Our Grades Were Broken: Overcoming Barriers and Challenges to Implementing Standards-Based Grading

The purpose of this study was to describe the barriers and challenges school leaders face as they implement a standards-based grading (SBG) system. The researchers used a multiple case study methodology to investigate how key school leaders described their implementation journey at three schools that differed in size, demographics, and location. Purposeful sampling was used to identify key administrators at three different schools who were in the process of implementing a SBG system. Data were collected primarily via semi-structured interviews. In the analysis, researchers used three phases: horizontalization, thematizing, and textural-structural synthesis. Each of the three schools had very different implementation stories. Barriers in the process included: student information and grading systems, parents/community members, the tradition of grading and fear of the unknown, and the implementation dip. This study suggests that implementation of SBG must be purposeful and well communicated. That is, in order to enhance the likelihood of success, an intentional plan with a reasonable timeline, ongoing professional development and collaboration, and effective two-way communication about the purpose of grading is needed. Also maintaining A-through-F final grades—even as they simultaneously implement more progressive assessment and reporting strategies—is often seen as a necessary concession. Finally, the authors explicate SBG’s relationship to competency-based education and professional learning communities (PLCs)

Relaxed Core Stability for Hedonic Games with Size-Dependent Utilities

We study relationships between different relaxed notions of core stability in hedonic games. In particular, we study (i) q-size core stable outcomes in which no deviating coalition of size at most q exists and (ii) k-improvement core stable outcomes in which no coalition can improve by a factor of more than k. For a large class of hedonic games, including fractional and additively separable hedonic games, we derive upper bounds on the maximum factor by which a coalition of a certain size can improve in a q-size core stable outcome. We further provide asymptotically tight lower bounds for a large class of hedonic games. Finally, our bounds allow us to confirm two conjectures by Fanelli et al. [Angelo Fanelli et al., 2021][IJCAI\u2721] for symmetric fractional hedonic games (S-FHGs): (i) every q-size core stable outcome in an S-FHG is also q/(q-1)-improvement core stable and (ii) the price of anarchy of q-size stability in S-FHGs is precisely 2q/q-1

Brief Announcement: An Effective Geometric Communication Structure for Programmable Matter

The concept of programmable matter envisions a very large number of tiny and simple robot particles forming a smart material that can change its physical properties and shape based on the outcome of computation and movement performed by the individual particles in a concurrent manner. We use geometric insights to develop a new type of shortest path tree for programmable matter systems. Our feather trees utilize geometry to allow particles and information to traverse the programmable matter structure via shortest paths even in the presence of multiple overlapping trees

Fast Reconfiguration for Programmable Matter

The concept of programmable matter envisions a very large number of tiny and simple robot particles forming a smart material. Even though the particles are restricted to local communication, local movement, and simple computation, their actions can nevertheless result in the global change of the material's physical properties and geometry. A fundamental algorithmic task for programmable matter is to achieve global shape reconfiguration by specifying local behavior of the particles. In this paper we describe a new approach for shape reconfiguration in the amoebot model. The amoebot model is a distributed model which significantly restricts memory, computing, and communication capacity of the individual particles. Thus the challenge lies in coordinating the actions of particles to produce the desired behavior of the global system. Our reconfiguration algorithm is the first algorithm that does not use a canonical intermediate configuration when transforming between arbitrary shapes. We introduce new geometric primitives for amoebots and show how to reconfigure particle systems, using these primitives, in a linear number of activation rounds in the worst case. In practice, our method exploits the geometry of the symmetric difference between input and output shape: it minimizes unnecessary disassembly and reassembly of the particle system when the symmetric difference between the initial and the target shapes is small. Furthermore, our reconfiguration algorithm moves the particles over as many parallel shortest paths as the problem instance allows