This presentation discusses Computational thinking, the four pillars of computational thinking which is decomposition, abstraction, algorithms and pattern recognition and lastly the benefits of teaching CT in schools to learners.
The introduction of Computational Thinking. What is the Computational Thinking? How to apply it into the real educational environment? You can find the solution in this slide.
Computational thinking involves breaking down complex problems into smaller, more manageable parts through decomposition. It utilizes concepts like pattern recognition, abstraction, algorithms, and evaluation. The key aspects of computational thinking are decomposing problems, recognizing patterns within data, abstracting away unnecessary details, designing algorithms to describe solutions, and evaluating whether the solution meets the problem's requirements.
Computational thinking (CT) is a problem-solving process that involves decomposition, pattern recognition, abstraction, and algorithm design. CT can be used to solve problems across many disciplines. The key principles of CT are: 1) Decomposition, which is breaking down complex problems into smaller parts; 2) Pattern recognition, which is observing patterns in data; 3) Abstraction, which identifies general principles; and 4) Algorithm design, which develops step-by-step instructions. CT is a concept that focuses on problem-solving techniques, while computer science is the application of those techniques through programming. CT can be applied to solve problems in any field, while computer science specifically implements computational solutions.
Productivity software includes word processors, spreadsheets, databases, and other programs designed for individual use. Productivity suites bundle these programs together under a common interface. Microsoft Office is the most popular productivity suite, including Word, Excel, PowerPoint, and Outlook. Other major productivity software includes Apple's iWork, IBM Lotus SmartSuite, Corel WordPerfect Office, and the open source OpenOffice.org. While Microsoft leads in market share, competition from Apple's iWork and other open source options continues to grow.
This document provides information about algorithms and flowcharts. It begins with defining an algorithm as a sequence of steps to solve a problem and discusses properties like finiteness, definiteness, inputs, outputs, and effectiveness. Examples of algorithms are provided for tasks like making noodles and checking voter eligibility. Flowcharts are introduced as a way to visually represent algorithms using standard symbols like rectangles, diamonds, and arrows. Advantages of algorithms and flowcharts are that they improve problem solving, communication, and programming. The document concludes with flowchart examples and a short class test.
Content:
1- Mathematical proof (what and why)
2- Logic, basic operators
3- Using simple operators to construct any operator
4- Logical equivalence, DeMorgan’s law
5- Conditional statement (if, if and only if)
6- Arguments
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
An algorithm is a set of steps to accomplish a specific task or solve a problem. It has a well-defined sequence of steps, will produce an output, and will eventually terminate. An algorithm describes the precise steps to solve a computational procedure from an input to an output in a finite number of steps. Examples of algorithms include step-by-step directions for driving to a friend's house or brushing your teeth.
The introduction of Computational Thinking. What is the Computational Thinking? How to apply it into the real educational environment? You can find the solution in this slide.
Computational thinking involves breaking down complex problems into smaller, more manageable parts through decomposition. It utilizes concepts like pattern recognition, abstraction, algorithms, and evaluation. The key aspects of computational thinking are decomposing problems, recognizing patterns within data, abstracting away unnecessary details, designing algorithms to describe solutions, and evaluating whether the solution meets the problem's requirements.
Computational thinking (CT) is a problem-solving process that involves decomposition, pattern recognition, abstraction, and algorithm design. CT can be used to solve problems across many disciplines. The key principles of CT are: 1) Decomposition, which is breaking down complex problems into smaller parts; 2) Pattern recognition, which is observing patterns in data; 3) Abstraction, which identifies general principles; and 4) Algorithm design, which develops step-by-step instructions. CT is a concept that focuses on problem-solving techniques, while computer science is the application of those techniques through programming. CT can be applied to solve problems in any field, while computer science specifically implements computational solutions.
Productivity software includes word processors, spreadsheets, databases, and other programs designed for individual use. Productivity suites bundle these programs together under a common interface. Microsoft Office is the most popular productivity suite, including Word, Excel, PowerPoint, and Outlook. Other major productivity software includes Apple's iWork, IBM Lotus SmartSuite, Corel WordPerfect Office, and the open source OpenOffice.org. While Microsoft leads in market share, competition from Apple's iWork and other open source options continues to grow.
