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

Pérez-Ortiz, María; Dormann, Claire; Rogers, Yvonne; Bulathwela, Sahan; Kreitmayer, Stefan; Yılmaz, Emine; Noss, Richard; Shawe‐Taylor, John

doi:10.1145/3397482.3450721

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…The immediate future work entails improving learner state visualisations via user studies. Integrating the library into a real-world e-learning platform (Perez-Ortiz et al 2021) is a top priority. Extending the current framework to podcasts and other information content while incorporating other feedback forms like educational questions (Bulathwela, Muse, and Yilmaz 2023;Fawzi, Amini, and Bulathwela 2023) remains in the future roadmap.…”

Section: Discussionmentioning

confidence: 99%

A Toolbox for Modelling Engagement with Educational Videos

Qiu,

Djemili,

Elezi

et al. 2024

AAAI

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With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. This work presents the PEEKC dataset and the TrueLearn Python library, which contains a dataset and a series of online learner state models that are essential to facilitate research on learner engagement modelling. TrueLearn family of models was designed following the "open learner" concept, using humanly-intuitive user representations. This family of scalable, online models also help end-users visualise the learner models, which may in the future facilitate user interaction with their models/recommenders. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytics practitioners. The experiments show the utility of both the dataset and the library with predictive performance significantly exceeding comparative baseline models. The dataset contains a large amount of AI-related educational videos, which are of interest for building and validating AI-specific educational recommenders.

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Section: Discussionmentioning

confidence: 99%

A Toolbox for Modelling Engagement with Educational Videos

Qiu,

Djemili,

Elezi

et al. 2024

AAAI

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“…With the ability to harness these AI-powered tools within our grasp, we should design these openly accessible tools in such a way that we avoid increasing the disparities in terms of access to quality education [1]. In a landscape where high-quality educational resources are available in abundance, having scalable educational recommenders that can connect distinct learning materials to individuals across languages, cultures, and content modalities over a lifelong journey of learning is a game changer [22].…”

Section: Opportunities and Challenges In Scaling Personalised Educationmentioning

confidence: 99%

Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

et al. 2022

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Educational recommenders have received much less attention in comparison with e-commerce- and entertainment-related recommenders, even though efficient intelligent tutors could have potential to improve learning gains and enable advances in education that are essential to achieving the world’s sustainability agenda. Through this work, we make foundational advances towards building a state-aware, integrative educational recommender. The proposed recommender accounts for the learners’ interests and knowledge at the same time as content novelty and popularity, with the end goal of improving predictions of learner engagement in a lifelong-learning educational video platform. Towards achieving this goal, we (i) formulate and evaluate multiple probabilistic graphical models to capture learner interest; (ii) identify and experiment with multiple probabilistic and ensemble approaches to combine interest, novelty, and knowledge representations together; and (iii) identify and experiment with different hybrid recommender approaches to fuse population-based engagement prediction to address the cold-start problem, i.e., the scarcity of data in the early stages of a user session, a common challenge in recommendation systems. Our experiments with an in-the-wild interaction dataset of more than 20,000 learners show clear performance advantages by integrating content popularity, learner interest, novelty, and knowledge aspects in an informational recommender system, while preserving scalability. Our recommendation system integrates a human-intuitive representation at its core, and we argue that this transparency will prove important in efforts to give agency to the learner in interacting, collaborating, and governing their own educational algorithms.

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“…The platform also includes other integrated features/tools that are intended to be used with the CFB. 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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X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI

Cited by 12 publications

References 8 publications

A Toolbox for Modelling Engagement with Educational Videos

A Toolbox for Modelling Engagement with Educational Videos

Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

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

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