Learning activities in a makerspace are hands-on and characterized by design and inquiry. Evaluation is needed both for learners and their coaches in order to effectively guide the learning process of the children and for feedback on the effectiveness of the after-school maker activities. Due to its constructionist nature, learning in a makerspace requires specific forms of evaluation. In this paper we describe the development of an instrument that facilitates and captures reflection on the activities that children undertook in a library makerspace. Our aim is to capture learning in this context with multiple instruments: analysis of the artifacts that are made, observation of hands-on activities and interviews - which all are time consuming methods. Hence, we developed an easy to use tool for self-evaluation of maker learner activities for children. We build on the design of a visual instrument used for learning by design and inquiry in primary education. The findings and results are transferable to (formative) assessment and evaluation of learning activities by learners in other types of education and specific in maker education.
In an image-saturated society, methods for visual analysis gain urgency. This special issue explores visual ways to study online images, focusing on their collection and circulation. The proposition we make is to stay as close to the material as possible. How to approach the visual with the visual? What type of images may one design to make sense of, reshape, and reanimate online image collections? How may arrangements of online images promote various analytical procedures, participatory actions, and design interventions? Furthermore, we focus on the role that algorithmic tools, including machine vision, can play in such research efforts while being sensitive to their flaws and shortcomings. Which kinds of collaborations between humans and machines can we envision to better grasp and critically interrogate the dynamics of today’s digital visual culture? The different practices and formats discussed in this special issue (including data feminism, visual scores, machine vision, image networks, field guides) offer a range of approaches that seek to understand, reanimate, and change perspectives on our digital visual culture.
MULTIFILE
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
