Universal Design for Learning in Open Educational ResourcesSam Johnston
CAST is a nonprofit focused on expanding learning opportunities through science and technology. They have developed many open educational resources (OERs) using the Universal Design for Learning (UDL) framework to make materials accessible and flexible for all learners. Their OER projects include Book Builder, UDL Editions, Science Writer, and the UDL Curriculum Toolkit. CAST promotes UDL principles of multiple means of representation, engagement, and expression. They advocate for inclusion, accessibility, mixed-use of OERs, facilitating discovery through distributed intelligence, and ensuring ongoing improvement through data and feedback.
In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the benefits of the students. In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models.
In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the
benefits of the students.
Using data mining in e learning-a generic framework for military educationElena Susnea
Susnea E. (2013). Using Data Mining in eLearning: A Generic Framework for Military Education, in Proceedings of "The International Scientific Conference eLearning and Software for Education", Iss. 01 (pp. 411-415).
Learning analytics involves analyzing educational data to understand students and improve teaching and learning. It can be performed at different scales from individual courses to institutions. Examples include using VLE data to track online discussion or predict student needs, and MOOC data to inform course design. Learning analytics can benefit students by personalizing support, teachers by informing instruction, and institutions by improving programs. Challenges include integrating diverse data sources and sharing insights appropriately.
The Future of Data Analytics Education_ Trends and Innovations (2).pdfUncodemy
The future of data analytics education, particularly the Data Analytics Course in Dehradun with Uncodemy, embodies dynamic innovation, adaptability, and an unwavering commitment to preparing individuals for the data-driven world. In an evolving industry, it's imperative to keep education aligned with shifting demands. This entails staying updated with swiftly evolving technologies, addressing concerns about equitable access, navigating the intricacies of data privacy and ethics, and ensuring high quality and consistency in online and micro-credential courses. To fully unlock the potential of data analytics education, it is of utmost importance to invest dedicated efforts, champion inclusivity, and uphold ethical standards. By doing so, we can empower individuals to embark on a journey of learning and professional growth in the field of data analytics, thereby fostering innovation and progress in our data-centric society. Explore the Data Analytics Course in Dehradun with Uncodemy and seize valuable opportunities in this dynamic field.
Enhancing Learning with Technology in Higher Educationjjulius
Originally developed in this form for Dr. Jana Pershing's SDSU class on Teaching Sociology, March 2008, though elements of the presentation were previously shared in other contexts.
The future of data analytics education is marked by diverse trends and innovations. Online learning, micro-credentials, and interdisciplinary approaches are democratizing access and specialization. Technology integration, such as AI and cloud-based labs, enhances learning experiences, while project-based and personalized learning foster practical skills and adaptability. Ethical considerations and industry collaboration are integrated, and interactive tools, gamification, and VR/AR provide engaging education. Challenges include content updates, equitable access, data privacy, and quality assurance. Overall, data analytics education is evolving to meet the demands of a data-driven world, emphasizing adaptability, inclusivity, and ethical practices.
Universal Design for Learning in Open Educational ResourcesSam Johnston
CAST is a nonprofit focused on expanding learning opportunities through science and technology. They have developed many open educational resources (OERs) using the Universal Design for Learning (UDL) framework to make materials accessible and flexible for all learners. Their OER projects include Book Builder, UDL Editions, Science Writer, and the UDL Curriculum Toolkit. CAST promotes UDL principles of multiple means of representation, engagement, and expression. They advocate for inclusion, accessibility, mixed-use of OERs, facilitating discovery through distributed intelligence, and ensuring ongoing improvement through data and feedback.
In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the benefits of the students. In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models.
In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the
benefits of the students.
Using data mining in e learning-a generic framework for military educationElena Susnea
Susnea E. (2013). Using Data Mining in eLearning: A Generic Framework for Military Education, in Proceedings of "The International Scientific Conference eLearning and Software for Education", Iss. 01 (pp. 411-415).
