Algorithmic Thinking: Basics for Gen Z and Gen Alpha
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Estimated reading time: 15 minutes
With the world becoming increasingly digital and automated, the younger generations need to develop algorithmic thinking skills.
Algorithmic thinking focuses on step-by-step processes or algorithms to solve problems. Artificial Intelligence (AI) is developing incredibly across the United States and other countries like India, China, and other European countries.
In this blog, we will discuss the meaning of algorithmic thinking and how it differs from critical thinking, why it is important for Generation Z and Alpha, and the base concepts of algorithmic thinking.
We will also explore examples of algorithmic thinking in everyday life and its relation to coding as a tool for problem-solving.
Help your children develop new age skills with our comprehensive guide to algorithmic thinking. Perfect for Generation Z and Alpha.
Photo by Jeffery Erhunse on Unsplash
What is Algorithmic Thinking?
Algorithmic thinking is looking at problems one step at a time, like following a recipe. It’s about breaking big problems into smaller parts and figuring out the best way to solve them. It also means looking for patterns and finding ways to do things faster and smarter.
Through algorithmic thinking, one can become a better problem solver and use creativity to derive cool solutions to everyday challenges in the digital world. It’s like having a superpower that helps us think logically and find the most efficient ways to get things done!
This process applies to various fields, including computer programming, mathematics, and science. When individuals develop algorithmic thinking skills, they become better at logical reasoning and critical thinking. It also helps them understand how technology works and how to create new solutions.
Understanding Algorithms
Algorithms are step-by-step instructions: Algorithms are like recipes that tell you exactly what to do in a specific order to solve a problem or complete a task.
They’re everywhere: Algorithms are not just for computers. They are in everyday life, like following instructions to bake a cake or solving a math problem using a specific method.
Algorithms make things efficient: By following a well-designed algorithm, you can solve problems faster and more efficiently. It helps you break down complex tasks into smaller, manageable steps.
Identifying patterns: Algorithms often involve recognizing patterns or similarities in the problem you’re trying to solve. By finding patterns, you can define a plan to solve the problem.
Algorithms in technology: Computers use algorithms to perform tasks. For example, search engines use algorithms to find relevant information when you type in a query.
Furthermore,
Different algorithms for different problems: There are many ways to solve a problem, and specific algorithms may work better for precise situations.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxklinda1
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxlesleyryder69361
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxklinda1
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Sharda_dss11_im_01.docChapter 1An Overview of Analy.docxlesleyryder69361
Sharda_dss11_im_01.doc
Chapter 1:
An Overview of Analytics, and AI
Learning Objectives for Chapter 1
· Understand the need for computerized support of managerial decision making
· Understand the development of systems for providing decision-making support
· Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence
· Describe the business intelligence (BI) methodology and concepts
· Understand the different types of analytics and review selected applications
· Understand the basic concepts of artificial intelligence (AI) and see selected applications
· Understand the analytics ecosystem to identify various key players and career opportunities
CHAPTER OVERVIEW
The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata.
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com
Discussion - Weeks 1–2COLLAPSETop of FormShared Practice—Rol.docxcuddietheresa
Discussion - Weeks 1–2
COLLAPSE
Top of Form
Shared Practice—Role of Business Information Systems
Note: This Discussion has slightly different due dates than what is typical for this program. Be mindful of this as you post and respond in the Discussion. Your post is due on Day 7 and your Response is due on Day 3 of Week 2.
As a manager, it is critical for you to understand the types of business information systems available to support business operations, management, and strategy. As of 2013, these include, but are certainly not limited to the following:
· Supply Chain Management (SCM)
· Accounting Information System
· Customer Relationship Management (CRM)
· Decision Support Systems (DSS)
· Enterprise Resource Planning (ERP)
· Human Resource Management
These types of systems support critical business functions and operations that every organization must manage. The effective manager understands the purpose of these types of systems and how they can be best used to manage the organization's data and information.
In this Discussion, you will share your knowledge and findings related to business information systems and the role they play in your organization. You will also consider your colleagues' experiences to explore additional ways business information systems might be applied in your colleagues' organizations, or an organization with which you are familiar.
By Day 7
· Describe two or three of the more important technologies or business information systems used in your organization, or in one with which you are familiar.
· Discuss two examples of how these business information systems are affecting the organization you selected. Be sure to discuss how individual behaviors and organizational or individual processes are changing and what you can learn from the issues encountered.
