4 Next Steps

4.1 Overview

We have now proposed one approach to better represent a modern individual’s knowledge by taking the world of unstructured educational content (Youtube videos, Medium articles, etc), classifying it by its concepts which belong to one or more subjects from an education-based knowledge graph.

We surveyed some of the relevant machine and deep learning research, proposed the Educational Content to Questions and Answers (DeepEdu) network, a novel neural network for taking in any type of educational content and generating a set of questions, answers, and the evaluation of a learner’s knowledge understanding without the need for the educator be involved.

We then proposed to tie these components together as an adaptive knowledge ecosystem consisting of an individual’s knowledge footprint, mapped to a central knowledge graph, visualised over time as a learner’s knowledge journey. This would serve to support the independent (aspiring or current) learner to pursue their life-long education. It would take into consideration the education they acquired from unstructured sources, thereby formalising their informal knowledge for themselves and others.

4.2 Challenges

There will be many challenges in coming up with solutions to enable knowledge acquisition and representation at the learner and group level. We will address some of the primary challenges to designing the components of the proposed knowledge ecosystem:

Datasets
DeepEdu dataset
Knowledge graph dataset
Deep Learning Architecture
DeepEdu network

For our purposes, we will focus on the DeepEdu network and dataset as the main focus from here.

4.3 DeepEdu dataset

The success of deep learning techniques is predicated on the assumption that one needs a significant amount of data to train a large neural network to learn the representation that can capture a set of admissible functions needed to learn the given concepts.

The DeepEdu network will also face the same challenges. It requires a large dataset of educational content (i.e. Youtube videos, Wikipedia pages, Medium articles) along with a set of questions that have a set of correct and incorrect answers for each question.

\[ \{content1: \{question1: \{answer1, answer2,...\},... \]

However, there are many approaches we can decrease the amount of data needed while still learning a good representation of the concepts. We will present an approach that we believe can best utilise the intrinsic structure of educational content and use the minimal amount of data.

The network can be trained on unstructured educational content without questions or answers to create an education embedding by employing unsupervised techniques like variational auto-encoder (VAE). This requires a dataset of educational content, possibly many media types(audio/video/text). This could also be done by using a semi-supervised technique by supervising the media type and learning representations for the types of educational content.
The network can then be trained on existing educational content that already has preexisting questions and answers for specific parts of the content (i.e. Khan Academy, Udacity, Coursera). The domain of structured educational content is surely very different from the unstructured content and may hinder the network but we believe it might be the case that it helps more than it hinders so it is worth experimenting.
The final approach is annotating a set of unstructured educational content with questions and answers.

4.4 Education Partners

The second (#2) approach requires a large amount of existing educational content paired with existing questions and answers. This data is rich and informative; it could help us learn an early representation and create a strong baseline model. Most of these datasets have been created by experts and educators and their efforts could be valuable for our model to learn from. For this purpose, we would need to partner with large (in terms of a library of content) educational institutions (i.e. Udacity, Coursera, MOOCs, Edx, Khan Academy) to work with their datasets for pretraining and collaborate on how we annotate the space of unstructured educational content.

4.5 Educator Enrichment

The third (#3) dataset that would be used as our primary data source to train and test the model’s performance, remains the most important component as well as the most time and labour intensive one. Partnering with educators and subject matter experts to source the subspace of unstructured educational content and to create questions and answers, we would eventually arrive at a curated dataset that our model would be primarily trained on. The end result would be a new benchmark that other researchers can begin to build newer architectures to make progress in the problem domain.

4.6 Minimum Viable Dataset (Benchmark)

We propose to narrow the problem space and jumpstart research in this area by focusing on only one subject (i.e. psychology, mathematics, design) rather than trying to take on mapping the whole space of unstructured educational content. This drastically reduces the data that is needed and the number of experts and partners needed to kickstart the initiative.

4.7 Call for collaborators

We hope to bring together collaborators that would include machine learning researchers, educators, technologists, and designers to begin our first foray into a modern knowledge ecosystem. Specifically starting with the DeepEdu dataset and network, one education subject, and at least one educational partner.

Please email us at two@dyadxmachina.com with the subject Knowledge Ecosystem Initiative or leave your email below for collaboration.

4.8 Conclusion

In conclusion, we presented a new perspective on knowledge acquisition and representation, proposed the concept of a modern and adoptive knowledge ecosystem that a learner can rely on upon through their entire educational lifetime.

Our main task was to take an individual and begin to get a true depiction of their knowledge beyond their traditional degree, which is only a small percentage of one’s education.

We focused on taking the world of unstructured educational content online, and providing structure in the form of testing and mapping it to a knowledge graph. We introduced the ideas of knowledge journeys and knowledge graph as means to make sense and structure a learner’s knowledge acquisition from 2 distinct perspectives - temporal representation vs thumbnail profile.

There is still much more research to be done in bringing to life the Educational Content to Questions and Answers (DeepEdu) neural networks as well our ultimate knowledge ecosystem and collaboration required amongst machine learning researchers, educators, and designers,

As deep learning researchers, we are looking forward to designing or seeing others design the minimum viable dataset, benchmark, and architecture motivated by this work.