The AFEL Project aims to create tools to support self-directed learners by analyzing data from their online activities. It collects browsing history and social media data to identify topics of interest and measure progress. Indicators show how learners engage with different topics over time. Learners can set goals which are checked against their daily activities. Recommendations guide further learning based on indicators and goals. The project developed a data platform, visual analytics tools, and a mobile app to help learners optimize their use of online resources for informal learning.
This document discusses web analytics and personal analytics for learning. It describes how web analytics can analyze user activities on websites and online systems. Personal analytics can help users improve their behavior by self-tracking. Learning analytics analyze student activities and data from university systems to provide recommendations and applications like vital signs dashboards for doctors. The goal of analytics for everyday learning (AFEL) is to create theory-backed methods and tools that support self-directed learners in making effective use of online resources according to their goals. A scenario is described of a learner who uses an AFEL dashboard to track her progress on different topics and set goals to focus more on areas she is weaker in, like statistics. Challenges discussed include collecting integrated personal data
This document discusses using data to support self-directed learning. It presents a simple model of online learning involving people, resources, topics, and organizations. A scenario is described of a learner named Jane who uses an online dashboard to view her learning activities and progress across different topics. The dashboard helps Jane realize she has been procrastinating on topics she enjoys less, like statistics, and set goals to focus more on those areas. Challenges discussed include recognizing and measuring learning in open online environments. The document also references a cognitive model of learning as a co-evolutionary process driven by "constructive friction," and identifies indicators of learning like coverage of topics.
Learning Analytics: understand learning and support the learnerMathieu d'Aquin
The document discusses learning analytics, which is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning and the environments where it occurs. It provides examples of how learning analytics can be used for prediction, exploration, and interpretation of learning data. It also discusses challenges in recognizing and measuring learning using data from open, unconstrained online environments. Finally, it presents a cognitive model of learning and knowledge construction that involves constructive friction as the driving force behind learning.
This document discusses how Google Apps can be used to enhance classroom instruction from a teacher's perspective. It provides examples of common problems teachers face when integrating new tools, such as students being unable to open files created on different platforms. The document then introduces Google Apps as a solution, highlighting features such as collaboration, accessibility from any device, and version tracking. It provides an overview of the Google Education version and demonstrates how documents, presentations, spreadsheets and other tools can be used for group projects, writing assignments, and lesson planning to encourage collaboration.
This document provides information about an upcoming webinar on developing talking points for meetings with institutional stakeholders about data management planning and the DMPTool. The webinar will discuss conducting an environmental scan to identify key stakeholders, developing an outreach plan and self-assessment, and creating effective talking points and other outreach materials. Examples of talking points tailored for different audiences like IT groups, grants offices, and researchers will also be covered. Resources for collaboration and additional webinars on related topics will be shared.
Scaling online and hybrid training rutgers university 1-14-15 submittedMarshall Sponder
The document discusses strategies for scaling online and hybrid education courses. It notes that good course design is vital for student outcomes and may be more important than course content, especially for online courses. Specific strategies proposed for improving scalability include streamlining grading tasks, limiting assessments, cutting long-form content, using checklists and rubrics sparingly, and leveraging tools like Google Forms to automate data collection. Co-curating courses between faculty and adjuncts is also suggested to provide better frameworks for connecting lessons across a program.
AFEL: Towards Measuring Online Activities Contributions to Self-Directed Lear...Mathieu d'Aquin
The document describes how Jane, a 37-year-old administrative assistant, uses the AFEL platform to track and improve her self-directed online learning activities related to her hobbies, career development, and math skills. Jane connects data from her browsing history, Facebook, and MOOCs to the AFEL dashboard. By reviewing her dashboard daily, Jane realizes she has been procrastinating on statistics and sets goals to focus more on it. The dashboard will now remind Jane of her goals and recommend additional learning activities.
This document summarizes Rose's e-portfolio presentation on her learning through the LTT program. It includes 3 key learning statements: 1) Learning about collaborative technology tools to support constructivist learning; 2) Needing to be organized when using digital technology; 3) Exploring how technology can serve student learning needs and teaching goals. Evidence provided includes using Google Docs, a SharePoint site, and video/audio casts. Analysis of student and parent surveys showed the websites improved communication and engagement. Rose's learning showed growth in using and integrating technology into her teaching practice and engaging in critical reflection to develop her skills.