Learning analytics involves analyzing educational data to understand students and improve teaching and learning. It can be performed at different scales from individual courses to institutions. Examples include using VLE data to track online discussion or predict student needs, and MOOC data to inform course design. Learning analytics can benefit students by personalizing support, teachers by informing instruction, and institutions by improving programs. Challenges include integrating diverse data sources and sharing insights appropriately.
The Future of Data Analytics Education_ Trends and Innovations (2).pdfUncodemy
The future of data analytics education, particularly the Data Analytics Course in Dehradun with Uncodemy, embodies dynamic innovation, adaptability, and an unwavering commitment to preparing individuals for the data-driven world. In an evolving industry, it's imperative to keep education aligned with shifting demands. This entails staying updated with swiftly evolving technologies, addressing concerns about equitable access, navigating the intricacies of data privacy and ethics, and ensuring high quality and consistency in online and micro-credential courses. To fully unlock the potential of data analytics education, it is of utmost importance to invest dedicated efforts, champion inclusivity, and uphold ethical standards. By doing so, we can empower individuals to embark on a journey of learning and professional growth in the field of data analytics, thereby fostering innovation and progress in our data-centric society. Explore the Data Analytics Course in Dehradun with Uncodemy and seize valuable opportunities in this dynamic field.
Enhancing Learning with Technology in Higher Educationjjulius
Originally developed in this form for Dr. Jana Pershing's SDSU class on Teaching Sociology, March 2008, though elements of the presentation were previously shared in other contexts.
The future of data analytics education is marked by diverse trends and innovations. Online learning, micro-credentials, and interdisciplinary approaches are democratizing access and specialization. Technology integration, such as AI and cloud-based labs, enhances learning experiences, while project-based and personalized learning foster practical skills and adaptability. Ethical considerations and industry collaboration are integrated, and interactive tools, gamification, and VR/AR provide engaging education. Challenges include content updates, equitable access, data privacy, and quality assurance. Overall, data analytics education is evolving to meet the demands of a data-driven world, emphasizing adaptability, inclusivity, and ethical practices.
This document discusses the potential for establishing a Center for Data Governance and Innovation to help facilitate learning analytics projects at UvA universities. It outlines some roles the center could play, such as approving learning analytics projects to ensure ethical and legal compliance, managing knowledge about data policies, and facilitating communication between stakeholders. The center could help address issues around data ownership, gatekeeper resistance, and complex infrastructure challenges. Establishing good data governance is important to enable learning analytics and other big data initiatives while protecting student privacy and ethical use of data.
2015 j. heinlein re-imagining the future of educationEADTU
1) edX is a non-profit online learning platform founded by Harvard and MIT to expand access to quality education through online courses.
2) edX's mission is to expand access to quality education, advance research, and improve on-campus education. The edX platform offers features like auto-grading, virtual labs, gamification, and social learning tools.
3) The future of education is predicted to be unbundled, accessible, global, lifelong, personalized, and blended. Trends include increasing access to education through online courses, personalized learning based on student data, and blending online and in-person education.
Learning analytics research informed institutional practiceYi-Shan Tsai
The document summarizes learning analytics research and initiatives at the University of Edinburgh. It discusses early MOOC and VLE analytics projects that aimed to understand student behaviors and identify patterns. It also describes the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) and efforts to build institutional capacity for learning analytics. Challenges discussed include the effort required to analyze raw data and involve stakeholders. The document advocates developing critical and participatory approaches to educational data analysis.
KeyNote Speech
10th International Conference of Science, Mathematics & Technology Education
Mauritius Institute of Education, Reduit, Mauritius
6 November 2019
This document summarizes several projects and resources related to learning analytics. It discusses the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) project at the University of Edinburgh which aims to develop critical and participatory approaches to educational data analysis. It also mentions the Learning Analytics Report Card (LARC) project which explores critical awareness with report cards. Additionally, it provides an overview of the Supporting Higher Education to Integrate Learning Analytics (SHEILA) project which developed a learning analytics policy framework through interviews and surveys. The document also shares findings from the SHEILA project about the adoption of learning analytics in higher education and key challenges identified. It outlines the principles and purposes of the University of Edinburgh's
Design, development and implementation of blended learningZalina Zamri
Share reflections of the three authors on the process of
instructional design and implementation of blended learning for teachers’ professional development
(PD) in rural western Kenya.
