4. After you have posted, read what others have said, then respond to someone else by posting a comment. Use the “Reply” button to address that student directly.
On Wednesday, Jan. 12, teachers from our collaborator, the American Statistical Association, will facilitate this discussion from 9 a.m. to 2 p.m. Eastern time.
5. By Friday morning, Jan. 14, we will reveal more information about the graph, including a free link to the article that includes this graph, at the bottom of this post. We encourage you to post additional comments based on the article, possibly using statistical terms defined in the Stat Nuggets.
Reveal
We’ll post more information here on Thursday afternoon. Stay tuned!
• Learn more about the notice and wonder teaching strategy from this 5-minute video and how and why other teachers are using this strategy from our on-demand webinar.
• Sign up for our free weekly Learning Network newsletter so you never miss a graph. Graphs are always released by the Friday before the Wednesday live-moderation to give teachers time to plan ahead.
• Go to the American Statistical Association K-12 website, which includes teacher statistics resources, Census in the Schools student-generated data, professional development opportunities, and more.
Students 13 and older in the United States and the Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
“Grieving Our Old Normal” is a four-minute film that touches on themes of loss, acceptance and our all-too-human struggle to make sense of life in a pandemic and all that we can never get back. In this Opinion video, Lindsay Crouse, one of the filmmakers, asks us to reckon with a new, cold reality: After all of the deaths, the canceled weddings, the lonely birthday celebrations, the boredom and the terror, “There is no going back to normal. Your old life is gone. But whether you wanted to or not, you were building a new one. You still are. What do you want your new life to be?”
How do you make sense of this moment, two years into the pandemic? Do you agree with Ms. Crouse’s advice? How do we mourn everything we’ve lost? How do we begin to accept — and perhaps, one day, attempt to move on?
Students
1. Watch the short film above. While you watch, you might take notes using our Film Club Double-Entry Journal (PDF) to help you remember specific moments.
2. After watching, think about these questions:
What questions do you still have?
What connections can you make between this film and your own life or experience? Why? Does this film remind you of anything else you’ve read or seen? If so, how and why?
3. An additional challenge | Respond to the essential question at the top of this post: How do we mourn everything we’ve lost to Covid?
4. Next, join the conversation by clicking on the comment button and posting in the box that opens on the right. (Students 13 and older are invited to comment, although teachers of younger students are welcome to post what their students have to say.)
5. After you have posted, try reading back to see what others have said, then respond to someone else by posting another comment. Use the “Reply” button or the @ symbol to address that student directly.
6. To learn more, read “Grieving Our Old Normal.” Lindsay Crouse, Kirby Ferguson and Emily Holzknecht, the filmmakers, write:
That’s right: We’re all two years older than when we first shut ourselves inside for lockdown. If we assumed the Covid pandemic would be brief, we were wrong. So if you feel like a withered old sloth these days wallowing around and yearning for your old life, that’s understandable. But there’s no use. It’s gone.
The stunning number of lives lost to Covid is its own appalling tragedy. But the video above is about a different kind of grief many of us are experiencing right now: the kind that comes with the gnawing realization that we really need to grieve the parts of our lives that have disappeared, even as they continue to slip away. As another year ends and Covid surges again, it’s clear nothing will change soon.
Forget resilience. Forget silver linings. Right now, it’s time to decide: How do we mourn everything we’ve lost? Once we do that, hopefully at some point, we can attempt something even harder: moving on.
Students 13 and older in the United States and Britain, and 16 and older elsewhere, are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.
A.I. Engineers are in demand in most industries, and there’s a good reason for this. If you’re wondering what an A.I. Engineer does, we’ll break it down for you.
Businesses can use the massive amounts of data they generate daily to improve and simplify common, everyday tasks. With the right A.I. systems, companies can take these tasks off the hands of their teams so they can focus on more important work. Technologies like speech recognition, business process management, and image processing are only some of the A.I. technologies changing the world.
