문가 3인, 수학 교육 전문가 2인, 클라우드 기반 학습도구 활용 경험이 있는 중등 교육 전문가 1인을 포함한 총 6인에게 두 차례의 내적 타당화를 실시하였다. 내적 타당화를 통해 도출된 3차 수업 모형과 전략에 대한 외적 타당화를 서울 소재의 한 중학교 3학년 수학 수업에서 실시하였다. 외적 타당화의 방법으로 유사실험과 면담을 시행하였다. 유사실험의 경우 수업 모형과 전략 그리고 클라우드 기반 학습도구를 활용한 실험 집단과 클라우드 기반 학습도구만 활용한 집단으로 이루어진 2개 학급 단위의 집단을 설정하였다. 수업 모형과 전략의 적용 전에 3학년 1학기 기말 고사 성적과 수학 학습 태도 관련 사전 검사 결과를 기준으로 집단의 동질성 여부를 검증하였다. 그리고 총 8차시의 현장 적용이 종료된 시점에 성취도 평가와 수학 학습에 대한 태도에 대한 검사를 시행하였다. 또한, 교수자 및 학습자 대상 면담 전사 기록과 학습자의 성찰일지를 분석하였다. 분석 내용을 바탕으로 수업 모형과 교수전략을 수정 보완하여, 최종 수업 모형 및 교수전략을 도출하였다. 연구 결과, 모형 전체 단계에 영향을 주는 1) 학습 데이터 분석과 이를 바탕으로 시행되는 2) 계획, 3) 실행, 4) 평가 그리고 이 4단계를 지원하는 5) 환경 구성으로 이루어진 5단계의 최종 모형이 도출되었다. 그리고 각각의 단계를 지원하는 13개의 교수전략과 41개의 상세지침 그리고 예시 및 해설이 개발되었다. 통제집단과 실험 집단의 사후검사 결과를 비교한 결과 다음과 같은 결과가 관찰되었다. 인지적 영역의 경우, 학습 성취도 검사 결과에서 집단 간의 유의한 차이가 발견되지 않았다. 이는 본 연구에서 개발된 수업 모형과 교수전략이 학습 성취도에 유의한 영향을 미치지 않음을 보여준다. 정의적 영역의 경우, 수학 학습에 대한 태도에 대한 사후검사 결에서 실험 집단이 통제 집단과 비교하여 수학 교과에 대한 흥미와 가치 인식 영역에서 유의한 차이가 있는 것으로 나타났다. 또한, 실험 집단 내의 변화를 살펴본 결과, 모형 적용 기간 중의 참여도가 모형 적용 기간 전의 참여도에 비해 통계적으로 유의하게 높은 것으로 확인되었다. 또한 저성취 학습자의 경우, 다른 상위 집단의 학습자들에 비해 사후 참여도 증가 폭이 크게 관찰되었다. 학습 성취 변화에서도 저성취 학습자들의 성취도 상승 폭이 다른 상위 성취를 보이는 학습자 집단에 비해 매우 크게 관찰되었다. 교수자와 학습자 대상으로 실시한 면담 결과, 개발된 수업 모형이 학습 데이터와 클라우드 기반 학습도구를 활용하여 데이터 기반 맞춤형 수업을 시행할 때 유용하게 활용되었음이 관찰되었으며, 특히 학습자들의 경우 기존 수업과 비교하였을 때 더 높은 만족스럽다는 반응이 관찰되었다. 본 연구에서는 이상의 연구 결과에 기초하여 최종 수업 모형과 수업 전략과 이에 대한 논의 및 시사점을 도출하였다. 또한, 본 연구가 갖는 한계를 바탕으로 후속 연구의 필요성을 제언하였다. 본 연구는 클라우드 기반 학습 환경이 조성된 학교 맥락에서 교수자가 데이터 기반 맞춤형 수업을 진행할 때, 수행해야 하는 단계와 활동을 구체적으로 제시하였다는 점, 다인수 학급에서 실천하기 어려웠던 맞춤형 수업을 학습 데이터와 클라우드 기반 학습도구를 활용하여 현실적으로 학교 수업에서 실현할 수 있는 방안을 탐색했다는 점, 그리고 클라우드 기반 학습도구와 학습 데이터를 활용하는 데 있어 교수 설계가 갖는 중요성을 보여주었다는 점에서 의의를 갖는다.I. 서론 1 1. 연구의 필요성과 목적 1 2. 연구 문제 7 3. 용어의 정의 8 II. 이론적 배경 10 1. 클라우드 기반 학습도구 10 가. 클라우드 컴퓨팅의 정의와 특징 10 나. 클라우드 기반 학습도구 15 다. 클라우드 기반 학습도구를 활용한 수업 모형 19 2. 데이터 기반 맞춤형 수업 25 가. 맞춤형 수업 25 나. 데이터 기반 맞춤형 수업 모형 29 3. 클라우드 기반 학습도구를 활용하는 수학 수업 모형 36 가. ICT 활용 수학 수업 모형 36 나. 클라우드 기반 학습도구를 활용한 수학 수업 모형의 가능성 40 III. 연구방법 42 1. 연구 절차 43 2. 초기 수업 모형 및 전략 개발 46 가. 경험적 탐색 46 나. 선행문헌 검토 46 3. 내적 타당화 49 가. 연구 참여자 49 나. 연구 도구 및 자료 분석 방법 50 4. 외적 타당화 53 가. 연구 참여자 54 나. 현장 적용 절차 57 다. 연구 도구 및 자료 분석 방법 83 Ⅳ. 연구 결과 88 1. 최종 모형 개발 89 가. 모형의 가정 및 특징 89 나. 최종 수업 모형과 교수전략 91 2. 초기 수업 모형 및 교수전략 118 가. 경험적 탐색과 선행문헌 검토를 통한 수업 모형 및 교수전략 도출 118 나. 초기 수업 모형 및 교수전략 개발 129 3. 내적 타당화 결과 139 가. 1차 전문가 타당화 결과 139 나. 2차 전문가 타당화 결과 167 4. 외적 타당화 결과 197 가. 사후검사 결과 및 분석 197 나. 실험 집단 내의 변화 분석 199 다. 수업 모형에 대한 교수자 및 학습자 반응 206 Ⅴ. 논의 및 결론 211 1. 논의 211 가. 클라우드 기반 학습도구를 활용한 데이터 기반 맞춤형 수업 모형과 교수전략 211 나. 클라우드 기반 학습도구를 활용한 데이터 기반 맞춤형 수업 모형과 교수전략에 대한 교수자 및 학습자 반응 215 2. 결론 및 제언 219 가. 결론 219 나. 제언 221 참고문헌 223 부 록 229 Abstract 262석

