Plenary address in reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014)
Conference Problem@Web | 2-4 May 2014 | Portugal
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
A Reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014) at the International Conference Problem@Web, Vilamoura, Portugal, 2-4 May 2014
This document outlines considerations for developing a scheme of work for the new UK computing curriculum. It provides an overview of the curriculum aims and expectations at different key stages, focusing on core concepts like computational thinking, digital literacy and computer science. It also discusses important themes, assessment approaches, and the balance between skills, knowledge and understanding. The document concludes by reflecting on key decisions needed to structure the scheme of work, including topics, format and ensuring it builds on children's interests in learning.
This document discusses key concepts related to computational thinking and systems thinking. It covers abstraction, data collection and representation, algorithms, specification, and implementation. Digital systems including hardware, software, and networks are explored. Interactions between people and digital systems and various impacts are also examined. The goal is for students to develop computational thinking skills to solve problems through project-based learning.
This document discusses teaching computational thinking through technologies education. It emphasizes developing students' thinking skills like design thinking, computational thinking, systems thinking and futures thinking through project-based learning. The document outlines curriculum outcomes, contexts, challenges and expectations for developing solutions across different year levels. It also discusses integrating different models of thinking, evaluating solutions, and the importance of creativity, innovation and accepting failure in the learning process.
The document discusses applying computational thinking in education. It defines several types of thinking including systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It then lists some big problems facing the world like global warming, food scarcity, and overpopulation. The document goes on to define key aspects of computational thinking including systems thinking, abstraction, data and information systems, algorithms and programming, digital systems, and implications and impacts. It provides examples of how computational thinking can be brought into the classroom through activities and projects involving things like Bee Bots, guessing games, computer games, mobile apps, websites, robotics, interfaces, wearables, and expert systems.
HCI has evolved over time from focusing on system components and tasks to considering socially embedded interactions. Early HCI emphasized usability and enabling human capabilities through technologies like graphical UIs [first sentence]. As computing expanded beyond workplaces, the field incorporated theories of context, activity, and culture to understand user experiences [second sentence]. Modern HCI focuses on designing with users through methods like prototyping and uses a range of qualitative research approaches to study technology use in natural settings [third sentence].
This document provides an overview of machine learning including definitions, types, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The three main types are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to make predictions, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns from interactions to maximize rewards. Applications discussed include face recognition, speech recognition, self-driving cars, medical analysis, and more. The future scope of machine learning is described as expanding across many industries with continued growth driven by improved algorithms, data, and computing power.
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
A Reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014) at the International Conference Problem@Web, Vilamoura, Portugal, 2-4 May 2014
This document outlines considerations for developing a scheme of work for the new UK computing curriculum. It provides an overview of the curriculum aims and expectations at different key stages, focusing on core concepts like computational thinking, digital literacy and computer science. It also discusses important themes, assessment approaches, and the balance between skills, knowledge and understanding. The document concludes by reflecting on key decisions needed to structure the scheme of work, including topics, format and ensuring it builds on children's interests in learning.
This document discusses key concepts related to computational thinking and systems thinking. It covers abstraction, data collection and representation, algorithms, specification, and implementation. Digital systems including hardware, software, and networks are explored. Interactions between people and digital systems and various impacts are also examined. The goal is for students to develop computational thinking skills to solve problems through project-based learning.
This document discusses teaching computational thinking through technologies education. It emphasizes developing students' thinking skills like design thinking, computational thinking, systems thinking and futures thinking through project-based learning. The document outlines curriculum outcomes, contexts, challenges and expectations for developing solutions across different year levels. It also discusses integrating different models of thinking, evaluating solutions, and the importance of creativity, innovation and accepting failure in the learning process.
The document discusses applying computational thinking in education. It defines several types of thinking including systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It then lists some big problems facing the world like global warming, food scarcity, and overpopulation. The document goes on to define key aspects of computational thinking including systems thinking, abstraction, data and information systems, algorithms and programming, digital systems, and implications and impacts. It provides examples of how computational thinking can be brought into the classroom through activities and projects involving things like Bee Bots, guessing games, computer games, mobile apps, websites, robotics, interfaces, wearables, and expert systems.