This document discusses web analytics and personal analytics for learning. It describes how web analytics can analyze user activities on websites and online systems. Personal analytics can help users improve their behavior by self-tracking. Learning analytics analyze student activities and data from university systems to provide recommendations and applications like vital signs dashboards for doctors. The goal of analytics for everyday learning (AFEL) is to create theory-backed methods and tools that support self-directed learners in making effective use of online resources according to their goals. A scenario is described of a learner who uses an AFEL dashboard to track her progress on different topics and set goals to focus more on areas she is weaker in, like statistics. Challenges discussed include collecting integrated personal data
This document discusses using data to support self-directed learning. It presents a simple model of online learning involving people, resources, topics, and organizations. A scenario is described of a learner named Jane who uses an online dashboard to view her learning activities and progress across different topics. The dashboard helps Jane realize she has been procrastinating on topics she enjoys less, like statistics, and set goals to focus more on those areas. Challenges discussed include recognizing and measuring learning in open online environments. The document also references a cognitive model of learning as a co-evolutionary process driven by "constructive friction," and identifies indicators of learning like coverage of topics.
Learning Analytics: understand learning and support the learnerMathieu d'Aquin
The document discusses learning analytics, which is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning and the environments where it occurs. It provides examples of how learning analytics can be used for prediction, exploration, and interpretation of learning data. It also discusses challenges in recognizing and measuring learning using data from open, unconstrained online environments. Finally, it presents a cognitive model of learning and knowledge construction that involves constructive friction as the driving force behind learning.
This document discusses how Google Apps can be used to enhance classroom instruction from a teacher's perspective. It provides examples of common problems teachers face when integrating new tools, such as students being unable to open files created on different platforms. The document then introduces Google Apps as a solution, highlighting features such as collaboration, accessibility from any device, and version tracking. It provides an overview of the Google Education version and demonstrates how documents, presentations, spreadsheets and other tools can be used for group projects, writing assignments, and lesson planning to encourage collaboration.
This document provides information about an upcoming webinar on developing talking points for meetings with institutional stakeholders about data management planning and the DMPTool. The webinar will discuss conducting an environmental scan to identify key stakeholders, developing an outreach plan and self-assessment, and creating effective talking points and other outreach materials. Examples of talking points tailored for different audiences like IT groups, grants offices, and researchers will also be covered. Resources for collaboration and additional webinars on related topics will be shared.
Scaling online and hybrid training rutgers university 1-14-15 submittedMarshall Sponder
The document discusses strategies for scaling online and hybrid education courses. It notes that good course design is vital for student outcomes and may be more important than course content, especially for online courses. Specific strategies proposed for improving scalability include streamlining grading tasks, limiting assessments, cutting long-form content, using checklists and rubrics sparingly, and leveraging tools like Google Forms to automate data collection. Co-curating courses between faculty and adjuncts is also suggested to provide better frameworks for connecting lessons across a program.
AFEL: Towards Measuring Online Activities Contributions to Self-Directed Lear...Mathieu d'Aquin
The document describes how Jane, a 37-year-old administrative assistant, uses the AFEL platform to track and improve her self-directed online learning activities related to her hobbies, career development, and math skills. Jane connects data from her browsing history, Facebook, and MOOCs to the AFEL dashboard. By reviewing her dashboard daily, Jane realizes she has been procrastinating on statistics and sets goals to focus more on it. The dashboard will now remind Jane of her goals and recommend additional learning activities.
This document summarizes Rose's e-portfolio presentation on her learning through the LTT program. It includes 3 key learning statements: 1) Learning about collaborative technology tools to support constructivist learning; 2) Needing to be organized when using digital technology; 3) Exploring how technology can serve student learning needs and teaching goals. Evidence provided includes using Google Docs, a SharePoint site, and video/audio casts. Analysis of student and parent surveys showed the websites improved communication and engagement. Rose's learning showed growth in using and integrating technology into her teaching practice and engaging in critical reflection to develop her skills.
This document summarizes Rose's e-portfolio presentation on her learning through the LTT program. She learned about using collaborative technology tools like SharePoint and Google Docs to support constructivist learning. She realized the importance of being organized when using digital tools as a teacher. Rose explored how technology can meet student learning needs and support her teaching goals, such as providing options for different types of learners. She conducted a study using her class website and found it improved communication between school and home and engaged students with writing. Overall, Rose demonstrated growth in using and evaluating technology in her teaching practice and engaging in reflection to improve.
The document discusses various ways that technology can be used to engage students and keep parents informed. It recommends creating a Yahoo group to share information with parents, using Microsoft Word to create a monthly newsletter for parents, and posting student grades and test analysis on the Yahoo group using identification codes. It also discusses using the ARIS system to track student data and make it available to teachers and parents.