Personalized Adaptive Teacher Education to Increase Self-Efficacy: Toward a Framework for Teacher Education

This study investigated personalized adaptive learning, teacher education, and self-efficacy to determine if personalized adaptive teacher education can increase self-efficacy. It is suggested that teachers with higher self-efficacy tend to stay in the teaching profession longer. Chapters 2 and 3 are literature reviews on personalizing adaptive learning to determine what common components are used in personalized adaptive learning systems to get a clear understanding of what previous literature suggests building this study on it. Chapter 4 investigates the data collected from 385 teachers to understand better what teachers report on factors that increase their self-efficacy. As a result, it was found that teachers' self-efficacy increases with more training, support, and resources. In chapter 5, a framework was developed based on previous findings, with components of personalized adaptive learning to provide support/help at the right time for teachers to increase their self-efficacy. An empirical study was conducted to validate this framework, where the framework was used as a guide to personalize and adapt summer teacher preservice training and survey teachers on their self-efficacy before and after the training to see its impact on teachers' self-efficacy. However, since summer preservice training was virtual, the framework could not be implemented fully, as we were not able to observe teachers' behaviors and monitor their learning to provide them help and support, as needed and being in the post-COVID-19 year as educators dealing with about two-year learning loss systemwide, seems decreased teachers' self-efficacy. The findings of this study can guide preservice teacher education institutions and decision-makers of teacher education to assess inservice teachers' needs and self-efficacy to help and support them with a more personalized adaptive education to improve their self-efficacy