This document provides information about algorithms and flowcharts. It begins with defining an algorithm as a sequence of steps to solve a problem and discusses properties like finiteness, definiteness, inputs, outputs, and effectiveness. Examples of algorithms are provided for tasks like making noodles and checking voter eligibility. Flowcharts are introduced as a way to visually represent algorithms using standard symbols like rectangles, diamonds, and arrows. Advantages of algorithms and flowcharts are that they improve problem solving, communication, and programming. The document concludes with flowchart examples and a short class test.
Content:
1- Mathematical proof (what and why)
2- Logic, basic operators
3- Using simple operators to construct any operator
4- Logical equivalence, DeMorgan’s law
5- Conditional statement (if, if and only if)
6- Arguments
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
An algorithm is a set of steps to accomplish a specific task or solve a problem. It has a well-defined sequence of steps, will produce an output, and will eventually terminate. An algorithm describes the precise steps to solve a computational procedure from an input to an output in a finite number of steps. Examples of algorithms include step-by-step directions for driving to a friend's house or brushing your teeth.
Course: Intro to Computer Science (Malmö Högskola):
A overview of computability and complexity (for non-mathematicians). definition of algorithm, turing machines, lambda, calculus and concepts of complexity
Modeling requirements involves developing functional requirements from customer views into something translatable to software. Techniques like use cases, state diagrams, UI mockups, storyboards and prototypes are used to understand current systems, business processes, and how users will interact with new systems. The software requirements document specifies what is required of the system and should focus on what the system should do rather than how. Requirements modeling is iterative and requirements change in agile methods.
Extreme Programming (XP) is an agile software development methodology that focuses on rapid feedback, simplicity, communication, and responsiveness to change. The core practices of XP include: short iterative release cycles, frequent planning games, simple design, pair programming, unit testing, collective code ownership, continuous integration, on-site customers, and 40-hour work weeks. By following these practices, XP aims to deliver working software frequently in a way that is adaptable to changing requirements.
The document discusses the process of writing a computer program. It explains that programming involves breaking a problem down into a logical sequence of steps. There are two main phases: the problem-solving phase where the problem is analyzed and an algorithm is developed, and the implementation phase where the algorithm is translated into a programming language and tested. The process also includes a maintenance phase to modify the program as needed over time.
This document provides an introduction to computer programming. It explains that a computer program is a set of instructions that a computer can execute. Programming allows humans to store and transmit knowledge via computer code. The document outlines some basic programming concepts like variables, conditional statements, lists, loops, and subroutines. It explains each concept using everyday examples and simple code snippets. The overall document serves as a starting point for learning computer programming fundamentals.
What is Software project management?? , What is a Project?, What is a Product?, What is Project Management?, What is Software Project Life Cycle?, What is a Product Life Cycle?, Software Project, Software Triple Constraints, Software Project Manager, Project Planning,
Software is a set of instructions to acquire inputs and to manipulate them to produce the desired output in terms of functions and performance as determined by the user of the software
This document provides an introduction to Python programming. It discusses problem solving techniques like algorithms, flowcharts, and pseudocode. It also covers Python concepts like variables, operators, control structures, strings, lists, tuples, and dictionaries. Functions and algorithms are presented as ways to organize Python code. The document is intended as an introductory guide to learning Python programming.
The document provides an outline for a course on data structures and algorithms. It includes topics like data types and operations, time-space tradeoffs, algorithm development, asymptotic notations, common data structures, sorting and searching algorithms, and linked lists. The course will use Google Classroom and have assignments, quizzes, and a final exam.
This document discusses function-oriented software design. It explains that function-oriented design represents a system as a set of functions that transform inputs to outputs. The chapter objectives are to explain function-oriented design, introduce design notations, illustrate the design process with an example, and compare sequential, concurrent and object-oriented design strategies. Topics covered include data-flow design, structural decomposition, detailed design, and a comparison of design strategies.
Este documento describe los pasos para elaborar una propuesta de desarrollo de proyecto, incluyendo la redacción de una propuesta y documentos de requisitos, el diseño y modelado del proyecto usando diagramas UML, y la documentación y presentación del proyecto final. El documento también incluye una evaluación de varias actividades relacionadas con el desarrollo del proyecto.
This document is a PDF version of the Python Programming Wikibook, which provides instruction on a variety of Python topics. It includes the LaTeX source code as an attachment, and specifies how to extract and decompress the source code from the PDF. The document also describes various licenses that may apply to parts of the content within, due to being derived from Wikibooks and Wikipedia projects.
