The concept and architecture of learning cellWei Cheng
The document proposes the concept of a "learning cell" as a new unit for organizing and sharing learning resources. A learning cell aggregates related educational content, activities, tools, and records into a single extensible resource that can evolve over time. It utilizes a semantic network model rather than a hierarchical structure to categorize information. The architecture supports sharing learning cells across systems and devices through a cloud-based runtime environment.
Modern learning models require linking experiences in training environments with experiences in the real-world. However, data about real-world experiences is notoriously hard to collect. Social spaces bring new opportunities to tackle this challenge, supplying digital traces where people talk about their real-world experiences. These traces can become valuable resource, especially in ill-defined domains that embed multiple interpretations. The paper presents a unique approach to aggregate content from social spaces into a semantic-enriched data browser to facilitate informal learning in ill-defined domains. This work pioneers a new way to exploit digital traces about real-world experiences as authentic examples in informal learning contexts. An exploratory study is used to determine both strengths and areas needing attention. The results suggest that semantics can be successfully used in social spaces for informal learning – especially when combined with carefully designed nudges.
Learning Analytics for Learning BlogospheresYiwei Cao
This document discusses learning analytics approaches for analyzing blogospheres. It proposes using structural network analysis (SNA) to identify social capital within blogging networks and detect hubs and closures. Content analysis would identify bursty topics that rise and fall over time, reflecting learning activities. The approaches would provide insights into bloggers' expertise and the dynamics of learning within blogospheres. The analyses could integrate SNA and content approaches to better understand learning analytics for informal learning environments like blogs.
This document discusses generating personalized web pages for tutoring systems using knowledge-based approaches. It covers key topics like ontologies, student modeling, cognitive psychology, and hypertext. Personalized web pages can be adapted based on a student's knowledge, learning style, goals, preferences and other factors inferred from their interactions. The document argues that web pages should be designed following principles of cognitive ergonomics and rhetoric to facilitate understanding and avoid issues like high cognitive load.
Combining Dialogue and Semantics for Learning and Knowledge MaturingSimone Braun
presentation of the paper "Combining Dialogue and Semantics for Learning and Knowledge Maturing: Developing Collaborative Understanding in the 'Web 2.0 Workplace'" at ICALT 2010 conference, Sousse, Tunisia, July 5 2010
This document provides a comprehensive survey of deep learning algorithms, techniques, and applications. It begins with an overview of the history and rapid growth of deep learning. Key deep learning networks like RNNs, CNNs, and generative models are then described. Challenges in deep learning related to parallelism, scalability, and optimization are discussed. Popular deep learning tools and frameworks are also reviewed. Finally, the document surveys applications of deep learning across various domains like computer vision, natural language processing, and speech recognition.
The concept and architecture of learning cellWei Cheng
The document proposes the concept of a "learning cell" as a new unit for organizing and sharing learning resources. A learning cell aggregates related educational content, activities, tools, and records into a single extensible resource that can evolve over time. It utilizes a semantic network model rather than a hierarchical structure to categorize information. The architecture supports sharing learning cells across systems and devices through a cloud-based runtime environment.
Modern learning models require linking experiences in training environments with experiences in the real-world. However, data about real-world experiences is notoriously hard to collect. Social spaces bring new opportunities to tackle this challenge, supplying digital traces where people talk about their real-world experiences. These traces can become valuable resource, especially in ill-defined domains that embed multiple interpretations. The paper presents a unique approach to aggregate content from social spaces into a semantic-enriched data browser to facilitate informal learning in ill-defined domains. This work pioneers a new way to exploit digital traces about real-world experiences as authentic examples in informal learning contexts. An exploratory study is used to determine both strengths and areas needing attention. The results suggest that semantics can be successfully used in social spaces for informal learning – especially when combined with carefully designed nudges.
Learning Analytics for Learning BlogospheresYiwei Cao
This document discusses learning analytics approaches for analyzing blogospheres. It proposes using structural network analysis (SNA) to identify social capital within blogging networks and detect hubs and closures. Content analysis would identify bursty topics that rise and fall over time, reflecting learning activities. The approaches would provide insights into bloggers' expertise and the dynamics of learning within blogospheres. The analyses could integrate SNA and content approaches to better understand learning analytics for informal learning environments like blogs.