HCI has evolved over time from focusing on system components and tasks to considering socially embedded interactions. Early HCI emphasized usability and enabling human capabilities through technologies like graphical UIs [first sentence]. As computing expanded beyond workplaces, the field incorporated theories of context, activity, and culture to understand user experiences [second sentence]. Modern HCI focuses on designing with users through methods like prototyping and uses a range of qualitative research approaches to study technology use in natural settings [third sentence].
This document provides an overview of machine learning including definitions, types, and applications. It defines machine learning as a field that gives computers the ability to learn without being explicitly programmed. The three main types are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to make predictions, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns from interactions to maximize rewards. Applications discussed include face recognition, speech recognition, self-driving cars, medical analysis, and more. The future scope of machine learning is described as expanding across many industries with continued growth driven by improved algorithms, data, and computing power.
Introduction to cognitive architectures, with a focus on those that have been implemented as software. Differentiates architectures from models and theories.
The document provides an overview of the computing curriculum in England, including aims, key stages, and learning objectives. At key stage 1, students will learn about algorithms, basic programming, data storage and retrieval, and online safety. At key stage 2, they will design and write programs, use logical reasoning, understand computer networks and the internet, use search engines effectively, and collect/analyze data. At key stage 3, topics include computational modeling, algorithms, programming languages, Boolean logic, computer systems, and data representation.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
Application of soft computing techniques in electrical engineeringSouvik Dutta
This document discusses the application of soft computing techniques in electrical engineering. It begins with an introduction to soft computing and its key elements including fuzzy logic, neural networks, evolutionary computation, machine learning and probabilistic reasoning. It then discusses hard computing versus soft computing, defining hard computing as requiring precise analytical models and definitions, while soft computing can handle imprecision. The document outlines several soft computing techniques - neural networks, fuzzy logic, and their applications in power system economic load dispatch and generation level determination to solve complex, non-linear optimization problems in electrical engineering. In conclusion, soft computing provides alternatives to traditional techniques for electrical engineering problems involving uncertainty.
The presentation of our Journal First (J1ST) paper entitled "Conceptualisation, measurement, and application of semantic transparency in visual notations" on MODELS 2021: ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)
This document discusses knowledge mapping and visualization techniques and their potential use in Latvia. It provides an overview of knowledge mapping methods like mind maps, concept maps, and argument maps that have been widely used in Western countries. These methods can effectively support problem solving, planning, decision making, and collaboration across various sectors like education, business, government. The document argues that while smartphone and tablet use is growing in Latvia, these visualization techniques are still underutilized and their benefits not fully realized. It advocates introducing these methods more broadly in Latvia to improve education, business, communities and cooperation.
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...mlaij
This study aims to introduce a discussion platform and curriculum designed to help people understand how
machines learn. Research shows how to train an agent through dialogue and understand how information
is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy
based on existing research and integrates a wide range of different subject documents into a set of key AI
literacy skills to develop a user-centered AI. This functionality and structural considerations are organized
into a conceptual framework based on the literature. Contributions to this paper can be used to initiate
discussion and guide future research on AI learning within the computer science community.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
Cybernetics, human-in-the-loop and probabilistic modelling for recommender sy...Eliezer Silva
Talk presented at BRAIN NTNU event
https://brainntnu.no/portfolio/brain-talks-big-data2-2/
Blog post about the talk https://brainntnu.no/2019/02/05/society-of-minds/
Reflections about cybernetics, bias in recommender systems and future of AI
Digital Technologies: What now?
Presentation by Dr Jason Zagami to the Queensland Studies Authority: Australian Curriculum conference on 22 March 2014 in Brisbane, QLD.
Australian Digital Technologies LeadersJason Zagami
Australian Digital Technologies Leaders
Presentation by Dr Jason Zagami to the Australian Digital Technologies Leaders (EdTechSA) on 13 April 2014 in Adelaide, SA.
Symbolic-Connectionist Representational Model for Optimizing Decision Making ...IJECEIAES
Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.