Companies need A.I. Engineers to put these systems in place, maintain them, and adapt them to changes in the business. In this article, we’ll explore what A.I. Engineers do, what kind of skills they need, and how you can get started on the A.I. engineering career path.
But first, let’s examine what A.I. engineering is and how it relates to machine learning.
What is artificial intelligence?
A.I., or artificial intelligence, uses computers and machines to emulate how the human mind operates to accomplish problem-solving and decision-making tasks. It combines the robust data sets we generate daily with computer science to achieve this goal in its simplest form.
In A.I., machines learn the outcomes of specific actions by crunching mountains of past data. They then use the insights gained from this process to make decisions about future actions and solve problems. At the same time, data is collected on the machine’s decisions and is used to correct and perfect future actions and decisions.
What’s the difference between A.I. and machine learning?
Machine learning and artificial intelligence are often lumped together in the same definition, but they aren’t necessarily the same. In our forums, one of our learners, J, provides a helpful explanation:
“Artificial intelligence can be described as when machines carry out tasks in an intelligent or smart way, based on set rules to solve certain problems. Artificial intelligence, or A.I., makes decisions, learns, and solves problems similar to how humans would.
Machine learning, on the other hand, is a subset of artificial intelligence. It’s when we give machines data and have them learn from that data on their own, without being explicitly programmed. Machine learning models learn from the data and try to make improvements to its predictions over time.”
So machine learning is a subset of the A.I. field, but not all A.I. is machine learning. A.I. is a broader field. Check out our article on what a Machine Learning Engineer does to learn more.
What does an A.I. Engineer do?
A.I. Engineers develop new applications and systems to:
Enhance the performance and efficiency of business processes
Help the business make better decisions
Lower costs
Increase revenue and profits
Simply put, they use software engineering and data science to streamline a business with automation.
Many of an A.I. Engineer’s tasks overlap with those of a Machine Learning Engineer. Some of the responsibilities of an A.I. Engineer include:
Coordinating with business leaders and software development teams to determine what business processes can be improved by using A.I.
Creating and maintaining the A.I. development process and the infrastructure that it runs on.
Applying machine learning techniques for image recognition.
Applying natural language processing techniques to text and voice transcripts to pull insights and analytics from this data.
Building and maintaining chatbots that interact with customers.
Developing AI-driven solutions that mimic human behavior to accomplish repetitive tasks currently done by people.
Building, training, and perfecting machine learning models.
Simplifying the machine learning process so that other business applications can interact with them using APIs.
Building recommendation engines for shopping sites, streaming services, and other applications.
Developing data pipelines that streamline the process of transforming raw data into the structured data necessary for A.I. processes.
An A.I. Engineer definitely needs to know at least one programming language and will usually end up learning multiple during their career. Many of the tools that A.I. Engineers use to make their job easier will require knowledge of Python, R, or Java.
To build and work with machine learning models, an A.I. Engineer will also need to know the fundamentals of various machine learning frameworks, like TensorFlow, Theano, PyTorch, and Caffe. They’ll also need to know how to turn raw data into the features that machine learning models use.
Additionally, an A.I. Engineer must have experience with a variety of machine learning model types and what type of jobs they work best for. These types include:
To actually create new models and understand how they work, an A.I. expert may have to know linear algebra, probability, and statistics instead of using pre-built models. These topics help you understand hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis — the techniques used in machine learning.
Data is also a vital part of an A.I. Engineer’s job. A lot of that data is stored in relational database management systems, so having a basic knowledge of SQL, the language of databases, comes in handy. Still, some of this data will be stored in unstructured or semi-structured data stores — so knowing big data technologies like Apache Spark, Apache Hadoop, Cassandra, and MongoDB is a big plus.
A.I. Engineers require more than technical skills, though. They must also:
Be meticulous and detail-oriented because small inconsistencies in data can cause big discrepancies in machine learning models.