This document discusses generating personalized web pages for tutoring systems using knowledge-based approaches. It covers key topics like ontologies, student modeling, cognitive psychology, and hypertext. Personalized web pages can be adapted based on a student's knowledge, learning style, goals, preferences and other factors inferred from their interactions. The document argues that web pages should be designed following principles of cognitive ergonomics and rhetoric to facilitate understanding and avoid issues like high cognitive load.
Combining Dialogue and Semantics for Learning and Knowledge MaturingSimone Braun
presentation of the paper "Combining Dialogue and Semantics for Learning and Knowledge Maturing: Developing Collaborative Understanding in the 'Web 2.0 Workplace'" at ICALT 2010 conference, Sousse, Tunisia, July 5 2010
This document provides a comprehensive survey of deep learning algorithms, techniques, and applications. It begins with an overview of the history and rapid growth of deep learning. Key deep learning networks like RNNs, CNNs, and generative models are then described. Challenges in deep learning related to parallelism, scalability, and optimization are discussed. Popular deep learning tools and frameworks are also reviewed. Finally, the document surveys applications of deep learning across various domains like computer vision, natural language processing, and speech recognition.
Institutional repositories are digital collections that capture and preserve the intellectual output of academic institutions. They contain scholarly works and research in various formats and stages of academic work. The goal is typically open access to research. Major systems for developing institutional repositories include DSpace, EPrints, Fedora, and Digital Commons. Key considerations for starting an institutional repository include getting faculty buy-in, submission policies, intellectual property issues, and interoperability through standards like OAI-PMH. Ensuring ongoing contributions and use remains a challenge.
Integrating digital traces into a semantic enriched dataDhaval Thakker
The document discusses integrating digital traces from social media into a semantic-enriched data cloud for informal learning. It outlines a processing pipeline that collects digital traces, semantically augments them using ontologies, and allows browsing and interaction through a semantic query service. An exploratory study on job interviews found that authentic examples from digital traces were useful learning stimuli but could be mistaken as norms without context. Semantic technologies provide opportunities to organize digital traces for informal learning but further work is needed to fully realize this potential.
Network Learning: AI-driven Connectivist Framework for E-Learning 3.0Neil Rubens
This document discusses the evolution of eLearning and introduces a connectivist framework for eLearning 3.0. It summarizes eLearning 1.0 which focused on reading content and behaviorism/cognitivism theories. eLearning 2.0 allowed writing and social interaction and incorporated constructivism and social learning theories. However, most created content is unused, redundant, or results in information overload. The document proposes connectivism which views knowledge as distributed across networks and learning as constructing/navigating these networks. It introduces a conceptual framework using AI to connect content, people, and models through different layers and modules.
The document discusses authoring learning objects and content using semantic learning object repositories (LORs). It describes moving from pre-semantic web authoring to semantic web-based authoring where learning design specifications and ontologies are used. Student interactions with learning objects in a learning environment can be semantically linked to concepts, problems, and hints in the LOR knowledge base. The document also briefly discusses the information lifecycle and interoperability challenges for LORs, including technical synchronization, shared ontologies between communities, and copyright models.
The document discusses learning analytics and the current and future state of higher education. It covers topics such as learning analytics frameworks including macro, meso, and micro levels; the convergence of learning analytics layers; and building an analytics ecosystem involving learners, educators, and various teams. It questions whether institutions will understand how to apply analytics at different levels or be dazzled by dashboards. It also discusses using analytics to identify effective learning conversations and different types of discourse.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses using personalized ontologies to improve web information gathering by representing user profiles. It proposes a model that constructs personalized ontologies by adopting user feedback from a world knowledge base. The model also uses users' local instance repositories to discover background knowledge and populate the ontologies. The proposed ontology model is evaluated against benchmark models through experiments using a large standard dataset.
The document discusses a proposal to automatically generate knowledge chains (KCs) to recommend to learners based on monitoring their web navigation. A software agent would observe the pages a learner visits and the time spent on each. It would then classify page content using an ontology and web mining techniques. Based on the related concepts identified across visited pages and the navigation path, the agent aims to build potential KCs representing that knowledge to recommend back to the learner. This approach intends to motivate learners to build their personal knowledge by creating KCs for them based on their own browsing behavior and content.