Digifest 2017 - Learning Analytics & Learning Design Patrick Lynch
- Patrick Lynch discusses learning analytics and emphasizes the importance of learning design. He argues that learning analytics cannot be used effectively without understanding the underlying learning design and that learning design needs learning analytics to validate itself.
- Lynch outlines his journey working with learning analytics since 2012 and describes how he uses analytics to inform course redesigns. He also discusses the need for learning design and analytics communities to work together to address the full lifecycle of curriculum development.
- At Hull University, Lynch advocates for design to be a recognized activity with clear goals that identify data collection methods up front and build knowledge through learning design patterns shown to work or not in specific contexts.
2021_01_15 «Applying Learning Analytics in Living Labs for Educational Innova...eMadrid network
The document describes research being conducted at Tallinn University in Estonia on applying learning analytics in living labs for educational innovation. It discusses 6 key points:
1) The research group uses living labs and involves practitioners in each step of the research to study new pedagogical methods and support teacher training and innovation adoption.
2) Six living lab case studies are exploring learning analytics for STEM education across 300+ schools, 800+ teachers, and 5000+ students.
3) The research aims to help gather evidence on innovations, support teacher professional development and decision making, and be flexible based on stakeholder needs.
4) Examples of research projects include using sensors and mobile analytics for outdoor collaborative learning
This document provides an introduction to project-based learning (PBL). It defines PBL as a student-centered pedagogical approach that utilizes real-world projects to help students gain deeper knowledge. It emphasizes that PBL involves sustained inquiry over extended periods of time and authentic assessment. The document also outlines why PBL is important for developing 21st century skills and preparing students for a knowledge-based society. It notes PBL promotes skills like collaboration, problem-solving, and self-directed learning.
Dr. Gábor Kismihók: Labour Market driven Learning AnalyticsTextkernel
Dr. Gábor Kismihók's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016.
Learning analytics is an emerging discipline in education, aiming at analysing (big) educational data in order to improve learning processes. In this talk, Dr. Gábor Kismihók will give an overview about the main challenges of this field, with a special emphasis on bridging the education - labour market divide.
Improving Research Productivity of Science Technology & Engineering Students ...Felipe De Oca
The document summarizes a study that evaluated the use of a learning management system (LMS) using Google web-based software among science, technology, and engineering students. The study used a concurrent mixed methods design to collect quantitative data through questionnaires and qualitative data through interviews and focus groups. The results showed that students were highly satisfied with the LMS and found it easy to use. Analysis of students' research manuscripts that were collaboratively developed using the LMS showed high quality in content, organization, and format. Students reported that the LMS enabled real-time collaboration beyond the classroom and helped them successfully complete and win awards for their research projects. In conclusion, the LMS was effective in facilitating collaboration, monitoring, and feedback on students
1. The document discusses the prospects for using learning analytics to achieve adaptive learning models. It describes adaptive learning and different levels of adaptive technologies, including platforms that react to individual user data and those that leverage aggregated data across users.
2. It outlines the pathway to achieving adaptive learning analytics, including using LMS analytics dashboards, predictive analytics, and adaptive learning analytics. Case studies and examples of existing applications are provided.
3. A proof of concept reference model for learning analytics is proposed, including a basic analytics process and an advanced process using predictive and adaptive algorithms. Linked open data for connecting curriculum standards and digital resources is also discussed.
UT-Austin Guest Lecture, ""Patterns and Outcomes of Youth Engagement in Colla...Rebecca Reynolds
Reports results of a program of game design learning in which information resource uses by students to solve programming challenges are explored. Students in MS and HS take a game design class daily, for credit and a grade for a full year and use a learning management system stocked with information resources to support their programming and game design. Results highlight types of inquiry they conduct, which strategies were more and less successful, and how their resource uses appear to connect to their learning outcomes. The results are discussed in relation to the overall landscape of educational technologies, considering the issue of structure.
