Investing In CS4All: Training Teachers and Helping Them Build Curricula

A. Jesse Jiryu Davis
October 6, 2016 | Updated: September 19, 2022
#Engineering#EngineeringBlog

Until last year, Jeremy Mellema was a history teacher. Now, he's teaching computer programming. When I visited his class in the Bronx this month, he had 30 students with 30 MacBooks, completing exercises in Python. They had just finished a lesson on data types, and now they were tackling variables. In Jeremy's class, the first variable assignment is:

tupac = "Greatest of All Time!!"

Computer science for all

A year ago, New York City mayor Bill de Blasio announced Computer Science for All, an $80 million public-private partnership. The goal is to teach computer science to every student at every public school. But first, the schools need curricula and 5000 teachers need training.

Here at MongoDB, our VP of Education Shannon Bradshaw oversees MongoDB University, which trains IT professionals to use MongoDB. When he heard about CS4All, he wanted us to contribute. He proposed that we set aside budget for two paid fellowships, and recruit public school teachers to spend the summer with us. We would develop them as teachers, and help build curricula they could take back into schools this fall. MongoDB staff would share our expertise, our office space, our equipment, and the MongoDB software itself.

Shannon pitched his proposal to the company like this: "As many of us know, it’s still unusual for students to encounter computer science, let alone databases, in their classrooms before entering college. I believe this absence directly contributes to the gender and racial disparity we see today across our industry." The CS4All project improves access to these subjects for many more students in our city, and MongoDB could be part of it from the beginning.

The teachers we hired for the summer are at opposite ends of a spectrum. At the more technical end is Tim Chen. He majored in Math and took some CS classes in college. When he applied to work for the NYC Department of Education, there was a job opening for a Tech Teacher. "I didn’t know what 'tech' meant at the time,” he said, “but I lucked out because it was teaching really basic software engineering." His first year teaching, he taught the 9th grade curriculum for the Software Engineering Program. Now, he's an experienced CS teacher, and his students this fall are comparatively advanced programmers. He spent his summer at MongoDB outlining an ambitious two-year curriculum in Python and Javascript.

Jeremy Mellema, on the other hand, was teaching 10th grade world history when he was tapped to teach a CS class last year. He took an intensive training with the NYC Department of Education's Software Engineering Program, where he spent a day apiece working in Python, HTML, CSS, JavaScript, Scratch, and Arduino. That fall was a struggle. "It was pretty overwhelming," he said. Jeremy is a skilled teacher, but on the topic of software, he "just didn’t know a whole lot."

Jeremy joined us this summer to meet software engineers and learn to code more like a professional. His presence is particularly valuable because he's not a specialist: he'll ensure the curriculum he builds can be taught by other teachers who aren't already coders. Shannon assigned him the book How to Think Like a Computer Scientist , and he spent the summer turning its contents into something high schoolers can learn from. He told me, "I was excited to actually get my hands dirty with what it means to be a programmer and learn how to do things besides basic Scratch programming, drag and drop, and to see how you actually use it in the real world."

Back to school

Now that school is back in session, I'm watching Tim and Jeremy's progress with fascination. Their students come to CS from a different angle than I did: I have the typical background of a computer programmer in this country, with college-educated parents and all the privileges that typically pave the way to a career in software. I taught myself to code in high school and got a Bachelor's in CS from Oberlin College. But the students in Tim and Jeremy's classes nearly all come from low-income families and qualify for federal meal assistance. These students have self-selected into the computer track, and they are mostly boys; but there are a half-dozen girls in both classes. The majority are Black or Hispanic, and Jeremy's class also includes many recent immigrants from the Carribbean and Bangladesh. If young people like them succeed as software engineers, it will go a long way to addressing the inequalities of our industry and our society as a whole.

But the goal for Tim and Jeremy this year is more modest. They will prove and refine their curricula in their classrooms; then the materials can be used by any teacher in the New York City public schools. I visited their classes to watch their plans being put to the test.

