In computational Making, Makers integrate areas like electronics and 3D fabrication with computation. When used for learning formal subjects like science, computational Making adds not only interdisciplinary content but also embodied, hands-on interaction that supports conceptual understanding. However, integrating these different areas to advance a Making-for-learning activity is non-trivial. While research has explored student engagement in Making, we lack an understanding of how students link knowledge across disciplines during these activities. This study investigates whether and how 5th and 6th-grade students in a public school create interdisciplinary linkages in computational Making activities for science learning. Drawing from 340 hours of classroom video data, we analyzed student connections across science, Making, and computing to understand how and in what contexts they emerged. Our findings reveal how hands-on experimentation and embodied metaphors contribute to disciplinary integration. We discuss implications for the design of computational Making activities to better support interdisciplinary learning in the classroom.

BACKGROUND: Filopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells. RESULTS: To address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing. CONCLUSION: To facilitate simple application within Amira or other frameworks, we share the source code, which is available at https://github.com/zibamira/filopodia-tool .

Situated Cognition Theory (SCT) has been widely used to ground designs of technology-based educational interventions. SCT argues that knowledge is inherently tied to the social, cultural and physical context of its acquisition. Hence, learning should be most effective when it takes place in authentic real-world contexts. While SCT has been studied for app design, most literature focuses solely on the social context. This study investigates the physical situatedness aspect of SCT in relation to everyday science learning from mobile apps. A mobile app, Objectica, was designed applying the idea of physical situatedness. A week-long between-subjects study with 56 participants compared Objectica with a control app. Results showed significant differences in learner engagement and motivation between the two apps, but no impact on learning. This study thus provides some evidence for the effectiveness of using SCT in educational mobile app designs based on physical situatedness, but also raises questions about its applicability.

Educators have perpetually strived to make learning content relevant to students. One way to create this relevance is contextualizing instruction with students’ out-of-classroom experiences. Out-of-classroom experiences happen in-the-world and are embodied experiences that are more likely to be of interest to students. However, integrating out-of-classroom experiences is non-trivial, placing additional burden on the teacher when designing lesson plans. We posit that technology in the form of teacher-facing lesson-planning interfaces can facilitate this effort. To be able to design such interfaces, we need understanding of how teachers contextualize their lessons leading to design insights. We carried out a series of design workshops to understand how teachers integrate students’ out-of-classroom experiences into their lesson plans. In this paper, we present results from the design workshops that lead to an understanding of teachers’ current approaches as well as the synthesis of design insights.

Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample complexity issues, recent approaches have used high-level task specifications, such as Linear Temporal Logic (LTLf) formulas or Reward Machines (RM), to guide the learning progress of the agent. In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions. Unlike previous work, LSTS does not assume information about the environment dynamics or the Reward Machine, and dynamically samples promising tasks that lead to successful goal policies. We evaluate LSTS on a gridworld and show that it achieves improved time-to-threshold performance on complex sequential decision-making problems compared to state-of-the-art RM and Automaton-guided RL baselines, such as Q-Learning for Reward Machines and Compositional RL from logical Specifications (DIRL). Moreover, we demonstrate that our method outperforms RM and Automaton-guided RL baselines in terms of sample-efficiency, both in a partially observable robotic task and in a continuous control robotic manipulation task.

Maker-based learning and Computational Thinking (CT) have increased in popularity in formal educational settings over the past decade. Particularly, the combination of CT and making seem to hold promise for providing opportunities for students to learn and use computing concepts outside of computing courses. This paper presents findings from a two year study of the integration of computational making into 5th and 6th grade science classrooms. Students participated in computational making interventions in which they programmed Arduino microcontrollers to create scientific models of concepts that aimed to help them engage with the science content while learning CT and making skills. In this paper, we explore the differences between the desired computing learning progressions, students' performance on assessments, and perceptions of computer science to answer: To what extent are middle school students able to learn computing through computing integrated science curriculum? We observed that the programming concepts taught were largely dependent on the needs of the science and making project. Our findings suggest that while students had opportunities to learn and use programming concepts, their performance on assessments was between 15% and 78% correct for conceptual and applied questions and their programming self-efficacy and their perceptions of computer science were lower than desired. We discuss the implications of these findings and the factors that impact the integration of CT in core disciplines and the challenges this presents as we aim to use integration approaches to effectively teach computing outside of computing courses and to broaden participation in computing.

