# data[].attributes.body
https://namle.net/journal-of-media-literacy-education/

# data[].attributes.attachment_link.description
The Journal of Media Literacy Education (JMLE.org) is an online interdisciplinary journal that supports the development of research, scholarship and the pedagogy of media literacy educati…

# data[].attributes.attachment_link.embed_code
Journal Of Media Literacy Education

# data[].attributes.body
https://www.iste.org/areas-of-focus/digital-citizenship

# data[].attributes.attachment_link.readable_text
Digital citizenship shouldn't be a long list of don'ts. It should be about the do's that help create thoughtful, empathetic digital citizens who can wrestle with the important ethical questions at the intersection of technology and humanity. Those do's include:

Using technology to make your community better.

Engaging respectfully online with people who have different beliefs than you.

Using technology to make your voice heard by public leaders and shape public policy.

Determining the validity of online sources of information.

Digital citizenship goes beyond conversations about personal responsibility. It's about being active citizens who see possibilities instead of problems, and opportunities instead of risks as they curate a positive and effective digital footprint.

# data[].attributes.attachment_link.description
Digital citizenship shouldn't be a long list of don'ts. It should be about the do's that help create thoughtful, empathetic digital citizens who can wrestle with the important ethical questions at the intersection of technology and humanity. Those do's include: Using technology to make your community better.

# data[].attributes.body
https://learn.kqed.org/

# data[].attributes.attachment_link.readable_text
Youth Media Challenges ask students to explore how they see themselves, share their view of the world, or envision a better future. Choose from a range of standards-aligned, media-making projects across content areas that help reach your learning objectives and engage students at the same time.

All challenges come with ready-to-use curriculum and every student submission is published on the KQED Youth Media Showcase. Select submissions will be aired on KQED broadcasts and shared with PBS and NPR member stations around the country.

Explore the 7 current challenges or browse all published student submissions.

# data[].attributes.attachment_link.description
Youth Media Challenges ask students to explore how they see themselves, share their view of the world, or envision a better future. Choose from a range of standards-aligned, media-making projects across content areas that help reach your learning objectives and engage students at the same time.

# data[].attributes.attachment_link.readable_text
Vocabulary

charged [chahrjd] ( adj) filled with excitement, tension or emotion

tone [tohn] ( noun) the general attitude communicated in a piece of writing

bias [ bahy-uh s] ( noun) prejudice; favoring one person or point of view more than others

point of view [point uhv vyoo] ( noun) a particular way of considering a matter; the position from which an event or topic is observed

implicit [im- plis-it] ( adj) suggested or assumed but not obvious or explicitly stated
Sources: Google Dictionary, dictionary.com, en.oxforddictionaries.com
Series Overview

As technology advances and the social landscape shifts, it is crucial for students to become digitally literate citizens. In this series, students will learn the ins and outs of media literacy, from choosing reliable sources and understanding online searches to navigating online security and participating in digital communities. More lessons in this series are listed below under "Related Resources."

Lesson Overview

This lesson addresses the importance of locating and verifying reliable sources when working with online information. Students will compare and contrast different sources on the same topic and think about what makes one source more reliable than another. They will then work collaboratively to develop a checklist of questions for source evaluation.

Students will also zero in on the significance of evaluating sources for bias. They will learn to identify the author's or designer's purpose in online information and use this skill to search out biased viewpoints. Students will react to sources presented online and identify common reasoning errors in reactions to digital information.

Procedure

1. Give students one news story about an event. Put students into groups, and ask them to collect a list of facts from the article using the exact words the author used in the article.

2. Give students the second article about the same event, ensuring that this one has a different tone. Ask the same groups to make a list of facts from the article, again using the exact words the author used in the article.

3. For the whole class, model creating a t-chart with words from each article that depict the same fact or event. For example, you might write murdered in one column and killed in the other. Ask students to complete their own t-charts independently.