The Bronx: Jeremy's class

When I saw Jeremy at Bronx Compass High School, he'd had the students for one week. He was nervous the month before about how class would go: "I’m always afraid it’s going to bomb, or the students won’t find it interesting." But the kids were hacking enthusiastically. Watching them reminded me of my own joy when I first learned to code.

Class time is spent working independently while Jeremy checks in with each student. The kids present a huge range of skills: some have taken computer science classes for several years, and others have just moved to New York and are getting their first exposure to CS. Jeremy worries about challenging all his students according to their level.

An advanced student, Tatyana, is able to use this wide skill range to make the class more effective for the novice students. I noticed her sitting sideways in her chair so she could coach the students to her side and behind her. "It's nice to help people who struggle with coding because it's like a mixture of math and grammar,” she said. “When you write 'print', if you write it with a capital P your code won't work. It's like teaching them a new language." Tatyana plans to keep coding after high school: "I like making things work the way I want them to work."

Since my formal CS education began in college, I've never sat in a high school programming class. To my surprise, 30 students can be trusted to stay on task, even when they're on laptops with the whole Internet in reach. Jeremy doesn't mind when they take detours to the Web. He said, "If they just need YouTube to play music to drown out their classmates that's fine." I saw his student Amaury with headphones on and YouTube playing in the background in order to concentrate, the same way I use music at the MongoDB office. Jeremy gives him space to work in his own style: "It's fine. As soon as I ask Amaury a question, his headphones are off and he answers."

I asked Amaury to take off his headphones for a minute and tell me how the class was going. "It's cool learning how a computer works using just a bunch of freaking inputs. Put in a couple lines of code and you can have the computer do crazy stuff." Then his headphones went back on and he got back to hacking.

By the end of this year, Jeremy hopes his kids will advance from sandboxed programming environments to real-world tools—that they'll install Python on their computers and code on their own. "The kids I’ve seen really do well,” he said, “this will open the door for them to take the world by storm. There’s a lot of really smart and talented kids who are not in an advantaged place. This will put them at an advantage."

Hell's Kitchen: Tim's class

Tim Chen teaches a few blocks from the MongoDB office, at Urban Assembly Gateway School for Technology on the west side of Manhattan. His students have specialized in CS more than Jeremy's, and he has more time with them: 45 minutes a day, 5 days a week. His curriculum is accordingly more ambitious. This year he's teaching introductory programming in Python, aligned with the upcoming version of the Advanced Placement test in Computer Science Principles. Beginning with Python makes entry to the subject easy for Tim's students, but by the end of the year they'll cover nearly every topic of a first semester college class. Next year they’ll switch to Javascript. They'll build web applications with MongoDB, Express, and Node. These three technologies (along with Angular.js) make up the famous MEAN Stack, which Tim learned this summer from MongoDB University.

When I asked Tim how his curriculum performed in its first week, he said there'd been no surprises yet—but he's still in the process of writing it. Only the first semester's material is complete. "My greatest fear is that they’ll move faster than I can create it," he said. Still, he expects his students to be forgiving if they hit any bumps. "I told them this is my first year trying this out, so we're going to try things, and if it doesn't work out we're going to try different things. They're cool with it."

Time to scale up

Teaching one class in Hell's Kitchen and one in the Bronx is only the start. Tim wants to create a curriculum that any teacher can pick up and deliver to their students. Jeremy has the same goal, and because of his background as a history teacher, rather than a computer scientist, he is focused on making his course effective for teachers like him.

For Shannon, this teacher-training is the most unexpected, and the most inspiring, aspect of our involvement. "Most of the people who will teach computer science in the New York City public schools are transitioning from another discipline,” he said. “They’re never going to be hardcore software engineers. They’re professional teachers; that’s what they want to do." But in nine years, computer science will be a core class in New York City, and the existing staff will have to teach it. "If we can contribute to figuring that out, how to make this transition as effective as possible for computing education in New York City, then we’ve gone miles beyond where I imagined us going."

