LEARNING OUTCOMES & RECOMMENDATION TEAM

Welcome to the future of education!

At Embibe, we have just one mission – to truly personalise education. Because every child deserves it. This has led us to embark on this noblest of journeys to deliver life and learning outcomes for every student! Rooted in consumer behavior, we are leveraging AI to deliver personalised achievement journeys for every student.

Embibe has traversed a long journey from a data-centric product to an AI platform. On this journey, we have realised that the most powerful teams are: 1. Vision Led  in understanding student context and obsessed with success; 2. Self-Organising  in defining their own agenda; 3. Intellectually Fierce and Globally Conscious  in their choices, and 4. Consistently Excellent  in their execution.

After exploring a deeply functional organisational structure in engineering, we are now evolving towards a problem-solving team structure that manifests at the platform and backend level as an agile team supporting a unified front-end and augmented by a strong Architect + Principal Engineer + Advisory group for technical mentoring. This document outlines the problem statement and other aspects of the Learning Outcomes & Recommendations Team.

THE PROBLEM STATEMENT

Every student is different. They have different strengths, weaknesses, needs and even learning goals. We want  to build the most personalised user journeys for students such that they can improve their learning outcomes with minimum time spent on the platform.

THE INSPIRATION

This function is inspired by leading global technology companies which operate at the intersection of content discovery and personalisation. The leaders in the space include:

• Google
• Netflix
• Spotify

It is the endeavour of this team to emulate the best practices of these global players into the content discovery and suggestions for users to spend time on learning, practice and testing aspects of the platform to enhance their speed of learning and knowledge assimilation.

THE OBJECTIVES

• Next Content Engine (NCE) – Continuous Journey, with Boundary Condition of Chapter
• Learn Intervention
• Test Feedback
• Learning Journey
• Achieve (Fixed Syllabus, Fixed Time)
• Embibe’s Teaching Algorithm (Gradeless, Timeless and Boundaryless Learning)
• Personalised Content Packaging
• Knowledge Buddy

PRODUCT MANIFESTATION OF YOUR EFFORT

• Achieve – Embibe’s personalised AI power learning outcomes delivery engine
• Improve – Syllabus boundary with no time constraints (e.g., chapter)
• eBuddy – Just a start button (no constraints) – experience Embibe teaching – grade-time-boundaryless learning
• CYOT, Personalised Test and Assignment Generation
• Doubt Resolution
• Revise with Mb

BUSINESS MANIFESTATION OF YOUR EFFORT

• Improved Customer Retention
• Lower Churn
• Higher NPS
• Higher Word of Mouth

METRICS YOU WILL OWN AND LIVE BY

L2 PROBLEMS OWNED

We believe in building an organisation at the intersection of domain modelling and   problem intuition. While the L1 teams give us the flexibility to have a multi-faceted view of the problem and cluster similar problems together, the L2 structure ensures independent and focused problem-solving. The following L2 teams have been suggested for the L1 problem stated above:  

• Learning Journey : To build the most personalised user journeys for the students so as to improve their learning outcomes with minimum time spent on the platform
• Learning Interventions : To build the most personalised interventions to help students improve their learning outcomes
• Content Packaging : To build content packaging for the consumer, school and parent application at scale with minimum latency
• Doubt Resolution + Knowledge Buddy (NLP Platform) : To build an intelligent real-time question answering platform leveraging image processing and natural language understanding, leveraging Embibe’s content and real-time solving capabilities; to deliver nudges to improve behaviour
• Transformative Content Generation – Text (NLP Platform) : To extend the universe of content at Embibe by generating, tagging and storing – text-based content
• Transformative Content Generation – Video (Computer Vision Platform) : To extend the universe of content by generating, tagging and storing – audiovisual content

L1 SKILLS REQUIRED

• Teaching Pedagogy
• Algorithms
• Recommendation System
• Optimisation Problems

IP DEVELOPED SO FAR

1. Framework for Predicting, Interpreting and Improving Learning Outcomes
2. Impacting Learning Outcomes in the Education Sector Through Artificial Intelligence
3. Student Calibration for Learning Outcomes
4. Behavioral Interventions for Learning Outcomes
5. Building the Learning Outcomes AI Stack
6. Behavioural Nudges That Work for Learning Outcomes
7. Attributes That Create Learning Outcomes
8. Adaptive Learning Machine for Score Improvement and Parts Thereof
9. Auto Generation of Diagnostic Assessments and Their Quality Evaluation
10. A Framework for Predicting Interpreting and Improving Learning Outcomes  
11. System and Method for Behavioural Analysis and Recommendations
12. Adaptive Learning Machine for Score Improvement and Parts Thereof
13. System and Method for Recommending Personalised Content Using Contextualised Knowledge Base
14. System and Method for Generating an Assessment Paper and Measuring the Quality Thereof
15. System and Method to Generate Sets of Similar Assessment Papers  
16. System for Generating Multiple Similar Questions and Their Solutions and Method Thereof