4. As a class, define and discuss tone and charged as these ideas apply to word choice. Then, ask students to discuss in their groups what biases they identify in the two articles based on the tone and charge of the words. Allow them to use the Evaluating Bias in a Newscast handout to record their observations.

5. Pose two questions to the class: (1) How do we figure out what really happened? (2) Why is it important to be aware of both stories? Discuss as a group or have students write their responses to each question.

6. Tell students they will now practice writing their own "charged" article based on a given scenario that is relevant to current events. Together, review the Creating a Biased News Story handout, which they can use to guide their process.

7. Finally, have students share their pieces with each other. Ask them to highlight the charged words and identify the bias in each other's work and explain how the bias is expressed.

Alignment to Common Core State Standards CCSS.ELA-LITERACY.RH.6-8.4
Determine the meaning of words and phrases as they are used in a text, including vocabulary specific to domains related to history/social studies.
CCSS.ELA-LITERACY.CCRA.R.6
Assess how point of view or purpose shapes the content and style of a text.
CCSS.ELA-LITERACY.RH.6-8.1
Cite specific textual evidence to support analysis of primary and secondary sources.
CCSS.ELA-LITERACY.CCRA.W.2
Write informative/explanatory texts to examine and convey complex ideas and information clearly and accurately through the effective selection, organization, and analysis of content.
CCSS.ELA-LITERACY.CCRA.W.4
Produce clear and coherent writing in which the development, organization, and style are appropriate to task, purpose, and audience.
CCSS.ELA-LITERACY.RH.6-8.6
Identify aspects of a text that reveal an author's point of view or purpose (e.g., loaded language, inclusion or avoidance of particular facts).

# data[].attributes.attachment_link.description
This lesson, part of the Digital Literacy series, focuses on teaching students to identify how writers can reveal their biases through their word choice and tone. Students will identify "charged" words that communicate a point of view. Students will understand how writers communicate a point of view implicitly by writing their own charged news stories.

# data[].attributes.attachment_link.readable_text
What is Checkology?

Checkology helps educators teach news literacy by providing free lessons, challenges, exercises, and missions about today's challenging information landscape. Students will learn how to identify credible information, seek out reliable sources, and apply critical thinking skills to separate fact-based content from falsehoods. All you'll need to get started is an up-to-date web browser.

(Watch the video or read the article below for a quick guide to getting started on Checkology.)

Registering an educator account

Visit checkology.org.

When you click "Register Now," you will see the Educator account registration button on the left.

You will be taken through three easy steps to register your educator account:

1. Select your school. Joining with your school means it will be easy to select your colleagues as co-teachers or transfer students across classes in Checkology. Just enter your school's information and select it from the dropdown menu. Can't find your school? No problem. Just select "Can't find a match? Request a new school" and follow the instructions.

2. Confirm your Checkology administrators. This step asks you to confirm any school or district-level Checkology administrators associated with the school you selected. By checking the box, you are acknowledging that you understand the individuals listed will have access to the data described. Please note: Some schools may have no administrators listed.

3. Create your account. In this step, you can select your preferred sign-in method. You can use one of the single sign-on options (such as Google or Microsoft) so you can login with just a click or you can use "Sign up using email" and set a password.

Creating a class

To create your first class, click the "+ Add class" button on your Dashboard. From there, just enter your class information at the top and then click the green "Create class" button when ready.

You can adjust class settings anytime to help tailor your Checkology experience for your learning environment. Just click "Edit" next to "Class settings" on a Dashboard class tile menu and then click "Open advance settings" to see more options. Remember to press "Save" after making any settings adjustments.

Teacher tip:

Set your Checkology video preference according to your school's preferred video streaming service. Checkology videos are hosted on three different platforms: Vimeo, YouTube and Wistia. To switch video preferences, hover over the icon in the top right corner and select "Manage Account" from the dropdown menu and, then, "Video Preference" from the side navigation menu.

Adding students

Once you have created a class, you can begin adding students to that class. Students can self-register OR you can register student accounts for them.