Have excellent communication skills because many of the people they work with won’t understand much of what they do. They’ll have to explain the results of their tasks in a way that anyone can understand.
Be good at big-picture thinking so they can understand business needs and build A.I. systems that benefit the company.
A.I. Engineer salary
A.I. Engineers make good money. The average salary for an A.I. Engineer in the U.S. is over $160,000. In states like California, the average reaches close to $200,000.
The demand for A.I. Engineers has always been high, so expect job openings and pay to increase in the future. The U.S. Bureau of Labor Statistics expects all Software Developer jobs to increase by 22% over the next decade, and this includes A.I. Engineers.
How to become an A.I. Engineer
Gone are the days when a computer science degree or even any college degree would be required to become an A.I. Engineer. Good Artificial Intelligence Engineers are just in too much demand to require a degree, and employers have learned that many skilled A.I. experts don’t even need one. They do it because they love the work.
If A.I. is the career path for you, and you don’t have a degree or want to spend four years learning artificial intelligence, you don’t have to. There are plenty of educational opportunities to learn A.I. online whenever you have the time and wherever you are in the world. Plus, most of the tools you need for the learning process are open-source and freely available online.
If you’re new to artificial intelligence and looking for the best place to start your journey, why not try Codecademy? Since knowing at least one programming language is a prerequisite for becoming an A.I. Engineer, a great place to start is our Learn Python 3 course.
Python is one of the top languages used by Data Scientists and A.I. Engineers. It’s also a requirement of our Learn the Basics of Machine Learning course, which will introduce you to the field. You can also check out our Data Scientist Career Path that covers many of the skills you’ll need as an A.I. Engineer.
While taking these courses, make sure to also learn and work on custom A.I. projects on your own time and add both your course projects and side projects to your portfolio. Also, keep your LinkedIn profile updated with your new learning achievements and projects to make it stand out for recruiters and companies looking for A.I. Engineers. It also pays to practice interviewing skills to be ready when you get a call from a recruiter.
Keep learning today
Never stop learning. A.I. is a broad field, and learning Python and machine learning fundamentals is a great start, but each skill you add to your resume can increase your value to a company. Building Chatbots with Python will teach you how to build software that can carry on conversations like a human. Learn to Program Alexa will teach you how to write software for Amazon’s bot.
For even more courses to build your A.I. skills, check out our machine learning course catalog and revisit the skills section of this article. Good luck with your A.I. career path!
8. Now that you have read the first chapter of “Chasing the Truth,” revisit your response to the warm-up. What did you learn about investigative journalism that you didn’t know? Why is it important? Would you want to be an investigative journalist? Why, or why not?
9. Make a prediction: After finishing the first chapter, how do you think Jodi and Megan will continue their investigation? What are some major steps they will need to take?
10. Finally, based on what you just read, what questions do you have for the authors? (We hope to feature video versions of some of them on our Jan. 27 panel, so if you’d like to ask yours that way, follow Step 2, below.)
Going Further | Plan an Investigative Project
Now that you’ve read Chapter 1, you probably realize that an investigative journalist is likely to spend considerably more time reporting a story than writing it up. Asking the right questions, finding sources who are willing to speak on the record, identifying clear evidence, and checking and double-checking all the facts takes time.
Using your own curiosity, some of the techniques you read about in “Chasing the Truth” and the advice from Megan and Jodi you’ll hear in our “Live Panel for Students: How Investigative Journalism Works,” think about what an investigative journalism project of your own could look like.
Before the Panel
Step 1: Brainstorm your topic.
On your own or with a classmate or small group, brainstorm a list of possible issues, large or small, you’d like to investigate. Keep in mind that you’re likely to have more success investigating topics in your school or community than chasing a national or international story. Why are you interested in these subjects? Why are they relevant or important?