← Previous

Getting Started with Python, PyMODM, and MongoDB Atlas

What is PyMODM PyMODM is an object modeling package for Python that works like an Object Relational Mapping (ORM) and provides a validation and modeling layer on top of PyMongo (MongoDB’s Python driver). Developers can use PyMODM as a template to model their data, validate schemas, and easily delete referenced objects. PyMODM can be used with any Python framework, is compatible with Python 3, and is supported by MongoDB. Benefits of PyMODM PyMODM allows developers to focus more on developing application logic instead of creating validation logic to ensure data integrity. Some key benefits of PyMODM are: Field Validation. MongoDB has a dynamic schema, but there are very few production use cases where data is entirely unstructured. Most applications expect some level of data validation either through the application or database tier. MongoDB provides Document Validation within the database. Users can enforce checks on document structure, data types, data ranges, and the presence of mandatory fields. Document validation is useful for centralizing rules across projects and APIs, as well as minimizing redundant code for multiple applications. In certain cases, application side validation makes sense as well, especially when you would like to obviate the need for a round trip between the application and database. PyMODM provides users the ability to define models and validate their data before storing it in MongoDB, thus eliminating the amount of data validation logic developers need to write in the application tier. Built In Reference Handling. PyMODM has built in reference handling to make development simpler. Developers don’t have to plan on normalizing data as much as they would with an RDBMS. PyMODM can automatically populate fields that reference documents in other collections, in a similar way to foreign keys in a RDBMS. For example, you might have a model for a blog post that contains an author. Let’s say we want to keep track of these entities in separate collections. The way we store this in MongoDB is to have the _id from the author document be stored as an author field in the post document: { "title": "Working with PyMODM", "author": ObjectId('57dad74a6e32ab4894ea6898') } If we were using the low-level driver, we would just get an ObjectId when we accessed post['author'] , whereas PyMODM will lazily dereference this field for you: >>> post.author Author(name='Jason Ma') In other words, PyMODM handles all the necessary queries to resolve referenced objects, instead of having to pull out the ids yourself and perform the extra queries manually. PyMODM also provides several strategies for managing how objects get deleted when they are involved in a relationship with other objects. For example, if you have a Book and Publisher class, where each Book document references a Publisher document, you have the option of deleting all Book objects associated with that Publisher. Familiar PyMongo Syntax. PyMODM uses PyMongo-style syntax for queries and updates, which makes it familiar and easy to get started with for those already familiar with PyMongo. Installing PyMODM Getting started with PyMODM is simple. You can install PyMODM with pip . pip install pymodm Connecting to MongoDB Atlas For developers that are interested in minimizing operational database tasks, MongoDB Atlas is an ideal option. MongoDB Atlas is a database as a service and provides all the features of the database without the heavy lifting of setting up operational tasks. Developers no longer need to worry about provisioning, configuration, patching, upgrades, backups, and failure recovery. Atlas offers elastic scalability, either by scaling up on a range of instance sizes or scaling out with automatic sharding, all with no application downtime. Setting up Atlas is simple. Select the instance size that fits your application needs and click “CONFIRM & DEPLOY”. Connecting PyMODM to MongoDB Atlas is straightforward and easy. Just find the connection string and plug it into the ‘connect’ method. To ensure a secure system right out of the box, authentication and IP Address whitelisting are automatically enabled. IP address whitelisting is a key MongoDB Atlas security feature, adding an extra layer to prevent 3rd parties from accessing your data. Clients are prevented from accessing the database unless their IP address has been added to the IP whitelist for your MongoDB Atlas group. For AWS, VPC Peering for MongoDB Atlas is under development and will be available soon, offering a simple, robust solution. It will allow the whitelisting of an entire AWS Security Group within the VPC containing your application servers. from pymodm import connect #Establish a connection to the database and call the connection my-atlas-app connect( 'mongodb://jma:PASSWORD@mongo-shard-00-00-efory.mongodb.net:27017,mongo-shard-00-01-efory.mongodb.net:27017,mongo-shard-00-02-efory.mongodb.net:27017/admin?ssl=true&replicaSet=mongo-shard-0&authSource=admin', alias='my-atlas-app' ) In this example, we have set alias=’my-atlas-app’ . An alias in the connect method is optional, but comes in handy if we ever need to refer to the connection by name. Remember to replace “PASSWORD” with your own generated password. Defining Models One of the big benefits of PyMODM is the ability to define your own models and apply schema validation to those models. The below examples highlight how to use PyMODM to get started with a blog application. Once a connection to MongoDB Atlas is established, we can define our model class. MongoModel is the base class for all top-level models, which represents data stored in MongoDB in a convenient object-oriented format. A MongoModel definition typically includes a number of field instances and possibly a Meta class that provides settings specific to the model: from