Most educators elect to have students self-register because self-registration allows students to link their Google or Microsoft account to Checkology. The easiest way to have students self-register is to copy the class URL and code (pictured below on the left) and then share that link wherever you communicate most easily with your whole class - perhaps in an email or a class discussion board. You can also copy this class URL and code from your class tile on your Dashboard (pictured below on the right).

When students click the join link, they will be sent directly to the student registration page and your unique class code will be auto-filled in their login information.

After sending students this registration information, you can check to see who has joined your class by clicking on "Manage" next to "Students" on your Dashboard.

Assigning a course

Courses are collections of news literacy lessons, challenges, exercises and missions. When you click "Assign course," you will automatically be served a "Checkology 101" default course based on your earlier "grade level" selection in the class settings area. To find more information about the default course, click "Preview course." To assign the course, click on the green "Assign to class" button. It will appear purple when your course has been successfully served to your students.

If you would like to browse or assign other courses curated by the News Literacy Project, click "change course." If you would like to curate your own course, click "customize this course." When customizing an existing course or create a brand new one, you will be prompted to name your new course and then be taken to the course builder.

Once you've assigned a course, your students will be able to access it from their student accounts. Your students will first be prompted to take a short pre-assessment survey to help you see their news literacy learning gains overtime. Once done, they can jump into their first news literacy assignment.

Managing a class

Even after your class is underway, you can still change your class settings, add and remove students, and edit your course at any time from your Dashboard class tile menu:

You can also use this class tile menu to enter the evaluation area or download a report of student work.

# data[].attributes.attachment_link.title
Getting started on Checkology (for educators)

# data[].attributes.attachment_link.description
What is Checkology? Checkology helps educators teach news literacy by providing free lessons, challenges, exercises, and missions about today's challenging information landscape. Students will learn how to identify credible information, seek out reliable sources, and apply critical thinking skills to separate fact-based content from falsehoods.

# data[].attributes.body
http://www.agoogleaday.com/

# data[].attributes.attachment_link.readable_text
Put your search skills and Google's tools to use. For instance, you can enter 30.891383, -102.885032 right inside the search bar, and learn that it's directly in the center of Ft. Stockton, TX. A quick Search for [Giant bird ft stockton TX] (or perhaps you're the adventurous type who'll "walk" there in Street View) and you'll have your bird: Paisano Pete.

# data[].attributes.attachment_link.description
Welcome to the new daily puzzle from Google. There is no right way to solve it, but there's only one right answer. Find the answer with your creativity and clever search skills.

# data[].attributes.body
https://support.google.com/websearch/answer/2466433?hl=en

# data[].attributes.attachment_link.description
There are different ways to filter your Google searches to be more precise or to expand in new directions. Advanced search Google offers pages designed to help you perform specialized web and image

# data[].attributes.body
https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency

# data[].attributes.attachment_link.readable_text
This story is part of a group of stories called

Uncovering and explaining how our digital world is changing - and changing us.

Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire.

But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. It's tough to figure out exactly how systems might be susceptible to algorithmic bias, especially since this technology often operates in a corporate black box. We frequently don't know how a particular artificial intelligence or algorithm was designed, what data helped build it, or how it works.

Typically, you only know the end result: how it has affected you, if you're even aware that AI or an algorithm was used in the first place. Did you get the job? Did you see that Donald Trump ad on your Facebook timeline? Did a facial recognition system identify you? That makes addressing the biases of artificial intelligence tricky, but even more important to understand.

Machine learning-based systems are trained on data. Lots of it.

When thinking about "machine learning" tools (machine learning is a type of artificial intelligence), it's better to think about the idea of "training." This involves exposing a computer to a bunch of data - any kind of data - and then that computer learns to make judgments, or predictions, about the information it processes based on the patterns it notices.

For instance, in a verysimplified example, let's say you wanted to train your computer system to recognize whether an object is a book, based on a few factors, like its texture, weight, and dimensions. A human might be able to do this, but a computer could do it more quickly.

