Questions on Computer networking

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

Title: The Adventures of Max and the Magical Computer (Shortened Version) Once upon a time, in a small town called Techville, there lived a curious fourth-grader named Max. One day, while exploring his grandma’s attic, he found an old, dusty computer. As he cleaned it, the screen lit up, and a cheerful voice said, “Hello, Max! I’m Compy, your magical computer. Let’s learn about operating systems, files, and folders!” Max was thrilled. “A talking computer? Let’s go!” Chapter 1: The World of Windows Compy explained, “I run on Windows, the brain of the computer. It uses a Graphical User Interface (GUI), so you can interact with me using icons, menus, and buttons. Let’s start by changing my desktop background—the image on the screen.” Max chose a spaceship picture. “Cool! Can I add a screen saver too?” “Of course!” said Compy. “It’s an image that appears when I’m inactive. Try this swirling galaxy!” Max set the screen saver and giggled as it appeared. “This is fun!” Chapter 2: Organizing with Folders and Files Compy’s screen filled with random icons. “Oh no! My files are a mess. Can you help?” “Sure! What are files and folders?” asked Max. “A file is information, like a picture or document. Files have names, like ‘Homework.docx.’ The part after the dot, like .docx, is the file extension. It tells you the file type,” Compy explained. Max pointed to “Game.exe.” “So, this is a program file?” “Yes!” said Compy. “To organize, we use folders—like drawers for files. You can even put folders inside folders!” Max created a “School” folder, added his homework files, and made a “Projects” folder inside it. “Now everything’s neat!” Chapter 3: The File Explorer Adventure Compy’s screen flickered. “Some files are missing. Let’s use Windows Explorer to find them. It’s like a map for files and folders.” Max opened Windows Explorer and saw a tree-like list of folders. “This is like a tree with branches!” “Exactly!” said Compy. “Search for the missing files and move them to the right folders.” Max found the files in “Downloads” and moved them. “I feel like a computer detective!” Chapter 4: The Final Challenge Compy’s screen turned into a game board. “Time for a quiz! What’s the purpose of an operating system? How do you change the desktop background? What’s the difference between a file and a folder?” Max answered all the questions correctly, and fireworks lit up the screen. “Congratulations, Max! You’re a computer whiz!” The End Max smiled. “Thanks, Compy! I can’t wait to teach my friends!” Compy replied, “Remember, Max, learning is an adventure. Keep exploring!” As Max turned off the computer, he knew his journey into technology had just begun.

Got it ✅ I’ll create 4 simple sections of assignments based on these two pages (1.1 What is a network? and 1.2 Network connections). Total marks = 25. Suitable for Oxford International Primary Computing Grade 5. --- Assignment – Computer Networks Grade 5 – Total Marks: 25 --- Section A: Choose the Correct Answer (6 marks) Circle the correct option. Each question = 2 marks. 1. A group of computers connected together is called a: a) Website b) Network c) Software 2. Which type of network connects computers inside a school? a) LAN b) WAN c) Wi-Fi 3. To use a network safely, you need: a) A strong password b) A printer c) A hotspot --- Section B: Fill in the Blanks (6 marks) Each correct answer = 2 marks. 1. We can use a network to share files and ________. 2. A wireless connection is also called ________. 3. Network ________ help devices work together. --- Section C: Short Answer Questions (7 marks) 1. Write two advantages of using a network. (2 marks) 2. What is the difference between a wired connection and a wireless connection? (2 marks) 3. Write one rule to keep your password safe. (1 mark) 4. Create a strong password example using numbers and symbols. (2 marks) --- Section D: Activity / Problem Solving (6 marks) 1. Web Valley School has 60 network connections. Each cable is 50 meters long. How many meters of cable are used in total? (2 marks) 2. Convert your answer into kilometers. (2 marks) 3. Why is it important to hide or lock network equipment? (2 marks) --- ✅ Total = 25 marks --- Would you like me to also make a teacher’s marking guide with answers and mark distribution?

