Prior research has shown how ‘content preview tools’ improve speed and accuracy of user relevance judgements across different information retrieval tasks. This paper describes a novel user interface tool, the Content Flow Bar, designed to allow users to quickly identify relevant fragments within informational videos to facilitate browsing, through a cognitively augmented form of navigation. It achieves this by providing semantic “snippets” that enable the user to rapidly scan through video content. The tool provides visually-appealing pop-ups that appear in a time series bar at the bottom of each video, allowing to see in advance and at a glance how topics evolve in the content. We conducted a user study to evaluate how the tool changes the users search experience in video retrieval, as well as how it supports exploration and information seeking. The user questionnaire revealed that participants found the Content Flow Bar helpful and enjoyable for finding relevant information in videos. The interaction logs of the user study, where participants interacted with the tool for completing two informational tasks, showed that it holds promise for enhancing discoverability of content both across and within videos. This discovered potential could leverage a new generation of navigation tools in search and information retrieval.
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.
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.
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.
The SUM’20 workshop was held at the 13th ACM International WSDM Conference on Web Search and Data Mining (WSDM 2020) in Houston, Texas. The purpose of the workshop was to stimulate the research community to explore open challenges in building systems that can capture the user’s state, context and goals, as well as effectively use these for leveraging intelligent user-centric systems in a wide range of applications. The workshop incorporated different plenary sessions and contributed talks. The workshop website and proceedings are available at https://www.k4all.org/event/wsdmsum20
Artifical Intelligence (AI) in Education has great potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. We believe this is a crucial moment for setting the foundations of AI in education in the beginning of this Fourth Industrial Revolution. This report aims to synthesize how AI might change (and is already changing) how we learn, as well as what technological features are crucial for these AI systems in education, with the end goal of starting this pressing dialogue of how the future of AI in education should unfold, engaging policy makers, engineers, researchers and obviously, teachers and learners. This report also presents the advances within the X5GON project, a European H2020 project aimed at building and deploying a cross-modal, cross-lingual, cross-cultural, cross-domain and cross-site personalised learning platform for Open Educational Resources (OER).
Open Education Resources (OERs) have increased significantly in the last decade, giving learners access to a wider range of educational material any time and anywhere in the world. However, this trend demands automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. Our work focuses on modelling learner engagement, which is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), as opposed to other contextualised and personalised approaches that focus more on individual learner engagement. We consider both context-agnostic and contextual engagement to be necessary for building effective recommender systems for education, e.g., context-agnostic engagement can be used to build a prior to solve the common cold-start problem and contextual personalised models be used upon an abundance of user data. However, research on context-agnostic engagement is surprisingly scarce and modality-specific. In this work, we explore the idea of building a predictive model for population-based engagement in education. We first propose two sets of relevant features for our predictive model: i) a set of cross-modal and language-based features that are easily applicable to OERs across multiple modalities and ii) a set of video-specific features. We then test different strategies for quantifying learner engagement signals. We further evaluate different machine learning models to predict population engagement in a large dataset of video lectures. We demonstrate the usefulness of our approach when compared to a personalised approach in a scenario of user data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.
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.
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.
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.
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 . 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.
With the emergence of Open Education Resources (OERs), educational content creation has boomed to a whole new scale. For AI-driven OER platforms such as X5GON, scalable quality assurance is highly impactful. As the quality of OERs could vary significantly, the quality assurance process plays a key role in maintaining a high-quality learner experience when using OERs. Managing this problem at large scale demands automating the quality assurance process as a whole or in parts. Prior research on automating quality assurance in the context of education is surprisingly scarce. We present our ongoing work of building Quality Assurance Models, a novel approach to using crossmodal features from OERs to predict quality using machine learning. While developing quality models, we extended our search beyond the education domain to identify features that indicate content quality that can be categorised into five main quality verticals. In the future, these features will enable us to leverage scalable quality assurance on OERs of different modalities. Furthermore, quality features will also become useful in learning quality preferences of learners when recommending content. Altogether, the expected outcomes of this research will mark a significant step towards Automatic, Scalable Quality Assurance in Open Education.
The objective of this study is to assess the extent to which academic history vis-à-vis other factors influence the academic performance of the undergraduate students in the Faculty of Agriculture, University of Peradeniya in Sri Lanka. A series of educational production functions were estimated treating Grade Point Average of students at semester level and cumulative Grade Point Average as the measures of academic performance. Academic history, engagements in student associations and other extra-curricular activities, learning environment, associations with the teaching staff, resources and support services provided by the university, social interactions, psychological factors, family background, funding and student inherent characteristics/personal background were hypothesized as the factors affecting academic performance. A structured questionnaire was distributed among 196 students in the final year of Agricultural Technology and Management degree program offered by the Faculty of Agriculture in 2011 to gather the needed data and 121 students responded to the survey. Results of the econometric models specified at semester levels reveal that during the first semester, English language proficiency, efforts made by the student and family background have positive and statistically significant effects on academic performance of undergraduates while the performance during semesters 2-7 is largely driven by the performance of the previous semester. A significant gender disparity in academic performance of undergraduates exists. In general, female students perform better than the male students and psychological factors explain a considerable proportion of the variability of academic performance. The results further reveal that the overall academic performance is influenced by the language proficiency and the academic efforts made by the students. Female students and those who came from privileged districts perform much better than their respective counterparts. Academic performance at school does not have a significant effect even when the other factors affecting undergraduate performance are controlled for. The study concludes that the English language proficiency, family background and academic efforts made by the students are the three key elements that determine the academic performance at every level. Contrary to the expectation, the performance at the Advanced Level examination, as measured by the Z score, does not seem to influence undergraduate academic performance in a statistically significant manner. The above findings imply a need to upgrade the facilities to improve English language proficiency and to create an enabling learning environment primarily through strengthening of social interactions and enhancing psychological spirits of undergraduates in order to obtain the best return for the investment made in higher education in Sri Lanka.
Chemical fertilization is very popular in Sri Lankan agriculture and application of chemicals has a major impact on the economy, health and environment of the country. Vegetable and home garden cultivation has recorded the most severe harm adding very high levels of heavy metals to the environment. Chemical Leasing is an innovative business model which is a good solution for this issue which will reduce chemical usage while maximizing profits to major stakeholders. During the study, chemical leasing approach was applied to potato cultivation parallel to conventional farming approach. The pytoprotection chemicals were reduced in the chemical leasing approach and the leaf area and height was used to measure plant growth. New software was developed to measure the plant leaf area more efficiently. A methodology is formulated to derive the unit of payment for chemical leasing. Results show that the new software based approach to measuring leaf area is very successful with both Average Absolute Error and Average Bias Error < 5%. It is very much suitable for developing countries as it is less expensive and less labour intensive. Furthermore, Profit against chemical costs (α) Chemical reduction proportion (β) and Profit sharing agreement between service provider and the farmer (γ) are the important determinants of the unit of payment. These determinants are not universal and are variable from one project to another depending on numerous domain specific factors