Helping teachers teach computer science

Posted by Karen Parker, Education Program Manager and Jason Ravitz, Education Evaluation Manager

(Cross-posted on the Google for Education Blog)

Since 2009, Google’s CS4HS (Computer Science for High School) grant program has connected more than 12,000 computer science (CS) teachers with skills and resources to teach CS in fun and relevant ways. An estimated 600,000 students have been impacted by the teachers who have completed CS4HS professional development workshops so far. Through annual grants, nearly 230 colleges and universities have hosted professional development workshops worldwide.

Grantees use the funds to develop CS curriculum and professional development workshops tailored for local middle and high school teachers. These workshops expose teachers to CS curriculum using real-world applications that spark students’ curiosity. As feedback from those teachers rolls in, we want to share some highlights from what we’ve learned so far.

What went well:

• 89% of participants reported they would recommend their workshop to others
• 44% more participants reported a “high” or “very high knowledge” of CS after their workshop vs. before
• More than half of participants said they would use “most” or “all” of the activities or resources presented during their workshop.
• In 2014 the number of teachers who took part in a CS4HS professional development workshop increased by 50%, primarily due to the funding of multiple MOOCs.

Ways to make a bigger impact:

• Just 53% of participants said they felt a sense of community among the other workshop participants. Research by Joyce & Showers (2002) and Wiske, Stone, & Levinson (1993) shows that peer-to-peer professional development, along with ongoing support, helps teachers implement new content, retain skills, and create lasting change. We’ll explore new ways to build community among participants as we plan future workshops.
• 83% of participants reported being Caucasian, which is consistent with the current demographics of CS educators. This indicates a need to increase efforts in diversifying the CS teacher population.
• Outcome measures show us that the most knowledge gains were among teachers who had no prior experience teaching CS or participating in CS professional development -- a population that made up just 30% of participants. While we see that the workshops are meeting a need, there remains an opportunity to develop materials geared toward more experienced CS teachers while also encouraging more new teachers to participate.

We know there are many challenges to overcome to improve the state of CS teacher professional development. We look forward to sharing new ideas for working in partnership with the CS education community to help address those challenges, in particular by helping more teachers teach computer science.

At the University of Sydney CS4HS workshop teachers are learning how to teach

Computer Science without a computer during a CS Unplugged activity.

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Teach Yourself Deep Learning with TensorFlow and Udacity

Posted by Vincent Vanhoucke, Principal Research Scientist

Deep learning has become one of the hottest topics in machine learning in recent years. With TensorFlow, the deep learning platform that we recently released as an open-source project, our goal was to bring the capabilities of deep learning to everyone. So far, we are extremely excited by the uptake: more than 4000 users have forked it on GitHub in just a few weeks, and the project has been starred more than 16000 times by enthusiasts around the globe.

To help make deep learning even more accessible to engineers and data scientists at large, we are launching a new Deep Learning Course developed in collaboration with Udacity. This short, intensive course provides you with all the basic tools and vocabulary to get started with deep learning, and walks you through how to use it to address some of the most common machine learning problems. It is also accompanied by interactive TensorFlow notebooks that directly mirror and implement the concepts introduced in the lectures.

The course consists of four lectures which provide a tour of the main building blocks that are used to solve problems ranging from image recognition to text analysis. The first lecture focuses on the basics that will be familiar to those already versed in machine learning: setting up your data and experimental protocol, and training simple classification models. The second lecture builds on these fundamentals to explore how these simple models can be made deeper, and more powerful, and explores all the scalability problems that come with that, in particular regularization and hyperparameter tuning. The third lecture is all about convolutional networks and image recognition. The fourth and final lecture explore models for text and sequences in general, with embeddings and recurrent neural networks. By the end of the course, you will have implemented and trained this variety of models on your own machine and will be ready to transfer that knowledge to solve your own problems!

Our overall goal in designing this course was to provide the machine learning enthusiast a rapid and direct path to solving real and interesting problems with deep learning techniques, and were now very excited to share what weve built! It has been a lot of fun putting together with the fantastic team of experts in online course design and production at Udacity. For more details, see the Udacity blog post, and register for the course. We hope you enjoy it!

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