The effectiveness of integrating educational robotic activities into higher education Computer Science curricula: a case study in a developing country

In this paper, we present a case study to investigate the effects of educational robotics on a formal undergraduate Computer Science education in a developing country. The key contributions of this paper include a longitudinal study design, spanning the whole duration of one taught course, and its focus on continually assessing the effectiveness and the impact of robotic-based exercises. The study assessed the students' motivation, engagement and level of understanding in learning general computer programming. The survey results indicate that there are benefits which can be gained from such activities and educational robotics is a promising tool in developing engaging study curricula. We hope that our experience from this study together with the free materials and data available for download will be beneficial to other practitioners working with educational robotics in different parts of the world

Accelerating K-12 computational thinking using scaffolding, staging, and abstraction

We describe a three-stage model of computing instruction beginning with a simple, highly scaffolded programming en-vironment (Kodu) and progressing to more challenging frame-works (Alice and Lego NXT-G). In moving between frame-works, students explore the similarities and differences in how concepts such as variables, conditionals, and looping are realized. This can potentially lead to a deeper under-standing of programming, bringing students closer to true computational thinking. Some novel strategies for teach-ing with Kodu are outlined. Finally, we briefly report on our methodology and select preliminary results from a pi-lot study using this curriculum with students ages 10–17, including several with disabilities

Learning to communicate computationally with Flip: a bi-modal programming language for game creation

Teaching basic computational concepts and skills to school children is currently a curricular focus in many countries. Running parallel to this trend are advances in programming environments and teaching methods which aim to make computer science more accessible, and more motivating. In this paper, we describe the design and evaluation of Flip, a programming language that aims to help 11–15 year olds develop computational skills through creating their own 3D role-playing games. Flip has two main components: 1) a visual language (based on an interlocking blocks design common to many current visual languages), and 2) a dynamically updating natural language version of the script under creation. This programming-language/natural-language pairing is a unique feature of Flip, designed to allow learners to draw upon their familiarity with natural language to “decode the code”. Flip aims to support young people in developing an understanding of computational concepts as well as the skills to use and communicate these concepts effectively. This paper investigates the extent to which Flip can be used by young people to create working scripts, and examines improvements in their expression of computational rules and concepts after using the tool. We provide an overview of the design and implementation of Flip before describing an evaluation study carried out with 12–13 year olds in a naturalistic setting. Over the course of 8 weeks, the majority of students were able to use Flip to write small programs to bring about interactive behaviours in the games they created. Furthermore, there was a significant improvement in their computational communication after using Flip (as measured by a pre/post-test). An additional finding was that girls wrote more, and more complex, scripts than did boys, and there was a trend for girls to show greater learning gains relative to the boys

An associative network with spatially organized connectivity

We investigate the properties of an autoassociative network of threshold-linear units whose synaptic connectivity is spatially structured and asymmetric. Since the methods of equilibrium statistical mechanics cannot be applied to such a network due to the lack of a Hamiltonian, we approach the problem through a signal-to-noise analysis, that we adapt to spatially organized networks. The conditions are analyzed for the appearance of stable, spatially non-uniform profiles of activity with large overlaps with one of the stored patterns. It is also shown, with simulations and analytic results, that the storage capacity does not decrease much when the connectivity of the network becomes short range. In addition, the method used here enables us to calculate exactly the storage capacity of a randomly connected network with arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency

A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components