The document discusses the SpeakApps project which aims to develop tools and tasks for oral production and interaction using a learning analytics approach. It provides an overview of learning analytics and references a learning analytics reference model. The model describes analyzing data from the SpeakApps platform to evaluate claims about task design, specifically regarding time limitations for recordings. Data sources would include behavioral logs from the platform and user generated content to assess the engagement and experiences of students, teachers, and instructional designers.
Qualitative IT Assignment on Data, Information & KnowledgeDavid Thompson
Define and describe knowledge, information and data in a general sense, as well as providing specific examples for the job that you have selected.
Describe how 21st Century technology assists the use of the knowledge, information and data.
Describe what challenges come with the use of the technology in relation to the knowledge, information and data.
Does the technology remove or alter the knowledge, information and data required?
Propose how the knowledge needed in this job contributes to organisational and personal operational efficiency and strategy.
This document discusses the potential for establishing a Center for Data Governance and Innovation to help facilitate learning analytics projects at UvA universities. It outlines some roles the center could play, such as approving learning analytics projects to ensure ethical and legal compliance, managing knowledge about data policies, and facilitating communication between stakeholders. The center could help address issues around data ownership, gatekeeper resistance, and complex infrastructure challenges. Establishing good data governance is important to enable learning analytics and other big data initiatives while protecting student privacy and ethical use of data.
2015 j. heinlein re-imagining the future of educationEADTU
1) edX is a non-profit online learning platform founded by Harvard and MIT to expand access to quality education through online courses.
2) edX's mission is to expand access to quality education, advance research, and improve on-campus education. The edX platform offers features like auto-grading, virtual labs, gamification, and social learning tools.
3) The future of education is predicted to be unbundled, accessible, global, lifelong, personalized, and blended. Trends include increasing access to education through online courses, personalized learning based on student data, and blending online and in-person education.
Learning analytics research informed institutional practiceYi-Shan Tsai
The document summarizes learning analytics research and initiatives at the University of Edinburgh. It discusses early MOOC and VLE analytics projects that aimed to understand student behaviors and identify patterns. It also describes the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) and efforts to build institutional capacity for learning analytics. Challenges discussed include the effort required to analyze raw data and involve stakeholders. The document advocates developing critical and participatory approaches to educational data analysis.
KeyNote Speech
10th International Conference of Science, Mathematics & Technology Education
Mauritius Institute of Education, Reduit, Mauritius
6 November 2019
This document summarizes several projects and resources related to learning analytics. It discusses the Learning Analytics Map of Activities, Research and Roll-out (LAMARR) project at the University of Edinburgh which aims to develop critical and participatory approaches to educational data analysis. It also mentions the Learning Analytics Report Card (LARC) project which explores critical awareness with report cards. Additionally, it provides an overview of the Supporting Higher Education to Integrate Learning Analytics (SHEILA) project which developed a learning analytics policy framework through interviews and surveys. The document also shares findings from the SHEILA project about the adoption of learning analytics in higher education and key challenges identified. It outlines the principles and purposes of the University of Edinburgh's
Design, development and implementation of blended learningZalina Zamri
Share reflections of the three authors on the process of
instructional design and implementation of blended learning for teachers’ professional development
(PD) in rural western Kenya.
Digifest 2017 - Learning Analytics & Learning Design Patrick Lynch
- Patrick Lynch discusses learning analytics and emphasizes the importance of learning design. He argues that learning analytics cannot be used effectively without understanding the underlying learning design and that learning design needs learning analytics to validate itself.
- Lynch outlines his journey working with learning analytics since 2012 and describes how he uses analytics to inform course redesigns. He also discusses the need for learning design and analytics communities to work together to address the full lifecycle of curriculum development.
- At Hull University, Lynch advocates for design to be a recognized activity with clear goals that identify data collection methods up front and build knowledge through learning design patterns shown to work or not in specific contexts.
