Problem:

Engauge is a product suite that provides real time feedback on engagement in order to optimize messaging for the target audience. Initially, Engauge will be focused on the education sector with broader future applications in areas such as live entertainment, television, and movies. Studies have shown that students with teachers that “make them feel excited about the future” and a school that “is committed to building the strengths of each student” are 30 times more likely to show engagement in the classroom than students who do not agreement with these statements. (source: http://www.edweek.org/ew/articles/2014/04/09/28gallup.h33.html). According to a report by Gallup education, engagement in the classroom is the key predictor of academic success. Students often have short attention spans and keeping them consistently engaged is a challenging endeavor. Unfortunately, current solutions that measure engagement rely on surveys and student feedback which are not done in real time and can often be extremely inaccurate. The Engauge solution will not only provide more accurate feedback that doesn’t rely on subjective measures, but will also provide this data in real time to teachers. This will allow teachers to adjust on the fly when engagement is slipping in order to ensure that students remain consistently engaged. Engauge will accomplish this by utilizing advanced real-time image and sound processing technology on an individual level.

 

The education sector represents a huge opportunity as represented by the massive scale of private schools within the US. Private school revenue totals $56.7 billion and it is estimated that 5.4 million students attend close to 34,000 private schools with average annual tuition of $13,640 ($22,440 excluding non-sectarian schools). Private schools are continually innovating to attract parents who are willing to pay a large annual fee so that their children can receive a superior education. Engauge will provide private schools with a significant advantage as they will be able to utilize the solution to maximize engagement and utilize analytics to continually improve their teaching methods and subject matter.

 

Solution:

Engauge will utilize advanced real-time image and sound processing technology to measure audience engagement in a variety of contexts. Our initial focus will be on the education sector. Some of the classroom use cases we are most excited about include:

• Diagnostic tool to understand key gaps in material and delivery
• Student-specific trend analysis to identify at-risk students early on
• Course-correct in real-time as students start to lose interest
• Individualized career counseling based on material that most piques each student’s interest

 

The technology that allows us to deliver these powerful insights is driven by a Convolutional Neural Network for both image recognition and natural language processing. The CNN model will collect a number of key inputs into our model:

1. Image:
   1. Identity of student
   2. Facial expression
   3. Eye focus (i.e. where they are looking)
   4. Body posture
   5. Body movements (e.g. fidgeting)
2. Audio:
   1. Tone
   2. Emphasis
   3. Topic / content
   4. Lesson type

 

By pairing the visual data with the audio data, we can deliver powerful insights on how students are engaging with a teacher’s material. The set-up is extremely simple – all that is required is to set up our plug-and-play video camera at the front of your class. Engauge will be a completely SaaS platform, so the feed from the camera will automatically be sent through our algorithms and the output and analysis will be communicated in real-time.

 

More detail on CNN:

There are 4 main operations in the CNN:

1. 1. Convolution: Extract features from the image

 

• Non-linearity: Real-world data is non-linear, so model include those elements

• Pooling: Make input representations smaller and more manageable

• Classification: Classify different components of an image

 

 

Pilot plan:

To apply and demonstrate this new technology, Engauge will initially target K-12 private schools. We believe private schools are the best initial target for Engauge, given how critical student engagement is to academic success. Private schools in particular have the resources to implement the technology and interpret results. They will also be more open to adjusting teaching methods to improve overall engagement levels. Once adopted, we believe parents will be interested in the information as well, and will use this data as a way of comparing potential schools in which to enroll their children.

We intend to pilot Engauge at The University of Chicago Lab School. This school in particular is a perfect playground to launch Engauge, as teachers and parents alike are interested in new technology and will be eager to analyze new data. Engauge monitors will be randomly placed in 50% of Lab School classrooms. Teachers and students will not know which classrooms have Engauge technology installed. For one week, teachers will be asked to fill out a brief survey after each class that assess student engagement. Survey questions will include:

• What topic was discussed in today’s class?
• In chronological order, what activities occurred (lecture, discussion, etc.)?
• On a scale of 1 to 5, how engaged were students in today’s class?
• What part of class did students find most compelling (and what time did this occur)?
• What part of class did students find least compelling (and what time did this occur)?

 

Over the pilot week, we will aggregate data from each grade and compare survey results to Engauge metrics. We believe Engauge will be able to more accurately pinpoint levels of engagement in each class and throughout the school day. Data will reveal which teaching methods worked in keeping students engaged in the subject, and which methods were not as well received. It will also show which teachers have overall higher and lower engagement levels, and also identify specific children that have downward trending engagement.

