Fostering Creative Visualisation Skills Through Data-Art Exhibitions
- URL: http://arxiv.org/abs/2408.16479v1
- Date: Thu, 29 Aug 2024 12:16:13 GMT
- Title: Fostering Creative Visualisation Skills Through Data-Art Exhibitions
- Authors: Jonathan C. Roberts,
- Abstract summary: We present our implementation of a data-art exhibition within a computing curriculum, for third-year degree-level students.
Students create art-based visualisations from selected datasets and present their work in a public exhibition.
- Score: 0.9463895540925061
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data-art exhibitions offer a unique and real-world setting to foster creative visualisation skills among students. They serve as real-world platform for students to display their work, bridging the gap between classroom learning and professional practice. Students must develop a technical solution, grasp the context, and produce work that is appropriate for public presentation. This scenario helps to encourage innovative thinking, engagement with the topic, and helps to enhance technical proficiency. We present our implementation of a data-art exhibition within a computing curriculum, for third-year degree-level students. Students create art-based visualisations from selected datasets and present their work in a public exhibition. We have used this initiative over the course of two academic years with different cohorts, and reflect on its impact on student learning and creativity.
Related papers
- Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives.
Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation.
We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
arXiv Detail & Related papers (2024-08-22T04:49:50Z) - Zero-Shot Object-Centric Representation Learning [72.43369950684057]
We study current object-centric methods through the lens of zero-shot generalization.
We introduce a benchmark comprising eight different synthetic and real-world datasets.
We find that training on diverse real-world images improves transferability to unseen scenarios.
arXiv Detail & Related papers (2024-08-17T10:37:07Z) - Representational Alignment Supports Effective Machine Teaching [81.19197059407121]
We integrate insights from machine teaching and pragmatic communication with the literature on representational alignment.
We design a supervised learning environment that disentangles representational alignment from teacher accuracy.
arXiv Detail & Related papers (2024-06-06T17:48:24Z) - PortfolioMentor: Multimodal Generative AI Companion for Learning and
Crafting Interactive Digital Art Portfolios [1.8130068086063336]
Digital art portfolios serve as impactful mediums for artists to convey their visions, weaving together visuals, audio, interactions, and narratives.
Without technical backgrounds, design students often find it challenging to translate creative ideas into tangible codes and designs.
This tool guides and collaborates with students through proactive suggestions and responsible Q&As for learning, inspiration, and support.
In detail, the system starts with the understanding of the task and artist's visions, follows the co-creation of visual illustrations, audio or music suggestions and files, click-scroll effects for interactions, and creative vision conceptualization.
arXiv Detail & Related papers (2023-11-23T16:36:40Z) - Diffusion Based Augmentation for Captioning and Retrieval in Cultural
Heritage [28.301944852273746]
This paper introduces a novel approach to address the challenges of limited annotated data and domain shifts in the cultural heritage domain.
By leveraging generative vision-language models, we augment art datasets by generating diverse variations of artworks conditioned on their captions.
arXiv Detail & Related papers (2023-08-14T13:59:04Z) - Review of Large Vision Models and Visual Prompt Engineering [50.63394642549947]
Review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering.
We present influential large models in the visual domain and a range of prompt engineering methods employed on these models.
arXiv Detail & Related papers (2023-07-03T08:48:49Z) - Towards Mining Creative Thinking Patterns from Educational Data [0.0]
Creativity is an essential 21st-century skill that should be taught in schools.
The use of educational technology to promote creativity is an active study field.
Despite the burgeoning body of research on adaptive technology for education, mining creative thinking patterns from educational data remains a challenging task.
arXiv Detail & Related papers (2022-10-12T12:24:49Z) - Self-Supervised Representation Learning: Introduction, Advances and
Challenges [125.38214493654534]
Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets.
This article introduces this vibrant area including key concepts, the four main families of approach and associated state of the art, and how self-supervised methods are applied to diverse modalities of data.
arXiv Detail & Related papers (2021-10-18T13:51:22Z) - Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool [0.9899017174990579]
We present a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators.
ReACT is a data-driven tool with a user-friendly graphical interface.
It analyzes students' performance data and provides context-based alerts as well as recommendations to educators for course planning.
arXiv Detail & Related papers (2021-07-30T03:09:59Z) - Broader terms curriculum mapping: Using natural language processing and
visual-supported communication to create representative program planning
experiences [62.997667081978825]
Communication difficulties between faculty and non-faculty groups leave unexplored an immense collaboration potential.
This paper presents a method to deliver program plan representations that are universal, self-explanatory, and empowering.
arXiv Detail & Related papers (2021-02-09T13:27:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.