_____________is a standard computer language for relational database management and data manipulation.

SQL or structured query language

____________is a form of computer memory that can be read and changed in any order, typically used to store working data and machine code.

RAM is a form of computer memory that can be read and changed in any order, typically used to store working data and machine code.

Activity 2: Computer and Network Basics

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?

How is personal data collected? There are several ways that an unauthorised person can try and collect your data. These include: •phishing •smishing •vishing •pharming. Phishing Phishing is when a person sends a legitimate looking email to a user. The email contains a link to a website that also looks legitimate. The user is encouraged to click the link and to input personal data into a form on the website. The email could also simply ask the user to reply to the email with their personal data. The user is tricked into giving their personal data to a source that they believe is legitimate. However, both the email and the linked website are from a fake unauthorised source. The personal data that is input is then collected by an unauthorised person. This person can then use this data for criminal acts, for example, to commit fraud or steal the person's identity. Intimidation has become a common feature of phishing emails, threatening the user that they must click the link and rectify a situation immediately, or there will be a further issue. The aim of a phishing attack is to steal the user's personal data. Figure 5.1: Phishing. A real-life example of phishing PayPal have been the subject of several different phishing emails. Users receive an email that looks as though it has been sent from PayPal, as it has the PayPal branding. The email normally warns of an issue such as unexpected activity on their account, or that some kind of verification of their account is required. The user is then asked to click a link to log into their account and resolve the issue. The link takes them to a webpage that looks like the PayPal login page. If the user inputs their login details into this page, they will not be taken to their account. It is often at this stage that the user may realise that the email and webpage are fake. However, they have already given the unauthorised person their PayPal login details. Figure 5.2: An example of a phishing email claiming to be from PayPal. How to recognise phishing There are several guidelines to be aware of regarding emails to avoid being subjected to phishing. These include: •Don't even open an email that is not from a sender that you recognise or a trusted source. •Legitimate companies will never ask you for your personal data using email. Be immediately suspicious of any email that requests your personal data. •Legitimate companies will normally address you by your name. Be suspicious of any email that addresses you as ‘Dear Member' or ‘Dear Customer'. •Legitimate companies will send an email that uses their domain name. If you hover your mouse over the sender's name, it will show the email address that the email is sent from. If this does not look legitimate, for example, does not contain the correct domain name, then it is probably fake. For example, if the sender's email is user@paypal1.com rather than user@paypal.com, this is from an incorrect domain name. •Legitimate companies are protective of their professional reputation and thoroughly check any communications. They will make sure that all information given is grammatically and correctly spelt. Be suspicious of any email that contains bad grammar or spelling mistakes. •A link in an email from a legitimate company will also normally contain the domain name of the company. You can sometimes hover over the link, or right click and inspect the link, to see the address of the URL that is attached. If the URL does not contain the domain name, or also contains typical errors such as spelling mistakes, then be suspicious of this. PRACTICAL ACTIVITY 5.02 Ask a friend or a member of your family if they have ever received an email that they believed was a phishing email. Ask them how they identified it was phishing. Ask them if they know all of the given guidelines for identifying phishing emails. Smishing Smishing (or SMS phishing) is a variant of phishing that uses SMS text messages to lure the user into providing their personal details. The user is sent an SMS text message that either contains a link to a website, in the same way that phishing does, or it will ask the user to call a telephone number to resolve an urgent issue. The same advice can be followed for smishing as given for phishing. The user must question at all times any links that are sent from an unknown or suspicious user. It is advisable that if a user believes the message may be legitimate, to type in the domain name for the legitimate company website into their web browser, rather than following the link in the message. Users should block any numbers that they believe are