Frugal Education:What, why, and how?

This paper explores how frugal innovation practices can challenge resource constraints by leveraging available resources in creative and innovative ways towards more affordable, practical, sustainable and resilient education practice. The education sector has faced many challenges when adapting practice to deliver quality education in the wake of a world-changing pandemic. There is a great deal we can learn from each other with regards to the frugal application of resources, such as time, money, people and space. However, forms of frugality in education design are driven by necessity and are reactive as opposed to proactive measures. We can, however, learn from educators and institutions that have been able to achieve significant educational impact at low cost with far fewer resources, adopting frugal approaches to education design and delivery. This paper proposes a set of frugal education aspects that demonstrate how frugal design practices can be organised and applied within an educational context. The aspects are outlined, and examples are presented to illustrate their effectiveness within existing education practice. This paper seeks to contribute to the existing knowledge base and research into frugal innovation practice as it applies within an education context, reframing the use of the term ‘frugal’ away from affordability and poor quality, towards a more expansive understanding that establishes a foundation on which to build, define, and contextualise frugality within an education context. The paper concludes with recommendations for the development of practical resources, informed by the research, to support educators in the design of frugal education practice

Motion Capture As Meeting Point:Seeding collaboration from the bottom-up

A Report on the Cross-Centre Motion Capture Lab. Dates: 27-30 November 2023. Location: Performance Studio. Institute for Creative Cultures (ICC), Parkside, Coventry CV1 2NE

Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies

Morphology is a powerful indicator of a galaxy's dynamical and merger history. It is strongly correlated with many physical parameters, including mass, star formation history and the distribution of mass. The Galaxy Zoo project collected simple morphological classifications of nearly 900,000 galaxies drawn from the Sloan Digital Sky Survey, contributed by hundreds of thousands of volunteers. This large number of classifications allows us to exclude classifier error, and measure the influence of subtle biases inherent in morphological classification. This paper presents the data collected by the project, alongside measures of classification accuracy and bias. The data are now publicly available and full catalogues can be downloaded in electronic format from http://data.galaxyzoo.org.Comment: Accepted by MNRAS, 14 pages. Updated to match final version; problem with table 7 header fixed. Full tables available at http://data.galaxyzoo.or

Balance trucks:Using crowd-sourced data to procedurally-generate gameplay within mobile games

Within the field of procedural content generation (PCG) research, the use of crowd-sensing data has, until now, primarily been used as a means of collecting information and generating feedback relating to player experience within games, and game aesthetics. However, crowd-sensing data can offer much more, supplying a seemingly untapped font of information which may be used within the creation of unique PCG game spaces or content, whilst providing a visible outlet for the dissemination of crowd-sensed material to users. This paper examines one such use of crowd-sensed data, the creation of a game which will reside within the CROWD4ROADS (C4RS) application, SmartRoadSense (SRS). The authors will open with a brief discussion of PCG. Following this, an explanation of the features and aims of the SRS application will be provided. Finally, the paper will introduce ‘Balance Trucks’, the SRS game, discussing the concepts behind using crowd-sensed data within its design, its development and use of PCG

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 to March 2022

The COVID-19 pandemic changed the way people lived, worked, and studied around the world, with direct consequences for domestic energy use. This study assesses the impact of COVID-19 lockdowns in the first two years of the pandemic on household electricity and gas use in England and Wales. Using data for 508 (electricity) and 326 (gas) homes, elastic net regression, neural network and extreme gradient boosting predictive models were trained and tested on pre-pandemic data. The most accurate model for each household was used to create counterfactuals (predictions in the absence of COVID-19) against which observed pandemic energy use was compared. Median monthly model error (CV(RMSE)) was 3.86% (electricity) and 3.19% (gas) and bias (NMBE) was 0.21% (electricity) and −0.10% (gas). Our analysis showed that on average (electricity; gas) consumption increased by (7.8%; 5.7%) in year 1 of the pandemic and by (2.2%; 0.2%) in year 2. The greatest increases were in the winter lockdown (January – March 2021) by 11.6% and 9.0% for electricity and gas, respectively. At the start of 2022 electricity use remained 2.0% higher while gas use was around 1.9% lower than predicted. Households with children showed the greatest increase in electricity consumption during lockdowns, followed by those with adults in work. Wealthier households increased their electricity consumption by more than the less wealthy and continued to use more than predicted throughout the two-year period while the less wealthy returned to pre-pandemic or lower consumption from summer 2021. Low dwelling efficiency was associated with a greater increase in energy consumption during the pandemic. Additionally, this study shows the value of different machine learning techniques for counterfactual modelling at the individual-dwelling level, and our approach can be used to robustly estimate the impact of other events and interventions