2016년 교육정보학회 동계학술대회 발표자료
일시: 2016년 1월 14일
장소: 광주교육대학교
<초록>
그동안 정보통신기술을 활용한 교육을 위해 수많은 디지털 자료와 콘텐츠들로 구성된 학습 환경을 경 험해 왔다. 그러한 학습 환경의 중심은 디지털 자원이었으며, 교육과정 정보는 제한적으로 자원의 메타데 이터 내에서 분류체계의 형태로만 활용되었다. 그러나 이러한 학습 환경은 개인의 필요나 학습자의 수준에 맞춘 자원들을 구성하는 데 한계가 있다. 이러한 제한을 극복하기 위해 교육과정과 성취기준을 링크드 데 이터로 발행하여 학습 자원을 연결하는 모델에 대한 연구를 추진하였다. 이 모델의 특징은 역량을 중심으 로 학습 자원을 재배치하는 접근법이다. 링크드 데이터로 발행된 성취기준들은 구조적.의미적으로 연결되 기 때문에 앞으로 개별화된 학습 경로를 추천할 때 기준이 되는 노드 정보로도 활용할 수 있다. 이 연구는 국내뿐 아니라 다른 나라의 성취기준과도 연결이 가능하고 풍부한 학습 자원을 주제 단위의 성취기준으로 탐색 및 활용할 수 있는 기반을 마련한 것이다.
Slides for conference program at e-Learning Korea 2016. Also this slides contain ISO/IEC TR 20748-1 Learning Analytics Interoperability - Part 1: Reference model as well as curriculum standards. Mainly this slides was prepared for LASI-Asia 2016 #lasiasia16.
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Slides for conference program at e-Learning Korea 2016. Also this slides contain ISO/IEC TR 20748-1 Learning Analytics Interoperability - Part 1: Reference model as well as curriculum standards. Mainly this slides was prepared for LASI-Asia 2016 #lasiasia16.
This slide was presented in International the 2015 Conference on Education Research.
I aggregated several my other partial slides and reports to describe adaptive learning model pertaining to concept of learning analytics as well as LOD for curriculum standards and digital resources. There is short introduction to the project of ISO/IEC 20748 Learning analytics interoperability - Part 1: Reference model.
Materials for introduction to adaptive learning and learning analytics as well as efforts of interoperability standardization. This slides treats brief concept of adaptive learning, reference model of learning analytics, data APIs for learning analytics, and topic list of standardization community (ISO/IEC JTC1 SC36).
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
A case of Mbeya University of Science and Technology(MUST)
By;
Dr. Joel S. Mtebe
Director of;
Center for Virtual Learning
University of Dar es Salaam
Tanzania
http://works.bepress.com/mtebe
Understanding, predicting and optimizing learning with Learning AnalyticsCITE
Author: Jingyan Lu, The University of Hong Kong
--------------------------------------------------------
http://www.cite.hku.hk/news.php?id=501&category=cite
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Predicting instructor performance using data mining techniques in higher educ...redpel dot com
Predicting instructor performance using data mining techniques in higher education
for more ieee paper / full abstract / implementation , just visit www.redpel.com
This is a proposal of Research Topic ( Student performance prediction) . DUET CSE 15 Batch.
http://www.duet.ac.bd/department/department-of-computer-science-engineering/
In the undergraduate engineering program at Griffith University in Australia, the unit 1006ENG Design and Professional Skills aims to provide an introduction to engineering design and professional practice through a project-based learning (PBL) approach to problem solving. It provides students with an experience of PBL in the first-year of their programme. The unit comprises an underpinning lecture series, design work including group project activities, an individual computer-aided drawing exercise/s and an oral presentation. Griffith University employs a ‘Student Experience of Course’ (SEC) online survey as part of its student evaluation of teaching, quality improvement and staff performance management processes. As well as numerical response scale items, it includes the following two questions inviting open-ended text responses from students: i) What did you find particularly good about this course? and ii) How could this course be improved? The collection of textual data in in student surveys is commonplace, due to the rich descriptions of respondent experiences they can provide at relatively low cost. However, historically these data have been underutilised because they are time consuming to analyse manually, and there has been a lack of automated tools to exploit such data efficiently. Text analytics approaches offer analysis methods that result in visual representations of comment data that highlight key individual themes in these data and the relationships between those themes. We present a text analytics-based evaluation of the SEC open-ended comments received in the first two years of offer of the PBL unit 1006ENG. We discuss the results obtained in detail. The method developed and documented here is a practical and useful approach to analysing/visualising open-ended comment data that could be applied by others with similar comment data sets.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
Application of Higher Education System for Predicting Student Using Data mini...AM Publications
The aim of research paper is to improve the current trends in the higher education systems to understand
from the outside which factors might create loyal students. The necessity of having loyal students motivates higher
education systems to know them well, one way to do this is by using valid management and processing of the students
database. Data mining methods represent a valid approach for the extraction of precious information from existing
students to manage relations with future students. This may indicate at an early stage which type of students will
potentially be enrolled and what areas to concentrate upon in higher education systems for support. For this purpose
the data mining framework is used for mining related to academic data from enrolled students. The rule generation
process is based on the classification method. The generated rules are studied and evaluated using different
evaluation methods and the main attributes that may affect the student’s loyalty have been highlighted. Software that
facilitates the use of the generated rules is built which allows the higher education systems to predict the student’s
loyalty (numbers of enrolled students) so that they can manage and prepare necessary resources for the new enrolled students.