We believe that overtime, Engauge will be a predictor of test scores and student satisfaction levels. Teachers will be able to improve their teaching methods, as well as experiment with new ways to keep students excited. Principals will be able to use Engauge data in aggregate to assess how well the school is doing as a whole. They will be able to assess which teachers are engaging their students best, and which subjects generate more student excitement. This technology provides a way of assessing new hires and monitor teachers that may be struggling. It will also allow schools to identify at-risk students that are losing interest earlier, and help them get back on the right track with tailored help based on engagement data.

 

Overall, Engauge will equip private schools with better information on how their teachers and students are performing. This technology will promote student participation, which has been shown to lead to better test scores and graduation rates. As Engauge’s algorithm strengthens which each class, we will be able to adapt the technology in other for-profit sectors, as well as public schools that will benefit even more from this data.

 

Funding ask:

We are asking for $90k to fund this pilot with the University of Chicago Lab School. Our technology is already built and ready to be tested in a classroom setting. We believe this pilot will give us the data and insights to launch Engauge to the next level.

Big Health

Opportunity:

Healthy eating has come front and center in consumers’ minds. Half (49%) of global respondents believe they are overweight, and half (50%) are trying to lose weight1. And they’re willing to pay to achieve that goal with consumers paying an average 38% premium for healthy alternatives1. However, today’s consumers lack an accurate and easy way to determine how what they’re about to eat will impact their health, both in the short and long-term. The only out-dated technology that exists today (nutrition labels) is hard to translate to your individual portions, not easy to keep track of, and only available for pre-packaged foods (vs. food in restaurants, for example). So, although consumers know a cheeseburger is not the healthiest choice, since they don’t know exactly how unhealthy it is – they will choose to eat it anyway. And these instances add up over time.

 

Novel augmented intelligence solution:

We, at Big Health, are building the solution to all of these problems. Our hypothesis is that by delivering nutrition facts in an easy-to-consume (i.e. smart dashboards) and salient (i.e. clear impacts on health) way, consumers will be able to fight the urge for that cheeseburger and instead grab a salad. With our app, you can simply snap a picture of food and we will tell you:

• The nutritional facts
• Potential impact on health
• Healthier alternatives

We are able to achieve this through our advanced image recognition algorithm. This algorithm has been trained on photos of food pulled from Facebook, Instagram, and other social media sources and can can recognize the individual components of a dish (e.g. chicken, noodles, etc.). Using the size of the food to estimate portions, the app can now tell you the precise nutritional facts of each component. A separate algorithm will use health care data from millions of patients to show you your overall health picture with specific risks called out. Based on the underlying causes of your health situations, we will tell you whether or not a particular food is risky because of its ingredients. Then, using another model that can simulate the taste of a certain food based on its chemical make-up, we will serve you recommendations for similar dishes that hit the same taste metrics. Armed with all of this information, we believe consumers will start to make healthier choices.

Our plan is to sell this technology as a per user license to health insurance companies and to companies that want to support the health of their employees. Additionally, we will open up another revenue stream from restaurants that want to advertise their healthier alternatives on our platform.

 

Design of empirical demonstration:

We will conduct a study in which we track food consumption across two equally sized and similar sets of users with the test set utilizing the Big Health program and the control set eating as they normally do. The study will be run for a period of sixty days and at the conclusion of the study we will measure average daily calories consumed, macronutrients consumed (protein, carbohydrates, fat), and analyze the overall health quality of food consumed using metrics such as glycemic index and levels of healthy vs unhealthy fats. A successful outcome would be a materially improved health profile of the food consumed by the target group. We will also assess consumer happiness through the use of a variety of daily surveys surrounding areas such as energy levels and overall happiness surrounding food choices.

 

Pilot to reveal its plausibility, promise and appropriate value

 

The pilot will utilize the data obtained in the empirical demonstration in order to build a model to predict the long term positive health impact expected from improved food choices over a longer time period. In this manner, we can quantify the direct benefits to health insurance companies and larger corporations. On the health insurance side, we anticipate a significantly reduced percentage of obesity in patients which will reduce the likelihood of costly health issues such as heart disease and type two diabetes. On the corporate side, we will utilize the data to show the expected increase in worker productivity due to higher levels or energy and focus and a reduced amount of anticipated sick days.

 

Source

1. https://www.nielsen.com/content/dam/nielsenglobal/eu/nielseninsights/pdfs/Nielsen%20Global%20Health%20and%20Wellness%20Report%20-%20January%202015.pdf