Good morning. My name is Pedro. I'm a new student. Can I ask you some questions? Yes! Is the library on the fourth floor? No, the library is on the fifth floor. It's next to the cafeteria. What about the computer lab? The computer lab is on the sixth floor. It has a red door. Wow, thank you, guys! You are very helpful. See you around! You're welcome, Pedro. We're happy to show you around!

We found you were making a quiz on the subject of "What is a rubric? A tool comprising a set of criteria (with possible levels of performance quality on the criteria) developed to assess learners’ work, from written to oral to visual. It is used tomeasureperformance,suchastheprocess of doing something (e.g.,playing a musical instrument, making a speech) or products of the learners’ work (e.g., concept map, laboratory report, bookshelf) (Brookhart, 2013). BENEFITS OF USING RUBRICS Support authentic assessment Reflects how well learners are able to apply knowledge inthe real-world context. Communicate expectations Gives learners an idea of what is expected of them. It is especially useful when the rubrics are communicated to the learners before they are assessed. Improve performance Explicit criteria and performance level descriptions allow learners to understand the desired performance. Learners are able to assess themselves by referring to the specific criteria and performance-level descriptions. Provide informative feedback Instructors are able to provide constructive feedback to learners on their weaknesses and strengths. Promote thinking andlearning 4 Provide informative feedback Instructors are able to provide constructive feedback to learners on their weaknesses and strengths. Learners are able to review and revise their work,thus reflecting on their learning experiences. Ensure fairness Learner performance assessed fairly given its objectivity. It helps avoid disputes between learners and instructors about the scores/grades achieved. TYPES OF RUBRIC ANALYTIC It consists of individual criterion with corresponding descriptor of performance. HOLISTIC It consists of performance descriptors that are placed together to refeclet to overalll performance. ANATOMY OF ANANALYTIC RUBRIC Rating scales with corresponding scores or weights The row represents the criteria for the desired performance, while the column represents the evaluation score. Under the rating scale (corresponding weights orscorescanbeassigned),theperformance descriptors are explicitly stated ANATOMYOF AHOLISTICRUBRIC Descriptions: It comprises the rating scale (corresponding weights or scores can be assigned) in the row while the combined desired performance descriptors are placed in the column. Description of the task The purpose of the assignment is to assess learner’s cognitive and analytic skills in applying knowledge gained and constructed throughout the course Diffusion of Innovation,bywatching the Surrogates movieand writing ananalytical review of the movie in the context of innovation diffusion.Iwant to provide learners with informative feedback on their cognitive and analytic skills such as the following: applying the concepts of innovation diffusion,making judgmentson the scenes related to innovation diffusion identified from the movie,selecting and critiquing theories of innovation diffusion and making connections between the theories,aswell asarguingand proposing necessary solutions to the problemss hown in the movie. ESTABLISHING ALTERNATIVEASSESSMENTINHIGHEREDUCATION VALIDITYAND RELIABILITYOF RUBRICS. Validity Measuring what is supossedto be measured. Reability Yielding consists results. Instruments that are used in the alternative assessment must be aligned to the learning outcomes and measure well what it intends to measure (valid) and produce consistent scores (reliable). The valid instrument will manifest the true ability (latent trait) of learners and permit appropriate inferences to be made about a specific group of people for specific purposes. TYPES OF VALIDITY FACE VALIDITY Simple form of validity thatapplies a superficial and subjective assessment whether the instrument measures what it is supposed to measure. CONTENT VALIDITY Refers to the extent to which the items on a measure assess the