Edualign

Over 1 million students enter countries like the United States for higher studies. Needless to say, almost all students aim to maximize support and opportunities during the period; a lion's share of students fail to seize the correct opportunities. Especially, in countries where education fairs are scarce, international communications are narrow and the curriculum is vastly different. In a survey by Cognita, over 85% of students who applied reported to have missed more relevant opportunities. 

While generalizing the first year, up to some extent, eased the issue with choosing unaligned majors; problems exist with scholarships, grants, programs, clubs, research facilities, university listing, etc. Unawareness exists vastly in developing and under-developed countries and thus more achieving and aspiring students miss valuable opportunities.

There remain agencies and brokers that mostly enforce being admitted to partner universities in exchange for guidance. No opportunity-based portals offer personalized filtering and some organizations which filter often do it through preferences and surveys rather than activities making for a much less efficient and non-practical experience.

With 73% of global schools lacking counselors guiding students with personalized opportunities, there requires a solution that can create personalized instruction based on a student's reports not only with results and preferences but all other fields.

Edualign allows students to independently enlist their results, ECAs, interests, and preferences, not within restrictions of quizzes or even surveys, but through open-ended input fields. Natural Language Processing (particularly automated topic and sub-topic modeling) extracts the quality and quantity of activities, interests, experience, and expertise to cross-check big-data sources of opportunities from subjects to clubs to scholarships. 

For example, if someone showed previous achievements on Climate Science Olympiad, attended agricultural conferences, and had certain interests in problem-solving, he/she will be alerted for opportunities like the World Food Program Agricultural Accelerator or Thought For Food Business Challenge. Similarly, if any displaced student showed aspiration for studying in the United States, Edualign will notify during the session of the Columbia University Scholarship for Displaced Students. Preferences, results, and interests may also act as a distinguishing factor as to which major from which university is most aligned with someone. 

The solution consists of behavioral and response-oriented analytic technologies centering around inferential models in NLP that allow personalized instruction on opportunities breaking through randomness and confusion amidst the vast world. It acts as a proxy to trained counselors providing personalized tracks and opportunity-based counseling.

The solution serves all aspiring students aiming to complete higher studies but is devoid of trained academic counselors. Currently, we have generated a back-end for all USA aspiring undergraduate and post-graduate students but the capacity is soon to increase exponentially. As of now, we aim to bring the power of analytics in channeling relevant opportunities and providing actionable insights. This is further extended as the platform can be used to directly connect alumni, instructors, and freelance counselors to underserved students as well. Our first batch of research and development is occurring with 1200 students from Bangladesh across 24 districts including some of the remotest regions. From A-Z of the process to tracking and notifying of the correct opportunities befitting their profile, we are speeding up with a vision to arrange a well-structured pilot by next month.

Our teams are extremely well-positioned for the following reasons:

1) Root-Level Activities: We are working directly with the students alongside Cognita Counseling which is helping us gather complete insights in urban regions and DYDF (a 25-year old NGO working across 1000 villages) in rural areas.

2) High Alumni Support: One alumnus is present in our annotation team from almost all universities across the USA providing accurate and structured opportunity information for the creation of the back-end.

3) NLP expertise: We have been working across advanced Natural Language Processing, especially in Deep Analytics, Sentiment Modeling, Topic Modeling, etc. since 2016 and have reached Seed Stage as a startup.

4) Scaling Experience: From Managing Director of Founders' Institute to Country Representative of Seedstars, we have high expertise on how to scale startups and solutions the proper way.

5) Diverse Partnerships: Our sister concerns and partners include BRAC (one of the largest NGOs in Bangladesh), Biddyanondo (excelling in women empowerment), DYDF (excelling in rural development), Cognita (excelling in foreign studies counseling), YouthOp (excelling in Youth opportunity enlisting, etc.)

6) Deep Investment Networks; We have ourselves run top accelerators like the Grameenphone Accelerator and have wide opportunities for fundraising.

7) Personal Background: This solution was inspired by the personal problem of the head of the initiative Adib Ahnaf (COO of Socian Ltd) who after completing High School faced huge challenges for lack of a counselor and opportunity tracking facilities. He is currently 19 and was accepted at the University of British Columbia.

• Our major target for applying at SOLVE is to get assistance in properly capturing the opportunities across all universities in North America. We aim to find a partner that may use our NLP extraction algorithm alongside their networks to find and annotate all opportunities at the earliest periods.
• Secondly, while we have scaled multiple companies, we still have pretty little experience in global expansion and development. We aim to hold partnerships across multiple nations to scale the product.
• Solve can be a platform for major marketing and publicity which will be crucial for gaining students to use the service.
• The credibility and authenticity of MIT SOLVE will enable us to gain trust in underserved regions.