This document discusses design patterns, beginning with how they were introduced in architecture in the 1950s and became popularized by the "Gang of Four" researchers. It defines what patterns are and provides examples of different types of patterns (creational, structural, behavioral) along with common patterns in each category. The benefits of patterns are that they enable reuse, improve communication, and ease the transition to object-oriented development. Potential drawbacks are that patterns do not directly lead to code reuse and can be overused. Effective use requires applying patterns strategically rather than recasting all code as patterns.
Productivity software includes applications like word processors, spreadsheets, databases, and presentation software. It is used by almost everyone with a computer to increase productivity. Word processors help create documents, spreadsheets allow analysis of data and creation of graphs/charts, presentation software aids visual communication, and database software organizes information. Productivity suites bundle these applications together. Popular suites include Microsoft Office and free web-based options. Productivity software enhances business performance and is essential for computer fluency in education and work.
If you want to make carrier in the field of computer science then programming language is how important for you to learn.
if need any programming assignment help then go through our no.1 website- programmingshark.com
The programming process involves 6 main steps: 1) Identifying the problem and requirements, 2) Designing a solution using techniques like top-down design and modularization, 3) Writing the program by choosing a language and following its syntax, 4) Testing for errors, 5) Documenting the program, and 6) Maintaining the program with user guides and code comments. Key parts of the design stage include breaking the problem into subproblems and designing algorithms using methods like pseudocode or flowcharts.
GPT-2: Language Models are Unsupervised Multitask LearnersYoung Seok Kim
This document summarizes a technical paper about GPT-2, an unsupervised language model created by OpenAI. GPT-2 is a transformer-based model trained on a large corpus of internet text using byte-pair encoding. The paper describes experiments showing GPT-2 can perform various NLP tasks like summarization, translation, and question answering with limited or no supervision, though performance is still below supervised models. It concludes that unsupervised task learning is a promising area for further research.
The document provides an introduction to programming concepts including:
- A computer program tells a computer step-by-step how to solve a problem. An algorithm is the sequence of steps to solve a problem.
- Programming languages allow communication between users and computers. The program design process involves problem solving, designing algorithms and flowcharts, coding, testing and debugging.
- Problem solving for programming breaks problems into subproblems, specifies requirements, analyzes the problem, designs and tests solutions, and maintains programs. This process is used to systematically solve any type of problem.
The document defines computational thinking as using concepts from computer science to solve problems. It identifies the four pillars as decomposition, abstraction, pattern recognition, and algorithms. Decomposition involves breaking problems into smaller parts, abstraction focuses on relevant information, pattern recognition analyzes data for connections, and algorithms provide step-by-step solutions. Teaching computational thinking at schools has benefits like developing problem-solving and technical skills to address future challenges.
The document defines computational thinking and its key concepts including decomposition, abstraction, pattern recognition, and algorithms. It discusses breaking down complex problems into smaller parts, simplifying details while focusing on essentials, identifying similarities between problems, and developing step-by-step instructions to solve problems. The benefits of computational thinking at schools are also outlined, such as improving problem-solving skills, logical and analytical thinking, creativity, and preparing students for the digital age. Resources and references are listed at the end.
Course: Intro to Computer Science (Malmö Högskola):
A overview of computability and complexity (for non-mathematicians). definition of algorithm, turing machines, lambda, calculus and concepts of complexity
Modeling requirements involves developing functional requirements from customer views into something translatable to software. Techniques like use cases, state diagrams, UI mockups, storyboards and prototypes are used to understand current systems, business processes, and how users will interact with new systems. The software requirements document specifies what is required of the system and should focus on what the system should do rather than how. Requirements modeling is iterative and requirements change in agile methods.
Extreme Programming (XP) is an agile software development methodology that focuses on rapid feedback, simplicity, communication, and responsiveness to change. The core practices of XP include: short iterative release cycles, frequent planning games, simple design, pair programming, unit testing, collective code ownership, continuous integration, on-site customers, and 40-hour work weeks. By following these practices, XP aims to deliver working software frequently in a way that is adaptable to changing requirements.
The document discusses the process of writing a computer program. It explains that programming involves breaking a problem down into a logical sequence of steps. There are two main phases: the problem-solving phase where the problem is analyzed and an algorithm is developed, and the implementation phase where the algorithm is translated into a programming language and tested. The process also includes a maintenance phase to modify the program as needed over time.
This document provides an introduction to computer programming. It explains that a computer program is a set of instructions that a computer can execute. Programming allows humans to store and transmit knowledge via computer code. The document outlines some basic programming concepts like variables, conditional statements, lists, loops, and subroutines. It explains each concept using everyday examples and simple code snippets. The overall document serves as a starting point for learning computer programming fundamentals.