To train the system, you show the computer metrics attributed to a lot of different objects. You give the computer system the metrics for every object, and tell the computer when the objects are books and when they're not. After continuously testing and refining, the system is supposed to learn what indicates a book and, hopefully, be able to predict in the future whether an object is a book, depending on those metrics, without human assistance.

That sounds relatively straightforward. And it might be, if your first batch of data was classified correctly and included a good range of metrics featuring lots of different types of books. However, these systems are often applied to situations that have much more serious consequences than this task, and in scenarios where there isn't necessarily an "objective" answer. Often, the data on which many of these decision-making systems are trained or checked are often not complete, balanced, or selected appropriately, and that can be a major source of - although certainly not the only source - of algorithmic bias.

Nicol Turner-Lee, a Center for Technology Innovation fellow at the Brookings Institution think tank, explains that we can think about algorithmic bias in two primary ways: accuracy and impact. An AI can have different accuracy rates for different demographic groups. Similarly, an algorithm can make vastly different decisions when applied to different populations.

Importantly, when you think of data, you might think of formal studies in which demographics and representation are carefully considered, limitations are weighed, and then the results are peer-reviewed. That's not necessarily the case with the AI-based systems that might be used to make a decision about you. Let's take one source of data everyone has access to: the internet. One study found that, by teaching an artificial intelligence to crawl through the internet - and just reading what humans have already written - the system would produce prejudices against black people and women.

Another example of how training data can produce sexism in an algorithm occurred a few years ago, when Amazon tried to use AI to build a résumé-screening tool. According to Reuters, the company's hope was that technology could make the process of sorting through job applications more efficient. It built a screening algorithm using résumés the company had collected for a decade, but those résumés tended to come from men. That meant the system, in the end, learned to discriminate against women. It also ended up factoring in proxies for gender, like whether an applicant went to a women's college. (Amazon says the tool was never used and that it was nonfunctional for several reasons.)

Amid discussions of algorithmic biases, companies using AI might say they're taking precautions, taking steps to use more representative training data and regularly auditing their systems for unintended bias and disparate impact against certain groups. But Lily Hu, a doctoral candidate at Harvard in applied mathematics and philosophy who studies AI fairness, says those aren't assurances that your system will perform fairly in the future.

"You don't have any guarantees because your algorithm performs 'fairly' on your old dataset," Hu told Recode. "That's just a fundamental problem of machine learning. Machine learning works on old data [and] on training data. And it doesn't work on new data, because we haven't collected that data yet."

Still, shouldn't we just make more representative datasets? That might be part of the solution, though it's worth noting that not all efforts aimed at building better data sets are ethical. And it's not just about the data. As Karen Hao of the MIT Tech Review explains, AI could also be designed to frame a problem in a fundamentally problematic way. For instance, an algorithm designed to determine "creditworthiness" that's programmed to maximize profit could ultimately decide to give out predatory, subprime loans.

Here's another thing to keep in mind: Just because a tool is tested for bias - which assumes that engineers who are checking for bias actually understand how bias manifests and operates - against one group doesn't mean it is tested for bias against another type of group. This is also true when an algorithm is considering several types of identity factors at the same time: A tool may deemed fairly accurate on white women, for instance, but that doesn't necessarily mean it works with black women.

In some cases, it might be impossible to find training data free of bias. Take historical data produced by the United States criminal justice system. It's hard to imagine that data produced by an institution rife with systemic racism could be used to build out an effective and fair tool. As researchers at New York University and the AI Now Institute outline, predictive policing tools can be fed "dirty data," including policing patterns that reflect police departments' conscious and implicit biases, as well as police corruption.

The foundational assumptions of engineers can also be biased

So you might have the data to build an algorithm. But who designs it, and who decides how it's deployed? Who gets to decide what level of accuracy and inaccuracy for different groups is acceptable? Who gets to decide which applications of AI are ethical and which aren't?

While there isn't a wide range of studies on the demographics of the artificial intelligence field, we do know that AI tends to be dominated by men. And the "high tech" sector, more broadly, tends to overrepresent white people and underrepresent black and Latinx people, according to the Equal Employment Opportunity Commission.