Learning Analytics – Opportunities for ISO/IEC JTC 1/SC36 standardisationTore Hoel
1) The document discusses opportunities for standardization in learning analytics, including harmonizing activity stream specifications, building vocabularies, storage designs, privacy and data protection, and sharing algorithms and predictive models.
2) It analyzes characteristics of educational big data like varied data sources and formats, and calls for standards to bring these diverse data together and make them interoperable and meaningful for learners and teachers.
3) The document reviews several emerging specifications and tools in learning analytics, and identifies challenges for standardization in areas like privacy, personal data stores, data analysis, and sharing models and algorithms.
The document describes the DALICC Vocabulary, which was developed as part of the DALICC project to represent legal expressions from licenses in a machine-readable way. The vocabulary extends the ODRL and CCRel ontologies with additional properties needed to capture the full semantic spectrum of copyright statements. Examples are provided showing how the BSD 3.0, CC-BY, and Apache licenses can be represented using the DALICC vocabulary. The goal is to significantly reduce the costs of license clearance for derivative works by developing a framework that can understand and process license information.
This document introduces the Learning Cell (LC) system. [1] The LC is a new form of learning resource for ubiquitous learning environments. [2] It consists of six key modules: Learning Cell, Knowledge Group, Knowledge Cloud, Learning Tool, Learning Community, and Personal Space. [3] The LC system aims to support resources library construction, digital resource publication, knowledge management, and organizational learning.
Instruction Designe for e-Content Development;UK-India ProspectiveMazhar Laliwala
The document discusses creating a virtual learning environment at the University of Delhi using open educational resources and networked delivery of education. Some key points:
1) It proposes a blended model combining physical and virtual elements for delivering quality education through a network-based approach.
2) Open educational resources like content, applications and infrastructure can be leveraged to create engaging, customized and modular educational resources.
3) Efforts include building curriculum-based content, collaborative project-based labs, and training teachers to effectively use technologies and design content.
4) Challenges include identifying appropriate platforms and pedagogical issues, but benefits include seamless access to educational resources across institutions.
This document discusses how educators can share learning resources through online communities of practice and the Learning Registry. It describes how the Learning Registry collects social data about resource usage to help solve the problem of locating relevant materials. The document explains how educators can get started adding data to the Learning Registry by using a Chrome browser plugin to align resources to standards, rate them, tag them, and publish information about favorite sources for others to find.
The Social Semantic Server: A Flexible Framework to Support Informal Learning...tobold
The document describes the Social Semantic Server (SSS), a flexible framework developed to support informal learning in workplace settings. The SSS was designed based on theories of distributed cognition and meaning making to help learners interact through shared digital artifacts. It implements a service-oriented architecture with various microservices to integrate different learning tools. Examples of tools built on the SSS include Bits & Pieces for sensemaking experiences, KnowBrain for collaborative discussions, and Bookmarker/Attacher for exploring online topics. The SSS aims to provide a technical infrastructure that can capture workplace learning interactions and support the social construction of shared meaning.
The Social Semantic Server - A Flexible Framework to Support Informal Learnin...Sebastian Dennerlein
The document describes the Social Semantic Server (SSS), a flexible framework developed to support informal learning in workplace settings. The SSS was designed based on theories of distributed cognition and meaning making to facilitate collaboration and knowledge sharing through artifacts. It implements a service-oriented architecture with various microservices to integrate tools for informal learning. Examples of tools built on the SSS include Bits & Pieces for sensemaking experiences, KnowBrain for collaborative discussions, and Bookmarker/Attacher for exploring topics. The SSS aims to provide a technical infrastructure that supports meaning making during artifact-mediated communication in the workplace.
Towards the Intelligent Internet of EverythingRECAP Project
In this presentation, Prof. Theo Lynn (DCU) was talking about observations on Multi-disciplinary Challenges in Intelligent Systems Research, at the RECAP consortium meeting in Dublin, Ireland on 06 November 2018.
Olympus is a multi-agent system that uses agents, ontologies, and web mining to create knowledge chains and recommend personal knowledge to learners. It monitors a learner's web navigation, classifies webpage contents using an ontology, and creates recommended knowledge chains for the learner based on the classified webpages. The system aims to motivate learners to create new knowledge chains by semi-automatically generating potential chains for them to accept, modify, or discard.