MAS Presentation: Using Digital Tools to Engage LearnersDean Phillips
This document discusses using digital tools like cell phones and social media to engage students. It provides tips for using tools like Twitter, Google Docs, Google Voice, and photos/videos to connect with students and encourage collaboration. Examples are given of how these tools can be used for assignments, presentations, organizing work, and administrative tasks. Educators are encouraged to think about how the prevalence of cell phones and smart devices can impact teaching and learning.
How has technology in education changed in the last five years rdScottKiser8
Technology in education has changed significantly in the last five years, with students now regularly creating digital projects using computers and Chromebooks in classrooms. To address how students can store and share their finished digital projects, teachers are instructing students to create e-portfolios - digital collections of their work that can be accessed online by parents. E-portfolios have many uses beyond education, such as for job applications and memory therapy for Alzheimer's patients. While versatile, e-portfolios' biggest application is in education, as students learn to create them at a young age and continue adding to them throughout their schooling.
The document discusses ways that teachers can integrate technology and social media platforms like Facebook into their classrooms. It provides examples of how Facebook has been used successfully in some classes for things like submitting assignments, facilitating discussions, and engaging students both in and outside of class. The document also outlines some suggested guidelines and best practices for teachers who set up educational Facebook pages or groups for their classes to use, such as using professional titles and images to identify class pages.
This document contains a collection of links related to mobile learning and building online communities. It discusses how mobile learning activities should be designed based on student ownership and use patterns of mobile devices. Research shows that activities should not require apps and should allow for transfer of learning outside the classroom. The document also references studies about teens' use of technology and smartphones, as well as implications for designing mobile learning experiences.
The document discusses how administrators and teachers can use various internet-based tools and applications to improve instruction and student academic performance. It provides examples of how tools like Google Docs, Twitter, Facebook, and SurveyMonkey can be used for tasks like classroom evaluations, communication, strategic planning, and professional development. It also discusses using tablets and apps to enhance learning for students.
Bitsboard is an educational app that offers games, lessons, quizzes and videos on a wide variety of subjects. It encourages higher-level thinking skills like remembering, understanding, applying and creating. The app is well organized and easy to use. User information is kept private between Bitsboard users. Skills taught can be applied to different curriculums. The app provides instructions for individual features and games.
These slides are part of Dr. Voltz's presentation for the ISBE administrator academy "Become an iAdministrator to Strengthen Your Leadership and Management Skills
This document provides information about a training program to help school administrators strengthen their leadership and management skills through the use of internet and technology-based applications. It lists several outcomes of the program, including using tools like Google Documents, Twitter, and surveys to communicate and create strategic plans. It also discusses how administrators can use tablets, social media, and other digital tools to engage staff and students and improve teaching and learning.
This is the presentation that was given on March 5, 2010 at the Lower Hudson Regional Information Center's Tech Expo at the Edith Macy Conference Center.
This document proposes training select students at Mt Roskill Grammar School to become experts in using digital learning tools like iLearn and MyPortfolio to support teachers and other students. The rationale is that with a school of 2400 students, one person cannot provide all the needed technical support. Some students already have experience with eLearning tools from primary school. This role would develop students' leadership and community service skills. The document includes reflections from an e-learning teacher and comments from students on how ICT helps their learning and how it could best be used to support them at the school. Students suggest providing more accessible online resources, allowing laptop/device use, improving internet speed, and enabling communication with teachers outside of class time.
Why Develop A Toolkit? (1/2 hour)
• Future building our classrooms - Planning for technology
trends and supporting evidence based practice
• Assistive Technology is too powerful not to have a plan
What is in my Toolkit? (3 hours)
1. Key components:
• Quality Indicators in Assistive Technology (QIAT)
• S.E.T.T. Framework
• Technology rubrics, decision-making frameworks and
AT search tools
• Action Research and data
2. What does your toolkit look like?
• Case studies and examples
• Developing your own toolkit – future actions
Why Develop A Toolkit? (1/2 hour)
• Future building our classrooms - Planning for technology
trends and supporting evidence based practice
• Assistive Technology is too powerful not to have a plan
What is in my Toolkit? (3 hours)
1. Key components:
• Quality Indicators in Assistive Technology (QIAT)
• S.E.T.T. Framework
• Technology rubrics, decision-making frameworks and
AT search tools
• Action Research and data
2. What does your toolkit look like?
• Case studies and examples
• Developing your own toolkit – future actions
The document contains questions from quizzes about educational concepts and appropriate instructional materials. It provides explanations for each answer, discussing concepts like realia, surveys, line graphs, Gardner's Multiple Intelligences Theory, and Bruner's cone of experience. It also addresses the appropriate uses of technology, tools for journal writing, presenting data, and creating periodicals.