What is Software project management?? , What is a Project?, What is a Product?, What is Project Management?, What is Software Project Life Cycle?, What is a Product Life Cycle?, Software Project, Software Triple Constraints, Software Project Manager, Project Planning,
Software is a set of instructions to acquire inputs and to manipulate them to produce the desired output in terms of functions and performance as determined by the user of the software
This document provides an introduction to Python programming. It discusses problem solving techniques like algorithms, flowcharts, and pseudocode. It also covers Python concepts like variables, operators, control structures, strings, lists, tuples, and dictionaries. Functions and algorithms are presented as ways to organize Python code. The document is intended as an introductory guide to learning Python programming.
The document provides an outline for a course on data structures and algorithms. It includes topics like data types and operations, time-space tradeoffs, algorithm development, asymptotic notations, common data structures, sorting and searching algorithms, and linked lists. The course will use Google Classroom and have assignments, quizzes, and a final exam.
This document discusses function-oriented software design. It explains that function-oriented design represents a system as a set of functions that transform inputs to outputs. The chapter objectives are to explain function-oriented design, introduce design notations, illustrate the design process with an example, and compare sequential, concurrent and object-oriented design strategies. Topics covered include data-flow design, structural decomposition, detailed design, and a comparison of design strategies.
Este documento describe los pasos para elaborar una propuesta de desarrollo de proyecto, incluyendo la redacción de una propuesta y documentos de requisitos, el diseño y modelado del proyecto usando diagramas UML, y la documentación y presentación del proyecto final. El documento también incluye una evaluación de varias actividades relacionadas con el desarrollo del proyecto.
This document is a PDF version of the Python Programming Wikibook, which provides instruction on a variety of Python topics. It includes the LaTeX source code as an attachment, and specifies how to extract and decompress the source code from the PDF. The document also describes various licenses that may apply to parts of the content within, due to being derived from Wikibooks and Wikipedia projects.
This document discusses design patterns, beginning with how they were introduced in architecture in the 1950s and became popularized by the "Gang of Four" researchers. It defines what patterns are and provides examples of different types of patterns (creational, structural, behavioral) along with common patterns in each category. The benefits of patterns are that they enable reuse, improve communication, and ease the transition to object-oriented development. Potential drawbacks are that patterns do not directly lead to code reuse and can be overused. Effective use requires applying patterns strategically rather than recasting all code as patterns.
Productivity software includes applications like word processors, spreadsheets, databases, and presentation software. It is used by almost everyone with a computer to increase productivity. Word processors help create documents, spreadsheets allow analysis of data and creation of graphs/charts, presentation software aids visual communication, and database software organizes information. Productivity suites bundle these applications together. Popular suites include Microsoft Office and free web-based options. Productivity software enhances business performance and is essential for computer fluency in education and work.
If you want to make carrier in the field of computer science then programming language is how important for you to learn.
if need any programming assignment help then go through our no.1 website- programmingshark.com
The programming process involves 6 main steps: 1) Identifying the problem and requirements, 2) Designing a solution using techniques like top-down design and modularization, 3) Writing the program by choosing a language and following its syntax, 4) Testing for errors, 5) Documenting the program, and 6) Maintaining the program with user guides and code comments. Key parts of the design stage include breaking the problem into subproblems and designing algorithms using methods like pseudocode or flowcharts.
GPT-2: Language Models are Unsupervised Multitask LearnersYoung Seok Kim
This document summarizes a technical paper about GPT-2, an unsupervised language model created by OpenAI. GPT-2 is a transformer-based model trained on a large corpus of internet text using byte-pair encoding. The paper describes experiments showing GPT-2 can perform various NLP tasks like summarization, translation, and question answering with limited or no supervision, though performance is still below supervised models. It concludes that unsupervised task learning is a promising area for further research.
The document provides an introduction to programming concepts including:
- A computer program tells a computer step-by-step how to solve a problem. An algorithm is the sequence of steps to solve a problem.
- Programming languages allow communication between users and computers. The program design process involves problem solving, designing algorithms and flowcharts, coding, testing and debugging.
- Problem solving for programming breaks problems into subproblems, specifies requirements, analyzes the problem, designs and tests solutions, and maintains programs. This process is used to systematically solve any type of problem.