A case of Mbeya University of Science and Technology(MUST)
By;
Dr. Joel S. Mtebe
Director of;
Center for Virtual Learning
University of Dar es Salaam
Tanzania
http://works.bepress.com/mtebe
Understanding, predicting and optimizing learning with Learning AnalyticsCITE
Author: Jingyan Lu, The University of Hong Kong
--------------------------------------------------------
http://www.cite.hku.hk/news.php?id=501&category=cite
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.
Predicting instructor performance using data mining techniques in higher educ...redpel dot com
Predicting instructor performance using data mining techniques in higher education
for more ieee paper / full abstract / implementation , just visit www.redpel.com
This is a proposal of Research Topic ( Student performance prediction) . DUET CSE 15 Batch.
http://www.duet.ac.bd/department/department-of-computer-science-engineering/
In the undergraduate engineering program at Griffith University in Australia, the unit 1006ENG Design and Professional Skills aims to provide an introduction to engineering design and professional practice through a project-based learning (PBL) approach to problem solving. It provides students with an experience of PBL in the first-year of their programme. The unit comprises an underpinning lecture series, design work including group project activities, an individual computer-aided drawing exercise/s and an oral presentation. Griffith University employs a ‘Student Experience of Course’ (SEC) online survey as part of its student evaluation of teaching, quality improvement and staff performance management processes. As well as numerical response scale items, it includes the following two questions inviting open-ended text responses from students: i) What did you find particularly good about this course? and ii) How could this course be improved? The collection of textual data in in student surveys is commonplace, due to the rich descriptions of respondent experiences they can provide at relatively low cost. However, historically these data have been underutilised because they are time consuming to analyse manually, and there has been a lack of automated tools to exploit such data efficiently. Text analytics approaches offer analysis methods that result in visual representations of comment data that highlight key individual themes in these data and the relationships between those themes. We present a text analytics-based evaluation of the SEC open-ended comments received in the first two years of offer of the PBL unit 1006ENG. We discuss the results obtained in detail. The method developed and documented here is a practical and useful approach to analysing/visualising open-ended comment data that could be applied by others with similar comment data sets.
This paper highlights important issues of higher education system such as predicting student’s academic performance. This is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents. For making analysis on the student data we selected algorithms like Decision Tree, Naive Bayes, Random Forest, PART and Bayes Network with three most important techniques such as 10-fold cross-validation, percentage split (74%) and training set. After performing analysis on different metrics (Time to build Classifier, Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error, Precision, Recall, F-Measure, ROC Area) by different data mining algorithm, we are able to find which algorithm is performing better than other on the student dataset in hand, so that we are able to make a guideline for future improvement in student performance in education. According to analysis of student dataset we found that Random Forest algorithm gave the best result as compared to another algorithm with Recall value approximately equal to one. The analysis of different data mini g algorithm gave an in-depth awareness about how these algorithms predict student the performance of different student and enhance their skill.
