Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract)

Bulathwela, Sahan; Pérez-Ortiz, María; Yılmaz, Emine; Shawe‐Taylor, John

doi:10.1609/aaai.v34i10.7151

Cited by 14 publications

(28 citation statements)

References 7 publications

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“…These are described in [6,29] and aim to support the learning experience (e.g. optimization of learning paths, annotation of videos, a recommender system [8,9], etc. ).…”

Section: Context Of Usementioning

confidence: 99%

Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?

Pérez-Ortiz¹,

Bulathwela²,

Dormann³

et al. 2022

Preprint

Self Cite

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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 visuallyappealing 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.

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“…These are described in [6,29] and aim to support the learning experience (e.g. optimization of learning paths, annotation of videos, a recommender system [8,9], etc. ).…”

Section: Context Of Usementioning

confidence: 99%

Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?

Pérez-Ortiz¹,

Bulathwela²,

Dormann³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…type of OER, such as video, PDF etc., license available, and preferred language). • X5GON TrueLearn: This is an AI-based recommendation tool that uses TrueLearn model [5,6] to provide transparent educational recommendations for lifelong learners.…”

Section: The Core Ai Tools and Ui Functionsmentioning

confidence: 99%

“…The notes can also be aggregated and downloaded to be shared between students or integrated to coursework. The playlist illustrates how the AI and HCI work together effectively: the AI backbone provides an "optimise learning" function which automatically reorders the video sequences based on its assumed best learning sequence [5,15], while the interface has been designed to enable the user to readily take over control and reorder the video sequence if they are not happy with the recommendations. The system will use interactions with the materials and the system to train the recommender [6].…”

Section: The Core Ai Tools and Ui Functionsmentioning

confidence: 99%

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

Pérez-Ortiz

Dormann

Rogers³

et al. 2021

26th International Conference on Intelligent User Interfaces

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X5Learn (available at https:// x5learn.org) is a human-centered AIpowered 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.

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“…Extending the TrueLearn model [8,7], the proposed Semantic TrueLearn, is a learner model that infers the knowledge state of learners in an online fashion. Semantic Tru-eLearn exploits its current knowledge of observed concepts and their SR to a novel concept to make a better prior estimation as per Figure 1.…”

Section: Semantic Truelearnmentioning

confidence: 99%

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

Bulathwela¹,

Pérez-Ortiz²,

Yılmaz³

et al. 2021

Preprint

Self Cite

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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 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.

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Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract)

Cited by 14 publications

References 7 publications

Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?

Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?

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

Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems

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