same content or how wellthe content material was sampled inthe measure. CONSTRUCT VALIDITY Refers to the extent to which the test may be said to measure a theoretical construct or trait. CONCURRENT VALIDITY Refers to the extent to which scores onanewmeasure are related to scores from a criterion measure administered at the same time. PREDICTIVE VALIDITY Refers to the uses of the scores from the new measure to predict performance on a criterion measure administered ata later time. STEPS TO CONSIDER WHEN ESTABLISHING CONTENT VALIDITY Calculate the level of expert agreeement for the content validity, get expert to verfy. Interview the expert ,make meta contentdata análisis from literatura. STEPS TO CONSIDER WHEN ESTABLISHING CONSTRUCCT VALIDITY Administer the instrument for alll learners, revise any item necccesay, run an apropriates statistical analiysis, administerthe instrument to learners as a pilot test . CONSTRUCTMAP Morepreciseconceptthan construct. Ranges from one extreme to another(fromhightolow,small tolarge,positivetonegative,or strongtoweak). Identifiesthepositionofthe respondentsinthisrange. Representativenessofsampling (questions and ability of respondents). EXAMPLEO FACONSTRUCTMAP:AFFECTIVE LEVELOF AFFECTIVE VARIABLES EXAMPLESOFITEMSIN MEASURINGTEAM WORKING SKILLS 5. Characterisation Learnersvolunteerstodothe groupworks. 4. Organisation Learners are willing to help others,althoughitisnottheir scopeoftask. 3. Valuing Learners respect other team members’opinionwhendoing thediscussion. 2. Responding Learnergivescooperationwhen neededingroupworks. 1. Receiving Learneracceptsthediversityof races and nationalities among groupmembers. EXAMPLEOFACONSTRUCTMAP:PSYCHOMOTOR LEVELOF PSYCHOMOTOR VARIABLES EXAMPLESOFITEMSIN MEASURING DIGITAL SKILLS 7.Origination Learnerscanmodifytheirowndevicesto performbetter. 6.Adaptation Learnerscansolveandtroubleshootthe problemwhileusingthecomputer. 5.ComplexOvertResponse Learnerscanusethecomputercompetently. 4.Mechanism Learners can use the computer independently,butstillmakeminorerrors. 3.GuidedResponses Learnerscanusethecomputer,butstill needguidance. 2.Set Learnersarereadytousethecomputer. 1.Perception Learnerscanobservehowtousecomputer. EXAMPLEOFACONSTRUCTMAP:COGNITIVE LEVELOF COGNITIV E VARIABLES EXAMPLESOFITEMS IN MEASURING THINKINGSKILLS 6. Creating Learners are able to suggest anewmodelorframeworkof learningdigitalcommunity. 5. Evaluating Learners are able to judge the impactofthescenariotowards educationperspective. 4. Analysing Learnerscandifferentiate the factsusingafew theories. 3. Applying Learnerscansolveproblems usingthefactsgiven. 2. Understanding Learnersareabletoexplainthe factsusingtheirownwords. 1. Remembering Learnersonlymemorisethe. Direction of Increasing “X” Learners Learners with high “X” Learners with mid range “X” Learners with low “X” Responses to Item Item response indicate highest level of X Item response indicate higher level of X Item response indicate lower level of X The construct map shows the lower ability students are in line with the lower level of items. This shows that when educators plan to develop an instrument, it Item response indicate lowest level of X Direction of Decreasing “X” is crucial to create an item difficulty thatrepresents learners’ ability. Learners’ ability Learners who engage in level characterisation Learners who engage in level organisation Learners who engage in level valuing Learners who engage in level responding Learners who engage in level receiving Direction of Decreasing“X” MEASURINGCONSTRUCTVALIDITY Unlike content validity, this construct validity can be analysed using statistical analysis. Use Exploratory FactorAnalysis [EFA], Confirmatory FactorAnalysis [CFA] or Unidimensionality to confirm all items are measuring the right construct and the raw variance explained for the latent variables is sufficient. Gap initem map also can show accuracy in construct validity. RELIABILITY The degree to which test scores are consistent over repeated administrations of the same/ equivalent test and therefore considered dependable and repeatable for an individual learner.A test thatproduces highly consistent and stable results (i.e. relative free from random error) is said to be highly reliable. TYPESOFRELIABILITY Test-retest demonstrates the stability of a measure over time 01 Internal consistency most of the items within a rating scale of a concept show consistency of scoring. Inter-rater the extent to which two or more independent raters are consistent in observing, recording and scoring data (should be 70% or higher agreement) 04 Intra-rater relies on one rater to rate an object or event twice (70% or higher of agreement) FACTORSAFFECTING VALIDITYANDHOWTO INCREASEVALIDITY? FACTORS AFFECTING VALIDITY HOWTO INCREASE VALIDITY? 