· Summarize what you have learned about the importance of business information systems and why managers need to understand how systems can be used to the organization's advantage.
You should find and use at least one additional current article from a credible resource, either from the Walden Library or the Internet. Please be specific, and remember to use citations and references as necessary.
General Guidance: Your initial Discussion post, due by Day 7, will typically be 3–4 paragraphs in length as a general expectation/estimate. Refer to the rubric for the Week 1 Discussion for grading elements and criteria. Your Instructor will use the rubric to assess your work.
Week 2
By Day 3
In your Week 1 Discussion you described how business information systems have been applied in an organization with which you are familiar. Read through your colleagues' posts and by Day 3 (Week 2), respond to two of your colleagues in one or more of the following ways:
· Examine how the business information systems described by your colleague could be or are being used by your organization. Offer additional ways either organization might take advantage of these systems.
· Examine how the b ...
If you’re learning data science, you’re probably on the lookout for cool data science projects. Look no further! We have a wide variety of guided projects that’ll get you working with real data in real-world scenarios while also helping you learn and apply new data science skills.
The projects in the list below are also designed to help you get a job! Each project was designed by a data scientist on our content team, and they’re representative examples of the real projects working data analysts and data scientists do every day. They’re designed to guide you through the process while also challenging your skills, and they’re open-ended so that you can put your own twist on each project and use it for your data science portfolio.
You can complete each project right in your browser, or you can download the data set to your computer and work locally! If you work on our site, you’ll also be able to download your code at any time so that you can continue locally, or upload your project to GitHub.
The sky is the limit here and what you decide to look into further is completely up to you and your imagination!
1. Learning by Doing
Learning by doing refers to a theory of education expounded by American philosopher John Dewey. It is a hands-on approach to learning, meaning students must interact with their environment in order to adapt and learn. This way of learning sharpen your current skills and knowledge and also helps in gaining new skills that could only be acquired by doing.
Car driving is a perfect example of this, you can read as much as you would like about the theory of driving and the rules, and this is very important, and the more you understand the theory the better you get in the practical part. But you will only be able to drive better by applying this knowledge on the real road. In addition to that, there are some skills and knowledge that will be only gained by actually driving.
Data science is the same as driving. It is very important to have solid theoretical knowledge and to regularly increase them to be able to get better while working on a project. However, you should always apply this theoretical knowledge to projects. By this, you will deepen your understanding of these concepts and Knowledge, have a better point of view of how they work in a real-life, and will also show others that you have strong theoretical knowledge and are able to put them into practice.
There are different types of guided projects. One of them is a guided project for
There are a lot of benefits for it:
It removes the barriers between you and doing projects
Saves you much time thinking about the project and preparing the data.
It allows you to apply the theoretical knowledge without getting distracted by obstacles.
Practical tips that can save your effort and time in the future.
#datasciencefree
#rohitdubey
#teachtechtoe
#linkedin.com/in/therohitdubey
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
The Systems Development Life Cycle Moderate and large firms with uni.pdfarwholesalelors
The Systems Development Life Cycle Moderate and large firms with unique information needs
often develop information systems in-house. That is to say that information technology (IT)
professional within the firm design and program the systems. A greater number of smaller
companies and large firms with relatively standardized information needs opt to purchase
information systems from software vendors. Both approaches represent significant financial and
operational risks. a model for reducing this risk through careful planning, execution, control, and
documentation of key activities.
The five phases of this model are:
1) Business Needs and Strategy
Systems Strategy –Assess Strategic Information Needs –Develop a Strategic Systems Plan
–Create an Action Plan
2) Project Initiation –
Systems Analysis –Conceptualization of Alternative Designs –Systems Evaluation and Selection
3.) In-House Systems Development –Construct the System –Deliver the System
4). Commercial Packages –Trends in Commercial Packages –Choosing a Package
5) Maintenance and Support
The participants in systems development can be classified into three broad groups: systems
professionals, end users, and stakeholders. Systems professionals are systems analysts, systems
designers, and programmers. These individuals actually build the system. They gather facts
about problems with the current system, analyze these facts, and formulate a solution to solve the
problems. The product of their efforts is a new system. End users are those for whom the system
is built. Many users exist at all levels in an organization. These include managers, operations
personnel, accountants, and internal auditors. In some organizations, it is difficult to find
someone who is not a user. During systems development, systems professionals work with the
primary users to obtain an understanding of the users’ problems and a clear statement of their
needs. As defined in Chapter 1, stakeholders are individuals either within or outside the
organization who have an interest in the system but are not end users. These include accountants,
internal auditors, external auditors, and the internal steering committee that oversees systems
development.