2021_01_15 «Applying Learning Analytics in Living Labs for Educational Innova...eMadrid network
The document describes research being conducted at Tallinn University in Estonia on applying learning analytics in living labs for educational innovation. It discusses 6 key points:
1) The research group uses living labs and involves practitioners in each step of the research to study new pedagogical methods and support teacher training and innovation adoption.
2) Six living lab case studies are exploring learning analytics for STEM education across 300+ schools, 800+ teachers, and 5000+ students.
3) The research aims to help gather evidence on innovations, support teacher professional development and decision making, and be flexible based on stakeholder needs.
4) Examples of research projects include using sensors and mobile analytics for outdoor collaborative learning
This document provides an introduction to project-based learning (PBL). It defines PBL as a student-centered pedagogical approach that utilizes real-world projects to help students gain deeper knowledge. It emphasizes that PBL involves sustained inquiry over extended periods of time and authentic assessment. The document also outlines why PBL is important for developing 21st century skills and preparing students for a knowledge-based society. It notes PBL promotes skills like collaboration, problem-solving, and self-directed learning.
Dr. Gábor Kismihók: Labour Market driven Learning AnalyticsTextkernel
Dr. Gábor Kismihók's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016.
Learning analytics is an emerging discipline in education, aiming at analysing (big) educational data in order to improve learning processes. In this talk, Dr. Gábor Kismihók will give an overview about the main challenges of this field, with a special emphasis on bridging the education - labour market divide.
Improving Research Productivity of Science Technology & Engineering Students ...Felipe De Oca
The document summarizes a study that evaluated the use of a learning management system (LMS) using Google web-based software among science, technology, and engineering students. The study used a concurrent mixed methods design to collect quantitative data through questionnaires and qualitative data through interviews and focus groups. The results showed that students were highly satisfied with the LMS and found it easy to use. Analysis of students' research manuscripts that were collaboratively developed using the LMS showed high quality in content, organization, and format. Students reported that the LMS enabled real-time collaboration beyond the classroom and helped them successfully complete and win awards for their research projects. In conclusion, the LMS was effective in facilitating collaboration, monitoring, and feedback on students
1. The document discusses the prospects for using learning analytics to achieve adaptive learning models. It describes adaptive learning and different levels of adaptive technologies, including platforms that react to individual user data and those that leverage aggregated data across users.
2. It outlines the pathway to achieving adaptive learning analytics, including using LMS analytics dashboards, predictive analytics, and adaptive learning analytics. Case studies and examples of existing applications are provided.
3. A proof of concept reference model for learning analytics is proposed, including a basic analytics process and an advanced process using predictive and adaptive algorithms. Linked open data for connecting curriculum standards and digital resources is also discussed.
UT-Austin Guest Lecture, ""Patterns and Outcomes of Youth Engagement in Colla...Rebecca Reynolds
Reports results of a program of game design learning in which information resource uses by students to solve programming challenges are explored. Students in MS and HS take a game design class daily, for credit and a grade for a full year and use a learning management system stocked with information resources to support their programming and game design. Results highlight types of inquiry they conduct, which strategies were more and less successful, and how their resource uses appear to connect to their learning outcomes. The results are discussed in relation to the overall landscape of educational technologies, considering the issue of structure.
The document discusses the SpeakApps project which aims to develop tools and tasks for oral production and interaction using a learning analytics approach. It provides an overview of learning analytics and references a learning analytics reference model. The model describes analyzing data from the SpeakApps platform to evaluate claims about task design, specifically regarding time limitations for recordings. Data sources would include behavioral logs from the platform and user generated content to assess the engagement and experiences of students, teachers, and instructional designers.
Qualitative IT Assignment on Data, Information & KnowledgeDavid Thompson
Define and describe knowledge, information and data in a general sense, as well as providing specific examples for the job that you have selected.
Describe how 21st Century technology assists the use of the knowledge, information and data.
Describe what challenges come with the use of the technology in relation to the knowledge, information and data.
Does the technology remove or alter the knowledge, information and data required?