1. Inaccuracy of items in measuringtheoutcomes 1. Vetting session to get reviewsfromtheexpert. 2. Pooritemsdevelopment 2. Followtheformatandtips indevelopinggooditems. 3. Unclearinstructions 3. Do pilot testing to measuretheusabilityof thetest. 4. Interveningevents 4. Controltheinternalthreats validityfactors. 5. Itemsdifficultyisnot suitableforthelearners 5. Create a construct map toensurethereisanitem thatrepresentslearners ability. FACTORS AFFECTING RELIABILIT Y HOWTOINCREASERELIABILITY? 1. TestLength 1. Thetestlengthshouldbeappropriate withtestdifficulty. 2. Test retest interval 2. Suggesteddurationisbetween3 weeksto2months. 3. Variability of scores 3. Doconstructmaptoensuretheitems aresuitablewithlearners’ability. 4. Guessing 4. Penalisetheguessinganswers.You alsocandetecteitherthelearnersare guessing or not using the statistical analysis named guessing analysis andpersonfitanalysis. 5. Inconsistency score from different raters 5. Appointtheratertomarkcertain questionsforalllearners(Thisalways happen when you have more than onesectionandhavemorethanone lecturer). CONCLUSION Coming back to the issue of validity and reliability in assessment, there is a need for educators to put an effort to ensurethattheitemsintheformofquestionsorinstructions arenotonlyclearbutalsoabletomeasurewhatitisintended tomeasurebasedontherelatedlearningoutcomes. Establishingvalidityandreliabilityofinstrumentscan provide educators with some indications of the quality of the measuring tools being used. Valid and reliable instruments enabletheeducatorstocontinuouslyusethemeasuringtools withoutreservation. Reliablenot valid Precisenot Accurate Reliableand valid Preciseand Accurate NotReliable butvalid NotPrecisebut Accurate NotReliable butNotvalid NotPrecisebut NotAccurate 94 ". Would you like to continue making it or start afresh?

In this video we take a look at the 0:02 fetch to code 0:03 execute cycle including its effect on 0:06 the various registers we've previously 0:12 [Music] 0:14 discussed a computer is defined Definition 0:17 as an electronic device that takes an 0:20 input 0:22 processes data 0:25 and delivers output 0:29 in this simple example you can see we're 0:31 taking the input 5 0:35 we're multiplying it by 2 that's our 0:37 process 0:39 and we're outputting 10. 0:44 but this could be way more complex for 0:46 example of a game console 0:48 the input could be the buttons you press 0:50 on a controller 0:53 the processes would then be carried out 0:55 by the console itself 0:59 and the output would be some form of 1:01 update to a monitor 1:02 and sound out for a speaker possibly 1:04 vibration feedback through the 1:06 controller 1:10 to process data a computer follows a set 1:13 of instructions 1:14 known as a computer program 1:18 if we take the lid off a typical desktop 1:20 computer we can identify 1:22 two critical components the memory 1:26 that stores the program and the central 1:29 processing unit or processor 1:31 which is under this large fan and 1:33 carries out the instructions 1:37 a computer carries out its function by 1:40 fetching 1:41 instructions decoding them and then 1:43 executing them 1:44 in a continuous repetitive cycle 1:46 billions of times a second 1:48 let's look at each of these stages in a 1:50 little more detail Fetch 1:53 so let's start with the fetch stage the 1:55 very first thing that happens 1:57 is the program counter is checked as it 2:00 holds the address 2:01 of the next instruction to be executed 2:07 the address stored is then copied into 2:09 the memory address register 2:14 the address is then sent along the 2:16 address bus to main memory 2:18 where it waits to receive