Cost/Time Analysis:
As stated before, the cost/time analysis is an attempt to calculate to what degree the project and
system will meet the objectives. The SDLC must address two topics in its support of this area:
the scope of the analysis and the algorithm for doing it. Cost/Benefit Scope The benefit side of
the analysis should be expressed in quantitative terms wherever possible. Qualitative or
intangible benefits usually are reflections of poorly analyzed tangible benefits. The SDLC should
support the process of quantifying all benefits. On the costs side, the SDLC must address
development costs, installation costs and ongoing operational costs. In doing these calculations it
should differentiate between capital costs and expense costs. Cost/Benefit Algorithm The method
of calcu.
The series of presentations contains the information about "Management Information System" subject of SEIT for University of Pune.
Subject Teacher: Tushar B Kute (Sandip Institute of Technology and Research Centre, Nashik)
http://www.tusharkute.com
Discussion - Weeks 1–2COLLAPSETop of FormShared Practice—Rol.docxcuddietheresa
Discussion - Weeks 1–2
COLLAPSE
Top of Form
Shared Practice—Role of Business Information Systems
Note: This Discussion has slightly different due dates than what is typical for this program. Be mindful of this as you post and respond in the Discussion. Your post is due on Day 7 and your Response is due on Day 3 of Week 2.
As a manager, it is critical for you to understand the types of business information systems available to support business operations, management, and strategy. As of 2013, these include, but are certainly not limited to the following:
· Supply Chain Management (SCM)
· Accounting Information System
· Customer Relationship Management (CRM)
· Decision Support Systems (DSS)
· Enterprise Resource Planning (ERP)
· Human Resource Management
These types of systems support critical business functions and operations that every organization must manage. The effective manager understands the purpose of these types of systems and how they can be best used to manage the organization's data and information.
In this Discussion, you will share your knowledge and findings related to business information systems and the role they play in your organization. You will also consider your colleagues' experiences to explore additional ways business information systems might be applied in your colleagues' organizations, or an organization with which you are familiar.
By Day 7
· Describe two or three of the more important technologies or business information systems used in your organization, or in one with which you are familiar.
· Discuss two examples of how these business information systems are affecting the organization you selected. Be sure to discuss how individual behaviors and organizational or individual processes are changing and what you can learn from the issues encountered.
· Summarize what you have learned about the importance of business information systems and why managers need to understand how systems can be used to the organization's advantage.
You should find and use at least one additional current article from a credible resource, either from the Walden Library or the Internet. Please be specific, and remember to use citations and references as necessary.
General Guidance: Your initial Discussion post, due by Day 7, will typically be 3–4 paragraphs in length as a general expectation/estimate. Refer to the rubric for the Week 1 Discussion for grading elements and criteria. Your Instructor will use the rubric to assess your work.
Week 2
By Day 3
In your Week 1 Discussion you described how business information systems have been applied in an organization with which you are familiar. Read through your colleagues' posts and by Day 3 (Week 2), respond to two of your colleagues in one or more of the following ways:
· Examine how the business information systems described by your colleague could be or are being used by your organization. Offer additional ways either organization might take advantage of these systems.
· Examine how the b ...
If you’re learning data science, you’re probably on the lookout for cool data science projects. Look no further! We have a wide variety of guided projects that’ll get you working with real data in real-world scenarios while also helping you learn and apply new data science skills.
The projects in the list below are also designed to help you get a job! Each project was designed by a data scientist on our content team, and they’re representative examples of the real projects working data analysts and data scientists do every day. They’re designed to guide you through the process while also challenging your skills, and they’re open-ended so that you can put your own twist on each project and use it for your data science portfolio.
You can complete each project right in your browser, or you can download the data set to your computer and work locally! If you work on our site, you’ll also be able to download your code at any time so that you can continue locally, or upload your project to GitHub.
The sky is the limit here and what you decide to look into further is completely up to you and your imagination!
1. Learning by Doing
Learning by doing refers to a theory of education expounded by American philosopher John Dewey. It is a hands-on approach to learning, meaning students must interact with their environment in order to adapt and learn. This way of learning sharpen your current skills and knowledge and also helps in gaining new skills that could only be acquired by doing.