In this poster, we present the design of a browser-based Arduino programming tool, CASMM, to support computational thinking and making in science classrooms. This tool allows for unique integration of research tools, lesson planning, and scaffolding for learning computational thinking concepts and block-based programming. This poster will describe four key features of a block-based LMS: (1) reduced-scoped programming toolbox, (2) block locking, (3) lesson plans and starter code templates; and (4) low-tech code replay for researchers. Through discussion of this tool, we aim to catalyze conversations about integrating new scaffolding techniques into block-based programming environments to better support classroom use and research.

This paper presents the design of a browser-based Arduino programming tool and learning management system (LMS), CASMM, that offers end-to-end support for learners utilizing Chromebooks in a classroom environment. This tool aims to support learners through the entire process of coding and using Arduinos in group projects at scale in formal classrooms. The novelty of this tool and its discussion for the VL/HCC community lies in the design and customization of this tool to meet real world constraints of formal classrooms. In addition, it encourages expansion of who we consider users and requires inclusion of where and how learning takes place to truly support human-centered development of programming tools. In this paper, we shift the focus from individual users to multiple groups of student users and 1–3 teachers/mentors in a classroom environment. In particular, this paper aims to make explicit the unique needs of teachers and students who may have limited technology expertise both in coding and using Arduinos in formal classroom environments, the human and technological constraints of a formal classroom and features we've designed into CASMM to address these needs. Through this paper, we aim to spark discussion about the human-centered requirements of these users and how tools that support learners end-to-end in the development process may be necessary to truly provide accessible programming languages and environments for a wide range of novices (i.e., students, classroom teachers, and college mentors/volunteers).

Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problems in the context of decoding. An important development in this regard was due to Varsamopoulos et at. who proposed a two-step decoder using neural networks. Subsequent work of Maskara et at. used the same concept for decoding for various noise models. We propose a similar two-step neural decoder using inverse parity-check matrix for topological color codes. We show that it outperforms the state-of-the-art performance of non-neural decoders for independent Pauli errors noise model on a 2D hexagonal color code. Our final decoder achieves a threshold of 10%. Our result is comparable to the recent work on neural decoder for quantum error correction by Maskara et at. It appears that our decoder has advantages with respect to training cost and complexity of the network for higher distances when compared to that of Maskara et at.

Maintaining a scalable high-performance virtual global address space using distributed memory hardware has proven to be challenging. In this paper we evaluate a new approach for such an active global address space that leverages the capabilities of the network fabric to manage addressing, rather than software at the endpoint hosts. We describe our overall approach, design alternatives, and present initial experimental results that demonstrate the effectiveness and limitations of existing network hardware.

Owing to the extreme parallelism and the high component failure rates of tomorrow's exascale, high-performance computing (HPC) system software will need to be scalable, failure-resistant, and adaptive for sustained system operation and full system utilizations. Many of the existing HPC system software are still designed around a centralized server paradigm and hence are susceptible to scaling issues and single points of failure. In this article, we explore the design tradeoffs for scalable system software at extreme scales. We propose a general system software taxonomy by deconstructing common HPC system software into their basic components. The taxonomy helps us reason about system software as follows: (1) it gives us a systematic way to architect scalable system software by decomposing them into their basic components; (2) it allows us to categorize system software based on the features of these components, and finally (3) it suggests the configuration space to consider for design evaluation via simulations or real implementations. Further, we evaluate different design choices of a representative system software, i.e. key-value store, through simulations up to millions of nodes. Finally, we show evaluation results of two distributed system software, Slurm++ (a distributed HPC resource manager) and MATRIX (a distributed task execution framework), both developed based on insights from this work. We envision that the results in this article help to lay the foundations of developing next-generation HPC system software for extreme scales.