Expression, as we know it, is purest without restriction. This is not the first solution that tried to personalize and use analytics to build college lists but this is the first to do so in a much better manner for two very significant points:

The first is that it extracts from open-ended responses. It doesn't set up a binary quiz, doesn't put together a numbered checkbox, doesn't ask students to prioritize or even write a survey promptly. It extracts from whatever the student reports and works with it. Students can report whatever they want from internships on a top AI company to a self-led research and the AI will put together most relevant opportunities. It analyzes from open-ended reports making it different.

Second innovation is its ability to quantify and qualify in a holistic manner. It is not limited to results or ECAs. It encompasses everything and finds out topics and levels. It will consider A-Z and put together a complete list of opportunities.

It's catalytic to all other companies aiding in foreign higher education as it represents how advanced technologies can be used to remodel manual services to reach all regions. With more intervention of industry 4.0 in education, the underserved regions can rightly be addressed.

We chartered a roadmap on all relevant milestones including impact metrics. 

Next 1-year Impact:

Within the next 3 months, we aim to automate completely the opportunity extraction services in Asia for America based education. By 6 months, the services should be able to reach all remote regions of Africa wherever the internet is available. By the 8th month, we aim to integrate all Canadian university offerings. The focus shall move to global opportunities by the 9th month. We shall offer a completed data structure with automated crowd-sourcing by the end of 1st year impacting over 100,000 students.

Next 5-year Impact:

In the early second year, we shall integrate the portal institutionally. The portal will play a significant role in counselor training. Trained counselors can further reinforce the model to increase efficiency substantially. By the end of the second year, counselors will be our secondary customers and the largest channels to reach students. By the third year, we aim to reach at least 10,000 counselors and 1 million students. During this period we will also create alumni counseling structures and alumni-sourced annotations that'll reduce drop-outs even more with much timely instructions and information. The third-year shall also mark out custom partnerships between the company and universities and will create a double-ended value proposition where universities can know in-depth about students. In the 4th-5th year, we aim to reach across the globe and initiate the vision of an SMS service. This is a modified version of a technology we built that allows students from areas without internet and smartphones to gather information by sending specific SMS which is processed by the same NLP models. By the end of 5 years, USA, UK, China, Japan, France, Australia, Russia, and Germany educational offerings along with global opportunities will be all covered as we reach around 5 million underserved students and 100,000 counselors.

Indicator-1: Participation Rates

The indicator quantifies and creates a ratio between qualified segment and actual participant. For example, 1% of students from refugee camps of a country may apply to a certain scholarship on refuge students. We target this to exceed 10% or higher.

Indicator-2: Net Drop-outs

One of the core targets is to reduce drastically finance-oriented or ignorance-oriented dropouts at school levels. The reduction of net dropouts is simultaneously measured.

Indicator-3: Opportunity pool and pool accuracy

The faster the annotation on opportunities is completed, the more structured the pool of opportunities will be. Both the quantity and quality of this structure are measured.

Indicator-4: Extraction Ability and Accuracy

The algorithm's ability to accurately assign relative weights on reports and extract relevant opportunities is something we measure and fine-tune on intervals.

Indicator-5: Relevancy Rate and Satisfaction Rate

Every accessed opportunity goes through a manual optional survey at the user end that enquires about its relevancy. Our target is to reach 100% relevancy. The same goes for satisfaction.

Indicator-6: Net Users (with Demographics)

Net users across age ranges, geographical regions, urban and remote areas, etc. are measured.

NLTK, MERN Stack, TensorFlow

We have always maintained a diverse structure in our leadership. Our board consists of 40% female members. We have contributors across many communities. 80% of our data annotation is done by the transgender community under the partnership with TransEnd led by our employee Tanha Tanjin. Another very significant contributor in the solution Susmit Chakma belongs to the indigenous community. Our team consists of individuals from 7 divisions with multifarious backgrounds.

Our value proposition is of a complementary connection of students to the most relevant financial and other domains of opportunities in order to avoid drop-outs. With long traction on NLP, we aim to use our massive data and capabilities to extract relevant opportunities from deep insights. Customer segments are ultimately students while channels will be via web-app, counselors, schools and colleges, and even the government. The partnership is needed to be done with educational institutions for faster reach and optionally with opportunity blogs for easier extraction. We aim to create an open-source paid service with a trial.

Our cost structure includes server costs, data costs, human resources.

Upon calculation, on average capacity, we can serve 100 students for 12$ per month. Our CAC will be around 10 cents. We aim to provide the service for 1.99$/mo in an annual module. Bulk licenses can be bought by the government and organizations which will allow certain extensions to use for free. Average Customer Lifetime Value is 25$ and estimated revenue is 25,000$ first year of operation with a 300% growth rate.

We received around 2400$ in revenue. Our company has gained investments by Grameenphone accelerator and is valued at 8 million USD.