This document provides guidance for teachers implementing a 1:1 classroom where every student has their own laptop or digital device. It recommends that teachers learn key digital tools like Google Classroom and apps, use project-based learning, and engage students on Twitter and blogs. Teachers are advised to plan their classroom layout without rows and to establish clear expectations around student preparation, data protection, and responsible use. The document also suggests collaborating with other teachers and having contingency plans for when technology issues arise. It concludes by noting that technology is just a tool and the teacher remains the most important factor in student engagement.
A factorial study of neural network learning from differences for regressionMathieu d'Aquin
The document describes a factorial study that trained neural networks to perform regression tasks using differences between cases rather than raw data. It varied factors like the amount of training data, number of epochs, number of similar cases used to determine differences, and whether original features were included with differences. The study found that learning from differences generally required similar data amounts but converged faster. Adding original features was not always beneficial but never significantly hurt performance. The best settings depended on the specific task. Learning from differences showed potential but has limitations like difficulty scaling to large datasets.
Recentrer l'intelligence artificielle sur les connaissancesMathieu d'Aquin
The document appears to contain rules for assigning values to variables (x[n]) based on logical conditions. It includes 14 rules using comparisons of the variable values, logical operators, and numeric values. It also reports the training and test accuracies of the rules as 92.13% and 89.3% respectively.
This document summarizes Rose's e-portfolio presentation on her learning through the LTT program. She learned about using collaborative technology tools like SharePoint and Google Docs to support constructivist learning. She realized the importance of being organized when using digital tools as a teacher. Rose explored how technology can meet student learning needs and support her teaching goals, such as providing options for different types of learners. She conducted a study using her class website and found it improved communication between school and home and engaged students with writing. Overall, Rose demonstrated growth in using and evaluating technology in her teaching practice and engaging in reflection to improve.
The document discusses various ways that technology can be used to engage students and keep parents informed. It recommends creating a Yahoo group to share information with parents, using Microsoft Word to create a monthly newsletter for parents, and posting student grades and test analysis on the Yahoo group using identification codes. It also discusses using the ARIS system to track student data and make it available to teachers and parents.
MAS Presentation: Using Digital Tools to Engage LearnersDean Phillips
This document discusses using digital tools like cell phones and social media to engage students. It provides tips for using tools like Twitter, Google Docs, Google Voice, and photos/videos to connect with students and encourage collaboration. Examples are given of how these tools can be used for assignments, presentations, organizing work, and administrative tasks. Educators are encouraged to think about how the prevalence of cell phones and smart devices can impact teaching and learning.
How has technology in education changed in the last five years rdScottKiser8
Technology in education has changed significantly in the last five years, with students now regularly creating digital projects using computers and Chromebooks in classrooms. To address how students can store and share their finished digital projects, teachers are instructing students to create e-portfolios - digital collections of their work that can be accessed online by parents. E-portfolios have many uses beyond education, such as for job applications and memory therapy for Alzheimer's patients. While versatile, e-portfolios' biggest application is in education, as students learn to create them at a young age and continue adding to them throughout their schooling.
The document discusses ways that teachers can integrate technology and social media platforms like Facebook into their classrooms. It provides examples of how Facebook has been used successfully in some classes for things like submitting assignments, facilitating discussions, and engaging students both in and outside of class. The document also outlines some suggested guidelines and best practices for teachers who set up educational Facebook pages or groups for their classes to use, such as using professional titles and images to identify class pages.
This document contains a collection of links related to mobile learning and building online communities. It discusses how mobile learning activities should be designed based on student ownership and use patterns of mobile devices. Research shows that activities should not require apps and should allow for transfer of learning outside the classroom. The document also references studies about teens' use of technology and smartphones, as well as implications for designing mobile learning experiences.
The document discusses how administrators and teachers can use various internet-based tools and applications to improve instruction and student academic performance. It provides examples of how tools like Google Docs, Twitter, Facebook, and SurveyMonkey can be used for tasks like classroom evaluations, communication, strategic planning, and professional development. It also discusses using tablets and apps to enhance learning for students.
Bitsboard is an educational app that offers games, lessons, quizzes and videos on a wide variety of subjects. It encourages higher-level thinking skills like remembering, understanding, applying and creating. The app is well organized and easy to use. User information is kept private between Bitsboard users. Skills taught can be applied to different curriculums. The app provides instructions for individual features and games.