Analytic and strategic challenges of serious gamesDavid Gibson
How higher education learning and teaching can learn from serious game developers. Keynote at the 5th annual SeGAH conference concurrent with WWW 2017 held in Perth, Western Australia
Impeaching With Prior Inconsistent StatementsMelissa Gomez
The document discusses the findings of a study on how jurors perceive witnesses who provide inconsistent testimony. Some key findings include:
1. Most jurors (72%) expect witnesses to be as honest as possible, though certain demographics like younger jurors are more skeptical.
2. When confronted with inconsistencies, 60% of jurors believe the witness is lying rather than making an honest mistake.
3. Younger jurors and those who are employed are more likely to think the witness is purposely lying, while older jurors are more likely to believe it is an honest mistake.
Introduction to cognitive architectures, with a focus on those that have been implemented as software. Differentiates architectures from models and theories.
The document provides an overview of the computing curriculum in England, including aims, key stages, and learning objectives. At key stage 1, students will learn about algorithms, basic programming, data storage and retrieval, and online safety. At key stage 2, they will design and write programs, use logical reasoning, understand computer networks and the internet, use search engines effectively, and collect/analyze data. At key stage 3, topics include computational modeling, algorithms, programming languages, Boolean logic, computer systems, and data representation.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
Application of soft computing techniques in electrical engineeringSouvik Dutta
This document discusses the application of soft computing techniques in electrical engineering. It begins with an introduction to soft computing and its key elements including fuzzy logic, neural networks, evolutionary computation, machine learning and probabilistic reasoning. It then discusses hard computing versus soft computing, defining hard computing as requiring precise analytical models and definitions, while soft computing can handle imprecision. The document outlines several soft computing techniques - neural networks, fuzzy logic, and their applications in power system economic load dispatch and generation level determination to solve complex, non-linear optimization problems in electrical engineering. In conclusion, soft computing provides alternatives to traditional techniques for electrical engineering problems involving uncertainty.
The presentation of our Journal First (J1ST) paper entitled "Conceptualisation, measurement, and application of semantic transparency in visual notations" on MODELS 2021: ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)
This document discusses knowledge mapping and visualization techniques and their potential use in Latvia. It provides an overview of knowledge mapping methods like mind maps, concept maps, and argument maps that have been widely used in Western countries. These methods can effectively support problem solving, planning, decision making, and collaboration across various sectors like education, business, government. The document argues that while smartphone and tablet use is growing in Latvia, these visualization techniques are still underutilized and their benefits not fully realized. It advocates introducing these methods more broadly in Latvia to improve education, business, communities and cooperation.
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...mlaij
This study aims to introduce a discussion platform and curriculum designed to help people understand how
machines learn. Research shows how to train an agent through dialogue and understand how information
is represented using visualization. This paper starts by providing a comprehensive definition of AI literacy
based on existing research and integrates a wide range of different subject documents into a set of key AI
literacy skills to develop a user-centered AI. This functionality and structural considerations are organized
into a conceptual framework based on the literature. Contributions to this paper can be used to initiate
discussion and guide future research on AI learning within the computer science community.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
Cybernetics, human-in-the-loop and probabilistic modelling for recommender sy...Eliezer Silva
Talk presented at BRAIN NTNU event
https://brainntnu.no/portfolio/brain-talks-big-data2-2/
Blog post about the talk https://brainntnu.no/2019/02/05/society-of-minds/
Reflections about cybernetics, bias in recommender systems and future of AI
Digital Technologies: What now?
Presentation by Dr Jason Zagami to the Queensland Studies Authority: Australian Curriculum conference on 22 March 2014 in Brisbane, QLD.
Australian Digital Technologies LeadersJason Zagami
Australian Digital Technologies Leaders
Presentation by Dr Jason Zagami to the Australian Digital Technologies Leaders (EdTechSA) on 13 April 2014 in Adelaide, SA.
Symbolic-Connectionist Representational Model for Optimizing Decision Making ...IJECEIAES
Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.
Analytic and strategic challenges of serious gamesDavid Gibson
How higher education learning and teaching can learn from serious game developers. Keynote at the 5th annual SeGAH conference concurrent with WWW 2017 held in Perth, Western Australia
Impeaching With Prior Inconsistent StatementsMelissa Gomez
The document discusses the findings of a study on how jurors perceive witnesses who provide inconsistent testimony. Some key findings include:
1. Most jurors (72%) expect witnesses to be as honest as possible, though certain demographics like younger jurors are more skeptical.
