Identifying Student Learning Styles

A vast majority of academic content includes information that is locked in images, equations, and symbols. The challenging problem of extracting semantic and contextual information from images and equations is very closely related to the problem of automated ingestion of content from unstructured data sources. Extracting semantic information from images is still a domain-dependent hard task requiring large datasets, complex machine vision, and deep learning approaches. Extracting meaningful information from images and equations is a domain-dependent task requiring a comprehensive understanding….

The online evaluation used to assess a student’s understanding of concepts requires the questions used in the evaluation system to be tagged with concepts and other metadata like difficulty level, the time needed to solve, skills etc., that can be used to identify concepts the student is weak at or the level of her understanding with respect to that content. Typically, metadata tagging is performed manually by expert faculty. However, this is prohibitively expensive when a large dataset of questions….

We believe measurability lies at the heart of improvement — what can be measured can be improved. Embibe Score Quotient is a numeric parameter that captures a student’s ability to score in an exam. The Embibe Sqore Quotient has the following characteristics: Reflective: Embibe Score Quotient should reflect a student’s potential based on latent attributes of the student’s performance. Predictive: It should be predictive based on the current trend of a student’s performance. Robust: A bad or good test should….

Embibe has been a data-driven, data-focused, data-hungry company right from its inception, having understood very early that data was the key ingredient in being able to personalise education for every student at scale. And yet, data alone completes only half the picture. The personalisation of education using technology is a challenging problem that requires the interplay of advanced algorithms that can leverage enormous amounts of data across multiple sub-domains. At Embibe, we believe leaders are not born; they are crafted….

When it comes to delivering information that users are seeking, there are broadly two user experience paradigms. The first one involves a well-crafted, menu-based navigation system. The second is Search which serves content based on user queries. Search is a far superior method by which we seek online information today. While the menu-based system iteratively leads the users to the exact information they are looking for, the limited number of menu options makes it a less feasible option, especially when….

Embibe is fanatical about instrumenting, measuring, collecting, mining, and archiving data. Embibe owns its data, our IP depends on it. At Embibe, we delay the release until adequate instrumentation is in place to measure how our users interact with our products and what factors lead to specific outcomes. This obsession with data led to many insightful revelations on how students study and achieve their goals. For instance, a student’s potential to score is a combination of two factors – their….

The world has entered the digital age. Technology today touches every aspect of human life, be it business, communication, travel, health or education. Globally, the education sector is embracing technology wholeheartedly, and the implications of advanced technologies are creating wonders in this field. Chief among these rapidly evolving technologies is Artificial Intelligence in education, and its effects are far-reaching. While much of Artificial Intelligence’s theoretical basis is decades old, the proliferation of commodity computing hardware is making it more accessible….

As an EdTech platform, Embibe curates and manages a huge pool of learning objects which can be served to the students to fulfil their learning requirements. This content pool primarily holds content like videos, explainers, and interactive learning elements to educate the user about any academic concept. Also, it contains questions that can be bundled together intelligently to provide gamified practice and test experiences. At Embibe, the user engagement under the practice and test storyline provides us with the crucial….

Introduction Embibe’s Knowledge Graph (KG) is a curriculum-agnostic multi-dimensional graph that consists of 75,000+ nodes. Each node represents a discrete unit of academic knowledge, also called concepts. The Knowledge Graph also has hundreds of thousands of interconnections (relations) between the nodes showing how concepts are not independent but are instead related to other concepts. The interconnections between nodes are assigned a type based on the kind of relationship that exists between them. Incomplete knowledge graphs and missing relationships have been….

Embibe is all about the personalisation of education, and our technology is very good at serving the right piece of content, to the right student, at the right time. It is for this reason having access to a large dataset of usable content, especially questions, is very important to us. Historically, human data entry operators prepared Embibe’s dataset of questions. They would source questions from various freely available question sets online or via tie-ups with our partner institutions. The core….

Introduction Embibe’s Knowledge Graph is a curriculum-agnostic multi-dimensional graph consisting of more than 75,000+ nodes, each representing a discrete unit of academic knowledge, also called concepts and the hundreds of thousands of interconnections between them showing how concepts are not independent but are instead related to other concepts.  As Embibe expands its content, the Knowledge diagram is also constantly evolving. Historically, it has been built using expert faculties’ manual effort and smart automation to curate portions of the graph. However,….