The presentation shows 5 main trends for e-learning - it is a starting point for discussions, slides can be re-used for workshops on trend identification and roadmapping
This document summarizes an article about online education and the changing landscape of pedagogical styles. It discusses the shift from traditional face-to-face education to incorporating more distributed, distance, blended, and open learning models. Key drivers of this change include flexibility, accessibility of new technologies, and demand for lifelong learning opportunities. The document also outlines principles for effective online course design and delivery, such as chunking materials into weekly segments and identifying open educational resources that can be adapted and shared.
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Institutional repositories are digital collections that capture and preserve the intellectual output of academic institutions. They contain scholarly works and research in various formats and stages of academic work. The goal is typically open access to research. Major systems for developing institutional repositories include DSpace, EPrints, Fedora, and Digital Commons. Key considerations for starting an institutional repository include getting faculty buy-in, submission policies, intellectual property issues, and interoperability through standards like OAI-PMH. Ensuring ongoing contributions and use remains a challenge.
Integrating digital traces into a semantic enriched dataDhaval Thakker
The document discusses integrating digital traces from social media into a semantic-enriched data cloud for informal learning. It outlines a processing pipeline that collects digital traces, semantically augments them using ontologies, and allows browsing and interaction through a semantic query service. An exploratory study on job interviews found that authentic examples from digital traces were useful learning stimuli but could be mistaken as norms without context. Semantic technologies provide opportunities to organize digital traces for informal learning but further work is needed to fully realize this potential.
Network Learning: AI-driven Connectivist Framework for E-Learning 3.0Neil Rubens
This document discusses the evolution of eLearning and introduces a connectivist framework for eLearning 3.0. It summarizes eLearning 1.0 which focused on reading content and behaviorism/cognitivism theories. eLearning 2.0 allowed writing and social interaction and incorporated constructivism and social learning theories. However, most created content is unused, redundant, or results in information overload. The document proposes connectivism which views knowledge as distributed across networks and learning as constructing/navigating these networks. It introduces a conceptual framework using AI to connect content, people, and models through different layers and modules.
The document discusses authoring learning objects and content using semantic learning object repositories (LORs). It describes moving from pre-semantic web authoring to semantic web-based authoring where learning design specifications and ontologies are used. Student interactions with learning objects in a learning environment can be semantically linked to concepts, problems, and hints in the LOR knowledge base. The document also briefly discusses the information lifecycle and interoperability challenges for LORs, including technical synchronization, shared ontologies between communities, and copyright models.
The document discusses learning analytics and the current and future state of higher education. It covers topics such as learning analytics frameworks including macro, meso, and micro levels; the convergence of learning analytics layers; and building an analytics ecosystem involving learners, educators, and various teams. It questions whether institutions will understand how to apply analytics at different levels or be dazzled by dashboards. It also discusses using analytics to identify effective learning conversations and different types of discourse.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses using personalized ontologies to improve web information gathering by representing user profiles. It proposes a model that constructs personalized ontologies by adopting user feedback from a world knowledge base. The model also uses users' local instance repositories to discover background knowledge and populate the ontologies. The proposed ontology model is evaluated against benchmark models through experiments using a large standard dataset.
The document discusses a proposal to automatically generate knowledge chains (KCs) to recommend to learners based on monitoring their web navigation. A software agent would observe the pages a learner visits and the time spent on each. It would then classify page content using an ontology and web mining techniques. Based on the related concepts identified across visited pages and the navigation path, the agent aims to build potential KCs representing that knowledge to recommend back to the learner. This approach intends to motivate learners to build their personal knowledge by creating KCs for them based on their own browsing behavior and content.
Learning Analytics – Opportunities for ISO/IEC JTC 1/SC36 standardisationTore Hoel
1) The document discusses opportunities for standardization in learning analytics, including harmonizing activity stream specifications, building vocabularies, storage designs, privacy and data protection, and sharing algorithms and predictive models.
2) It analyzes characteristics of educational big data like varied data sources and formats, and calls for standards to bring these diverse data together and make them interoperable and meaningful for learners and teachers.
3) The document reviews several emerging specifications and tools in learning analytics, and identifies challenges for standardization in areas like privacy, personal data stores, data analysis, and sharing models and algorithms.