Quantification of Portrayal Concepts using tf-idf Weightingijistjournal
Term frequencies and inverse document frequencies have been successfully applied in determining
weighting for document rankings. However these have been more successful in text mining and in
extraction techniques used in the web. Concept mining has become increasingly popular in the research
and application areas of Computer Science. This paper attempts to demonstrate the limited usage of term
frequency and inverse document frequency for the application of weighting calculations for ranking
documents that are based on concept quantifications. The case study considered for experiment in this
paper, is based on concept terms of David Merrill’s First Principles of Instruction (FPI). Merrill’s FPI
applies cognitive structures explicitly for analyzing instructional materials. Therefore it is justified that the
terms categorized under each cognitive structure (or portrayal) of FPI can be taken as respective concept
of that portrayal. As question papers are representative of cognitive structures in a more clear and logical
way, four question papers on ‘C Language’ have been considered for the experimental study, that are
detailed in this paper. Manual method has been adopted for the computation of quantities of portrayals in
selected documents for the purpose of comparative study. As manual method is accurate, the values
(results) are considered as benchmark values. These benchmark values are considered for comparing with
normalized term frequencies that are derived (experimented) from automated extractions from the same
selected documents. The study is however limited to four documents only. Conclusions are drawn from this
experimental study, which will be of immense use to concept mining researchers as well as for instructional
designers.
Quantification of Portrayal Concepts using tf-idf Weighting ijistjournal
Term frequencies and inverse document frequencies have been successfully applied in determining weighting for document rankings. However these have been more successful in text mining and in extraction techniques used in the web. Concept mining has become increasingly popular in the research and application areas of Computer Science. This paper attempts to demonstrate the limited usage of term frequency and inverse document frequency for the application of weighting calculations for ranking documents that are based on concept quantifications. The case study considered for experiment in this paper, is based on concept terms of David Merrill’s First Principles of Instruction (FPI). Merrill’s FPI applies cognitive structures explicitly for analyzing instructional materials. Therefore it is justified that the terms categorized under each cognitive structure (or portrayal) of FPI can be taken as respective concept of that portrayal. As question papers are representative of cognitive structures in a more clear and logical way, four question papers on ‘C Language’ have been considered for the experimental study, that are detailed in this paper. Manual method has been adopted for the computation of quantities of portrayals in selected documents for the purpose of comparative study. As manual method is accurate, the values (results) are considered as benchmark values. These benchmark values are considered for comparing with normalized term frequencies that are derived (experimented) from automated extractions from the same selected documents. The study is however limited to four documents only. Conclusions are drawn from this experimental study, which will be of immense use to concept mining researchers as well as for instructional designers.
Interlinking educational data to Web of Data (Thesis presentation)Enayat Rajabi
This is a thesis presentation about interlinking educational data to Web of Data. I explain how I used the Linked Data approach to expose and interlink educational data to the Linked Open Data cloud
Frameworking craap how we're correlating the acrl framework to content evalu...Derek Malone
Presentation detailing the adaptation of the CRAAP test evaluation method to the Framework for Information Literacy for Higher Education, given at the Georgia International Conference on Information Literacy, Savannah, GA.
A brief and simplified introduction to the ACRL Frameworks & Standards for Information Literacy to improve student learning in Higher Education classrooms.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
1 Nova Southeastern University College of Computing.docxShiraPrater50
1
Nova Southeastern University
College of Computing and Engineering
Master of Management Information Systems
MMIS 643 Data Mining
Fall 2019
(August 19 – December 8, 2019)
Class Project
Due Date: November 17, 2019 (Firm)
Instructor: Dr. Junping Sun
In this project, you will be expected to do a comprehensive literature search and survey, select and
study a specific topic in one subject area of data mining and its applications in business
intelligence and analytics (BIA), and write a research paper on the selected topic by yourself. The
research paper you are required to write can be a detailed comprehensive study on some specific
topic or the original research work that will have been done by yourself.
Requirements and Instructions for the Research Paper:
1. The objective of the paper should be very clear about subject, scope, domain, and the goals to be
achieved.
2. The paper should address the important advanced and critical issues in a specific area of data
mining and its applications in business intelligence and analytics. Your research paper
should emphasize not only breadth of coverage, but also depth of coverage in the specific area.
3. The research paper should give the measurable conclusions and future research directions (this is
your contribution).
4. It might be beneficial to review or browse through about 15 to 20 relevant technical articles
before you make decision on the topic of the research project.
5. The research paper can be:
a. Literature review papers on data mining techniques and their applications for business
intelligence and analytics.
b. Study and examination of data mining techniques in depth with technical details.
c. Applied research that applies a data mining method to solve a real world application in terms
of the domain of BIA.
6. The research paper should reflect the quality at certain academic research level.
7. The paper should be about at least 3000-3500 words double space.
8. The paper should include adequate abstraction or introduction, and reference list.
9. Please write the paper in your words and statements, and please give the names of
references, citations, and resources of reference materials if you want to use the statements from
other reference articles.
2
10. From the systematic study point of view, you may want to read a list of technical papers from
relevant magazines, journals, conference proceedings and theses in the area of the topic you
choose.