pymodm import MongoModel, fields from pymongo.write_concern import WriteConcern class User(MongoModel): email = fields.EmailField(primary_key=True) first_name = fields.CharField() last_name = fields.CharField() class Meta: connection_alias = 'my-atlas-app' write_concern = WriteConcern(j=True) In this example, the User model inherits from MongoModel , which means that the User model will create a new collection in the database ( myDatabase.User ). Any class that inherits directly from MongoModel will always get it’s own collection. The character fields ( first_name , last_name ) and email field ( email ) will always store their values as unicode strings. If a user stores some other type in first_name or last_name (e.g. Python ‘bytes’) then PyMODM will automatically convert the field to a unicode string, providing consistent and uniform access to that field. A validator is readily available on CharField, which will validate the maximum string length. For example, if we wanted to limit the length of a last name to 30 characters, we could do: last_name = fields.CharField(max_length=30) For the email field, we set primary_key=True . This means that this field will be used as the id for documents of this MongoModel class. Note, this field will actually be called _id in the database. PyMODM will validate that the email field contents contain a single ‘@’ character. New validators can also be easily be created. For example, the email validator below ensures that the email entry is a Gmail address: def is_gmail_address(string): if not string.endswith(‘@gmail.com’): raise ValidationError(‘Email address must be valid gmail account.’) class User(MongoModel): email = fields.EmailField(validators=[is_gmail_address]) Here, PyMODM will validate that the email field contains a valid Gmail address or throw an error. PyMODM handles field validation automatically whenever a user retrieves or saves documents, or on-demand. By rolling validation into the Model definition, we reduce the likelihood of storing invalid data in MongoDB. PyMODM fields also provide a uniform way of viewing data in that field. If we use a FloatField, for example, we will always receive a float , regardless of whether the data stored in that field is a float, an integer, or a quoted number. This mitigates the amount of logic that developers need to create in their applications. Finally, the last part of our example is the Meta class, which contains two pieces of information. The connection_alias tells the model which connection to use. In a previous code example, we defined the connection alias as my-atlas-app . The write_concern attribute tells the model which write concern to use by default. You can define other Meta attributes such as read concern, read preference, etc. See the PyMODM API documentation for more information on defining the Meta class . Reference Other Models Another powerful feature of PyMODM is the ability to reference other models. Let’s take a look at an example. from pymodm import EmbeddedMongoModel, MongoModel, fields class Comment(EmbeddedMongoModel): author = fields.ReferenceField(User) content = fields.CharField() class Post(MongoModel): title = fields.CharField() author = fields.ReferenceField(User) revised_on = fields.DateTimeField() content = fields.CharField() comments = fields.EmbeddedDocumentListField(Comment) In this example, we have defined two additional model types: Comment and Post . Both these models contain an author , which is an instance of the User model. The User that represents the author in each case is stored among all other Users in the myDatabase.User collection. In the Comment and Post models, we’re just storing the _id of the User in the author field. This is actually the same as the User’s email field, since we set primary_key=True for the field earlier. The Post class gets a little bit more interesting. In order to support commenting on a Post , we’ve added a comments field, which is an EmbeddedDocumentListField . The EmbeddedDocumentListField embeds Comment objects directly into the Post object. The advantage of doing this is that you don’t need multiple queries to retrieve all comments associated with a given Post . Now that we have created models that reference each other, what happens if an author deletes his/her account. PyMODM provides a few options in this scenario: Do nothing (default behaviour). Change the fields that reference the deleted objects to None . Recursively delete all objects that were referencing the object (i.e. delete any comments and posts associated with a User). Don’t allow deleting objects that have references to them. If the deleted object was just one among potentially many other references stored in a list, remove the references from the list. For example, if the application allows for Post to have multiple authors we could remove from the list just the author who deleted their account. For our previous example, let’s delete any comments and posts associated with a User that has deleted his/her account: author = fields.ReferenceField(User, on_delete=ReferenceField.CASCADE) This will delete all documents associated with the reference. In this blog, we have highlighted just a few of the benefits that PyMODM provides. For more information on how to leverage the powerful features of PyMODM, check out this github example of developing a blog with the flask framework. Summary PyMODM is a powerful Python ORM for MongoDB that provides an object-oriented interface to MongoDB documents to make it simple to enforce data validation and referencing in your application. MongoDB Atlas helps developers free themselves from the operational tasks of scaling and managing their database. Together, PyMODM and MongoDB Atlas provide developers a compelling solution to enable fast, iterative development, while reducing costs and operational tasks. Get Started with PyMODM