Car driving is a perfect example of this, you can read as much as you would like about the theory of driving and the rules, and this is very important, and the more you understand the theory the better you get in the practical part. But you will only be able to drive better by applying this knowledge on the real road. In addition to that, there are some skills and knowledge that will be only gained by actually driving.
Data science is the same as driving. It is very important to have solid theoretical knowledge and to regularly increase them to be able to get better while working on a project. However, you should always apply this theoretical knowledge to projects. By this, you will deepen your understanding of these concepts and Knowledge, have a better point of view of how they work in a real-life, and will also show others that you have strong theoretical knowledge and are able to put them into practice.
There are different types of guided projects. One of them is a guided project for
There are a lot of benefits for it:
It removes the barriers between you and doing projects
Saves you much time thinking about the project and preparing the data.
It allows you to apply the theoretical knowledge without getting distracted by obstacles.
Practical tips that can save your effort and time in the future.
#datasciencefree
#rohitdubey
#teachtechtoe
#linkedin.com/in/therohitdubey
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
The Systems Development Life Cycle Moderate and large firms with uni.pdfarwholesalelors
The Systems Development Life Cycle Moderate and large firms with unique information needs
often develop information systems in-house. That is to say that information technology (IT)
professional within the firm design and program the systems. A greater number of smaller
companies and large firms with relatively standardized information needs opt to purchase
information systems from software vendors. Both approaches represent significant financial and
operational risks. a model for reducing this risk through careful planning, execution, control, and
documentation of key activities.
The five phases of this model are:
1) Business Needs and Strategy
Systems Strategy –Assess Strategic Information Needs –Develop a Strategic Systems Plan
–Create an Action Plan
2) Project Initiation –
Systems Analysis –Conceptualization of Alternative Designs –Systems Evaluation and Selection
3.) In-House Systems Development –Construct the System –Deliver the System
4). Commercial Packages –Trends in Commercial Packages –Choosing a Package
5) Maintenance and Support
The participants in systems development can be classified into three broad groups: systems
professionals, end users, and stakeholders. Systems professionals are systems analysts, systems
designers, and programmers. These individuals actually build the system. They gather facts
about problems with the current system, analyze these facts, and formulate a solution to solve the
problems. The product of their efforts is a new system. End users are those for whom the system
is built. Many users exist at all levels in an organization. These include managers, operations
personnel, accountants, and internal auditors. In some organizations, it is difficult to find
someone who is not a user. During systems development, systems professionals work with the
primary users to obtain an understanding of the users’ problems and a clear statement of their
needs. As defined in Chapter 1, stakeholders are individuals either within or outside the
organization who have an interest in the system but are not end users. These include accountants,
internal auditors, external auditors, and the internal steering committee that oversees systems
development.
Cost/Time Analysis:
As stated before, the cost/time analysis is an attempt to calculate to what degree the project and
system will meet the objectives. The SDLC must address two topics in its support of this area:
the scope of the analysis and the algorithm for doing it. Cost/Benefit Scope The benefit side of
the analysis should be expressed in quantitative terms wherever possible. Qualitative or
intangible benefits usually are reflections of poorly analyzed tangible benefits. The SDLC should
support the process of quantifying all benefits. On the costs side, the SDLC must address
development costs, installation costs and ongoing operational costs. In doing these calculations it
should differentiate between capital costs and expense costs. Cost/Benefit Algorithm The method
of calcu.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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Algorithmic Thinking_ Basics for Gen Z and Gen Alpha.pdf
1. Algorithmic Thinking: Basics for Gen Z and
Gen Alpha
Home » Generation Alpha » Algorithm thinking for kids
Estimated reading time: 15 minutes
With the world becoming increasingly digital and automated, the younger generations
need to develop algorithmic thinking skills.
Algorithmic thinking focuses on step-by-step processes or algorithms to solve problems.
Artificial Intelligence (AI) is developing incredibly across the United States and other
countries like India, China, and other European countries.
In this blog, we will discuss the meaning of algorithmic thinking and how it differs from
critical thinking, why it is important for Generation Z and Alpha, and the base concepts of
algorithmic thinking.
We will also explore examples of algorithmic thinking in everyday life and its relation to
coding as a tool for problem-solving.
2. Photo by Jeffery Erhunse on Unsplash
What is Algorithmic Thinking?