Propose how the knowledge needed in this job contributes to organisational and personal operational efficiency and strategy.
Mobility opportunities with Erasmus+ (action line KA171 & KA171) - Larissa Sl...EADTU
This document provides information about the Erasmus+ program for higher education mobility opportunities. Erasmus+ is a European subsidy program that covers education, training, youth, and sport with a budget of €26 billion for 2021-2027. It aims to promote economic growth, employment, equal opportunities, and social inclusion in Europe. The program offers students and staff the opportunity to study, train, teach, and volunteer abroad. Key actions under Erasmus+ for higher education include KA131 for mobility within Europe and KA171 for mobility outside of Europe.
Overcoming Barriers to Online Engagement through carefull design and delivery...EADTU
Empower Webinar Week. Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
Active participation in online tutorials - Jon Rosewell and Karen Kear (Open ...EADTU
Empower Webinar Week.Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
Get Success with the Latest UiPath UIPATH-ADPV1 Exam Dumps (V11.02) 2024yarusun
Are you worried about your preparation for the UiPath Power Platform Functional Consultant Certification Exam? You can come to DumpsBase to download the latest UiPath UIPATH-ADPV1 exam dumps (V11.02) to evaluate your preparation for the UIPATH-ADPV1 exam with the PDF format and testing engine software. The latest UiPath UIPATH-ADPV1 exam questions and answers go over every subject on the exam so you can easily understand them. You won't need to worry about passing the UIPATH-ADPV1 exam if you master all of these UiPath UIPATH-ADPV1 dumps (V11.02) of DumpsBase. #UIPATH-ADPV1 Dumps #UIPATH-ADPV1 #UIPATH-ADPV1 Exam Dumps
How to Create a Stage or a Pipeline in Odoo 17 CRMCeline George
Using CRM module, we can manage and keep track of all new leads and opportunities in one location. It helps to manage your sales pipeline with customizable stages. In this slide let’s discuss how to create a stage or pipeline inside the CRM module in odoo 17.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
How to Create User Notification in Odoo 17Celine George
This slide will represent how to create user notification in Odoo 17. Odoo allows us to create and send custom notifications on some events or actions. We have different types of notification such as sticky notification, rainbow man effect, alert and raise exception warning or validation.
Artificial Intelligence (AI) has revolutionized the creation of images and videos, enabling the generation of highly realistic and imaginative visual content. Utilizing advanced techniques like Generative Adversarial Networks (GANs) and neural style transfer, AI can transform simple sketches into detailed artwork or blend various styles into unique visual masterpieces. GANs, in particular, function by pitting two neural networks against each other, resulting in the production of remarkably lifelike images. AI's ability to analyze and learn from vast datasets allows it to create visuals that not only mimic human creativity but also push the boundaries of artistic expression, making it a powerful tool in digital media and entertainment industries.
2. • The Information Expertise Center (IEC) at the Open University (OU) operates
on a "hub and spoke" model.
• This model features a central team, the "Hub," focused on data-driven
innovation, closely linked with various decentralized work teams, the
"Spokes."
• The IEC facilitates collaboration and exchange of best practices and data
among teams through the central hub.
• It offers flexibility in allocating analytics resources to optimize innovation
realization, productivity, and knowledge sharing among work teams.
• The Hub plays a vital role in talent strategy and development, ensuring the
right deployment of resources to the Spokes and facilitating data and
knowledge sharing across the organization.
INFORMATION EXPERTISE CENTER (IEC)
pagina 2
3. Context: a new LMS (Brightspace) in 23/24 operational
Dream: Education shouldn’t be a one-size-fits-all experience but
tailored to individual needs and aspirations.
Focus: we delve into the realm of personalized education,
exploring the technical challenges lurking beneath
the surface.
