Getting too personal(ized): The importance of feature choice in online adaptive algorithms

Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of policies that benefit all students. We explore these issues in the context of using multi-armed bandit (MAB) algorithms to learn a policy for what version of an educational technology to present to each student, varying the relation between student characteristics and outcomes and also whether the algorithm is aware of these characteristics. Through simulations, we demonstrate that the inclusion of student characteristics for personalization can be beneficial when those characteristics are needed to learn the optimal action. In other scenarios, this inclusion decreases performance of the bandit algorithm. Moreover, including unneeded student characteristics can systematically disadvantage students with less common values for these characteristics. Our simulations do however suggest that real-time personalization will be helpful in particular real-world scenarios, and we illustrate this through case studies using existing experimental results in ASSISTments. Overall, our simulations show that adaptive personalization in educational technologies can be a double-edged sword: real-time adaptation improves student experiences in some contexts, but the slower adaptation and potentially discriminatory results mean that a more personalized model is not always beneficial.Comment: 11 pages, 6 figures. Correction to the original article published at https://files.eric.ed.gov/fulltext/ED607907.pdf : The Thompson sampling algorithm in the original article overweights older data resulting in an overexploitative multi-armed bandit. This arxiv version uses a normal Thompson sampling algorith

Replicative aging impedes stress-induced assembly of a key human protein disaggregase

The collapse of protein homeostasis manifests itself in a toxic protein aggregation cascade, which is associated with degenerative diseases and aging. To solubilize aggregates, dedicated protein disaggregases exist in unicellular organisms, but these have no nuclear/cytosolic orthologs in metazoa. Alternative metazoan disaggregation machines have been described, but how these are operated and regulated in vivo remained unknown. We show that protein disaggregases are functionally diversified in human cells to efficiently target different types of stress-induced aggregates in sequential and temporally distinct phases. In particular, we show the selective assembly of an Hsp70-DNAJA1-DNAJB1 trimeric disaggregase that forms during late phase of stress recovery., i.e., after VCP-dependent solubilization of non-native proteins that accumulate in cellular condensates such as nucleoli or stress granules. When activated, the trimeric disaggregase provides resistance to stress toxicity and contributes to amyloid disposal. Strikingly, this disaggregase collapses early in cells undergoing replicative aging with important underlining pathophysiological consequences