11. For the format and style of your research paper, please make reference to CEC Dissertation
Guide (http://cec.nova.edu/doctoral/documents/nsu-cec-dissertation-guides.html), Publication
Manual of APA, or the format of ACM and IEEE journal publications.
Suggested and Possible Topics for Written Report (But Not Limited)
Supervised Learning Methods:
Classification Methods:
Regression Methods
Multiple Linear Regression
Logistic Regression ...
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디지털 전환과 에듀테크의 성장을 촉진하기 위해 표준화를 고려해야 할 부분도 적지 않다. 먼저 에듀테크 국제 표준화가 지난 20년간 어떤 큰 흐름으로 전개되어 왔는지, 지금은 어떤 방향성을 가지고 표준화가 추진되고 있는지를 설명한다. 에듀테크 분야도 공적 표준(de-jure) 영역과 사실상 표준(de-facto) 영역이 존재하는데, 공적 표준화 기구로는 JTC1 산하의 SC36(Information Technology for Learning, Education, and Training)이 있고, 사실상 표준화 기구 중에는 IMS Global Learning Consortium과 IEEE 산하의 LTSC가 있다.
요즘 많은 관심과 빠른 성장을 거듭하고 있는 학습 분석 관련 표준화 동향과 특히 학습분석이 인공지능 기술과의 융합을 통해 어떤 사례들이 시도되고 있는지를 설명한다. 그 밖에 메타데이터 표준, e포트폴리오 표준, 접근성 표준들의 특성도 짧게 설명한다.
This slides was introduced in the 2020 Ed Tech Forum which was online conference due to COVID-19 pandemic.
Abstract:
In many areas of society pertaining to education, digital transformation is said to be an irreversible trend. In fact, many of daily lives are tightly connected with digital media, and we are experiencing the need to switch to digital and online in more areas during the period of social distancing due to COVID-19 pandemic. However, it is less persuasive to convert the existing off-line services and analog-based jobs into an online non-face-to-face format since the situation with consumers has changed. Consensus is needed on what problems or changes need to be made in digital transformation. Especially in the field of education, it is necessary to bridge the educational gap that has been left as a challenge for a long time, improve the efficiency of learning, and make automation for mundane tasks which are repetitive and take a long time. Through these efforts we are able to determine what form of digital transformation is needed to solve the complex educational problems. This session reviews the problems that can be solved by utilizing the functions of artificial intelligence in the field of education, and introduces the use cases currently being tried and prospective changes. In the conclusion, we will discuss and share some idea to solve the problems we face, emphasizing that the use of artificial intelligence technology should not be the purpose in itself.
Note: I'm afraid the event link was not available anymore, but some of my friends want to see this slides for their use case collection. Even if this upload is very late, but hopefully it can be helpful for your interest or works.
Introduction to KERIS Issue Report about prospects for educational purposes of Virtual Reality and Mixed Reality pertaining to Augmented Reality. This material was used at the JTC1/SC24 WG9 Seoul meeting.
KERIS 이슈리포트: 가상현실 및 혼합현실 활용 가능성 및 전망을 소개하는 슬라이드입니다. JTC1/SC24 WG9 서울 회의에서 소개된 자료입니다.
This slide was introduced ISO/IEC JTC1 SC36 (Information Technology for Learning, Education and Training, ITLET) Prague meeting. Thanks to this brief contribution. SC36 established Ad-hoc Group (AHG) on environments and resources for AR and VR in June 25 2016.
Acronyms:
LET: Learning, Education and Training
AR: Augmented Reality
VR: Virtual Reality
Presentation of the article at Workshop of Learning Analytics & Knowledge 2016 in April 25, 2016.
Note: full paper is available on http://www.laceproject.eu/wp-content/uploads/2015/12/ep4la2016_paper_4.pdf
2016년 교육정보학회 동계학술대회 발표자료
일시: 2016년 1월 14일
장소: 광주교육대학교
<초록>
데이터 분석 기술의 발전으로 인해 교육 분야에서도 다양한 학습 활동 데이터를 수집 및 분석하는 학 습 분석 기술이 주목을 받고 있다. 새로운 기술과 시스템을 도입하는 초기에는 참고할 수 있는 모델이나 가이드라인 정보가 부족하기 때문에 교육기관뿐 아니라 많은 이해관계자들이 혼란을 겪게 된다. 국제 표준 화 기구에서는 학습 분석 기술 도입을 촉진하기 위해서 참조모델을 개발하고 있다. 그러나 참조모델은 워 크플로우와 기능을 정의하는 데 초점을 맞추고 있기 때문에 실제 시스템을 도입하는 데 필요한 체크리스 트나 시스템 구성과 배치 등에 대한 정보는 별개의 문제이다. 이 연구는 학습 분석 시스템 도입 시 고려할 수 있는 시스템 요구사항을 정의하였다. 각 요구사항들은 학습 분석 참조모델에 근거하여 정의하였다.