The document describes the DALICC Vocabulary, which was developed as part of the DALICC project to represent legal expressions from licenses in a machine-readable way. The vocabulary extends the ODRL and CCRel ontologies with additional properties needed to capture the full semantic spectrum of copyright statements. Examples are provided showing how the BSD 3.0, CC-BY, and Apache licenses can be represented using the DALICC vocabulary. The goal is to significantly reduce the costs of license clearance for derivative works by developing a framework that can understand and process license information.
This document introduces the Learning Cell (LC) system. [1] The LC is a new form of learning resource for ubiquitous learning environments. [2] It consists of six key modules: Learning Cell, Knowledge Group, Knowledge Cloud, Learning Tool, Learning Community, and Personal Space. [3] The LC system aims to support resources library construction, digital resource publication, knowledge management, and organizational learning.
Instruction Designe for e-Content Development;UK-India ProspectiveMazhar Laliwala
The document discusses creating a virtual learning environment at the University of Delhi using open educational resources and networked delivery of education. Some key points:
1) It proposes a blended model combining physical and virtual elements for delivering quality education through a network-based approach.
2) Open educational resources like content, applications and infrastructure can be leveraged to create engaging, customized and modular educational resources.
3) Efforts include building curriculum-based content, collaborative project-based labs, and training teachers to effectively use technologies and design content.
4) Challenges include identifying appropriate platforms and pedagogical issues, but benefits include seamless access to educational resources across institutions.
This document discusses how educators can share learning resources through online communities of practice and the Learning Registry. It describes how the Learning Registry collects social data about resource usage to help solve the problem of locating relevant materials. The document explains how educators can get started adding data to the Learning Registry by using a Chrome browser plugin to align resources to standards, rate them, tag them, and publish information about favorite sources for others to find.
The Social Semantic Server: A Flexible Framework to Support Informal Learning...tobold
The document describes the Social Semantic Server (SSS), a flexible framework developed to support informal learning in workplace settings. The SSS was designed based on theories of distributed cognition and meaning making to help learners interact through shared digital artifacts. It implements a service-oriented architecture with various microservices to integrate different learning tools. Examples of tools built on the SSS include Bits & Pieces for sensemaking experiences, KnowBrain for collaborative discussions, and Bookmarker/Attacher for exploring online topics. The SSS aims to provide a technical infrastructure that can capture workplace learning interactions and support the social construction of shared meaning.
The Social Semantic Server - A Flexible Framework to Support Informal Learnin...Sebastian Dennerlein
The document describes the Social Semantic Server (SSS), a flexible framework developed to support informal learning in workplace settings. The SSS was designed based on theories of distributed cognition and meaning making to facilitate collaboration and knowledge sharing through artifacts. It implements a service-oriented architecture with various microservices to integrate tools for informal learning. Examples of tools built on the SSS include Bits & Pieces for sensemaking experiences, KnowBrain for collaborative discussions, and Bookmarker/Attacher for exploring topics. The SSS aims to provide a technical infrastructure that supports meaning making during artifact-mediated communication in the workplace.
Towards the Intelligent Internet of EverythingRECAP Project
In this presentation, Prof. Theo Lynn (DCU) was talking about observations on Multi-disciplinary Challenges in Intelligent Systems Research, at the RECAP consortium meeting in Dublin, Ireland on 06 November 2018.
Olympus is a multi-agent system that uses agents, ontologies, and web mining to create knowledge chains and recommend personal knowledge to learners. It monitors a learner's web navigation, classifies webpage contents using an ontology, and creates recommended knowledge chains for the learner based on the classified webpages. The system aims to motivate learners to create new knowledge chains by semi-automatically generating potential chains for them to accept, modify, or discard.
The presentation shows 5 main trends for e-learning - it is a starting point for discussions, slides can be re-used for workshops on trend identification and roadmapping
This document summarizes an article about online education and the changing landscape of pedagogical styles. It discusses the shift from traditional face-to-face education to incorporating more distributed, distance, blended, and open learning models. Key drivers of this change include flexibility, accessibility of new technologies, and demand for lifelong learning opportunities. The document also outlines principles for effective online course design and delivery, such as chunking materials into weekly segments and identifying open educational resources that can be adapted and shared.
https://www.youtube.com/watch?v=5ZUlVlumIQo&list=PLqJzTtkUiq54DDEEZvzisPlSGp_BadhNJ&index=10
Over the last years, deep learning is rapidly advancing with impressive results obtained in several areas including computer vision, machine translation and speech recognition. Deep learning attempts to learn complex function through learning hierarchical representation of data. A deep learning model is composed of non-linear modules that each transforms the representation from lower layer to the higher more abstract one. Very complex functions can be learned using enough composition of the non-linear modules. Furthermore, the need for manual feature engineering can be obviated by learning features themselves through the representation learning. In this talk, we first explain how deep learning architecture in particular and neural networks in general are loosely inspired by mammalian visual cortex and nervous system respectively. We also discuss about the reason for big and successful comeback of neural networks with the deep learning models. Finally, we give a brief introduction of various deep structures and their applications to several domains.