These slides are part of Dr. Voltz's presentation for the ISBE administrator academy "Become an iAdministrator to Strengthen Your Leadership and Management Skills
This document provides information about a training program to help school administrators strengthen their leadership and management skills through the use of internet and technology-based applications. It lists several outcomes of the program, including using tools like Google Documents, Twitter, and surveys to communicate and create strategic plans. It also discusses how administrators can use tablets, social media, and other digital tools to engage staff and students and improve teaching and learning.
This is the presentation that was given on March 5, 2010 at the Lower Hudson Regional Information Center's Tech Expo at the Edith Macy Conference Center.
This document proposes training select students at Mt Roskill Grammar School to become experts in using digital learning tools like iLearn and MyPortfolio to support teachers and other students. The rationale is that with a school of 2400 students, one person cannot provide all the needed technical support. Some students already have experience with eLearning tools from primary school. This role would develop students' leadership and community service skills. The document includes reflections from an e-learning teacher and comments from students on how ICT helps their learning and how it could best be used to support them at the school. Students suggest providing more accessible online resources, allowing laptop/device use, improving internet speed, and enabling communication with teachers outside of class time.
Why Develop A Toolkit? (1/2 hour)
• Future building our classrooms - Planning for technology
trends and supporting evidence based practice
• Assistive Technology is too powerful not to have a plan
What is in my Toolkit? (3 hours)
1. Key components:
• Quality Indicators in Assistive Technology (QIAT)
• S.E.T.T. Framework
• Technology rubrics, decision-making frameworks and
AT search tools
• Action Research and data
2. What does your toolkit look like?
• Case studies and examples
• Developing your own toolkit – future actions
Why Develop A Toolkit? (1/2 hour)
• Future building our classrooms - Planning for technology
trends and supporting evidence based practice
• Assistive Technology is too powerful not to have a plan
What is in my Toolkit? (3 hours)
1. Key components:
• Quality Indicators in Assistive Technology (QIAT)
• S.E.T.T. Framework
• Technology rubrics, decision-making frameworks and
AT search tools
• Action Research and data
2. What does your toolkit look like?
• Case studies and examples
• Developing your own toolkit – future actions
The document contains questions from quizzes about educational concepts and appropriate instructional materials. It provides explanations for each answer, discussing concepts like realia, surveys, line graphs, Gardner's Multiple Intelligences Theory, and Bruner's cone of experience. It also addresses the appropriate uses of technology, tools for journal writing, presenting data, and creating periodicals.
This document provides guidance for teachers implementing a 1:1 classroom where every student has their own laptop or digital device. It recommends that teachers learn key digital tools like Google Classroom and apps, use project-based learning, and engage students on Twitter and blogs. Teachers are advised to plan their classroom layout without rows and to establish clear expectations around student preparation, data protection, and responsible use. The document also suggests collaborating with other teachers and having contingency plans for when technology issues arise. It concludes by noting that technology is just a tool and the teacher remains the most important factor in student engagement.
A factorial study of neural network learning from differences for regressionMathieu d'Aquin
The document describes a factorial study that trained neural networks to perform regression tasks using differences between cases rather than raw data. It varied factors like the amount of training data, number of epochs, number of similar cases used to determine differences, and whether original features were included with differences. The study found that learning from differences generally required similar data amounts but converged faster. Adding original features was not always beneficial but never significantly hurt performance. The best settings depended on the specific task. Learning from differences showed potential but has limitations like difficulty scaling to large datasets.
Recentrer l'intelligence artificielle sur les connaissancesMathieu d'Aquin
The document appears to contain rules for assigning values to variables (x[n]) based on logical conditions. It includes 14 rules using comparisons of the variable values, logical operators, and numeric values. It also reports the training and test accuracies of the rules as 92.13% and 89.3% respectively.
This document summarizes Mathieu d'Aquin's career path and research interests. It notes that he has worked at LORIA in Nancy, France from 2002-2006, at the Knowledge Media Institute at the Open University in Milton Keynes, UK from 2006-2017, and at the Data Science Institute at NUI Galway in Ireland from 2017-2021. His research has focused on using knowledge-driven and hybrid data-driven/knowledge-driven approaches to understand data provenance, content, and results from data analysis in order to achieve intelligent data understanding.
Unsupervised learning approach for identifying sub-genres in music scoresMathieu d'Aquin
This document discusses an unsupervised learning approach to identify sub-genres in music scores. It explores different ways of representing musical features like pitch and timing in vector formats that can be analyzed using clustering algorithms. Evaluating different feature representations on a sample of folk tunes, the best results were obtained using a combined weighting of pitch, timing, beats extracted from audio files. This approach shows potential for applications like music information retrieval, studying musical genres and connections between tunes.