a signal from 2:21 the control 2:22 bus so it knows what to do 2:27 as we want to read the data that's 2:29 stored in memory address 2:30 0 0 0 0 the control unit sends 2:34 a read signal along the control bus to 2:36 main memory 2:41 now main memory knows the data needs to 2:44 be read 2:45 the content stored in memory address 000 2:49 can be sent along the data bus to the 2:51 memory data register 2:56 now as we're currently in the process of 2:58 fetching an instruction 3:00 the data received by the memory data 3:03 register gets copied 3:04 into the current instruction register 3:11 the instruction effectively has now been 3:14 fetched from memory 3:16 just before we proceed to the decode 3:18 phase we now 3:19 increment the program counter so that 3:22 the address it contains 3:24 points to the address of the next 3:26 instruction which will need to be 3:30 executed 3:32 the instruction now being held in the 3:33 current instruction register 3:35 is ready to be decoded 3:39 now as we mentioned in the previous 3:41 video the instruction is made up of two 3:43 parts 3:44 we have the op code that's what it is we 3:47 need to do 3:50 and we have the operand what are we 3:53 going to do it to 3:55 now the operand could contain the actual 3:57 data 3:58 or indeed it could contain an address of 4:01 where the data is to be found 4:06 by decoding this instruction we can see 4:08 the operation we need 4:10 is a load operation so we need to load 4:14 the contents of memory location0101 4:18 into the cpus accumulator 4:25 in the exam a simple model will be used 4:27 to describe the 4:29 structure of any given instruction 4:32 you're not going to be expected to 4:34 define how an opcode is made up 4:36 but simply to interpret opcodes in the 4:39 given context of an exam 4:40 question in the example here 4:44 you can see there's a total of 16 4:46 different opcodes available 4:48 and this is because we're using four 4:50 bits for our representation 4:56 so now we've fetched the instruction and 4:59 we've decoded it so we know what we need 5:00 to do 5:01 we're finally ready to execute it 5:05 so we now send address 0101 5:08 to the memory dress register 5:13 now we're in the memory address register 5:15 we can finally send the address 5:18 down the address bus to main memory 5:24 this time we want to read the data 5:26 that's stored in memory 5:28 and so the control unit again sends a 5:30 read signal along the control bus 5:36 so main memories now receive an address 5:38 and a read signal 5:40 so the content stored at memory location 5:43 0101 5:44 can now be sent along the data bus back 5:46 to the cpu 5:47 and into the memory data register 5:54 finally the contents of the memory data 5:56 register are copied to the accumulator 5:59 and this is one of a number of general 6:00 purpose registers found in the cpu 6:04 this first instruction is now complete Branching 6:11 so what does this program actually do 6:14 you should be able to work it through 6:16 carefully and figure it out 6:19 we're now pointing instructions zero 6:21 zero zero one in the program counter 6:23 and we're ready to fetch the second 6:25 instruction 6:27 at the end of this video we're gonna 6:29 provide you with the answer 6:34 so let's talk a second about programs 6:37 that branch 6:40 on the left here we have a very simple 6:42 piece of pseudo code 6:44 line zero says first execute this line 6:46 of code 6:47 line 1 now execute this line and then 6:50 line 2 says 6:52 if the age is greater than 18 then 6:56 we're going to execute lines 3 and 4 6:58 otherwise 6:59 we're going to execute lines six and 7:02 seven 7:03 so this program doesn't necessarily 7:05 follow strictly in sequence from line 7:07 zero through to seven there's a chance 7:10 here the program may branch and jump 7:14 around 7:16 so we're going to pretend that this 7:17 program has been loaded into memory 7:20 each line of code on the left here has 7:23 ended up 7:24 as a location in memory now this is not 7:27 strictly how this would happen in this 7:28 one-to-one way 7:29 but for the purpose of example it's 7:31 absolutely fine 7:35 so the program counter starts by 7:37 pointing to memory address zero 7:39 and we fetch the first