How Beaufort, Neumann and Gates met? Subject integration with
spreadsheeting
- URL: http://arxiv.org/abs/2309.12353v1
- Date: Thu, 31 Aug 2023 20:02:42 GMT
- Title: How Beaufort, Neumann and Gates met? Subject integration with
spreadsheeting
- Authors: Maria Csernoch and Julia Csernoch
- Abstract summary: It is found that both students content knowledge and their digital skills developed more efficiently than in traditional course book and decontextualized digital environments.
The method presented here can be adapted to any paper-based problems whose solutions would be more effective in a digital environment.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computational thinking should be the fourth fundamental skill, along with
reading, writing, and arithmetic (3R). To reach the level where computational
thinking skills, especially digital problem solving have their own schemata,
there is a long way to go. In the present paper, a novel approach is detailed
to support subject integration and building digital schemata, on the well-known
Beaufort scale. The conversion of a traditional, paper-based problem and a data
retrieval process are presented within the frame of a Grade 8 action research
study. It is found that both students content knowledge and their digital
skills developed more efficiently than in traditional course book and
decontextualized digital environments. Furthermore, the method presented here
can be adapted to any paper-based problems whose solutions would be more
effective in a digital environment and which offer various forms for building
schemata both in the subject matter and informatics.
Related papers
- Unlocking Comics: The AI4VA Dataset for Visual Understanding [62.345344799258804]
This paper presents a novel dataset comprising Franco-Belgian comics from the 1950s annotated for tasks including depth estimation, semantic segmentation, saliency detection, and character identification.
It consists of two distinct and consistent styles and incorporates object concepts and labels taken from natural images.
By including such diverse information across styles, this dataset not only holds promise for computational creativity but also offers avenues for the digitization of art and storytelling innovation.
arXiv Detail & Related papers (2024-10-27T14:27:05Z) - A Document-based Knowledge Discovery with Microservices Architecture [0.0]
We point out the key challenges in the context of knowledge discovery and present an approach to addressing these using a database architecture.
Our solution led to a conceptual design focusing on keyword extraction, calculation of documents, similarity in natural language, and programming language independent provision of the extracted information.
arXiv Detail & Related papers (2024-06-13T09:28:31Z) - Digital Transformation of Education, Systems Approach and Applied Research [0.0]
This article proposes the construction of a systemic model of digital education as part of research applied to public policy.
Considering the digital domain in its pervasiveness, it highlights the importance of a complex approach to understanding the transformation of practices.
arXiv Detail & Related papers (2024-04-10T07:45:29Z) - A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing
Objects in 3D Scenes [80.20670062509723]
3D dense captioning is an emerging vision-language bridging task that aims to generate detailed descriptions for 3D scenes.
It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning.
Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field.
arXiv Detail & Related papers (2024-03-12T10:04:08Z) - Towards a Holistic Understanding of Mathematical Questions with
Contrastive Pre-training [65.10741459705739]
We propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo.
We first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes.
Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy.
arXiv Detail & Related papers (2023-01-18T14:23:29Z) - A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty
Quantification and Optimization, a Battery Digital Twin, and Perspectives [11.241244950889886]
Second paper presents a literature review of key enabling technologies of digital twins.
Third paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed.
arXiv Detail & Related papers (2022-08-27T01:36:15Z) - A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning
Enabling Technologies [11.241244950889886]
Digital twin is an emerging technology in the era of Industry 4.0.
Digital twins can model the physical world as a group of interconnected digital models.
In part two of this review, the role of uncertainty quantification and optimization are discussed.
arXiv Detail & Related papers (2022-08-26T15:01:26Z) - Automatic Image Content Extraction: Operationalizing Machine Learning in
Humanistic Photographic Studies of Large Visual Archives [81.88384269259706]
We introduce Automatic Image Content Extraction framework for machine learning-based search and analysis of large image archives.
The proposed framework can be applied in several domains in humanities and social sciences.
arXiv Detail & Related papers (2022-04-05T12:19:24Z) - Digital Editions as Distant Supervision for Layout Analysis of Printed
Books [76.29918490722902]
We describe methods for exploiting this semantic markup as distant supervision for training and evaluating layout analysis models.
In experiments with several model architectures on the half-million pages of the Deutsches Textarchiv (DTA), we find a high correlation of these region-level evaluation methods with pixel-level and word-level metrics.
We discuss the possibilities for improving accuracy with self-training and the ability of models trained on the DTA to generalize to other historical printed books.
arXiv Detail & Related papers (2021-12-23T16:51:53Z) - Neural Fields in Visual Computing and Beyond [54.950885364735804]
Recent advances in machine learning have created increasing interest in solving visual computing problems using coordinate-based neural networks.
neural fields have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation.
This report provides context, mathematical grounding, and an extensive review of literature on neural fields.
arXiv Detail & Related papers (2021-11-22T18:57:51Z) - Towards Digital Engineering -- The Advent of Digital Systems Engineering [6.034469109312663]
Digital Engineering, the digital transformation of engineering to leverage digital technologies, is coming globally.
This paper explores digital systems engineering, which aims at developing theory, methods, models, and tools to support the emerging digital engineering.
arXiv Detail & Related papers (2020-02-21T04:58:20Z)
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.