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  • 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.

  • Smart Autofill - Harnessing the Predictive Power of Machine Learning in Google Sheets
    Posted by Konstantin Davydov, Software Engineer and Afshin Rostamizadeh, Research Scientist

    Much of Google’s work on language, speech, translation, and visual processing relies on machine learning, where we construct and apply learning algorithms that make use of labeled data in order to make predictions for new data. What if you could leverage machine learning algorithms to learn patterns in your spreadsheet data, automatically build a model, and infer unknown values?

    You can now use machine learning to make predictions in Google Sheets with the newly launched Smart Autofill Add-on. With a single click, Smart Autofill predicts the missing values of a partially filled column in your spreadsheet by using the data of other related columns. Smart Autofill uses the non-missing data to learn patterns and differs from the standard "Auto-fill" feature of Sheets, which attempts to fill in only simple patterns that it already knows (e.g. calendar dates, days of the week, ordered numbers).

    As an example, in the screenshots below, we give four very simple characteristics of used vehicles (year, number of miles, number of doors, and type: car or truck) as well as the price for some of the vehicles. Since the prices are probably correlated with the characteristics of the vehicle, we can use Smart Autofill to estimate what the missing prices should be. The rows that do contain a price will be used as examples to learn from in order to fill in the rows with a missing price.

    Smart Autofill uses Google's cloud-based machine learning service Prediction API, which trains several linear as well as non-linear classification and regression models. The best model is automatically chosen for your problem by finding the one with the smallest misclassification error (for categorical data) or root-mean-squared error (for numeric data) calculated by using cross-validation on the labeled (non-empty) set of examples.

    To use Smart Autofill, after following the installation procedure, simply select "Add-ons > Smart Autofill > Start" which will open a sidebar. Select a block of data that includes the column to Autofill and click "Next". Finally, from the selected data, choose a target column to Autofill and click "Start" (Figure 1). Now just sit back as Smart Autofill does its work and fills in the missing values (Figure 2).
    Figure 1: Highlighting the dataset and selecting the target column.
    Figure 2: After clicking "Start" a model is trained and applied to automatically fill in the missing values of the target column. Note, the estimated error of the model is reported in the sidebar.
    An estimate of the error-rate of the model (based on the non-missing data) is shown in the sidebar after the missing values are filled. The accuracy of Smart Autofill (as well as the accuracy of the estimated error) depends on many factors, including the amount and quality of the data provided. While not all datasets will be ideally suited for machine learning, we hope our more in-depth tutorial will provide an idea of the range of problems where Smart Autofill can be effective.

    While the vehicle pricing example is relatively simple (in reality used vehicle prices are a function of more than just four variables), more complex datasets could have many more non-target columns as well as data rows. Also, the target column does not need to be numeric, since Smart Autofill can also predict categorical values (i.e. in the car example the target column value could have contained the categories "expensive", "moderate", "affordable" instead of price). Other illustrative scenarios include:

    • You have a spreadsheet that holds the results of a customer survey, but one of the columns (e.g. "overall satisfaction 1-5") has some missing values. If the other columns of the survey can help indicate overall satisfaction then you can try using Smart Autofill to estimate the missing values.
    • You keep a spreadsheet of restaurants that you've visited and their characteristics (type: Italian, ambiance: quiet, cost: $$$, etc.) and whether you enjoyed the restaurant or not. Now you can add the characteristics of new restaurants to your spreadsheet and use Smart Autofill to guess at which ones you might enjoy.

    The example dataset and more detailed tutorial for the add-on can be found here. We hope you discover new and useful ways to incorporate the predictive power of machine learning with your data.

  • All the News that's Fit to Read: A Study of Social Annotations for News Reading
    Posted by Chinmay Kulkarni, Stanford University Ph.D candidate and former Google Intern, and Ed H. Chi, Google Research Scientist

    News is one of the most important parts of our collective information diet, and like any other activity on the Web, online news reading is fast becoming a social experience. Internet users today see recommendations for news from a variety of sources; newspaper websites allow readers to recommend news articles to each other, restaurant review sites present other diners’ recommendations, and now several social networks have integrated social news readers.