Think about the problems or injustices in your community or school. Consider questions you or others have about how local systems work — or don’t. Think about big investigative pieces you have read in national news outlets, and “localize” them: How does that same issue look in your area? (To help you brainstorm, you might scroll through this list of investigative pieces that have won Pulitzer Prizes or through this list of “21 Excellent Stories of Student Journalism Against the Odds.”)
When you have some ideas, we encourage you to post them and read what other students from around the world have to say in the comments section of our forum. Or, proceed to Step 2 to make a video.
Use Ms. Kantor and Ms. Twohey’s groundbreaking 2017 article as your guide. You can read it either as it was originally published in The Times or via this PDF from their book, in which the authors annotate their work throughout to show you their reporting process.
Two decades ago, the Hollywood producer Harvey Weinstein invited Ashley Judd to the Peninsula Beverly Hills hotel for what the young actress expected to be a business breakfast meeting. Instead, he had her sent up to his room, where he appeared in a bathrobe and asked if he could give her a massage or she could watch him shower, she recalled in an interview.
“How do I get out of the room as fast as possible without alienating Harvey Weinstein?” Ms. Judd said she remembers thinking.
In 2014, Mr. Weinstein invited Emily Nestor, who had worked just one day as a temporary employee, to the same hotel and made another offer: If she accepted his sexual advances, he would boost her career, according to accounts she provided to colleagues who sent them to Weinstein Company executives. The following year, once again at the Peninsula, a female assistant said Mr. Weinstein badgered her into giving him a massage while he was naked, leaving her “crying and very distraught,” wrote a colleague, Lauren O’Connor, in a searing memo asserting sexual harassment and other misconduct by their boss.
“There is a toxic environment for women at this company,” Ms. O’Connor said in the letter, addressed to several executives at the company run by Mr. Weinstein.
An investigation by The New York Times found previously undisclosed allegations against Mr. Weinstein stretching over nearly three decades, documented through interviews with current and former employees and film industry workers, as well as legal records, emails and internal documents from the businesses he has run, Miramax and the Weinstein Company.
During that time, after being confronted with allegations including sexual harassment and unwanted physical contact, Mr. Weinstein has reached at least eight settlements with women, according to two company officials speaking on the condition of anonymity. Among the recipients, The Times found, were a young assistant in New York in 1990, an actress in 1997, an assistant in London in 1998, an Italian model in 2015 and Ms. O’Connor shortly after, according to records and those familiar with the agreements.
Here is the December, 2021 edition of Teenagers in The Times, a roundup of the news and feature stories about young people that have recently appeared across sections of NYTimes.com. We publish a new edition on the first Thursday of each month.
For ideas about how to use Teenagers in The Times with your students, please see our lesson plan and special activity sheet, both of which can be used with this or any other edition.
A 15-year-old accused of killing four of his classmates and wounding seven other people had described wanting to attack the school in cellphone videos and a journal, the authorities said.
Across the country, an unregulated system is severing parents from children, who often end up abandoned by the agencies that are supposed to protect them.
A YouTuber who investigates cold cases found a missing Tennessee teenager’s car submerged in a nearby river. It is at least the fourth such discovery by amateur investigators in two months.
The family of Valentina Orellana Peralta described the terrifying moments before the 14-year-old girl, who had recently moved to Los Angeles from Chile, was killed by a stray bullet fired by a police officer.
Unlike almost anyone else in Hollywood, McGowan had a history
of speaking out against sexism, even if it meant she wouldn’t get movie roles. She once tweeted out the insulting clothing requirements on a casting notice for an Adam Sandler movie: “tank that shows off cleavage (push-up bras encouraged).” In general, her tone on social media
was tough, confrontational: “It is okay to be angry. Don’t be afraid of it,”
she had tweeted a month earlier, later adding: “dismantle the system.”
a
If McGowan, as much an activist as an actress, would not have one off
the-record conversation with Jodi, which would be kept confidential,
who would?