The document defines computational thinking as using concepts from computer science to solve problems. It identifies the four pillars as decomposition, abstraction, pattern recognition, and algorithms. Decomposition involves breaking problems into smaller parts, abstraction focuses on relevant information, pattern recognition analyzes data for connections, and algorithms provide step-by-step solutions. Teaching computational thinking at schools has benefits like developing problem-solving and technical skills to address future challenges.
The document defines computational thinking and its key concepts including decomposition, abstraction, pattern recognition, and algorithms. It discusses breaking down complex problems into smaller parts, simplifying details while focusing on essentials, identifying similarities between problems, and developing step-by-step instructions to solve problems. The benefits of computational thinking at schools are also outlined, such as improving problem-solving skills, logical and analytical thinking, creativity, and preparing students for the digital age. Resources and references are listed at the end.
This document discusses instructional design and media selection. It provides an agenda for an instructional design course that includes updating students on assignments, discussing learner motivation theories, and media selection. The document outlines an assignment for students to create an instructional blueprint applying their instructional design model. It provides grading criteria, discusses applying instructional design models and theories to the blueprint, and considers issues to examine when selecting media. Checklists are presented to help instructional designers evaluate the effectiveness of media in supporting instructional goals and learner interaction.
The document discusses the concept of backward design in planning instruction, which involves starting from desired learning outcomes and assessing if goals are met, as described by Ralph Tyler in 1949 and further popularized by Wiggins and McTighe. Backward design results in more clearly defined goals, appropriate assessments, aligned lessons, and purposeful teaching compared to traditional planning. The backward design process explained by Wiggins and McTighe begins with identifying the desired results and understanding before determining acceptable evidence and planning learning experiences.
As part of the 2024 ASCCC Noncredit Institute, we explored the current landscape of instructional design in California Community Colleges and provided insights into the traditional use of instructional designers' skills and explored innovative approaches to maximize these resources to achieve better student outcomes and cultivate equitable learning environments.
ICT Integration programme contributes to the Kenya Education Sector Support Programme (KESSP 2005 - 2010). KESSP joins ministries, donors, NGOs and other partners for improved quality of education in Kenya. A Ministerial ICT integration team has been set up and is responsible for the coordination and harmonization of all ICT initiatives within KESSP.
The VVOB ICT integration programme partners with the Kenyan Ministry of Education in two subprogramme components: ICT Integration in education and capacity building. The subprogramme of ICT integration in education is cross cutting and works with all directorates and units within the Ministry of Education.
The education sector in Kenya is still in its infancy in the inclusion and use of ICT. To integrate ICT appropriately in order to increase the quality of education, technology and teaching methods and education should go hand in hand. VVOB pursues an integrated approach and we simultaneously work with several national institutions that have mandates to strengthen the capacity of education managers at different levels, as well as that of teachers.
Technology is transforming the way we shop, communicate, eat, transact, consume media and pretty much every aspect of our lives. Education is another sector that's massively been impacted by tech, especially over the last 2 years.
We're excited about how edtech is transforming from Massive Open Online Courses (MOOCs) designed for passive online content consumption to high-intent Cohort Based Courses (CBCs) with tangible and oftentimes monetary outcomes.
CBCs are time-bound, highly interactive community based courses addressing skill-based topics across various verticals with clear-cut incentives, leading to near-perfect completion rates. These courses can range from software engineering up-skilling to learning how to bake a lemon meringue pie. Read our research on the cohort based learning space and where we see the future.
A PRELIMINARY STUDY ON MULTIDISCIPLINARY DESIGN FRAMEWORK IN A VIRTUAL REALIT...ijma
This article presents a preliminary study on the effectiveness of the multidisciplinary design framework
(MDF) for teaching and learning in a Virtual Reality Learning Environment (VRLE). The aim of the study
was to investigate the students’ learning experiences with fully remote multidisciplinary groups, practicing
collaborative design in a VRLE. The objective was to introduce and implement a synchronous
multidisciplinary design teaching and learning engagement framework with asynchronous online
documentation that manages and evaluates evidence of learning outcomes. This study employed a
sequential explanatory mixed method research on a quasi-experiment involving 30 undergraduate students
from the creative media specializations in collaboration with 39 other students from the business,
computing, communication, and product design degree students over a 14- weeks duration. Students were
surveyed using online questionnaires, interviews, and observations by the module facilitator for the
quantitative and qualitative data collection. A triangulation protocol was used for the convergence coding
of three data sets. Results revealed that there were 85% students scoring grade A’s as compared to 69.3%
from the previous cohort that was without the framework and VRLE support. Overall, the students’
commented that the multidisciplinary design collaboration was beneficial, realizing the advantage of
collaborating to merge various skill sets and knowledge to solve problems that couldn’t be solved alone.