    With news article recommendations and endorsements coming from a combination of computers and algorithms, companies that publish and aggregate content, friends and even complete strangers, how do these explanations (i.e. why the articles are shown to you, which we call “annotations”) affect users' selections of what to read? Given the ubiquity of online social annotations in news dissemination, it is surprising how little is known about how users respond to these annotations, and how to offer them to users productively.

    In All the News that’s Fit to Read: A Study of Social Annotations for News Reading, presented at the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems and highlighted in the list of influential Google papers from 2013, we reported on results from two experiments with voluntary participants that suggest that social annotations, which have so far been considered as a generic simple method to increase user engagement, are not simple at all; social annotations vary significantly in their degree of persuasiveness, and their ability to change user engagement.
    News articles in different annotation conditions
    The first experiment looked at how people use annotations when the content they see is not personalized, and the annotations are not from people in their social network, as is the case when a user is not signed into a particular social network. Participants who signed up for the study were suggested the same set of news articles via annotations from strangers, a computer agent, and a fictional branded company. Additionally, they were told whether or not other participants in the experiment would see their name displayed next to articles they read (i.e. “Recorded” or “Not Recorded”).

    Surprisingly, annotations by unknown companies and computers were significantly more persuasive than those by strangers in this “signed-out” context. This result implies the potential power of suggestion offered by annotations, even when they’re conferred by brands or recommendation algorithms previously unknown to the users, and that annotations by computers and companies may be valuable in a signed-out context. Furthermore, the experiment showed that with “recording” on, the overall number of articles clicked decreased compared to participants without “recording,” regardless of the type of annotation, suggesting that subjects were cognizant of how they appear to other users in social reading apps.

    If annotations by strangers is not as persuasive as those by computers or brands, as the first experiment showed, what about the effects of friend annotations? The second experiment examined the signed-in experience (with Googlers as subjects) and how they reacted to social annotations from friends, investigating whether personalized endorsements help people discover and select what might be more interesting content.

    Perhaps not entirely surprising, results showed that friend annotations are persuasive and improve user satisfaction of news article selections. What’s interesting is that, in post-experiment interviews, we found that annotations influenced whether participants read articles primarily in three cases: first, when the annotator was above a threshold of social closeness; second, when the annotator had subject expertise related to the news article; and third, when the annotation provided additional context to the recommended article. This suggests that social context and personalized annotation work together to improve user experience overall.

    Some questions for future research include whether or not highlighting expertise in annotations help, if the threshold for social proximity can be algorithmically determined, and if aggregating annotations (e.g. “110 people liked this”) help increases engagement. We look forward to further research that enable social recommenders to offer appropriate explanations for why users should pay attention, and reveal more nuances based on the presentation of annotations.

  • Announcing the Google CS Engagement Small Awards Program
    Posted by Leslie Yeh Johnson, University Relations

    (cross-posted on the Google for Education blog)

    College students are more interested than ever in studying computer science. There has been an unprecedented increase in enrollment in Computer Science undergraduate programs over the past six years. Harvard University’s popular introductory CS course CS50 has recently claimed the spot as the most enrolled course on campus. An astounding 50% of Harvey Mudd’s graduates received engineering degrees this year. However, while the overall number of students in introductory computer science courses continue to climb, the number of students who go on to complete undergraduate degrees in this field, particularly among women and under-represented minorities, does not match this increase in individual course enrollment (2013 Taulbee Survey).

    Recent findings show that while students may begin a CS degree program, retaining students after their first year remains an issue. Research indicates that one of the strongest factors in the retention of students in undergraduate CS degrees is early exposure to engaging courses and course material, such as high quality assignments that are meaningful and relevant to the student’s life or classroom activities that encourage student-to-student interaction. When an instructor or department imbeds these practices into the introductory CS classroom, students remain excited about CS and are more likely to complete their undergraduate CS degree.