Turner-Lee emphasizes that we need to think about who gets a seat at the table when these systems are proposed, since those people ultimately shape the discussion about ethical deployments of their technology.

But there's also a broader question of what questions artificial intelligence can help us answer. Hu, the Harvard researcher, argues that for many systems, the question of building a "fair" system is essentially nonsensical, because those systems try to answer social questions that don't necessarily have an objective answer. For instance, Hu says algorithms that claim to predict a person's recidivism don't ultimately address the ethical question of whether someone deserves parole.

"There's not an objective way to answer that question," Hu says. "When you then insert an AI system, an algorithmic system, [or] a computer, that doesn't change the fundamental context of the problem, which is that the problem has no objective answer. It's fundamentally a question of what our values are, and what the purpose of the criminal justice system is."

That in mind, some algorithms probably shouldn't exist, or at least they shouldn't come with such a high risk of abuse. Just because a technology is accurate doesn't make it fair or ethical. For instance, the Chinese government has used artificial intelligence to track and racially profile its largely Muslim Uighur minority, about 1 million of whom are believed to be living in internment camps.

Transparency is a first step for accountability

One of the reasons algorithmic bias can seem so opaque is because, on our own, we usually can't tell when it's happening (or if an algorithm is even in the mix). That was one of the reasons why the controversy over a husband and wife who both applied for an Apple Card - and got widely different credit limits - attracted so much attention, Turner-Lee says. It was a rare instance in which two people, who at least appeared to be exposed to the same algorithm and could easily compare notes. The details of this case still aren't clear, though the company's credit card is now being investigated by regulators.

But consumers being able to make apples-to-apples comparisons of algorithmic results are rare, and that's part of why advocates are demanding more transparency about how systems work and their accuracy. Ultimately, it's probably not a problem we can solve on the individual level. Even if we do understand that algorithms can be biased, that doesn't mean companies will be forthright in allowing outsiders to study their artificial intelligence. That's created a challenge for those pushing for more equitable, technological systems. How can you critique an algorithm - a sort of black box - if you don't have true access to its inner workings or the capacity to test a good number of its decisions?

Companies will claim to be accurate, overall, but won't always reveal their training data (remember, that's the data that the artificial intelligence trains on before evaluating newdata, like, say, your job application). Many don't appear to be subjecting themselves to audit by a third-party evaluator or publicly sharing how their systems fare when applied to different demographic groups. Some researchers, such as Joy Buolamwini and Timnit Gebru, say that sharing this demographic information about both the data used to train and the data used to check artificial intelligence should be a baseline definition of transparency.

Artificial intelligence is new, but that doesn't mean existing laws don't apply

We will likely need new laws to regulate artificial intelligence, and some lawmakers are catching up on the issue. There's a bill that would force companies to check their AI systems for bias through the Federal Trade Commission (FTC). And legislation has also been proposed to regulate facial recognition, and even to ban the technology from federally assisted public housing.

But Turner-Lee emphasizes that new legislation doesn't mean existing laws or agencies don't have the power to look over these tools, even if there's some uncertainty. For instance, the FTC oversees deceptive acts and practices, which could give the agency authority over some AI-based tools.

The Equal Employment Opportunity Commission, which investigates employment discrimination, is reportedly looking into at least two cases involving algorithmic discrimination. At the same time, the White House is encouraging federal agencies that are figuring out how to regulate artificial intelligence to keep technological innovation in mind. That raises the challenge of whether the government is prepared to study and govern this technology, and figure out how existing laws apply.

"You have a group of people that really understand it very well, and that would be technologists," Turner-Lee cautions, "and a group of people who don't really understand it at all, or have minimal understanding, and that would be policymakers."
Open Sourced is made possible by Omidyar Network. All Open Sourced content is editorially independent and produced by our journalists.
That's not to say there aren't technical efforts to "de-bias" flawed artificial intelligence, but it's important to keep in mind that the technology won't be a solution to fundamental challenges of fairness and discrimination. And, as the examples we've gone through indicate, there's no guarantee companies building or using this tech will make sure it's not discriminatory, especially without a legal mandate to do so. It would seem it's up to us, collectively, to push the government to rein in the tech and to make sure it helps us more than it might already be harming us.