Knowledge engineering remains relevant for developing knowledge-based systems and representing knowledge on the semantic web and in knowledge graphs. It also has applications in data science for understanding the relationships between data, models, and techniques. Recent work has applied knowledge engineering to explain data patterns, propagate data policies, and make technological artifacts more accessible to non-experts. The field can help scale and integrate tools for knowledge curation, explanation, and knowledge-driven data access and interpretation.
This document discusses the need to study data science as a discipline through examining the processes, techniques, and outputs. It presents data science as consisting of iterative steps like forming hypotheses, collecting and analyzing data, and extracting results. Ontologies and platforms are proposed as tools to systematically describe datasets, licenses, models, and tasks. Case studies examine modeling data flows and understanding patterns in large data science systems. The document argues for an interdisciplinary approach and using techniques like science fiction to ensure data science is developed and applied responsibly through considering social and ethical implications.
This document discusses dealing with open domain data and recent examples. It begins by explaining that typical knowledge systems are closed domain, while open domain systems can answer unknown questions. It then discusses early work using the Watson ontology and Semantic Web to build open domain question answering. A core assumption was that the Semantic Web would know everything if it continued growing, which did not occur. However, recent projects like AFEL have shown the Semantic Web and DBpedia can represent data from many domains and be used for tasks like detecting topics in activity streams, explaining patterns in data, and finding biases. While applications using open domain linked data are still limited, the ability to represent diverse data in a single graph remains important.
Assessing the Readability of Policy Documents: The Case of Terms of Use of On...Mathieu d'Aquin
This document summarizes a study that assessed whether the readability of terms of use documents from various websites is adapted to the education levels of their target audiences. It finds that readability is often not well-adapted, using two main methods: analyzing over 1500 terms of use with the SMOG readability index and comparing typical education levels of website audiences in different countries. Results show mismatches between document complexity and user education levels for many US and India-based sites. The study concludes readability assessment is useful but has limitations when applied broadly.
Towards an “Ethics in Design” methodology for AI research projects Mathieu d'Aquin
The document proposes an "Ethics in Design" methodology for AI research projects. It argues that current ethics debates focus too much on technical data protection and not broader societal impacts. The methodology calls for a reflective, dialectic process involving data scientists and social scientists throughout a project's lifecycle to identify ethical issues, minimize risks, and increase positive societal impact. It explores applying this approach to two case studies and outlines principles of being dialectic, reflective, creative, and all-encompassing. The document concludes by advocating adopting these guidelines and collaborating across fields to further develop ethics methodologies.
From Knowledge Bases to Knowledge Infrastructures for Intelligent SystemsMathieu d'Aquin
1) The document discusses how knowledge representation and ontologies have evolved from closed knowledge bases for specific domains to open knowledge infrastructures that can handle large amounts of diverse data and information at scale.
2) It provides examples of how ontologies and semantic technologies are being used to build intelligent systems that can search, integrate, and automatically process and analyze large datasets.
3) Going forward, ontologies will play an important role in populating knowledge from data and dialog, enabling the automatic exploitation of data by autonomous agents, and enhancing data analytics and mining through semantic representation of datasets, tools, and policies.
Data analytics beyond data processing and how it affects Industry 4.0Mathieu d'Aquin
The document discusses how data analytics is moving beyond just data processing to affect Industry 4.0. It summarizes the research areas and industry partnerships of the Insight Centre for Data Analytics in NUI Galway, including linked data, machine learning, and media analytics. Key applications discussed are monitoring energy consumption using stream processing and event detection, predicting future behavior through machine learning, and detecting and classifying anomalies to inform predictive maintenance decisions.
Shared data infrastructures from smart cities to educationMathieu d'Aquin
This document discusses shared data infrastructures for smart cities and education. It outlines the challenges of data heterogeneity and diversity that arise from integrating multiple datasets from different sources. It proposes taking a linked data approach to query disparate data sources virtually through templates rather than fully integrating the data into a single warehouse. This allows new data sources to be added more easily. It also advocates using semantic representations of data policies and licenses to help navigate different access conditions. Examples are provided of applications developed for the city of Milton Keynes that integrate hundreds of datasets through an "Entity API" to provide insights. Similar solutions are suggested for educational data integration and analytics.