Agenda topics:
1. Tracking Learning Progress
2. Aggregating Diverse Learning Paths
3. Clustering Students in Mass Approach:
NEW DATA, BIG AMBITIONS AND NEW CHALLENGES
pagina 3
4. 12/5/24
Raw, Unstructured, and Messy Data
Actionable Information
Categorized Data
Sorted Data
Arranged & Filtered Data
Visually Presented Data
Explained Data
Our Focus in
this presentation
Dealing with data
6. pagina 6
Using Big Data for actionable learning and
teaching
How can we compare diverse learners' paths
over different course contents?
Our LMS Data
400
Courses
35000
Topics
9000
Modules
100000
Learning Activities
Our Challenge
COURSE STRUCTURE STANDARDIZATION – THE CHALLENGE
7. pagina 7
Using Big Data for actionable learning and
teaching
Reading Course Content Watching a knowledge clip Categorizing Content based on
Topic Activities
Beginning
Orienting
Reading
Executing
Participating
Feedback
Assessment
Finishing
From 100.000 Data Points to 9 Categories
COURSE STRUCTURE STANDARDIZATION – CATEGORIZING
8. pagina 8
Using Big Data for actionable learning and
teaching
COURSE STRUCTURE STANDARDIZATION – MAPPING PRACTICALITIES AND SAMPLE USAGE
Example Topic Title Mapping
Learning Unit, Chapter, Study Unit Mapped to “Reading - Study Content”
Tool Linkage
(Quiz, Assignment, etc.)
File Extension
(.ppt, .xlsx, .py, etc.)
External Tools
(Kaltura, Ans, etc.)
Topic Title
(Mapping on keywords)
4 steps of data mapping
9. pagina 9
Using Big Data for actionable learning and
teaching
FOLLOWING STUDENTS THROUGH THE COURSE STRUCTURES
The Data
1.2 million aggregated events (From 01.06.2023 until today)
The Challenge
How can we follow the students in their learning journey?
Orienting Reading Reading Assessment Reading Orienting Executing PASS
Finishing Reading Reading Orienting Reading Assessment Executing PASS
Expectation
Reality
11. pagina 11
Using Big Data for actionable learning and
teaching
FOLLOWING STUDENTS THROUGH THE COURSE STRUCTURES
Passive Passive Passive Active Passive Passive Active PASS
Passive Passive Passive Passive Passive Passive Passive FAIL
Active Active Active Active Passive Passive Active PASS
Categorizing Content
based on Topic Activities
Beginning
Orienting
Reading
Executing
Participating
Feedback
Assessment
Finishing
Categorizing Content
based on Type of Activity
Passive
Active
Process Map: Active - Passive
Process Map: Course Planning Conformance
13. pagina 13
Using Big Data for actionable learning and
teaching
CLUSTERING STUDENT ENGAGEMENT
Expectation
Source: Vrije Universiteit Amsterdam (2014)
Reality
14. pagina 14
CLUSTERING STUDENT ENGAGEMENT
Obtaining a Certificate Taking an Exam
Activity Type: Orienting Activity Type: Doing
Blue:
Certificate Not Obtained
Green:
Certificate Obtained
Red:
Certificate Obtained
Red:
Took an exam
Other Color:
Did not take an exam
Blue Shades:
Did less than 2 Orienting activities
Red:
Did More than
16 Orienting activities
Green:
Did between 7 and 12
Orienting activities
Blue Shades:
Did less than 13 Activities
Green:
Between 25 - 38 activities
Orange/Red:
More than 50 activities
Learning Must Be Student-Centered - Education Evolving
Point on the transformation
Self-directed learning
Due to the fact that all the learning happens online, the physical, psychological and the communicational gaps between students and teachers are very high (this is called transactional distance). It can negatively impact learning outcomes. At the same time, social presence plays an important role in impacting student engagement, satisfaction and learning outcomes. Social presence, in essence, is the degree to which participants in a virutal learning environment perceive each other as real people.
Jürgen Habermas – Wikipedia
The Theory of Communicative Action - Wikipedia
Self-directed learning
Imagine you have a library with 70 million books, each representing a row of data.
Transactional distanace
Social presence (the more that I know about the student the better social presence, so the impact on transactional distance is)
Why do we really need the profiles?