How Much Guidance Do Students Need? An Intervention Study on Kindergarten Mathematics with Manipulatives

Research has shown that the efficacy of learning with manipulatives (e.g., fingers, blocks, or coins) is affected by multiple variables, including the amount of guidance teachers provide during learning. However, there is no consensus on how much guidance is necessary when learning with manipulatives. The goal of this study was to examine the optimal level of guidance during instruction with manipulatives. The focus was on the timing and level of guidance. The researcher taught students a lesson on counting from one to 10 with pennies and nickel strips. Kindergarten students were taught over five consecutive days in one of four conditions: high guidance, low guidance, high guidance that transitioned to low guidance, and low guidance that transitioned to high guidance. Results showed no difference in learning across the conditions. These results provide valuable information to teachers on the areas of mathematics that do not require the effort of high guidance.

Domain adaptation for reinforcement learning on the Atari

Deep Reinforcement learning is a powerful machine learning paradigm that has had significant success across a wide range of control problems. This success often requires long training times to achieve. Observing that many problems share similarities, it is likely that much of the training done could be redundant if knowledge could be efficiently and appropriately shared across tasks. In this paper we demonstrate a novel adversarial domain adaptation approach to transfer state knowledge between domains and tasks on the Atari game suite. We show how this approach can successfully transfer across very different visual domains of the Atari platform. We focus on semantically related games that involve returning a ball with the user controlled agent. Our experiments demonstrate that our method reduces the number of samples required to successfully train an agent to play an Atari game

Acceleration Profiles and Processing Methods for Parabolic Flight

Parabolic flights provide cost-effective, time-limited access to "weightless" or reduced gravity conditions experienced in space or on planetary surfaces, e.g. the Moon or Mars. These flights facilitate fundamental research - from materials science to space biology - and testing/validation activities that support and complement infrequent and costly access to space. While parabolic flights have been conducted for decades, reference acceleration profiles and processing methods are not widely available - yet are critical for assessing the results of these activities. Here we present a method for collecting, analyzing, and classifying the altered gravity environments experienced during a parabolic flight. We validated this method using a commercially available accelerometer during a Boeing 727-200F flight with $20$ parabolas. All data and analysis code are freely available. Our solution can be easily integrated with a variety of experimental designs, does not depend upon accelerometer orientation, and allows for unsupervised and repeatable classification of all phases of flight, providing a consistent and open-source approach to quantifying gravito-intertial accelerations (GIA), or $g$ levels. As academic, governmental, and commercial use of space increases, data availability and validated processing methods will enable better planning, execution, and analysis of parabolic flight experiments, and thus, facilitate future space activities.Comment: Correspondence to C.E. Carr ([email protected]). 15 pages, 4 figures, 3 supplemental figures. Code: https://github.com/CarrCE/zerog, Dataset: https://osf.io/nk2w4

The Histone Deacetylase Complex (HDC) 1 protein of Arabidopsis thaliana has the capacity to interact with multiple proteins including histone 3-binding proteins and histone 1 variants

Intrinsically disordered proteins can adopt multiple conformations thereby enabling interaction with a wide variety of partners. They often serve as hubs in protein interaction networks. We have previously shown that the Histone Deacetylase Complex (HDC) 1 protein from Arabidopsis thaliana interacts with histone deacetylases and quantitatively determines histone acetylation levels, transcriptional activity and several phenotypes, including ABA-sensitivity during germination, vegetative growth rate and flowering time. HDC1-type proteins are ubiquitous in plants but they contain no known structural or functional domains. Here we explored the protein interaction spectrum of HDC1. In addition to binding histone deacetylases, HDC1 directly interacted with core histone H3-binding proteins and co-repressor associated proteins, but not with H3 or the co-repressors themselves. Surprisingly, HDC1 was also able to interact with variants of the linker histone H1. Truncation of HDC1 to the ancestral core sequence narrowed the spectrum of interactions and of phenotypic outputs but maintained binding to a H3-binding protein and to H1. The results indicate a potential link between H1 and histone modifying complexes