This slides was used in ISO/IEC JTC1 SC36/WG8 meeting in December 1 2015. Also this is following thinking from “quick review for xAPI and IMS Caliper” (ISO/IEC JTC1 SC36/WG8 first webinar in Nov. 11, 2015). Through this slides I'm thinking two phases for mapping both data format. One is structural and syntactic mapping and the other is ontological mapping. Enjoy this trivial idea and please give my your valuable comments.
다양한 접근성 기술에 대한 소개와 함께 최근의 혁신적인 기술 변화에 따른 디지털 격차를 해소할 방안에 대한 고민. Inclusive Design과 GPII가 해법이 될 수는 없을지 이슈를 던지는 슬라이드.
In this slides you can find diverse concepts of accessibility technology and you may have questions to yourself that current innovative technology soared causes digital divide more deeply. This slides raise the issues that Inclusive design and GPII may be the solution?
This slides is following thinking from “quick review for xAPI and IMS Caliper” (ISO/IEC JTC1 SC36/WG8 first webinar in Nov. 11, 2015). Through this slides I'm thinking two phases for mapping both data format. One is structural and syntactic mapping and the other is ontological mapping. Enjoy this trivial idea and please give my your valuable comments.
2015년 11월 9일 TTA에서 개최된 K-ICT 표준화 전략맵 2016 - 실감형 콘텐츠 분야 발표회 자료. 보고서 원문은 12월경에 일반에 공개될 예정인 것으로 압니다.
실감형 콘텐츠를 구성하는 다섯 가지 세부 기술의 구성요소와, 표준화 추진을 위한 전략 포지셔닝에 대한 내용들로 구성된 요약본입니다. 곧 공개될 보고서 원문에는 국내외 기술 및 표준화 동향, 특허 동향, 각 기술에 대한 설명이 담겨있으니 참고하시기 바랍니다.
This slide is an instance meeting material for the study group of ISO/IEC JTC1 SC36/WG8. This study group had a meeting in Nov. 11, 2015. The questions or ideas described in this slide were just informal and personal thought upon very slight knowledge and experience for IMS Caliper and xAPI.
This slide was used in ISO/IEC JTC1 SC36 Plenary Meeting in June 22, 2015.
Title of this slide is 'Proof of Concept for Learning Analytics Interoperability and subtitle is 'Reference Model based on open source SW'.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
1. 교육 분야 성취기준
링크드 데이터 프로파일 설계
한국교육학술정보원
조용상, Ph.D
zzosang@keris.or.kr
FB: /zzosang Twitter: @zzosang
2016년 한국교육정보학회 동계 학술대회
(2016. 1. 14)
2. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
3. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
4. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
5. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
6. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
7. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
8. Goal of achievement
School level
Curriculum standard per school grade
Achievement statement (third level)
First criteria of science subject (top level)
(Case Study) 미국-교육과정 문서 구조 분석
9. Area of content
Grade group
Primary school 3-4 grade group Primary school 5-6 grade group
Middle school 1-3 grade group
Section
(Case Study) 한국-교육과정 문서 구조 분석
10. (Case Study) 한국-교육과정 문서 구조 분석
Section of science subject (middle school)
Content of
curriculum
Criteria of achievement
Core achievement criteria
Reason and explanation
for core achievement
13. Described competency
Target competency
Broad
Narrow
Exact
Minor
Major Learning resource
Target competency
Described competency
Target competency
Learning resource
Target competency
Described competency
Target competency
Learning resource
Target competency
Described competency
Target competency
Learning resource
Target competency
Described competency
Target competency
Learning resource
Target competency
(A ∧ B)
A
B
A
B
A
B
A
B
A
B
(A ∧ B)
(A = B)
(A > B)
(A < B)
성취기준과 성취기준 / 성취기준과 학습자원 간 관계
15. <Source: The Achievement Standards Network (ASN) - Framework A Linked Data Approach to Learning
Objectives and Educational Content, Christopher Chung (2014)>
16. 감사합니다 !!!
Thanks you !!!
Korea Education & Research Information Service
Yong-Sang CHO, Ph.D
zzosang@gmail.com
FB: /zzosang Twitter: @zzosang