Harvey Weinstein was not the man of the moment. In recent years, his moviemaking magic had faltered. But he still had power, specifically the power to make and boost careers. First he had invented him
self, going from a modest upbringing in Queens, New York, to concert promotion to film distribution and production, and he seemed to know how to make everything around him bigger-films, parties, and most of all, people. Over and over, he had propelled young actors to star
dom: Gwyneth Paltrow, Matt Damon, Michelle Williams, and Jennifer
Lawrence. He could turn tiny independent movies into phenomena. He
had pioneered the modern Oscar awards campaign, winning five Best
Picture statues for himself and armloads for others. His record of rais
ing money for Hillary Clinton, and joining her at countless fundraisers,
was almost two decades long. When President Obama’s daughter Malia
had sought an internship in film, she worked for “Harvey”—first name only, used even by many strangers. By 2017, even though his movies were
less successful than they used to be, his reputation remained outsized.
Rumors had long circulated about his treatment of women. But
Register for the live student panel at 1 p.m. Eastern on January 27.
In October 2017, New York Times journalists Jodi Kantor and Megan Twohey broke the story of the Hollywood producer Harvey Weinstein’s alleged sexual misconduct. Their reporting helped ignite the #MeToo movement and initiate policy changes around the world.
In “Chasing the Truth,” their new book for young people, Ms. Kantor and Ms. Twohey share the methods behind their reporting process as a guide for aspiring journalists. They argue that you don’t have to be a New York Times journalist to conduct investigations that reveal the truth and make a difference in your community.
Ms. Kantor and Ms. Twohey will speak directly to students about investigative journalism at our live panel on Jan. 27. We recommend that classrooms prepare for the panel with the two teaching resources below, which include excerpts from “Chasing the Truth.” We also encourage students to submit video questions by Jan. 20that may be played during the panel.
Prepare for the Panel
Before viewing the panel, we ask teachers and students to use one or more of the following resources, each of which features an excerpt from the book, “Chasing the Truth.”
Our Lesson of the Day invites students to read the first chapter of “Chasing the Truth” and examine how Ms. Kantor and Ms. Twohey began their investigation.
Submit Video Questions
What questions do you have for Ms. Kantor and Ms. Twohey? Submit a video through this formby 11:59 Pacific time on Jan. 20, and we may play it during the panel. Each video should be no longer than 30 seconds.
Students are also invited to submit a short video sharing what they would like to investigate and why. Learn more in our Student Opinion question.
Get to Know the Panelists
For their work on Harvey Weinstein, which helped to shift attitudes, and spur new laws, policies and standards of accountability around the globe, Ms. Kantor and Ms. Twohey, together with a team of colleagues who exposed harassment across industries, were awarded the Pulitzer Prize for public service, journalism’s highest award. They also received or shared in numerous other honors, including a George Polk award and being named to Time magazine list of 100 most influential people of the year.“She Said,” the book Ms. Kantor and Ms. Twohey wrote recounting the Weinstein investigation, was called “an instant classic of investigative journalism” by the Washington Post. “Chasing the Truth” is a young readers’ adaptation of that work.
But before they broke the Weinstein story, both journalists had done extensive reporting that has helped to spur societal change. Jodi Kantor’sarticle about the havoc caused by automated scheduling systems in Starbucks workers’ lives led to changes at the company and helped spark a national fair-scheduling movement. After she and David Streitfeld investigated punishing practices at Amazon’s corporate headquarters, the company changed its human resources policies, introducing paternity leave and eliminating its employee ranking system. And Ms. Kantor’s report on working mothers and breastfeeding inspired two readers to create the first free-standing lactation suites for nursing mothers, now available in hundreds of airports and stadiums. Ms. Kantor has also done extensive reporting on Barack and Michelle Obama, and her best-selling book “The Obamas,” about their behind-the-scenes adjustment to the jobs of president and first lady, was published in 2012.