A vast majority of legacy runtime systems and middleware prevalent in cluster and supercomputing environments are static in nature. Due to the rising scale and complexity of high-performance computing systems, the static nature of systems software would prospectively impede its scalability and resilience. Traditionally, the mobility of servers is further limited since services are statically bound to specific communication endpoints. To address these challenges imminent for exascale-class systems, distributed middleware needs to support dynamic reconfiguration, redundant and replicated state, and adaptation where the number of servers can vary according to the load in the system. We identify the key features necessary from the underlying network infrastructure to support dynamic adaptation and elasticity in distributed system software, and describe the implementation of a high-performance middleware library that implements the proposed interface. We discuss several novel approaches for dynamic resolution using range computations performed by hosts (in software) and by switches (in hardware), and compare the performance on contemporary Ethernet networks. Finally, we validate the benefits offered by our library with two different applications -- a scalable DHCP server and an elastic key-value store.

In distributed applications, the transmission of non-contiguous data structures is greatly slowed down by the need to serialize them into a buffer before sending. We describe Compact Normal Forms, an API that allows programmers to explicitly place immutable heap objects into regions, which can both be accessed like ordinary data as well as efficiently transmitted over the network. The process of placing objects into compact regions (essentially a copy) is faster than any serializer and can be amortized over a series of functional updates to the data structure in question. We implement this scheme in the Glasgow Haskell Compiler and show that even with the space expansion attendant with memory-oriented data structure representations, we achieve between x2 and x4 speedups on fast local networks with sufficiently large data structures.

Owing to the significant high rate of component failures at extreme scales, system services will need to be failure-resistant, adaptive and self-healing. A majority of HPC services are still designed around a centralized paradigm and hence are susceptible to scaling issues. Peer-to-peer services have proved themselves at scale for wide-area internet workloads. Distributed key-value stores (KVS) are widely used as a building block for these services, but are not prevalent in HPC services. In this paper, we simulate KVS for various service architectures and examine the design trade-offs as applied to HPC service workloads to support extreme-scale systems. The simulator is validated against existing distributed KVS-based services. Via simulation, we demonstrate how failure, replication, and consistency models affect performance at scale. Finally, we emphasize the general use of KVS to HPC services by feeding real HPC service workloads into the simulator and presenting a KVS-based distributed job launch prototype.

Long-running HPC applications guard against node failures by writing checkpoints to parallel file systems. Writing these checkpoints with petascale class machines has proven difficult and the increased concurrency demands of exascale computing will exacerbate this problem. To meet checkpointing demands and sustain application-perceived throughput at exascale, multi-tiered hierarchical storage architectures involving solid-state burst buffers are being considered. In this paper, we describe the design and implementation of cento, a multi-level, content-addressable checkpoint file system for large-scale HPC systems. cento achieves in-flight checkpoint data reduction across all compute nodes through compression and elimination of duplicate blocks over a series of checkpoints. Through a detailed analysis of checkpoint dumps, we assess the benefits of data reduction for scientific applications that are representative of production workloads. We observe upto 40% data reduction within a limited sample of representative workloads. Finally, experiments on existing systems show a decrease in checkpoint commit latencies by 5 to 20 % reducing the load on the parallel file system.

Modern parallel computing hardware demands increasingly specialized attention to the details of scheduling and load balancing across heterogeneous execution resources that may include GPU and cloud environments, in addition to traditional CPUs. Many existing solutions address the challenges of particular resources, but do so in isolation, and in general do not compose within larger systems. We propose a general, composable abstraction for execution resources, along with a continuation-based meta-scheduler that harnesses those resources in the context of a deterministic parallel programming library for Haskell. We demonstrate performance benefits of combined CPU/GPU scheduling over either alone, and of combined multithreaded/distributed scheduling over existing distributed programming approaches for Haskell.