2. When confronted with inconsistencies, 60% of jurors believe the witness is lying rather than making an honest mistake.
3. Younger jurors and those who are employed are more likely to think the witness is purposely lying, while older jurors are more likely to believe it is an honest mistake.
This document discusses developing a coaching mindset for selling financial products and services. It introduces Dr. Shayne Tracy, the presenter, and outlines his background and qualifications. The rest of the document focuses on defining coaching and differentiating it from consulting, training, mentoring, and counseling. It discusses key aspects of coaching like creating a safe environment, using questions, listening skills, and building trust. The document also outlines a four dimensional model of coaching including the client's story, thinking, feelings, and the coach's use of self.
This document discusses the merging of search and social media and the implications for marketers. It notes that Google has integrated search results with a user's Google+ social network. This increases the importance of building a strong brand community and engaging customers on social media. It also means marketers have less time to make a good first impression with users as search results become more personalized based on social connections and preferences.
Social media engagement in India has become desperate as brands try numerous engagement tactics to attract users. In 2011, the year that saw the rise of Anna Hazare's anti-corruption movement on social media, Airtel spent 300 crores on rebranding but got no results. Brands want high engagement from every campaign but end up resorting to desperate measures like paying for interactions or overpromising giveaways. True engagement comes from relating campaigns to the brand through authentic and transparent communication. Success in social media also requires some failure as outcomes cannot always be controlled.
Networlding introduces some key Web 2.0 tools and terms like blogs, podcasts, RSS, and wikis. It also lists several blog hosting sites and provides additional resources for blogs and articles. The document aims to inform readers about popular Web 2.0 technologies and resources.
Staff Services provides membership benefits to various roles working in the NHS including porters, doctors, nurses, secretaries, lab assistants, admin staff, caterers, social care workers, ambulance officers, care assistants, day care workers, dentists, IT officers and midwives. To become a member of Staff Services, visit their website at www.staffservices.co.uk/membership and fill out the online application form.
This document discusses strategies for optimizing enterprise search marketing at a global scale. It recommends establishing a search center of excellence to develop best practices, standards, and governance. Key steps include conducting an audit of the current search effectiveness, deploying standards and training, continuous optimization, and implementing analytics to prove the value of the program. Centralizing processes while allowing for local optimization is important to achieve scale.
Jai Saxena, from ERNST & YOUNG gives an analysts perspective on how to Build Businesses in a Meltdown.
Jai presents as part of the "Surviving and Winning in a Downturn" a one day conference by TiE Internet SiG, Delhi.
To catch all the action from the event, visit www.omshare.com (India's first Online Platform for taking Events and Conferences Global) - an Initiative by OMLogic
La Unión Europea ha acordado un paquete de sanciones contra Rusia por su invasión de Ucrania. Las sanciones incluyen restricciones a las transacciones con bancos rusos clave y la prohibición de la venta de aviones y equipos a Rusia. Los líderes de la UE esperan que las sanciones aumenten la presión económica sobre Rusia y la disuadan de continuar su agresión contra Ucrania.
This document summarizes interviews with two Polish entrepreneurs, Zenon and Pawel, about starting businesses after the fall of communism in Poland in 1989. Zenon started growing vegetables and opening stalls, but his business was impacted by the Chernobyl disaster. He later found success in Warsaw operating a manufacturing business, but struggled with corruption and theft when opening a subsidiary and restaurant in a small town. Pawel took advantage of new laws allowing private businesses to leave his job in a state-owned company and traded computers, but faced harassment from the secret police before transitioning into the paper industry. Both discuss the challenges of changing mindsets and overcoming a culture of corruption as Poland transitioned to a capitalist system.
The document discusses the importance of visual literacy and how it involves not just interpreting existing visual images but also creating new visual interpretations through a process of questioning, research, analysis, and hypothesis testing. It emphasizes that understanding this process requires gaining experience through creating and reflecting on one's own visual works. The document also references quotes about using shape and form to make sense of information and a photography project that took a photo every 5 minutes over a long period of time.