Megan Twohey has reported on Donald J. Trump, helping to reveal allegations of sexual misconduct against him, his business interests in Russia and illegal efforts to silence two women who claimed they had affairs with him. She exposed an underground network where parents gave away adopted children they no longer wanted to strangers met on the Internet in a practice known as private re-homing. The series, “The Child Exchange,” showed how the dangerous black market took place with no government oversight and at great risk to children, and was a finalist for the 2014 Pulitzer Prize for Investigative Reporting. Ms. Twohey was one of the first journalists to expose how police and prosecutors were shelving DNA evidence collected after sex crimes, robbing victims of the chance for justice. In response to her stories, Illinois passed the first state law mandating the testing of every rape kit.
If you’re getting ready for your first interview for an Object-Oriented Programming (OOPs) role, congratulations! We know you’ve put in a lot of time and effort to get this far. You’re probably now wondering what kinds of OOPs interview questions to expect.
That’s why we’ve put together this list of 13 common OOPs interview questions and how to answer them. Between studying these OOPs-specific questions and our guide on technical interviews, you’ll feel confident and prepared before the big day.
1. What are OOPs?
Object-oriented programming is a programming model based on objects rather than procedures or functions. Objects contain data as well as methods, or functions, that operate on that data.
2. What are the advantages of OOPs?
There are many advantages to OOPs, including:
OOPs abstraction helps make code easier to use and understand.
OOPs reuse code which helps reduce redundancy and save coding time.
OOPs can easily handle and manipulate complex datasets.
OOPs support a lot of flexibility through polymorphism.
3. What are some examples of OOP languages?
The most popular OOP languages are:
4. What are the four main concepts of OOPs?
The four main concepts or principles of OOPs are:
Encapsulation
Polymorphism
Inheritance
Abstraction
5. What is encapsulation?
Encapsulation refers to the consolidation or bundling of all data and methods into a single unit. At the same time, encapsulation hides or restricts any unnecessary data and methods to minimize complexity.
For example, if you define an object that contains a list of integers, your object will always include the method used to calculate the average of those integers, even if you can’t access the source code directly.
6. What is polymorphism?
Polymorphism is the concept of having a single function or method do different things depending on the object class. For example, using the “+” operator will perform a summation if the inputs are numbers. But the same “+” operator will perform concatenation if the inputs are strings. In other words, the “+” is an operator that takes on several forms — it’s polymorphic.
7. What is inheritance?
Inheritance is the concept of a class or object gaining certain properties and methods from a parent class.
For example, suppose we created an “Animals” class with properties like age and weight. If we create a child class called “Mammals,” we might add mammal-specific traits like hair color. But, as a child class of “Animals,” the “Mammals” class would automatically inherit properties of age and weight. That’s how inheritance works in OOPs.
8. What is abstraction?
Abstraction is the concept of hiding unnecessary details about an object, method, or function from someone using your program. For example, when you use a coffeemaker to brew a cup of coffee in the morning, all you need to do is add water and coffee beans. You don’t need to understand the mechanics of your coffeemakers or how the sensors are calibrated.
So, while your code may include a complex function or method, following abstraction means that anyone using your program only needs to provide the necessary inputs for the function or method to work. And the only outputs are those that are useful to the person using your program.
9. What’s the difference between a class and an object?
Both classes and objects are essential components of OOPs. A class is essentially a template, or blueprint, for an object. An object is a member of, or an instance of, a particular class.
In other words, all objects belong to a class that defines the object’s properties and how that object can interact with other objects. And, while you need to declare a class only once, you can create as many objects as you need to — even objects that are part of the same class.
10. What are subclasses and superclasses?
A subclass is a class that inherits from another class. For example, you can create a class called “Animals” that includes properties such as age, weight, and natural habitat. These properties can apply to any animal.
When you create a subclass called “Birds,” the subclass, or child class, inherits everything from the parent “Animals” class — age, weight, and natural habitat — but can also include bird-specific attributes like egg size and wingspan.