suspicious to prevent any further risk of smishing from that number. Figure 5.3: Smishing. Vishing Vishing (or voice phishing) has the same aim as phishing, to obtain a user's personal details. The user receives a telephone call that could either be an automated system or could be a real person. An automated voice could speak to the user and advise them that an issue has occurred, such as there has been suspicious activity regarding their bank account. The user may then be asked to call another number, or just to simply press a digit and be directed to another automated system. This system will ask them to provide their bank account details to resolve the issue. The bank account details have then been obtained by the unauthorised user and can be used to commit a crime against the user. The automated system could be replaced by a real person who will try to do the same thing. They will try to convince the user that there has been an issue with an account they have and to provide the log-in details or PIN for the account to verify who they are so the issue can be resolved. The precaution to take for vishing is that no company will ever call you and ask you to provide any log-in details or PIN details over the telephone. They may ask you to provide other personal information, and if you are in doubt that the person on the other end of the phone is legitimate, it is always advisable to put the phone down and call the company back on a legitimate number that you may already know or can obtain. Figure 5.4: Vishing. Pharming Pharming is when an unauthorised user installs malicious code on a person's hard drive or server. The malicious code is designed to redirect a user to a fake website when they type in the address of a legitimate one. The fake website is designed to look like the legitimate one, to trick the user and make sure they are not aware that their request has been redirected. The user will then enter their personal details into the fake website, believing it is the legitimate one, and the unauthorised person will now have their personal data. A common technique used in pharming is called domain name server (DNS) cache poisoning. This technique exploits vulnerabilities in the DNS and diverts the internet traffic intended for a legitimate server toward a fake one instead. The unauthorised user needs to find a way to install the malicious code on the computer. They often hide the malicious code in an email attachment or link. When the user opens the email attachment or clicks the link, the malicious code is downloaded also. Figure 5.5: Pharming. The aim of a pharming attack is also to steal a user's personal data. A real-life example of pharming In 2007 50 different companies all over the world were subject to a pharming attack, these included PayPal, eBay, Barclays bank and American Express. Over a three-day period, hackers managed to infect over 1000 PCs a day with a malicious pharming code. When users who had been infected visited the websites of the different companies, they were redirected to a legitimate-looking version of the site that was designed to steal their personal data. The original email, containing the malicious code, was set up to look like a shocking news story. Users were encouraged to click a link in the email to find out more information. The code was downloaded when the user clicked the link. This was quite a sophisticated attack that required legitimate looking websites to be set up for a large number of companies. It is not known how much money the hackers were able to retrieve as a result. How to prevent pharming All of the guidelines to avoid being subjected to phishing are also relevant for recognising pharming. There are also several other precautions that can be taken to check for pharming attacks. These include: •Have a firewall installed and operational. A firewall monitors incoming and outgoing traffic from your computer. It checks this traffic against set criteria and will flag and stop any traffic that does not meet the criteria. A firewall could detect and block suspicious traffic, such as a malicious code trying to enter your system. •Have an anti-virus program installed that is designed to detect malicious pharming code. You need to scan your computer on a regular basis to check for any malicious code. It is advisable to set up an automatic scan on a daily basis at a time when your computer will normally be switched on. •Be aware when using public Wi-Fi connections. A hacker could look to directly access your computer and install the malicious code if you are connected to a public Wi-Fi connection. It is often advisable to use a VPN when using public Wi-Fi. This will help shield your internet activity and personal details from a hacker, making it more difficult for them to access your computer. Smishing can also be used as a form of pharming. A user is sent a link, that when they click is designed to download malware onto their mobile device. Therefore, it is advisable to have security software installed on your mobile and also scan it regularly to detect any presence of malware.