# data[].attributes.attachment_link.description
Uncovering and explaining how our digital world is changing - and changing us. Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. Still, the tech is already making important decisions about your life and potentially ruling over which political advertisements you see, how your application to your dream job is screened, how police officers are deployed in your neighborhood, and even predicting your home's risk of fire.

# data[].attributes.body
https://www.iste.org/explore/5-competencies-digital-citizenship

# data[].attributes.attachment_link.readable_text
The digital world is evolving rapidly and so must the way we educate students about navigating it. For over a decade now, digital citizenship has been mainly about the don'ts: Don't share your passwords, don't steal the intellectual property of others, don't post or share inappropriate content on social media.

But it's time to rethink that approach, says ISTE CEO Richard Culatta.

"Far too often, digital citizenship is taught in a negative way here's the list of all the stuff you should not do online. And while I appreciate the intent behind anti-cyberbullying campaigns, we don't teach other things as "anti-" in schools. I mean, we don't have anti-illiteracy campaigns we teach kids to love to read. Digital Citizenship shouldn't be a list of don'ts, but a list of do's."

The other way we need to evolve our thinking, Culatta says, is by recognizing that the skills required to thrive as a digital citizen go beyond just online safety. They include recognizing fact from fiction online, using technology to engage in civic action and being respectful of people with differing viewpoints from our own.

"These are skills that students aren't learning from watching the adults around them either.," Culatta says. "And so we must challenge ourselves to do better."

That's why ISTE has partnered with some like-minded organizations to redefine digital citizenship. This coalition has launched an international DigCitCommit campaign to get students, educators and their communities familiar with the five competencies of digital citizenship.

Watch the video below and then commit to the five competencies of digital citizenship.

The five competencies of digital citizenship

Inclusive:

I am open to hearing and respectfully recognizing multiple viewpoints, and I engage with others online with respect and empathy.

Informed:

I evaluate the accuracy, perspective, and validity of digital media and social posts.

Engaged:

I use technology and digital channels for civic engagement, to solve problems and be a force for good in both physical and virtual communities.

Balanced:

I make informed decisions about how to prioritize my time and activities online and off.

Alert:

I am aware of my online actions, and know how to be safe and create safe spaces for others online.

As an educator, here are three things you can do today:

# data[].attributes.attachment_link.title
The 5 competencies of digital citizenship

# data[].attributes.attachment_link.description
The digital world is evolving rapidly and so must the way we educate students about navigating it. For over a decade now, digital citizenship has been mainly about the don'ts: Don't share your passwords, don't steal the intellectual property of others, don't post or share inappropriate content on social media.

# data[].attributes.body
https://www.learningforjustice.org/classroom-resources/lessons/minimizing-the-impact-of-biases

# data[].attributes.attachment_link.readable_text
Activities will help students:

Practice critical reading skills.

Use expressive oral language to stand up against prejudice.

Practice metacognitive reading strategies in viewing advertisements.

What biases do advertisements most commonly perpetuate?

What happens in our own minds when we see and hear these biases on a constant basis?

How can we prevent the biases in ads from affecting the way we think and feel about ourselves and others?

Activities

1. Have the students collectively view the advertisement below or a similar advertisement that perpetuates stereotypes. The advertisement here perpetuates the idea that girls should look a certain way, that girls care primarily about their appearances and are somewhat ditzy, that it is "cool" to be a fake sort of tough, etc. Guide students to use critical reading strategies developed in lesson two to analyze the advertisement.

2. Explain to students that, as they have seen over the course of this series, some advertisements are full of biases. They perpetuate stereotypes, use unfair representations, and teach unjust ideas about how people are and how people should be.