A superclass is a class that acts as a parent to one or more child classes or subclasses.
11. What is method overloading?
Method overloading is creating different methods that are defined under different classes but share the same name. For example, if you have two classes, “Dogs” and “Cats,” then you may assign both classes a method with the same name, “Speak.”
For “Dogs,” the “Speak” method outputs “Woof,” while for “Cats,” the method outputs “Meow.” When you create a new object, “MyPet,” and call the “Speak” method, the output will be different, depending on whether “MyPet” falls within the “Dogs” or “Cats” class.
12. What is a constructor?
A constructor is a special method or subroutine of a class that initializes a new object of that class. When the object is first declared or created, the constructor sets values to properties of the object, which can be default values or user-defined. Constructors will have the same names as the class itself, but they’re not considered to be true methods because they don’t have a return type.
13. What is an exception?
An exception is a problem that interrupts program execution. For example, an exception may occur when a user inputs a string into a function that only accepts integers. Proper exception handling can resolve many exceptions — for example, checking that input data is the correct type and displaying a notification that the function only accepts integers.
Be prepared for your next OOPs interview
We know you have what it takes to succeed as an OOPs developer. Once you’ve mastered these OOPs interview questions, make sure to check out our guide to technical interviews as well as our technical portfolio guide to make sure you’re ready for anything. A little interview prep goes a long way toward impressing the hiring manager!
Then, once you’re ready to start practicing with real technical interview questions, test your coding skills with one of our Code Challenges. Not only will they give you a sense of what to expect when you walk into your first technical interview, but they’ll also help you identify the concepts you’ll need to refresh your knowledge of if you find yourself stuck.
For more tips on launching your career in development, visit our Career Center.
Alyse Wicentowski, “Heart” Angel Zhao and Fiona Xing, “teenage homage” Bobby Goldyn, 14, “Lost Voices” Cassie Garrett, 14, “Kindness in the Pandemic” Dorothy Du, 16, “The Sound of Rain” Eliana Robin, 15, “Warning: The World is Crumbling” Eveleen Jiang, 17, “Out of Touch” Hailey Jones, 15, “Growing Up in 2021” Hiewon Ahn, “A Cutlery Awakening” Ilona Lebron, 18, “Painting Something New” James Kim, 17, “Life was rough.” Jingyi Yang, 17, “Daydream in Heatwave” Kalina May, 15, “Shame of Experimenting (in highschool)” Kasie Leung, 15, “neurons and wings and other fleeting things” Kevin Park, 16, “The March of Nature” Kia Brazhnikova, 15, “Impassive” Leona Su, 15, “Chinatown” Phoebe Han, 16, “Burdened Puppet” Selina Zhan, 13; Mia Kang, 14, “Diving into the Device” Sena Chang, “Triptych: INFODEMIC” Stella Turowsky-Ganci, 15, “what’s inside my head” Surya Newa, 17, “To Each Their Own” Xinyi Zhang, 16, “Me in the Mirror” Ziqi, “Nest” Zoey Lestyk, 14, “Just Do It”
Honorable Mentions
Find the full list of students whose submissions received an honorable mention here.
Thank You to Our Judges
The team that helped choose these finalists included educators, Learning Network staff and Times journalists, as well as teenagers who have won previous Learning Network contests. In alphabetical order they were:
Adee Braun, Amanda Brown, William Chesney, Nicole Daniels, Shannon Doyne, Jeremy Engle, Nora Fellas, Ross Flatt, Annissa Hambouz, Henry Hsiao, Michael Gonchar, Karen Hanley, Callie Holtermann, Susan Josephs, Isabel Hui, Isabel Hwang, Phoebe Lett, Simon Levien, Rachel Manley, Sue Mermelstein, John Otis, Natalie Proulx, Katherine Schulten, Ana Sosa, Ananya Udaygiri, Emma Weber, and Clare Zhang