“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.

Q1. A teacher designs a lesson where students compute real-life percentages such as discounts and savings. 👉 A student calculates 15% of 200 to determine savings in a purchase. What is the correct result? A. 20 B. 25 C. 30 D. 35 Q2. In a classroom activity, learners compare numbers to find the highest common factor for grouping materials evenly. 👉 What is the GCF of 24 and 36? A. 6 B. 8 C. 12 D. 18 📘 FRACTIONS, DECIMALS, AND POWERS Q3. A learner converts fractions into percentages for data interpretation. 👉 What is 3/4 expressed as a percentage? A. 50% B. 60% C. 75% D. 80% Q4. A student models exponential growth using repeated multiplication. 👉 What is the value of 252^525? A. 25 B. 30 C. 32 D. 64 📘 ALGEBRA (EQUATIONS AND EXPRESSIONS) Q5. A teacher guides students to solve equations that represent real-life situations. 👉 Solve: 2x+8=202x + 8 = 202x+8=20 A. x = 4 B. x = 6 C. x = 8 D. x = 10 Q6. Students simplify expressions to understand relationships between quantities. 👉 Simplify: 3(x+4)−2x3(x + 4) - 2x3(x+4)−2x A. x + 12 B. x + 4 C. 5x + 4 D. 5x + 12 📘 FUNCTIONS AND GRAPHING Q7. A student analyzes a linear equation to determine its rate of change. 👉 What is the slope of y=3x−5y = 3x - 5y=3x−5? A. -5 B. -3 C. 3 D. 5 Q8. A learner evaluates functions to predict outcomes. 👉 If f(x)=2x+3f(x) = 2x + 3f(x)=2x+3, what is f(4)f(4)f(4)? A. 7 B. 9 C. 11 D. 14 📘 GEOMETRY Q9. Students explore geometric shapes and their properties through visual models. 👉 What is the sum of interior angles of a triangle? A. 90° B. 180° C. 270° D. 360° Q10. A student calculates the area of a classroom table with dimensions 8 cm by 5 cm. 👉 What is the area? A. 26 sq cm B. 30 sq cm C. 40 sq cm D. 48 sq cm 📘 MEASUREMENT AND FIGURES Q11. A learner determines the volume of a cube used in a science experiment. 👉 What is the volume of a cube with side 4 cm? A. 16 cubic cm B. 32 cubic cm C. 48 cubic cm D. 64 cubic cm Q12. Students identify shapes used in design projects. 👉 How many sides does a hexagon have? A. 5 B. 6 C. 7 D. 8 📘 STATISTICS AND PROBABILITY Q13. A teacher helps students interpret data sets using measures of central tendency. 👉 What is the mean of 4, 6, 8, 10, 12? A. 6 B. 8 C. 10 D. 12 Q14. A class experiment involves flipping a fair coin. 👉 What is the probability of getting heads? A. 1/4 B. 1/3 C. 1/2 D. 2/3 📘 WORD PROBLEMS (APPLICATION) Q15. A car travels 180 km in 3 hours during a learning task on speed. 👉 What is its average speed? A. 45 km/h B. 60 km/h C. 75 km/h D. 90 km/h Q16. Students analyze work efficiency in a project. 👉 If 5 workers complete a task in 12 days, how long will 10 workers take? A. 3 days B. 6 days C. 8 days D. 12 days Q17. A student solves a problem involving ratios in a classroom population. 👉 If the ratio of boys to girls is 3:2 and there are 30 students, how many boys are there? A. 12 B. 15 C. 18 D. 20 Q18. A learner determines the duration of a scheduled trip. 👉 A journey starts at 8:30 AM and ends at 11:15 AM. How long is the trip? A. 2 hrs 15 mins B. 2 hrs 30 mins C. 2 hrs 45 mins D. 3 hrs 15 mins Q19. A student computes simple interest for financial literacy. 👉 What is the simple interest on ₱1000 at 5% for 2 years? A. ₱50 B. ₱75 C. ₱100 D. ₱150 Q20. A learner solves a perimeter problem involving a rectangle. 👉 A rectangle has a length of 12 cm and perimeter of 34 cm. What is the width? A. 5 cm B. 7 cm C. 10 cm D. 11 cm ✅ ANSWER KEY (BASED ON YOUR REVIEWER) (All verified from your uploaded file) [ilide.info...002acd4e5a | PDF] QAnswer1C2C3C4C5B6A7C8C9B10C11D12B13B14C15B16B17C18C19C20A

Computer Architecture and Organization - Activity I

5 Kenji COMPUTER Book Activity Page 61

COMPUTER SCIENCE - ACTIVITY

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