Supporting the use of data: From data repositories to service discoveryMathieu d'Aquin
The document discusses supporting the use of data through repositories and service discovery. It provides the example of the MK Data Hub, which allows users to find, share, develop, and get data as well as create applications. Ongoing work involves developing ontological representations of data characteristics like exploitability and applicability, as well as policies and rights. Service discovery requires adequate representation of both available data services and data, including licensing and policy aspects.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
1. The AFEL Project
Mathieu d’Aquin | @mdaquin | @afelproject
Insight Centre for Data Analytics
National University of Ireland, Galway
Angela Fessl, Dominik Kowald, Elisabeth Lex and Stefan Thalmann
Know Center, Graz, Austria
The AFEL Consortium
3. Analytics for Everyday Learning
=
Learning Analytics for online, social,
self-directed learning
4. Learning Analytics
According to Wikipedia (and past LAK CFPs, and some papers from relevant people)
Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes of
understanding and optimizing learning and the environments in which it
occurs.
5. Learning Analytics
According to Wikipedia (and past LAK CFPs, and some papers from relevant people)
Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes of
understanding and optimizing learning and the environments in which
it occurs.
6. Learning Analytics
According to Wikipedia (and past LAK CFPs, and some papers from relevant people)
Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes of
understanding and optimizing learning and the environments in which
it occurs.
not only students
not only the classroom,
university, library or VLE
7. Learner
Platform
Analytics
VLE | Website | Library
Assessment | Enrollment
School/University
Prediction Drop out
BI
Planning
Recommendation
However, typically, Learning Analytics is...
A university uses data on the
students and their activities
collected through the
institution's information
systems with the goal to
predict their success so they
can improve help them
improve….
… and improve their teaching,
offering, environments as well
10. Learner
Platform
Analytics
VLE | Website | Library
Assessment | Enrollment
School/University
Prediction Drop out
BI
Planning
Recommendation
Sentiment Analysis
Collective Intelligence Behaviour Analysis
Collaboration
Community Support
But: Everyday Learning
11. Objective
To create theory-backed methods and tools
supporting self-directed learners and the
people helping them in making more effective
use of online resources, platforms and networks
according to their own goals.
12. as a testbed
Objective
To create theory-backed methods and tools
supporting self-directed learners and the
people helping them in making more effective
use of online resources, platforms and networks
according to their own goals.
Cognitive Theories
of Learning
Technology
research
Technical
developm
ent
13. Guided by scenarios
Jane is 37 and works as an administrative assistant in a local medium-sized company. As a hobbies, she enjoyed sewing
and cycling in the local forests. She is also interested in business management, and is considering either developing
in her current job to a more senior level or making a career change.
Jane spends a lot of time online at home and at her job. She has friends on facebook with whom she shares and
discusses local places to go biking, and others with whom she discusses sewing techniques and possible projects,
often through sharing youtube videos.
Jane also follows MOOCs and forums related to business management, on different topics. She often uses online
resources such as Wikipedia and online magazine on the topics. At school, she was not very interested in maths,
which is needed if she want to progress in her job. She is therefore registered on Didactalia, connecting to resources
and communities on maths, especially statistics.
Jane has also decided to take her learning seriously: She has registered to use the AFEL dashboard through the
Didactalia interface. She has also installed the browser extension to include her browsing history, as well as the
facebook app. She has not included in her dashboard her emails, as they are mostly related to her current job, or
twitter, since she rarely uses it.
Jane looks at the dashboard more or less once a day, as she is prompted by a notification from the AFEL smartphone
application or from the facebook app, to see how she has been doing the previous day in her online social learning. It
might for example say “It looks like you progressed well with sewing yesterday! See how you are doing on other
topics…”
Jane, as she looks at the dashboard, realises that she has been focusing a lot on her hobbies and procrastinated on the
topics she enjoys less, especially statistics. Looking specifically at statistics, she realises that she almost only works
on it in Friday evenings, because she feels guilty of not having done much during the week. She also sees that she is
not putting as much effort into her learning of statistics as other learners, and not making as much progress. She
therefore makes a conscious decision to put more focus on it. She adds the dashboard goals of the form “to work on
statistics during my lunch break every week day” or “to have achieved a 10% progress compared to now by the same
time next week”. The dashboard will remind her how she is doing against those goals as she go about her usual
online social learning activities. She also gets recommendation of things to do on Didactalia and Facebook based on
the indicators shown on the dashboard and her stated goals.
14. Guided by scenarios
Jane is 37 and works as an administrative assistant in a local medium-sized company. As a hobbies, she enjoyed sewing
and cycling in the local forests. She is also interested in business management, and is considering either developing
in her current job to a more senior level or making a career change.