3. Ask students to talk with a partner about what goes through their minds when they see an advertisement. Encourage them to be as honest as possible: Maybe the first thing they think of is whether or not they want the product being sold. Have them be really specific in explaining what they think and how the advertisements affect them.

4. Then tell students that their task is to come up with a list of three to five techniques for "reading" an advertisement such as the one presented in this lesson without letting its biased messages influence them. In other words, since we cannot avoid, how can we work to minimize their impact on our self-esteem and the way we view others different from ourselves? Explain to students that there is no right or wrong here, but they should think of what strategies would be genuinely effective. Students may work independently, in partnership or as a group, depending on the needs of the class.

5. After students have developed their lists, publish them and display them prominently. Encourage other students to read the lists and practice putting the strategies into action!

Reflection

The real test of the strategies students come up with is whether they work outside the classroom. Give students photocopies of their lists to take home and post in their rooms or elsewhere in their homes. Challenge them to put the strategies into action over the course of the week. Then give them a chance to report back on whether and how the experience has changed the way they look at advertisements.

# data[].attributes.attachment_link.description
This is the eleventh lesson in the Reading Ads with a Social Justice Lens series.Children need empowerment strategies for what to do when faced with biased messages. In other words, they need to learn how to look critically at stereotypes without letting them in, to see and be conscious of biases without taking them on.

# data[].attributes.headline
Essential Elements of Digital Citizenship

# data[].attributes.subject
Essential Elements of Digital Citizenship

# data[].attributes.body
https://www.iste.org/explore/digital-citizenship/essential-elements-digital-citizenship

# data[].attributes.attachment_link.readable_text
Kids today have always had technology in their lives, and many educators assume their students are competent tech users - more competent, in fact, than themselves.

In reality, though, students may be comfortable using technology, but they still might not understand how to use it appropriately or how to harness the power of technology to pursue their passions and contribute to their community.

In my book, Digital Citizenship in Schools, I explain the three categories of digital citizenship respect, educate, protect and lay out a framework that educators of all subject areas and grade levels can use to teach the basics of digital citizenship.

As you embed digital citizenship lessons into your curriculum, you'll feel more comfortable empowering your students to be active, vocal digital citizens who use their voice to do good in the world and promote the causes they care about.

Read on to get an overview of each of the nine elements:

Respect

Digital access: Advocating for equal digital rights and access is where digital citizenship starts.

Digital etiquette: Rules and policies aren't enough - we need to teach everyone about appropriate conduct online.

Digital law: It's critical that users understand how to properly use and share each other's digital property.

Educate

Digital communication: With so many communication options available, students need to learn how to choose the right tools according to their audience and message.

Digital literacy: This involves more than being able to use tools. Digital literacy is about how to find, evaluate and cite digital materials.

Digital commerce: As students make more purchases online, they must understand how to be effective consumers in a digital economy.

Protect

Digital rights and responsibilities: Students must understand their basic digital rights to privacy, and freedom of speech.

Digital safety and security: Digital citizens need to know how to safeguard their information by controlling privacy settings.

Digital health and wellness: One important aspect of living in a digital world is knowing when to unplug. Students need to make informed decisions about how to prioritize their time and activities online and off. Balance is one of the five competencies of DigCitCommit.

Mike Ribble is author of The Digital Citizenship Handbook for School Leaders and Digital Citizenship in Schools, 3rd Edition.
This is an updated version of a post that was originally published on June 25, 2014.

# data[].attributes.attachment_link.title
Essential Elements of Digital Citizenship

# data[].attributes.attachment_link.description
Digital citizenship is more important than ever as learning moves online. Here are the nine essential elements of digital citizenship and how to teach them to students.

# data[].attributes.body
https://mediaeducationlab.com/publications

# data[].attributes.attachment_link.description
To advance the digital literacy competencies of educators, create opportunities for them to reflect on their motivations for using digital media, make collaborative inquiry a substantive component of the hands- on learning experience, and create opportunities to put teachers and learners (not mac