This document outlines the structure and content of an art studio class for pre-service teachers. It includes sections on themes like the fragmented body, home and space, forms like balls and globes, sketchpads, and final projects. Specific artworks are referenced, and the instructors for the class - Aileen Wilson, Hannah Brenner-Leonard, Jane Rothberg, and Danielle Barbaro - are listed. The goal of the class seems to be giving future teachers hands-on experience with different artistic media and themes through simulated lessons and projects.
Evoloop is a web design company based in Sri Lanka that offers professional web design and development services. Their goal is to create appealing, user-friendly websites that effectively communicate clients' messages and help businesses succeed online. They emphasize creative solutions, continuous improvement, and competitive pricing. Evoloop utilizes a collaborative process involving strategy, design, technology, project management and quality assurance to deliver customized websites that meet clients' needs and expectations.
The document discusses strategies for social media engagement. It considers what topics to discuss like tasting notes, brand history, and events. It examines questions around audience reaction, post frequency, and tone. Metrics are presented showing the page has over 135,000 fans in 7 months, high user engagement, and is among the top Scotch pages in India. Lessons learned are to remain relevant, analyze and learn from data, and build a social media strategy or path.
This is the presentation of the Juan Cruz-Benito’s PhD “On data-driven systems analyzing, supporting and enhancing users’ interaction and experience” that was defended on September 3rd, 2018 in the Faculty of Sciences at University of Salamanca Spain. This PhD was graded with the maximum qualification “Sobresaliente Cum Laude”.
Invited Talk:
Challenge-Based Learning: Creating engagement by learning from games and gamification
Speaker: Dr. David Gibson, Curtin University
Time: 9:15 – 10:00, 29 May 2015 (Friday)
Venue: Room 408A, 409A & 410, 4/F, Meng Wah Complex, The University of Hong Kong
http://citers2015.cite.hku.hk/program-highlights/talk-gibson/
Chapter 3 - HCI Human Factors Cognition Perception.pptxNjeruDaniel1
Human factors and cognition must be considered in HCI design. Cognition involves how we process and understand information. Key cognitive theories for HCI include information processing models, which view interaction as involving sensory memory, working memory, and long-term memory. Mental models and conceptual models help explain how users understand a system. Gulf of execution and evaluation describe differences between user goals/mental models and the actual system. Distributed and external cognition frameworks emphasize how knowledge is situated across internal and external representations. Embodied interaction also notes how the body influences cognition. Perception involves visual, auditory, and haptic senses. Constructivist and ecological theories describe how perception involves constructing meaning or directly detecting information. Gestalt laws and affordances respectively influence how
Guest Lecture: Restoring Context in Distance Learning with Artificial Intelli...Daniele Di Mitri
Presentation given on the February 1st, 2022 at the "Brown Bag" presentation series organised by the Faculty of NYU Educational Communication and Technology which is part of the Steinhardt School of Culture, Education, and Human Development.
https://www.ectstudent.info/news-events/brown-bag-dr-daniele-di-mitri
Presentation Abstract:
The COVID-19 pandemic forced more than 1.6 billion learners out of school, becoming the most challenging disruption ever endured by the global education systems. In many countries, education institutions decided to move their regular activities online, opting for remote teaching as an emergency solution to continue their education. Meanwhile, physical distancing and learning in isolation heavily challenge learners and hinder their study success. There is a compelling need to make education systems more resilient and less vulnerable to future disruptions in such a critical landscape. In particular, we have to reconsider how digital technologies can support online and hybrid teaching. If digital education technologies such as video conferencing tools and learning management systems have improved to make educational resources more available and education more flexible, the modes of interaction they implement remain essentially unnatural for the learner due to a substantial lack of context. Modern sensor-enabled computer systems allow extending the standard human-computer interfaces and facilitate richer multimodal interaction. Furthermore, advances in AI allow interpreting the data collected from multimodal and multi-sensor devices. These insights can be used to support online teaching and learning in isolation with personalised feedback and adaptation through Multimodal Learning Experiences (MLX). This guest lecture elaborates on existing approaches, architectures, and methodologies. I illustrate use cases that employ multimodal learning analytics applications that can shape the online teaching of the future.