References:
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444.
Socher, Richard, Yoshua Bengio, and Chris Manning. "Deep learning for NLP." Tutorial at Association of Computational Logistics (ACL), 2012, and North American Chapter of the Association of Computational Linguistics (NAACL) (2013).
Lee, Honglak. "Tutorial on deep learning and applications." NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. 2010.
LeCun, Yann, and M. Ranzato. "Deep learning tutorial." Tutorials in International Conference on Machine Learning (ICML’13). 2013.
Socher, Richard, et al. "Recursive deep models for semantic compositionality over a sentiment treebank." Proceedings of the conference on empirical methods in natural language processing (EMNLP). Vol. 1631. 2013.
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
https://www.udacity.com/course/deep-learning--ud730
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
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A brief introduction to learning cell
1. A New Form of Learning Resource in u-Learning Environmen
—A Brief Introduction to Learning Cell
Xianmin Yang
PhD Candidate in BNU
Email: yangxianmin8888@163.com
Ubiquitous Learning Team, Beijing Normal University
4. New emerging technologies 1
Virtual Reality Things of Internet
Pervasive Computing
Semantic Web Clound Computing
5. New emerging technologies 1
The world is changing!
Our life, work, learning….everything is in
the tide of changing!
6. Popular learning theories 2
Social Constructivism
Connectivism
Distributed Cognition
Situated Cognition
…..
7. New characteristics in learning 2
ubiquitous Learning
social
informal
connective
situational
adaptive
Learning
Technology
theory
8. Next generation of e-Learning: 3
U-Learning
Next phase of e-Learning development
5A Learning (anyone, anywhere, anytime,
any device, any learning resource)
Key features
Ubiquitous
Informal
Situated
Social
Context-aware
Distributed cognition
10. Deficiencies of Current Learning Technologies 3
Born in Web1.0 era
Only supports one-way information
communication
More focus on formal learning, less on
informal learning
11. Deficiencies of Current Learning Technologies 3
Emphasize learning resources and
activities sharing in a close structure
Ignores the continuous updating of learning
resources
Ignores dynamic and generative connection of
learning resources
Ignores building up dynamic relationships
between learners and teachers with learning
resources
12.
13. Proposal of Learning Cell
Year 2008
Prof. Yu Shenqquan
Propose
Learning Cell
The organizational model and key technologies
of u-Learning resource
21. What is Learning Cell? 1
A new kind of learning resource designed
for u-Learning
Make a big improvement of Learning
Object
Supports sharing of the valuable generative
information
Supports sharing of social cognitive network
Can realize self-evolution
Keep a small granularity and more intelligent
37. Unique features 3
七、Dynamic metadata searching
•Reference the search method of Googlebase and Freebase
•Realize more precise retrieval of learning resource
42. Resources library construction
Publishment of digital resources
Knowledge management
organizational learning
For more information:
http://lcell.bnu.edu.cn/help/FAQ.jsp#four_1
44. Learning resource evolution 1
How to make LC evolve orderly?
Web1.0: closed, evolve less
HOW?
Continually, orderly evolution
Web2.0: open, evolve in disorder
45. Adaptive recommendation of LC 2
How to recommend LCs to users
adaptively?
Adaptively
Personally
User-centered Learning Environment
46. u-Learning model based on LC 3
How to implement multidimensional
learning based on learning?
How to design and verify the learning
effects of learning models based on LC?
47. Automatic Social Network Construction 4
How to construct social network dynamically
through mining all kinds of learning process
information?
48. Self-adaption of Learning content
5
on multi-devices
How to display correctly learning contents
on different learning devices?
49. Spread of diffusion of organizational Knowledge
6
based on LC
How to build a learning organization
based on LC?