More Like this

1. Personalized Neural Speech Codec

   https://doi.org/10.1109/ICASSP48485.2024.10446067  

   Jang, Inseon; Yang, Haici; Lim, Wootaek; Beack, Seungkwon; Kim, Minje (April 2024, Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing)
2. Automatic Interpretable Personalized Learning

   https://doi.org/10.1145/3491140.3528267  

   Prihar, Ethan; Haim, Aaron; Sales, Adam; Heffernan, Neil (June 2022, Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S ’22)

   Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students’ education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students’ learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms. 

   more » « less
3. AdvPL: Adversarial Personalized Learning

   https://doi.org/10.1109/dsaa49011.2020.00021  

   Du, Wei; Wu, Xintao (November 2020, 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA))

   null (Ed.)
4. Personalized Fall Detection System

   Ngu, Anne H; Metsis, Vangelis; Coyne, Shaun; Chung, Brian; Pai, Rachel; Chang, Joshua (January 2020, 5th IEEE PerCom Workshop on Pervasive Health Technologies)

   This paper explores the personalization of smartwatch-based fall detection models trained using a combination of deep neural networks with ensemble techniques. Deep neural networks face practical challenges when used for fall detection, which in general tend to have limited training samples and imbalanced datasets. Moreover, many motions generated by a wrist-worn watch can be mistaken for a fall. Obtaining a large amount of real-world labeled fall data is impossible as fall is a rare event. However, it is easy to collect a large number of non-fall data samples from users. In this paper, we aim to mitigate the scarcity of training data in fall detection by first training a generic deep learning ensemble model, optimized for high recall, and then enhancing the precision of the model, by collecting personalized false positive samples from individual users, via feedback from the SmartFall App. We performed real-world experiments with five volunteers and concluded that a personalized fall detection model significantly outperforms generic fall detection models, especially in terms of precision. We further validated the performance of personalization by using a new metric for evaluating the accuracy of the model via normalizing false positive rates with regard to the number of spikes of acceleration over time. 

   more » « less
5. Automatic Interpretable Personalized Learning

   Prihar, Ethan; Haim, Aaron; Sales, Adam; Heffernan, Neil (June 2022, Learning at Scale)

   Personalized learning stems from the idea that students benefit from instructional material tailored to their needs. Many online learning platforms purport to implement some form of personalized learning, often through on-demand tutoring or self-paced instruction, but to our knowledge none have a way to automatically explore for specific opportunities to personalize students’ education nor a transparent way to identify the effects of personalization on specific groups of students. In this work we present the Automatic Personalized Learning Service (APLS). The APLS uses multi-armed bandit algorithms to recommend the most effective support to each student that requests assistance when completing their online work, and is currently used by ASSISTments, an online learning platform. The first empirical study of the APLS found that Beta-Bernoulli Thompson Sampling, a popular and effective multi-armed bandit algorithm, was only slightly more capable of selecting helpful support than randomly selecting from the relevant support options. Therefore, we also present Decision Tree Thompson Sampling (DTTS), a novel contextual multi-armed bandit algorithm that integrates the transparency and interpretability of decision trees into Thomson sampling. In simulation, DTTS overcame the challenges of recommending support within an online learning platform and was able to increase students’ learning by as much as 10% more than the current algorithm used by the APLS. We demonstrate that DTTS is able to identify qualitative interactions that not only help determine the most effective support for students, but that also generalize well to new students, problems, and support content. The APLS using DTTS is now being deployed at scale within ASSISTments and is a promising tool for all educational learning platforms. 

   more » « less