This document provides an overview of the topics to be covered in Week 2 of an Intelligent Interfaces course. It discusses the need for intelligent interfaces and the difference between intelligent interfaces and interfaces for intelligent systems. It describes the components of intelligent interfaces and various theories of human information processing, including methods and models. Learning outcomes focus on describing intelligent interfaces, explaining the difference between intelligent interfaces and interfaces for intelligent systems, listing intelligent interface components, and comparing information processing theories.
Project – YUC Intelligent System Interface Design C.docxwkyra78
Project – YUC Intelligent System
Interface Design Competition
(Innovation and Creative System)
INSTRUCTIONS:
Please submit your completed work in proper PRINTED DOCUMENT and via Schoology.
Your work must be your own group work. Act of copying is strictly prohibited and every party involved
will be penalized by deducting 70% of their marks.
DESCRIPTION:
Working in groups of 5 people (maximum), search any topic from internet to stimulate your idea and
creativity. You might identify the main problems that the user faced in their daily life or analyze the
weaknesses of existing YUC-SIS and YUC E-learning systems. Then you will provide creative solution
using innovative technology/system (upgrade the system functionality into Intelligent System or
Business Intelligent System). To realize the designs, gives added value to your system design. Each
group should able to present the idea of how the system works into graphic/ visual (interface design).
TASKS:
1. Read about Creativity below.
2. Do literatures review from Google or from given list of Bibliography.
3. Design your invention into Interface Design and using any solution models
4. Goto http://www.scoop.it/t/kaymarlyn and select ‘Tools’ tags under ‘Search in topic’ menu. Study
and learn about “60 User Interface Design Tools A Web Designer Must Have” and other prototyping
and mockup tools from the page.
5. Illustrate your idea into interface design using the selected best tool for your Design Category and
provide the explanation. You might search from the Internet using keywords to view other example of
process or models.
6. Disseminate your idea and how your system works into proper formatted report.
7. Presentation will determined the winners ranking and will contribute max 35/50 marks from the
total marks.
8. Shows all the workload distribution among your group members in the given table.
9. Lastly, provide all the references and websites that you visited and used in the report.
DESIGN CATEGORIES:
Academic System
Students Manager
University DSS
Mobile Apps
Student Work/ Activities Application
MANAGEMENT SCIENCE DEPARTMENT
MIS440 MANAGEMENT SUPPORT SYSTEM
http://www.scoop.it/t/kaymarlyn
Creativity
Creativity involves the generation of new ideas or the recombination of known elements into something new,
providing valuable solutions to a problem. It also involves motivation and emotion. Creativity “is a fundamental
feature of human intelligence in general. It is grounded in everyday capacities such as the association of ideas,
reminding, perception, analogical thinking, searching a structured problem-space, and reflecting self-criticism. It
involves not only a cognitive dimension (the generation of new ideas) but also motivation and emotion, and is
closely linked to cultural context and personality factors.” (Boden 1998).
Fundamental concepts for all creative techniques are:
The suspension of p ...
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Similaire à Snips and snails and puppy dog tails: the need to preserve complexity in mathematics learning analysis (20)
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
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واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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Snips and snails and puppy dog tails: the need to preserve complexity in mathematics learning analysis
1. Snips and snails and puppy dog tails: the
need to preserve complexity in
mathematics learning analysis
A Reply to “The Use of Digital Tools in Web-based
Mathematical Problem Solving: different levels of
sophistication in Solving-and-Expressing” (Jacinto,
Nobre, Carreira & Amado, 2014)
João Filipe Matos
jfmatos @ ie.ulisboa.pt
Instituto de Educação, Universidade de Lisboa
Conference Problem@Web | 2-4 May 2014 | Portugal
5. solve-and-express as mathematizing
• is solving inherent to expressing?
• what consequences do emerge from that
point of view?
• how powerful is digital technology in
solving-and-expressing?
6. how digital technologies constitute
human activity?
• all artifacts enable and constrain human
activity
7. a need to enlarge the research object
• consciousness of how digital technologies
constitute human being and activity
• mathematics as human activity can not be
addressed without considering the ways how
digital technologies constitute practices
• a need to consider a broader perspective
9. a need to enlarge the research object
• why isolate the system human-computer?
