Young children between the ages of 3 and 6 often struggle with identifying, expressing, and managing their emotions, especially when it comes to negative emotions. Research indicates that children with higher emotional skills tend to experience greater long-term mental health and well-being benefits. Building on our previous work, we introduce Tingets 2.0, an interactive storytelling app powered by computer vision to facilitate social and emotional learning (SEL) through tangible and digital play. We propose the TinkeRERR framework: Reflect, Express, Regulate, and Repeat, and utilize computer vision technology to combine tangible and digital play seamlessly. Tingets 2.0 aims to help children understand their own and others’ emotions, express emotions constructively, manage negative emotions and cultivate positive emotions. Early results indicate that our novel approach has effectively increased the depth and length of conversations around emotions among children and their parents.
3到6岁的孩子经常在识别、表达和管理自己的情绪方面遇到困难，尤其是在消极情绪方面。研究表明，情感技能较高的孩子往往会经历更大的长期心理健康和福祉。在我们之前工作的基础上，我们推出了Tingets 2.0，这是一款由计算机视觉驱动的交互式讲故事应用程序，通过有形和数字游戏促进社交和情感学习(SEL)。我们提出了TinkeRERR框架:Reflect, Express, regulation, and Repeat，利用计算机视觉技术将有形和数字游戏无缝结合。Tingets 2.0旨在帮助孩子理解自己和他人的情绪，建设性地表达情绪，管理负面情绪，培养积极情绪。早期的结果表明，我们的新方法有效地增加了孩子和父母之间围绕情感的对话的深度和长度。
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Multimodal data enables us to capture the cognitive and affective states of students to provide a holistic understanding of learning processes in a wide variety of contexts. With the use of sensing technology, we can capture learners’ states in near real-time and support learning. Moreover, multimodal data allows us to obtain early-predictions of learning performance, and support learning in a timely manner. In this contribution, we utilize the notion of “carry forward effect”, an inferential and predictive modelling approach that utilizes multimodal data measurements detrimental to learning performance to provide timely feedback suggestions. carry forward effect can provide a way to prioritize conflicting feedback suggestions in a multimodal data based scaffolding tool. We showcase the empirical proof of carry forward effect with the use of two different learning scenarios: debugging and game-based learning.
{"title":"Carry-Forward Effect: Early scaffolding learning processes","authors":"K. Sharma, M. Giannakos","doi":"10.1145/3594781.3594786","DOIUrl":"https://doi.org/10.1145/3594781.3594786","url":null,"abstract":"Multimodal data enables us to capture the cognitive and affective states of students to provide a holistic understanding of learning processes in a wide variety of contexts. With the use of sensing technology, we can capture learners’ states in near real-time and support learning. Moreover, multimodal data allows us to obtain early-predictions of learning performance, and support learning in a timely manner. In this contribution, we utilize the notion of “carry forward effect”, an inferential and predictive modelling approach that utilizes multimodal data measurements detrimental to learning performance to provide timely feedback suggestions. carry forward effect can provide a way to prioritize conflicting feedback suggestions in a multimodal data based scaffolding tool. We showcase the empirical proof of carry forward effect with the use of two different learning scenarios: debugging and game-based learning.","PeriodicalId":367346,"journal":{"name":"Proceedings of the 2023 Symposium on Learning, Design and Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117319727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rotem Israel-Fishelson, Peter F. Moon, Rachel E. Tabak, David Weintrop
Data science education has gained momentum in recent years. Along with the development of curricula to teach data science, the number and diversity of tools for introducing data science to learners are also multiplying. The tools used to teach data science play a central role in shaping the learning experience. Therefore, it is important to carefully choose which tools to use to introduce learners to data science. This article presents a systematic review of 25 data science tools that are, or can be, used in introductory data science education for K-12 students. The identified tools list includes spreadsheets, visual analysis tools, and scripting environments. For each tool, we examine facets of its capabilities, interactions, educational support, and accessibility. This paper advances our understanding of the current state of introductory data science environments and highlights opportunities for creating new tools to better prepare learners to navigate the data-rich world surrounding them.
{"title":"Preparing K-12 Students to Meet their Data: Analyzing the Tools and Environments used in Introductory Data Science Contexts","authors":"Rotem Israel-Fishelson, Peter F. Moon, Rachel E. Tabak, David Weintrop","doi":"10.1145/3594781.3594796","DOIUrl":"https://doi.org/10.1145/3594781.3594796","url":null,"abstract":"Data science education has gained momentum in recent years. Along with the development of curricula to teach data science, the number and diversity of tools for introducing data science to learners are also multiplying. The tools used to teach data science play a central role in shaping the learning experience. Therefore, it is important to carefully choose which tools to use to introduce learners to data science. This article presents a systematic review of 25 data science tools that are, or can be, used in introductory data science education for K-12 students. The identified tools list includes spreadsheets, visual analysis tools, and scripting environments. For each tool, we examine facets of its capabilities, interactions, educational support, and accessibility. This paper advances our understanding of the current state of introductory data science environments and highlights opportunities for creating new tools to better prepare learners to navigate the data-rich world surrounding them.","PeriodicalId":367346,"journal":{"name":"Proceedings of the 2023 Symposium on Learning, Design and Technology","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114585656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cassia Fernandez, R. de Deus Lopes, Paulo Blikstein
In this paper, we analyze how middle schoolers engaged in data visualization activities using PlayData, an educational tool designed to create representations for data by taking advantage of the flexibility and low entry point of block-based programming environments. Drawing on the analysis of artifacts and videos collected during a three-day workshop, we explore the types of visualizations created by participants and the process they engaged with to produce visualizations. Although the representational forms chosen by students were mainly traditional, our findings indicate that they were engaged in authentic data visualization practices throughout their programming process. These practices included translating ideas into programs, selecting parameters (such as color scheme and space between data points), inspecting the output, and adding annotations to provide context and better communicate the desired information. Moreover, our analysis pointed out opportunities for improving PlayData, mainly by the addition of new primitives for automating labeling and performing data transformations.
{"title":"Programming Representations: Uncovering the Process of Constructing Data Visualizations in a Block-based Programming Environment","authors":"Cassia Fernandez, R. de Deus Lopes, Paulo Blikstein","doi":"10.1145/3594781.3594783","DOIUrl":"https://doi.org/10.1145/3594781.3594783","url":null,"abstract":"In this paper, we analyze how middle schoolers engaged in data visualization activities using PlayData, an educational tool designed to create representations for data by taking advantage of the flexibility and low entry point of block-based programming environments. Drawing on the analysis of artifacts and videos collected during a three-day workshop, we explore the types of visualizations created by participants and the process they engaged with to produce visualizations. Although the representational forms chosen by students were mainly traditional, our findings indicate that they were engaged in authentic data visualization practices throughout their programming process. These practices included translating ideas into programs, selecting parameters (such as color scheme and space between data points), inspecting the output, and adding annotations to provide context and better communicate the desired information. Moreover, our analysis pointed out opportunities for improving PlayData, mainly by the addition of new primitives for automating labeling and performing data transformations.","PeriodicalId":367346,"journal":{"name":"Proceedings of the 2023 Symposium on Learning, Design and Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 2023 Symposium on Learning, Design and Technology","authors":"","doi":"10.1145/3594781","DOIUrl":"https://doi.org/10.1145/3594781","url":null,"abstract":"","PeriodicalId":367346,"journal":{"name":"Proceedings of the 2023 Symposium on Learning, Design and Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126766310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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