Jane spends a lot of time online at home and at her job. She has friends on facebook with whom she shares and
discusses local places to go biking, and others with whom she discusses sewing techniques and possible projects,
often through sharing youtube videos.
Jane also follows MOOCs and forums related to business management, on different topics. She often uses online
resources such as Wikipedia and online magazine on the topics. At school, she was not very interested in maths,
which is needed if she want to progress in her job. She is therefore registered on Didactalia, connecting to resources
and communities on maths, especially statistics.
Jane has also decided to take her learning seriously: She has registered to use the AFEL dashboard through the
Didactalia interface. She has also installed the browser extension to include her browsing history, as well as the
facebook app. She has not included in her dashboard her emails, as they are mostly related to her current job, or
twitter, since she rarely uses it.
Jane looks at the dashboard more or less once a day, as she is prompted by a notification from the AFEL smartphone
application or from the facebook app, to see how she has been doing the previous day in her online social learning. It
might for example say “It looks like you progressed well with sewing yesterday! See how you are doing on other
topics…”
Jane, as she looks at the dashboard, realises that she has been focusing a lot on her hobbies and procrastinated on the
topics she enjoys less, especially statistics. Looking specifically at statistics, she realises that she almost only works
on it in Friday evenings, because she feels guilty of not having done much during the week. She also sees that she is
not putting as much effort into her learning of statistics as other learners, and not making as much progress. She
therefore makes a conscious decision to put more focus on it. She adds the dashboard goals of the form “to work on
statistics during my lunch break every week day” or “to have achieved a 10% progress compared to now by the same
time next week”. The dashboard will remind her how she is doing against those goals as she go about her usual
online social learning activities. She also gets recommendation of things to do on Didactalia and Facebook based on
the indicators shown on the dashboard and her stated goals.
16. Difficulté #1: D'où viennent les données
learner
activités générant des traces et
(des fois, différents) identifiants
browser
AFEL Data Platform
Extension
app
Tracker
Crawler
Crawler
Traces and
metadata
AFEL identifier
and local
identifier
AFEL identifier
and local
identifier
17. learner
activités générant des traces et
(des fois, différents) identifiants
browser
AFEL Data Platform
Analytics platform
VisualisationAFEL identifier
Analysis
Integrated
personal data
Extension
app
Tracker
Crawler
Crawler
AFEL Core Data
Model (based on
schema.org)
Learning
indicators
Traces and
metadata
AFEL identifier
and local
identifier
AFEL identifier
and local
identifier
Challenge #1: Collecting data
18. Example : Browser Extension
Same model used
for Facebook
application, Twitter,
Didactalia analytics..
19. Challenge #2: Indicators of learning?
Maximising what? Minimising what?
teacher analyst
Ratio :
students’ success
cost in effort/resources
(?)
learner
In the context of informal,
self-directed learning, what
is success?
What are the relevant
notions of effort and cost?
20. The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
Understanding learning where it can’t be measured
21. Understanding learning where it can’t be measured
The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
22. The dynamic processes of learning and knowledge construction from
Kimmerle, Moskaliuk, Oeberst, and Cress, 2015.
“constructive friction is the driving force behind
learning” -- AFEL Deliverable 4.1, [CK08]
Understanding learning where it can’t be measured
23. Indicators of learning, based on measuring friction!
So to favour activities that generate a constructing fiction..
What kinds of frictions?
- In topic: In what way the activity introduces topics/themes/concepts
that have not been seen before?
- In complexity: In what way the activity introduces a further level of
complexity which was not accessible before and requires further
efforts.
- In view: In what way the activity introduces new points of view on an
already encountered topic, covering a different aspect.
24. Example - Analysing topic coverage from traces in web
browsing
Text analysis Clustering
Progress
analysis
Browser history
Learning scopes
(topics)
29. Overview of the technology in AFEL
AFEL Data Platform
Storage Catalog APIs
Data
Extractor
Data
Extractor
Data
Extractor
Recommender
services
Indicator
services
Data enrich. & feature extraction
Visualisation
GNOSS Tools
Other platforms
Tools
36. Conclusion
The Objective of the project…
To create theory-backed methods and tools
supporting self-directed learners and the people
helping them in making more effective use of online
resources, platforms and networks according to their
own goals.
… is being accomplished.
The basic theories, data collection, data
refinement, data visualisation and integration
mechanisms are in place.
Many other activities, including work on Data
Ethics, echo chambers, author profiling not
detailed here.
37. Examples of other useful outcomes
Public data release Learning Analytics
Glossary
38. Conclusion
And of course, we are open to requests
to test our tools on other platforms and
for collaborations, and welcome early
adopters!