• consider the whole activity system
…trends in HCI
10. a need to enlarge the research object
• the tool mediating perspective but a need
to conceptualize 2 interfaces, not 1:
• 2 borders separating:
1st
) the user from the computer
2nd
) the user and the computer from the
outside world
12. a need to enlarge the research object
• where is the boundary between the individual
who uses the tool and the external world?
• does the boundary coincide with the individual-
tool boundary or with the tool-world one?
subject-technology technology-mathematics
13. a need to enlarge the research object
• The answer from AT: concept of functional organ
(Leont’ev)
functionally integrated, goal directed
configurations of internal and external
resources
external tools: support and complement natural human abilities in
building up a more efficient system that can lead to higher
accomplishments
14. a need to enlarge the research object
external tools: support and complement natural human abilities in
building up a more efficient system that can lead to higher
accomplishments
efficient cutting organ
improve human vision
enhance memory
external tools integrated into functional organs are experienced as a
property of the individual (improving the transparency of the interface)
15. why functional organs?
• extension of the internal plane of actions (human
ability to perform manipulations with an
internal representation of external objects before
starting actions with the objects in reality)
17. why computerized systems / digital
technologies?
• computerized systems (digital technologies)
easily create controllable models of target objects
and provide opportunities to evaluate and
manipulate
18. • what are the needs that require the
development of a new functional organ (for
problem solving)?
what is the structure of students’ activity before
the assimilation of the tool and what is the
previous experience of students with the
uncomputerized equivalent of the activity?
19. the symbiotic unit human-with-media
and co-action
• visible through co-action
• but bringing in the context
… echoing the concept of digital habitat
… and distinguishing ‘context’ from
‘setting’
21. Example 1: humans-with-GeoGebra
• 3 scripts emerge from data
• but do not unveil the associated cognitive
processes involved in mathematical
thinking for the production of the solution
23. Example 2: humans-with-spreadsheet
• connecting 2 forms of representation
• the spreadsheet allows ways of expressing
functional relations but at the same time it
hides mathematical thinking (as it reifies
it)
24. co-action
• conceptual models or forms of resolution
that are sophisticated are associated to a
stronger co-action with the spreadsheet
25. co-action
• the relevance of co-action with the digital
artifact is the criteria used to define the
degree of robustness (visible in the level of
sophistication of solutions)
26. co-action
• it is not enough to distinguish between
different ways of addressing a problem and
solve it and to consider the emergent
conceptual models; we need to understand
how different conceptual models are
produced and materialized in relation to the
form of use of he digital artifact
29. degree of co-action
• Geogebra / Spreadsheet
• is paper & pencil a zero co-action habitat?
• what about web 2.0 tools?
30. criteria to assess co-action
… lack of criteria
•robustness
•simplicity
•efficiency (metrics in space and time)
•…
31. where to with geometric dynamic
systems?
1) gaining insight and intuition,
2) discovering new patterns and relationships,
3) graphing to expose math principles,
4) testing and especially falsifying conjectures,
5) exploring a possible result to see if it merits formal proof,
6) suggesting approaches for formal proof,
7) computing replacing lengthy hand derivations, and
8) confirming analytically derived results
32. where to with spreadsheets?
• Framing the problem,
• Exploring the solution,
• Conjecturing,
• Justifying or investigating the conjecture
33. call for a conceptual framework for the
analysis
“A conceptual framework is an argument including
different points of view and culminating in a series of
reasons for adopting some points – i.e., some ideas or
concepts – and not others. The adopted ideas or concepts
then serve as guides: to collecting data in a particular
study, and/or to ways in which the data from a particular
study will be analyzed and explained”
•
(Eisenhart, 1991, p. 209)
34. • nature of the framework: structure and
justification
• role of the framework: argumentative
network of analytical concepts
Mutual constitution = Individuals, practices/actions and world of objects
Social-cultural dimension of development = transformative/ emancipative character
Considering people-in-action Relevance of intencionality Evolutive role of contradictions and reflection A situated, dynamic, relational, emergent and expansive perspective
Mutual constitution = Individuals, practices/actions and world of objects
Social-cultural dimension of development = transformative/ emancipative character
Considering people-in-action Relevance of intencionality Evolutive role of contradictions and reflection A situated, dynamic, relational, emergent and expansive perspective