Algorithmic thinking is looking at problems one step at a time, like following a recipe. It’s
about breaking big problems into smaller parts and figuring out the best way to solve
them. It also means looking for patterns and finding ways to do things faster and smarter.
Through algorithmic thinking, one can become a better problem solver and use creativity
to derive cool solutions to everyday challenges in the digital world. It’s like having a
superpower that helps us think logically and find the most efficient ways to get things
done!
This process applies to various fields, including computer programming, mathematics,
and science. When individuals develop algorithmic thinking skills, they become better at
3. logical reasoning and critical thinking. It also helps them understand how technology
works and how to create new solutions.
Understanding Algorithms
1. Algorithms are step-by-step instructions: Algorithms are like recipes that
tell you exactly what to do in a specific order to solve a problem or
complete a task.
2. They’re everywhere: Algorithms are not just for computers. They are in
everyday life, like following instructions to bake a cake or solving a math
problem using a specific method.
3. Algorithms make things efficient: By following a well-designed algorithm,
you can solve problems faster and more efficiently. It helps you break
down complex tasks into smaller, manageable steps.
4. Identifying patterns: Algorithms often involve recognizing patterns or
similarities in the problem you’re trying to solve. By finding patterns, you
can define a plan to solve the problem.
5. Algorithms in technology: Computers use algorithms to perform tasks.
For example, search engines use algorithms to find relevant information
when you type in a query.
Furthermore,
1. Different algorithms for different problems: There are many ways to solve
a problem, and specific algorithms may work better for precise situations.
So, choose the algorithm for the task at hand.
2. Testing and refining: Algorithms can be tested and improved upon. By
trying different approaches and analyzing their effectiveness, algorithms
get better.
3. Algorithmic decision-making: Algorithms can help make decisions based
on predefined rules and conditions. For example, recommendation
algorithms suggest movies or products based on your preferences.
4. Learning from algorithms: Studying algorithms can improve your
problem-solving skills and critical thinking. You can apply the concepts of
algorithms to various areas of life to find efficient solutions.
5. Practice makes perfect: The more you work with algorithms, the better
you understand and use them. Practice solving different problems using
algorithms to sharpen your skills.
4. Algorithmic Thinking Process Infographic
Algorithmic Thinking Vs Critical Thinking
Algorithmic Thinking Critical Thinking
Focuses on systematic
problem-solving using algorithms and
logical steps.
Emphasizes analyzing, evaluating, and
reasoning to form judgments and make
decisions.
5. Involves breaking down complex
problems into smaller, manageable
steps.
Requires breaking down complex
information or arguments to understand their
components and relationships.
Utilizes pattern recognition to identify
similarities, trends, and sequences in
data or problems.
Encourages identifying patterns,
assumptions, and biases in information or
arguments.
Prioritizes efficiency and optimization
in finding the most effective solutions.
Prioritizes accuracy, coherence, and sound
reasoning in analyzing information and
forming conclusions.
Relies on computational tools, coding,
and algorithm design to solve
problems.
Relies on critical questioning, evidence
evaluation, and logical reasoning to analyze
and solve problems.
Used extensively in computer science,
data analysis, and automation.
Used in various domains, including research,
decision-making, and problem-solving in
diverse fields.
Enables automation, predictive
modeling, and efficient data
processing.
Promotes informed decision-making,
effective problem-solving, and informed
judgments.
Can be learned through studying
algorithms, coding, and computational
thinking.
Can be developed through practice, logical
reasoning, and exposure to diverse
perspectives.
Encourages a structured, step-by-step
approach to problem-solving.
Encourages open-mindedness, skepticism,
and considering alternative perspectives.
Applies well to repetitive or
deterministic problems with clear
rules and constraints.
Applies to complex and ambiguous problems
that require analysis, interpretation, and
evaluation.
Table explaining Critical Thinking Vs Algorithm Thinking
6. Why is Algorithmic Thinking important for Gen Z and Gen
Alpha?
Generation Z and Alpha must develop the ability to break down complex problems into
smaller, more manageable steps. People with algorithmic thinking skills are better
equipped to navigate the technological landscape of the 21st century.
In addition to honing logical, critical, and analytical skills, algorithmic thinking
encourages creativity and innovation in fields such as science and engineering.