instruction 7:41 decode it and execute it 7:44 it then updates and tells us the next 7:47 instruction 7:48 is zero zero zero one because remember 7:50 the program counter is being incremented 7:52 so we fetch it decode it and we execute 7:55 line one of our program 7:59 we then fetch line two which in binary 8:01 is one 8:02 zero 8:06 now at this point depending on what 8:10 happens during the execution 8:11 of line two the program may be required 8:15 to fetch line three from memory or 8:18 line five from memory 8:25 so let's look at how this actually works 8:27 because we've said the program counter 8:28 simply gets incremented 8:31 well in the current instruction register 8:33 we have an instruction with the op code 8:36 0 1 1 0. 8:41 now when we look this up in the decode 8:43 unit we discover that this 8:45 code means branch always 8:51 this replaces the value held in the 8:54 program counter 8:56 with the contents of the operand that's 8:58 the second part of the instruction 9:01 from the current instruction register so 9:03 this case 9:04 one zero zero one 9:09 now when the next fetch cycle begins the 9:12 program counter is obviously checked 9:14 and as its contents have been previously 9:16 updated to a new memory location 9:19 and not simply incremented the program 9:22 effectively is able to jump 9:24 around memory 9:28 so having watched this video you should 9:30 be able to answer the following key 9:32 question 9:33 how does a cpu work 9:39 okay so let's um answer the question we 9:41 posed 9:42 earlier what did that program actually 9:48 do 9:50 so this is the first fetch to code 9:53 execute cycle 9:55 and this is the one that we ran through 9:57 in detail earlier 9:58 it effectively loaded the contents of 10:01 the memory 10:02 stored at location location0101 10:05 into the accumulator in other words 10:08 the dna number 3 is moved 10:11 from memory into the cpu 10:18 we then proceed onto the second fetch 10:20 decode execute cycle 10:23 now this one adds the contents of memory 10:27 located at 0 1 1 0 10:30 to the current contents of the 10:32 accumulator 10:34 so in other words the dna number one 10:38 because that's what's stored at address 10:40 zero one one zero 10:43 is added to the number three that was in 10:45 the accumulator 10:46 the results are stored back over the 10:48 accumulator 10:49 so effectively we've done three plus one 10:53 equals four 10:58 the third fetch to code execute cycle 11:00 stores the contents which are in the 11:02 accumulator 11:03 into memory location zero one one one 11:07 and that's because the op code the first 11:09 part of this current instruction 11:10 zero zero one one is the command to 11:13 store when we look it up in the decoder 11:15 unit 11:16 so in other words the result of the 11:17 previous calculation three plus one 11:19 equals four 11:20 is now written back into main memory 11:28 the fourth fetch decode execute cycle 11:30 outputs the contents of the accumulator 11:33 remember they were copied into main 11:34 memory but they're still held in the 11:35 accumulator 11:37 so in this simple abstraction the number 11:40 four is now 11:41 output to the user so they can see the 11:43 result of the calculation 11:49 the fifth and final fetch code execute 11:51 cycle 11:52 brings a halt to the current program 11:58 so this very simple program which has 12:01 five 12:02 fetch decode execute cycles has 12:04 performed the calculation 12:06 three plus one is then stored the result 12:09 in main memory 12:10 and displayed the result four to the 12:12 user 12:13 and in a high-level language this may 12:15 look something very similar to the 12:17 following two lines of code 12:20 sum variable equals num1 plus num2 12:24 print sum to the user 12:27 so you can start to get an appreciation 12:29 here of how the high level code you 12:32 write actually ends up being fetched 12:34 decoded 12:35 and executed inside a processor 12:38 of course your processor is doing 12:40 billions and billions of these 12:42 operations a second 12:43 which when you think about it is really 12:45 very impressive 12:52 [Music] 13:03 you. make 10 questions for a standerd of a level

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