The study’s finding implied that the MDF effectively achieved the teaching and learning outcomes and
could be applied to all higher education multidisciplinary collaborations in a VRLE.
A Preliminary Study on Multidisciplinary Design Framework in a Virtual Realit...ijma
This article presents a preliminary study on the effectiveness of the multidisciplinary design framework (MDF) for teaching and learning in a Virtual Reality Learning Environment (VRLE). The aim of the study was to investigate the students’ learning experiences with fully remote multidisciplinary groups, practicing collaborative design in a VRLE. The objective was to introduce and implement a synchronous multidisciplinary design teaching and learning engagement framework with asynchronous online documentation that manages and evaluates evidence of learning outcomes. This study employed a sequential explanatory mixed method research on a quasi-experiment involving 30 undergraduate students from the creative media specializations in collaboration with 39 other students from the business, computing, communication, and product design degree students over a 14- weeks duration. Students were surveyed using online questionnaires, interviews, and observations by the module facilitator for the quantitative and qualitative data collection. A triangulation protocol was used for the convergence coding of three data sets. Results revealed that there were 85% students scoring grade A’s as compared to 69.3% from the previous cohort that was without the framework and VRLE support. Overall, the students’ commented that the multidisciplinary design collaboration was beneficial, realizing the advantage of collaborating to merge various skill sets and knowledge to solve problems that couldn’t be solved alone. The study’s finding implied that the MDF effectively achieved the teaching and learning outcomes and could be applied to all higher education multidisciplinary collaborations in a VRLE.
Big, small or medium: what kind of data can help us improve learning design?Jisc
Speakers:
Sarah Knight, head of change: student experience, Jisc
Samantha Ahern, learning technology project officer, University College London (UCL)
Gill Ferrell, consultant: learning, teaching and student experience
Patrick Lynch, technology-enhanced learning adviser, University of Hull
Natasa Perovic, digital education adviser, UCL
Clive Young, advisory team leader, digital education, UCL
Institutions increasingly promote blended approaches to learning and in parallel are gathering data across all areas of the student experience. This workshop explores the emerging relationship between data and learning design.
It includes: how to ensure a sound pedagogic purpose to your blend; how to tell if your pedagogic approach is working; how and when to take action based on what your data is telling you and what the data can't tell you.
Try out the ABC, learning design approach, explore curriculum redesign using tools and techniques - all available via the new Jisc guide on designing learning and assessment in a digital age.
HEA STEM seminar-2013 Embracing employability in HEIsEISLibrarian
This document outlines the agenda and participating institutions of a seminar on embracing employability in higher education institutions. The seminar featured keynote speeches from industry professionals and academics on engaging employers in curriculum delivery and the role of stakeholders in curriculum development. Breakout sessions discussed approaches to embedding employability skills at different institutions, including through volunteering, core skills training, and work placements. Employability frameworks like the CBI guidelines and SFIA were mapped to curriculum delivery and modules in science and technology programs. Collaboration between academic departments, language centers, and libraries integrated employability support into coursework and assessments.
Teaching with digital badges best practices for librariescredomarketing
University at Albany librarians Kelsey O'Brien and Trudi Jacobson discuss the digital badging program they’ve implemented at their library, and outline tips and best practices regarding badging. The presenters, editors of Teaching with Digital Badges: Best Practices for Libraries (Rowman & Littlefield, 2018), will provide attendees with lessons learned and advice on how to launch your own micro-credentialing effort and make it a success.
Engineering Futures through Engineering EducationGary Wood
Keynote talk from UK and Ireland Engineering Education Research Network workshop 'What happens post-COVID? How engineering education has evolved for a digital future'. Thursday, 8 September 2021.
This document outlines Professor Steven Warburton's approach to designing digital futures for organizations facing accelerating technological change. It discusses the need to shift organizational culture through developing a digital mindset, processes, and capabilities. A design studio approach is proposed to scaffold design activities, using methods like narrative case studies, design patterns, challenges/scenarios, and prototypes. Participants investigate problems, prototype solutions, and provide feedback through critiques. The goal is to empower teams to design successful innovations through a user-centered process informed by past successes.
Beyond the blend: practical approaches to designing fully online learningJisc
A presentation from Connect More 2020 by Kate Lindsay, head of digital education, University College of Estate Management.