Benefits of Algorithmic Thinking for Gen Z and Gen
Alpha:
1. Enhanced problem-solving skills
2. Improved logical reasoning
3. Development of computational thinking
4. Fostered creativity and innovation
5. Increased digital literacy
6. Future-proof skills
7. Automation and efficiency
8. Data literacy and analysis
9. Adaptability and agility
10. Empowerment and independence
Algorithmic Thinking: Basics for Gen Z and Gen Alpha (Concepts)
Gen Z and Gen Alpha can understand the algorithms working nature and apply them to
real-world problems by mastering the below concepts.
Furthermore, it will enhance their problem-solving abilities and prepare them for future
job opportunities related to computer science, data analysis, machine learning,
computational methods, and artificial intelligence.
Basic concepts of Algorithmic thinking include the below
1. Abstraction
2. Pattern recognition
3. Decomposition
4. Algorithm design
5. Efficiency and optimization
7. 6. Logical and sequential thinking
7. Algorithm evaluation
8. Iterative problem-solving
Abstraction:
1. Identify essential aspects: Determine the core elements of a problem,
focusing on the key factors for the solution.
2. Simplify complexity: Break down the problem into its fundamental
components, disregarding unnecessary details.
3. Generalize the solution: Create a generalized representation or model that
captures the underlying principles for applying the solution to similar
situations.
Pattern Recognition:
1. Analyze data/problem: Examine information or problem to identify
recurring patterns, similarities, or trends.
2. Extract the Pattern: Determine the common characteristics or
relationships.
3. Apply the Pattern: Use the recognized pattern to guide the solution
process or make predictions based on the identified relationships.
Decomposition:
1. Identify the main problem: Understand the overarching problem or task
that needs to be solved.
2. Break down into subproblems: Divide the main problem into smaller,
manageable subproblems.
3. Solve each subproblem: Focus on solving each subproblem independently,
considering their contribution to solving the main problem.
Algorithm Design:
1. Define the problem: Understand the requirements, constraints, and desired
outcomes.
2. Design logical steps: Determine the sequence of steps needed to solve
the problem.
3. Refine and optimize: Continuously improve the algorithm, considering
efficiency and reducing complexity.
Efficiency and Optimization:
8. 1. Analyze problem/task: Understand the resources, time, or steps involved.
2. Identify bottlenecks: Identify areas causing delays or inefficiencies.
3. Optimize the solution: Improve the algorithm to minimize resource usage,
reduce touchpoints and increase speed without compromising quality.
Logical and Sequential Thinking:
1. Establish logical flow: Determine the sequence of actions required to
reach the desired outcome.
2. Follow predetermined order: Execute steps in the established order,
building upon the previous.
3. Maintain consistency and coherence: Ensure decisions align with the
logical flow and contribute to the overall goal.
Algorithm Evaluation:
1. Determine evaluation criteria: Define factors for assessing the algorithm’s
effectiveness, such as accuracy, speed, and resource usage.
2. Test the algorithm: Execute the algorithm using test cases or real-world
scenarios.
3. Analyze and refine: Evaluate results, make necessary adjustments, and
improve the algorithm’s effectiveness and efficiency.
Iterative Problem-Solving:
1. Start with an initial solution: Develop a solution based on available
knowledge.
2. Test and evaluate: Implement the solution, gather feedback, and check
effectiveness.
3. Refine and iterate: Make adjustments and improvements based on
evaluation, repeating the process until you reach an optimal solution.
Automation and Computational Tools:
1. Identify automation tasks: Recognize repetitive or time-consuming tasks
suitable for automation.
2. Select appropriate tools: Choose relevant computational tools or
programming languages.
3. Implement and integrate automation: Utilize selected tools to automate
tasks, integrating them into the workflow for improved efficiency and
productivity.
9. Stay connected with Hoomale to access free articles about Generation Alpha,
Corporate Culture, Emerging Tech, Thought Leadership, and Human Behaviour.
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Real Life Examples of Algorithmic Thinking
Algorithmic thinking has unlimited applications, from computers and technology to our
daily lives. From following a recipe to using navigation apps, Algorithmic Thinking is
ubiquitous in our everyday activities. For instance,
1. Following a recipe: When cooking a meal, following a recipe involves
following a step-by-step process, understanding the order of ingredients
and instructions, and making adjustments based on personal preferences
or dietary restrictions.
2. Solving a Rubik’s Cube: Solving a Rubik’s Cube requires analyzing the
patterns and relationships of the colored squares, breaking the problem
down into smaller steps, and following a set of algorithms to solve each
layer.