    At Google we believe in the importance of preparing the next generation of computer scientists. To this end, we’ve created the CS Engagement Small Grants Program to support educators teaching introductory computer science courses in reaching their engagement and retention goals. We’ll give unrestricted gifts of $5,000 to the selected applicants’ universities, towards the execution of engaging CS1 or CS2 courses in the 2014-2015 school year. We encourage educators who are teaching CS1 and CS2 courses at the post-secondary level to apply to the Google CS Engagement Small Grants Program. Applications will be accepted through November 15, 2014 and will be evaluated on an ongoing basis. If you’re interested in applying, please check out the Call for Proposals.

  • Sudoku, Linear Optimization, and the Ten Cent Diet
    Posted by Jon Orwant, Engineering Manager

    (cross-posted on the Google Apps Developer blog, and the Google Developers blog)

    In 1945, future Nobel laureate George Stigler wrote an essay in the Journal of Farm Economics titled The Cost of Subsistence about a seemingly simple problem: how could a soldier be fed for as little money as possible?

    The “Stigler Diet” became a classic problem in the then-new field of linear optimization, which is used today in many areas of science and engineering. Any time you have a set of linear constraints such as “at least 50 square meters of solar panels” or “the amount of paint should equal the amount of primer” along with a linear goal (e.g., “minimize cost” or “maximize customers served”), that’s a linear optimization problem.

    At Google, our engineers work on plenty of optimization problems. One example is our YouTube video stabilization system, which uses linear optimization to eliminate the shakiness of handheld cameras. A more lighthearted example is in the Google Docs Sudoku add-on, which instantaneously generates and solves Sudoku puzzles inside a Google Sheet, using the SCIP mixed integer programming solver to compute the solution.
    Today we’re proud to announce two new ways for everyone to solve linear optimization problems. First, you can now solve linear optimization problems in Google Sheets with the Linear Optimization add-on written by Google Software Engineer Mihai Amarandei-Stavila. The add-on uses Google Apps Script to send optimization problems to Google servers. The solutions are displayed inside the spreadsheet. For developers who want to create their own applications on top of Google Apps, we also provide an API to let you call our linear solver directly.
    Second, we’re open-sourcing the linear solver underlying the add-on: Glop (the Google Linear Optimization Package), created by Bruno de Backer with other members of the Google Optimization team. It’s available as part of the or-tools suite and we provide a few examples to get you started. On that page, you’ll find the Glop solution to the Stigler diet problem. (A Google Sheets file that uses Glop and the Linear Optimization add-on to solve the Stigler diet problem is available here. You’ll need to install the add-on first.)

    Stigler posed his problem as follows: given nine nutrients (calories, protein, Vitamin C, and so on) and 77 candidate foods, find the foods that could sustain soldiers at minimum cost.

    The Simplex algorithm for linear optimization was two years away from being invented, so Stigler had to do his best, arriving at a diet that cost $39.93 per year (in 1939 dollars), or just over ten cents per day. Even that wasn’t the cheapest diet. In 1947, Jack Laderman used Simplex, nine calculator-wielding clerks, and 120 person-days to arrive at the optimal solution.

    Glop’s Simplex implementation solves the problem in 300 milliseconds. Unfortunately, Stigler didn’t include taste as a constraint, and so the poor hypothetical soldiers will eat nothing but the following, ever:

    • Enriched wheat flour
    • Liver
    • Cabbage
    • Spinach
    • Navy beans

    Is it possible to create an appealing dish out of these five ingredients? Google Chef Anthony Marco took it as a challenge, and we’re calling the result Foie Linéaire à la Stigler:
    This optimal meal consists of seared calf liver dredged in flour, atop a navy bean purée with marinated cabbage and a spinach pesto.

    Chef Marco reported that the most difficult constraint was making the dish tasty without butter or cream. That said, I had the opportunity to taste our linear optimization solution, and it was delicious.

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