This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections.
The user questionnaire revealed that participants found the Content Flow Bar helpful and enjoyable for finding relevant information in videos.
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 then show that in data starvation regimes, holding out data for the test set bounds adversely affects generalisation performance, while self-certified strategies based on PAC-Bayes bounds do not suffer from this drawback, proving that they might be a suitable choice for the small data regime.
We experiment on 6 datasets with different strategies and amounts of data to learn data-dependent PAC-Bayes priors, and we compare them in terms of their effect on test performance of the learnt predictors and tightness of their risk certificate.
One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets.
As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4, 000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets.
This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement.
In this paper we propose ASAP, an active sampling algorithm based on approximate message passing and expected information gain maximization.
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning.
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.
Data augmentation is rapidly gaining attention in machine learning.
Most popular strategies to capture subjective judgments from humans involve the construction of a unidimensional relative measurement scale, representing order preferences or judgments about a set of objects or conditions.