3. Planning a daily schedule: Organizing your daily activities involves
prioritizing tasks, determining the most efficient order to complete them,
and optimizing your time by considering dependencies and deadlines.
4. Playing a musical instrument: Learning to play a musical instrument
involves breaking down a piece of music into smaller sections, practicing
each section separately, and gradually combining them to play the entire
bit.
5. Searching for information online: When conducting an online search,
algorithmic thinking comes into play as search engines use algorithms to
analyze your query, rank and filter relevant results, and display them based
on relevance and popularity.
6. Solving a math problem: Applying algorithmic thinking to solve a math
problem involves identifying the problem’s key components, breaking it
down into smaller steps, and systematically applying mathematical
operations or formulas to reach a solution.
7. Building with LEGO bricks: Creating structures with LEGO bricks requires
following instructions that outline the sequential steps to assemble the
pieces, understanding the spatial relationships, and making adjustments
based on the desired outcome.
8. Planning a travel route: When planning a trip, algorithmic thinking helps
determine the efficient way of considering factors like distance, traffic
patterns, and possible stops.
10. 9. Playing chess: Chess involves thinking several moves ahead, analyzing
the potential consequences of different actions, and using strategic
algorithms to make optimal decisions and outmaneuver opponents.
10. Debugging a computer program: When debugging a program, algorithmic
thinking helps to identify and isolate the source of an error, systematically
analyze the code, and apply logical steps to fix the issue
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Algorithmic Thinking and Coding for Kids
Learning algorithmic thinking and coding can help children develop logical reasoning,
problem-solving skills, creativity, and adaptability. These skills are valuable for future
careers in technology and for a profession that requires analytical thinking and
problem-solving abilities.
As more industries become digitized, algorithmic thinking and coding will be
fundamental skills needed in the workforce.
Resources are available for children to learn algorithmic thinking and coding, including
online courses, coding camps, and educational games.
Here is a list that you can try,
● Enroll Now for Design Patterns Certification Training By Edureka and
increase your chances to get hired by Top Tech Companies ”
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● Enroll Now for React with Redux Certification Training By Edureka and
increase your chances to get hired by Top Tech Companies ”
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Solving Strategies to Develop Algorithmic Thinking Skills
in Children
Developing algorithmic thinking skills requires a multifaceted approach that involves
practice, foundational knowledge, and collaboration.
11. In addition to problem-solving, developing a strong foundation in math and logic is also
essential for algorithmic thinking.
It involves learning the mechanics of algorithms through computer programming
languages, such as Python or Java.
Engaging in activities that require critical thinking skills, such as puzzles or games, can
further help improve algorithmic thinking abilities.
Collaborating with others on projects can also provide valuable insights and
perspectives for problem-solving.
Gamification and Puzzles
Gamification and puzzles are effective ways to develop algorithmic thinking skills in
children. Kids can learn valuable skills while having fun by turning problem-solving into a
game. Games like Minecraft, Scratch, and CodeCombat can help kids learn programming
concepts in a fun and engaging way.
Furthermore, these games allow players to experiment with code and see the real-world
outcomes of their actions, fostering an understanding of how algorithms work.
Puzzles such as Sudoku, Rubik’s Cube, and logic puzzles can improve problem-solving
and critical thinking skills. These activities require careful analysis of patterns and
relationships, key components of algorithmic thinking.
Project-based Learning
Project-based learning is a powerful teaching method that enables students to develop
algorithmic thinking skills through hands-on projects. Students can apply the algorithmic
concepts they have learned in a practical setting by working on real-world problems.
Furthermore, this approach allows them to explore and innovate solutions using
algorithms. Project-based learning also encourages critical thinking, problem-solving
skills, and collaboration among students.
Teachers can guide their students through project-based learning by providing support
and feedback.
Moreover, teachers can motivate their students by showing them how algorithmic
thinking skills applies in various fields such as computer science, engineering, and
mathematics.
12. Computational Thinking Tools and Resources
Computational thinking tools have become essential for developing algorithmic thinking
skills in young learners.
Also, these tools provide interactive and engaging platforms for learning programming
concepts and help students understand the practical applications of algorithmic thinking
in various fields such as computer science, engineering, and mathematics.
Furthermore, Scratch, Code.org, and Khan Academy are popular computational thinking
resources that offer problem-solving challenges to enhance a child’s algorithmic thinking
abilities.
So, parents and educators can use these tools to supplement traditional classroom
instruction and encourage independent learning.