The University College of Estate Management has been delivering remote teaching and learning for over a century. Their current programme of digital transformation puts their students learning experience at it heart with a focus on flexibility and embedding active online pedagogies. Based on experience and evidence from practice, this presentation will outline the changes and methods we have put in place to design online education, along with a set of resources to share with the sector.
Mdb016 Sequencing Learning Experiences ITS and ICT SAS Queensland SyllabusMsButow
The document summarizes two learning sequences for a course on information and communication technologies (ICT).
Sequence One focuses on collaborative learning and has students work in groups on a multimedia project. They discuss skills needed, plan and evaluate the project, and work collaboratively over several weeks to complete it.
Sequence Two focuses on more complex learning involving 3D modeling. Students are given an ill-defined task and must find their own answers through guided discovery and collaboration. They create a project specification and presentation without step-by-step instructions. Both sequences aim to engage students in higher-order thinking through complex, open-ended tasks.
Activity-Oriented Design Methods (AODM): A way of making sense of the CENLeRoy Hill
Online social networking is an emerging area of interest to educational professionals and researchers alike. Unfortunately, more attention is paid to the process of knowledge-building resulting in less being paid to the processes of design which might enable sustainable collaborative knowledge-building (CKB) to flourish. The relative lack of attention to design, points to the need for methods to guide the development of CKB environments. This paper therefore draws on a larger study within an online social networking setting and focuses on the use of Activity Oriented Design Methods (Mwanza 2002) as a way to facilitate designers to capture a far-reaching perspective of the research and design context. In this presentation, I show how the AODM is used as a guide to operationalise various methods of data collection to gain a deeper insight into the context for further research. I argue that the development of a design framework to support sustainable CKB in online social networking environments is a complex process that demands a comprehensive activity-oriented approach to get a full picture the activity system in order to be responsive to learner needs. This approach suggests that there are implications for the way design for CKB is contextualised in such settings
This document summarizes two instructional design models: the Bates model and the Gentry IPDM model.
The Bates model, called ACTIONS, focuses on seven key factors to consider when selecting learning technologies: access, costs, teaching/learning implications, interaction, organizational issues, novelty, and speed. It also outlines four phases of instructional design.
The Gentry IPDM model emphasizes communication between the instructional development components and supporting components. It represents the necessary design components as 14 interconnected circles to complete an instructional unit using various techniques.
The interdependent dynamic relationship between individuals, communities.pptxAmatullah Daya
This Presentation discusses the interdependent dynamic relationship between individuals and communities and how the relationship is justified on moral and practical grounds. It is based on the theories of R.S Peters and discusses the moral requirements for education, the aims of education and the relationship between the community and the individual in terms of education.
This powerpoint presentation details Networking technologies, what they are and the purpose of a network. It also gives the advantages and disadvantages of networking and details what is needed to set up a network. Lastly, it describes the advantages and disadvantage of personal area networks.
This powerpoint presentation describes E-communication, what it is and the importance of e-communication. Furthermore, it presents email etiquette with examples of good and bad emails and the importance of following a professional email format. Lastly, it discusses social media, what it is and the guidelines for using it in education.
This presentation details the basic structure of a bacteria cell, including the types of bacteria there are and touches upon two diseases caused by bacteria, namely Tuberculosis and Cholera. It provides the symptoms of the diseases as well as some treatments and prevention methods.
Ferns are a group of plants adapted to live on land. The dominant generation in ferns is the sporophyte generation, which represents the adult fern plant with true roots, an underground stem, and large compound leaves. Spores are produced in clusters called sori on the underside of leaves and are dispersed by wind. When spores germinate, they produce a small heart-shaped gametophyte generation that bears male and female sex organs requiring water for fertilization to occur and form a new sporophyte fern plant.
Respiratory System and Gaseous Exchange.pdfAmatullah Daya
Cellular respiration requires oxygen to produce energy from glucose and produces carbon dioxide as a byproduct, so gaseous exchange is necessary to supply oxygen and remove carbon dioxide from the body. Breathing involves three steps - ventilation, external respiration, and internal respiration - to take in oxygen through inspiration and expiration and exchange gases between the air, blood, and tissue cells to supply cells with oxygen and remove carbon dioxide. The human respiratory system includes the nose, air passages like the pharynx, trachea, bronchi and bronchioles, and lungs containing alveoli to facilitate this gas exchange through breathing.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
2. Table of Contents:
1. What is computational thinking?
2. The 4 Pillars of computational thinking
explained
3. The 4 pillars of computational thinking
explained (continue)
4. Summary of the 4 pillars of
computational thinking
5. The benefits of teaching computational
thinking in schools
6. References
3. WHAT IS COMPUTATIONAL
THINKING?