October 5, 2016
Next →

Women Leaders at MongoDB: Why Kanika Khurana is Leading with Transparency

March is Women’s History Month. Our women leaders series highlights MongoDB women who are leading teams and empowering others to own their career development and build together. Kanika Khurana, Technical Services Manager, shares how she leads with transparency, the importance of taking smart risks, and enabling team members to have the “courage to fall and rise again”. Tell me a bit about your team. I oversee the Cloud Technical Services team in India. Our team provides technical advice and support to MongoDB customers by acting as subject matter experts to clear blockers and recommend best practices, enabling customers to build next-generation applications. What characteristics make a good leader? I think that a good leader comes to know and value their employees' unique skills and abilities. They determine how to capitalize on their team’s strengths and tweak the environment to meet their larger goals. By taking the time to understand each employee, a great manager shows that they see their people for who they are. Have you faced any challenges as a woman growing your career in leadership? One of the criticisms I’ve faced over the years is that I’m an emotional thinker, which somehow hampers my decision-making. However, while I tend to be a more relationally-oriented decision maker, I’ve used this characteristic to help advance my career. Listening to and involving team members in essential conversations has enabled me to make more logical, reasonable, and healthier decisions. What is the biggest lesson you’ve learned throughout your career? The best leaders are transparent. They admit mistakes, ask for forgiveness, and make bad situations right. These “failures” aren’t signs of weakness but rather strengths. Mistakes are inevitable, and what we learn from them is what determines the course of our success. Trying to look perfect isn’t authentic, creates stress, and models unhealthy perfectionism. Through transparency, you build stronger relationships and an environment where a commitment to doing the right thing impacts the culture and the bottom line. The best thing you could do is to offer an Eden to your team, which allows them to grow and thrive, rather than creating an environment where the fear of making a mistake overtakes the courage to fall and rise again. What’s your advice to other women looking to grow their careers as leaders? I advise other women to be brave and take risks. Sticking to the safest option can be tempting, but you are unlikely to achieve growth and innovation if you’re not open to new steps or strategies. Of course, risks should be calculated, but carefully considering risks can progress your career. Be a little risky, take a leap, give it a try, speak up, and be kind but convicted in your effort to take a seat at the table. Join us to make an impact on your career and the future of technology. Find open roles on our careers site today.

March 21, 2023