Check the below resources for a literature review:
● ScienceDirect
● Department Of Education
● ResearchGate
● MDPI
Common Challenges in Developing Algorithmic Thinking
Skills
Challenges are everywhere when we intend to learn something new. It must not slow us
down. Here are some common roadblocks.
● Lack of Access to Technology
● Resistance to Change in Educational Systems
● Limited Exposure to Real-World Problems
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Frequently Asked Questions
How can algorithmic thinking be applied in everyday life?
Algorithmic thinking applies in fields like finance, engineering, and healthcare for data
analysis and decision-making. Practicing algorithmic thinking can improve critical
thinking skills and prepare individuals for tech, software engineering, and STEM fields.
So, it is an essential skill that can enhance problem-solving abilities and overall
effectiveness in various areas of life.
What are some examples of algorithms that we use in our daily routines?
Algorithms are everywhere, and we use them daily without even realizing it. Examples
include recipes for cooking, Google’s search algorithm for finding information online,
social media algorithms (Facebook, Instagram, TikTok) that determine what content we
see in our feeds, and navigation apps like Google Maps or Waze that use algorithms to
determine the fastest route to our destination.
How can learning algorithmic thinking benefit future career prospects?
Learning algorithmic thinking can benefit future career prospects by developing critical
thinking skills, which are increasingly important in many industries like technology,
finance, and healthcare. Knowledge of algorithms and programming languages can be
valuable in various job roles.
Furthermore, algorithmic thinking can help individuals solve complex problems
efficiently, improving productivity and job performance. As technology advances, the
ability to think algorithmically will become even more critical in the workforce.
What is algorithmic thinking, and why is it important?
Algorithmic thinking is a problem-solving process that involves breaking down complex
problems into smaller, more manageable steps. It helps develop critical thinking skills
14. and improves problem-solving abilities.
This thinking applies to various fields like computer science, engineering, mathematics,
and everyday life. Individuals can approach problems logically and systematically,
leading to more efficient and effective solutions by mastering algorithmic thinking.
What is the learning style of Gen Alpha?
Gen Alpha, born after 2010, is still young. Their learning style is not fully understood yet.
However, they are known to be digital natives or digital handshakers comfortable with
technology from a very young age. They prefer interactive and visually stimulating
educational content over traditional lectures.
To adapt to the needs of Gen Alpha, educators should consider incorporating technology
and interactivity in their teaching methods. It could include using educational apps or
games and providing hands-on activities encouraging exploration and experimentation.
Are Gen Z and Gen Alpha the same?
No, Gen Z and Gen Alpha are not the same. Gen Z refers to individuals born between
1996 and 2010, while Gen Alpha refers to those born after 2010. Both generations are
considered digital natives and are growing up with technology.
Understanding algorithmic thinking is necessary for both generations as technology
continues to shape our world. However, there are distinct differences between the two
generations that we must look at when it comes to marketing and communication
strategies.
What skills do you need for Generation Alpha?
To succeed in the future, members of Generation Z and Alpha will need a range of skills,
including critical thinking, problem solving, digital literacy, technological proficiency,
communication, collaboration, deep learning, adaptability, emotional intelligence,
ethics, and empathy.
These skills will be essential for navigating an increasingly complex world evolving due
to technology and global challenges. As such, it is necessary to foster these skills to
ensure that individuals understand what lies ahead.
Remember, you are in an exciting era of progress that the previous generations could not
explore.
How can I learn more about algorithms?
There are many ways to learn more about algorithms. You could consider taking an
online course or enrolling in a computer science degree program. Reading books on
algorithms, such as “Introduction to Algorithms” by Cormen, Leiserson, Rivest, and Stein,
15. can also be helpful.
Moreover, participating in coding challenges and competitions is another great way to
practice using algorithms.
Finally, listening to millennials and following blogs and social media accounts of experts
in the field can provide you with updates and insights on algorithmic thinking.
Conclusion
As the world relies on technology, algorithmic thinking is essential for individuals of all
ages.
Generation Alpha and Gen Z will need many skills to succeed in future higher education,
including critical thinking, problem-solving, digital literacy, machine learning, and
technological proficiency.
Moreover, learning about algorithms can be done through online courses, degree
programs, reading books, participating in coding challenges and competitions, and
following experts in the field on social media.
So, you can be better prepared for what lies ahead by fostering these skills. Stay updated
on algorithmic thinking by subscribing to relevant blogs and social media accounts.
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