Pre-training with Scientific Text Improves Educational Question Generation

With the boom of digital educational materials and scalable e-learning systems, the potential for realising AI-assisted personalised learning has skyrocketed. In this landscape, the automatic generation of educational questions will play a key role, enabling scalable self-assessment when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our initial experiments demonstrate that EduQG can produce superior educational questions by pre-training on scientific text

TrueLearn: A family of bayesian algorithms to match lifelong learners to open educational resources

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowledge states of masses of learners for significantly longer periods of time (ideally, a lifetime). This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner’s knowledge from implicit data in the form of engagement with open educational resources. We i) use a text ontology based on Wikipedia to automatically extract knowledge components of educational resources and, ii) propose a set of online Bayesian strategies inspired by the well-known areas of item response theory and knowledge tracing. Our proposal, TrueLearn, focuses on recommendations for which the learner has enough background knowledge (so they are able to understand and learn from the material), and the material has enough novelty that would help the learner improve their knowledge about the subject and keep them engaged. We further construct a large open educational video lectures dataset and test the performance of the proposed algorithms, which show clear promise towards building an effective educational recommendation system

Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution

Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. Millions of students are already starting to benefit from the use of these technologies, but millions more around the world are not. If this trend continues, the first delivery of AI in Education could be greater educational inequality, along with a global misallocation of educational resources motivated by the current technological determinism narrative. In this paper, we focus on speculating and posing questions around the future of AI in Education, with the aim of starting the pressing conversation that would set the right foundations for the new generation of education that is permeated by technology. This paper starts by synthesising how AI might change how we learn and teach, focusing specifically on the case of personalised learning companions, and then move to discuss some socio-technical features that will be crucial for avoiding the perils of these AI systems worldwide (and perhaps ensuring their success). This paper also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We also emphasise the need for collectively designing human-centered, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as support new emerging pedagogies. Finally, we ask what would it take for this educational revolution to provide egalitarian and empowering access to education, beyond any political, cultural, language, geographical and learning ability barriers

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards building a state-aware educational recommendation system that incorporates semantic relatedness between knowledge topics, propagating latent information across semantically related topics. We introduce a novel learner model that exploits this semantic relatedness between knowledge components in learning resources using the Wikipedia link graph, with the aim to better predict learner engagement and latent knowledge in a lifelong learning scenario. In this sense, Semantic TrueLearn builds a humanly intuitive knowledge representation while leveraging Bayesian machine learning to improve the predictive performance of the educational engagement. Our experiments with a large dataset demonstrate that this new semantic version of TrueLearn algorithm achieves statistically significant improvements in terms of predictive performance with a simple extension that adds semantic awareness to the model

X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI

X5Learn (available at https://x5learn.org ) is a human-centered AI-powered platform for supporting access to free online educational resources. X5Learn provides users with a number of educational tools for interacting with open educational videos, and a set of tools adapted to suit the pedagogical preferences of users. It is intended to support both teachers and students, alike. For teachers, it provides a powerful platform to reuse, revise, remix, and redistribute open courseware produced by others. These can be videos, pdfs, exercises and other online material. For students, it provides a scaffolded and informative interface to select content to watch, read, make notes and write reviews, as well as a powerful personalised recommendation system that can optimise learning paths and adjust to the user's learning preferences. What makes X5Learn stand out from other educational platforms, is how it combines human-centered design with AI algorithms and software tools with the goal of making it intuitive and easy to use, as well as making the AI transparent to the user. We present the core search tool of X5Learn, intended to support exploring open educational materials

SUM’20: State-based user modelling

Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges

Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts

Extracting useful information from the user history to clearly understand informational needs is a crucial feature of a proactive information retrieval system. Regarding understanding information and relevance, Wikipedia can provide the background knowledge that an intelligent system needs. This work explores how exploiting the context of a query using Wikipedia concepts can improve proactive information retrieval on noisy text. We formulate two models that use entity linking to associate Wikipedia topics with the relevance model. Our experiments around a podcast segment retrieval task demonstrate that there is a clear signal of relevance in Wikipedia concepts while a ranking model can improve precision by incorporating them. We also find Wikifying the background context of a query can help disambiguate the meaning of the query, further helping proactive information retrieval

Scalable Educational Question Generation with Pre-trained Language Models

The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our extensive experiments demonstrate that EduQG can produce superior educational questions by further pre-training and fine-tuning a pre-trained language model on the scientific text and science question data

What's in it for me?: Augmenting recommended learning resources with navigable annotations

This paper introduces an interface that enables the user to quickly identify relevant fragments within multiple long documents. The proposed method relies on a machine-generated layer of annotations that reveals the coverage of topics per fragment and document. To illustrate how the annotations double as a tool for preview as well as navigation, an example application is presented in the form of a personalised learning system that recommends relevant fragments of video lectures according to user's history. Potential implications of this approach for lifelong learning are discussed. We argue that this approach is generally applicable to recommender and information retrieval systems, across multiple knowledge domains and document types

Leveraging Semantic Knowledge Graphs in Educational Recommenders to Address the Cold-Start Problem

In informational recommenders, significant challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to make advances toward building a semantically aware educational recommendation system, where the aim is to estimate the knowledge/interests of learners to leverage suitable recommendations. To do so, our proposed model incorporates notions of semantic relatedness between knowledge topics, propagating latent information across those semantically related topics. To introduce this novel learner model that exploits semantic relatedness, we make use of the Wikipedia link graph. Our final aim is to better predict learner engagement and latent knowledge in a lifelong learning scenario and evaluate how semantic knowledge graphs can facilitate this task. Our proposal, Semantic TrueLearn, one of the first attempts at fusing probabilistic graphical models and semantic knowledge graphs, is much more accurate than its non-semantic counterpart and builds a humanly intuitive knowledge representation, crucial in the context of education. Our experiments with a large dataset indicate that modeling semantic relatedness can improve user models that go beyond Semantic TrueLearn, showing the generalizability of our approach