• Computational thinking is the ability to take a complex
problem and break it down into smaller steps in order
to solve the problem. This is operating the same way in
which a computer operates when solving a problem,
hence the term “computational thinking”. It is the set of
skills needed to solve problems in a way a computer
would (Victoria, 2022).
Click the video above for more
information on CT
5. THE 4 PILLARS OF COMPUTATIONAL THINKING
EXPLAINED:
DECOMPOSITION: Decomposition
in computational thinking refers to
the breaking down of a large or
complex problem into smaller
chunks so that the problem
becomes easier to analyze and
solve (Q2 Decomposition, 2022).
ABSTRACTION: In abstraction, we
look at those characteristics of
the problem that are necessary to
solve the problem and we filter
out the characteristics that are
unimportant to the solution of
the problem.
Video about Decomposition Video about Abstraction
6. THE 4 PILLARS OF
COMPUTATIONAL THINKING
EXPLAINED:
• PATTERN RECOGNITION: Pattern
recognition involves recognizing the
patterns in the problem against other
problems in order to solve the
problem more efficiently.
• ALGORITHMS: The development of a
step-by-step procedure to solve a
problem so that others can solve the
problem in the same way (McVeigh-
Murphy, 2019).
7. THE 4 PILLARS OF COMPUTATIONAL THINKING
SUMMARY:
DECOMPOSITION:
The breaking down of a
problem into smaller
and achievable
portions.
PATTERN
RECOGNITION:
One looks for the
similarities between
the problems
ALGORITHMS:
Algorithms are a step-
by-step process that is
used to solve the
problem
ABSTRACTION:
Ignore the
unnecessary
information and
focus on the details
that are important
(Macann, 2022)
8. BENEFITS OF TEACHING CT IN SCHOOLS:
• Computational thinking leads to problem solving. By teaching CT in the
classroom, the educator is fostering learners who are problem solvers and
enhances their ability to solve problems.
• Computational thinking in the classroom leads to the creation of new
ideas. Innovation plays a large role in CT and allows for learners to solve
problems creatively and apply their ideas to create something new
(Cummins, 2020).
• Computational thinking creates learners that are producers of knowledge
and not just the consumers of knowledge. This allows learners to expand
their knowledge by creating rather than just following instructions.
• Computational Thinking is a lifelong skill that can be learned and used in
many fields of work. It also enables one to view the world differently and
opens doors of opportunities for learning and creating (Cummins, 2020).
• It is important to keep up with technology as the world is becoming
increasingly reliant on technology, hence computational thinking will
afford all learners the ability to successfully use the 21st century tools that
will enable them to stay ahead in this fast-paced world.
(The computational
thinkers, 2021)
9. REFERENCES:
• Cummins, K., 2020. Five reasons why computational thinking is an essential tool for teachers and students. — Innovative Teaching Ideas.
[online] Innovative Teaching Ideas. Available at: https://innovativeteachingideas.com/blog/five-reasons-why-computational-thinking-is-an-
essential-tool-for-teachers-and-students [Accessed 10 October 2022].
• Macann, V., 2022. The 4 parts of computational thinking in the digital technologies curriculum > Learning Architects. [online] Learning
Architects. Available at: https://www.learningarchitects.com/the-4-parts-of-computational-thinking-in-the-digital-technologies-curriculum/
[Accessed 10 October 2022].
• McVeigh-Murphy, A., 2019. Computational Thinking, Algorithmic Thinking, & Design Thinking Defined. [online] Equip.learning.com.
Available at: https://equip.learning.com/computational-thinking-algorithmic-thinking-design-thinking [Accessed 10 October 2022].
• Remc.org. 2022. Q2 Decomposition. [online] Available at: https://www.remc.org/21Things4Students/21/21-computational-thinking/q2-
decomposition/ [Accessed 10 October 2022].
• Victoria, K., 2022. Why thinking like a computer builds skills for success. [online] Teach Your Kids Code. Available at:
https://teachyourkidscode.com/what-is-computational-thinking/ [Accessed 10 October 2022].
• 2021. The computational thinkers. [image] Available at: https://blog.playosmo.com/teaching-computational-thinking-to-kids/ [Accessed 10
October 2022].