Data science education across the disciplines: Underexamined opportunities for K‐12 innovation

Jiang, Shiyan; Lee, Victor R.; Rosenberg, Joshua M.

doi:10.1111/bjet.13258

Cited by 12 publications

(6 citation statements)

References 26 publications

(23 reference statements)

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“…As aspects of concreteness, virtual, remote, or hybrid education, educational innovation processes with ICT, digital transformation, and technological innovation find dynamizing aspects in the pedagogical models of STEAM from the recognition of interdisciplinary skills and the commitment of educational actors [82][83][84][85], education 4.0 [86][87][88][89][90][91][92][93], Big Data [94][95][96][97][98][99][100][101], Data Mining [102,103], Data Science [104][105][106][107][108][109][110][111], and Artificial Intelligence [112][113][114][115][116][117]. However, one should constantly reflect on the ethics, potentialities, and challenges of artificial intelligence in the framework of quality education [118].…”

Section: Educational Innovation and Ictmentioning

confidence: 99%

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

et al. 2023

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The Sustainable Development Goals are part of the formative management in multiple universities that are committed to a better and more sustainable future for all; contributing to this from the postgraduate management in education involves rethinking educational research from lines that ensure that the research work has a real impact on the 2030 agenda. In this regard, the objective of this study was to identify and characterize specific lines of educational research for sustainable development in a private university in Lima, specifically in Postgraduate Education (Doctorate, Master’s and Second Specialty). The methodology deployed a qualitative approach, synchronizing the methods of systematization of experiences and grounded theory. A total of 186 students and 16 teachers participated, and the collection of information used three techniques: (1)—documentary review (5398 titles and other normative documents), (2)—asynchronous focus group (16 experienced teachers), and (3)—interview (202 participants). The results show four specific lines of research: (1)—Quality educational management, (2)—Teaching–learning–assessment, (3)—Educational innovation and ICT, and (4)—Training, research, and psychosocial factors in education. It concludes with a characterization of these lines based on an epistemic foundation that integrates current trends in educational research for sustainable development.

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Section: Educational Innovation and Ictmentioning

confidence: 99%

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

et al. 2023

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“…These include situating understandings in disciplinary practices and broader reach beyond CS classes. Furthermore, educators from many disciplines have growing interest in and questions on teaching about AI (Jiang, Lee, and Rosenberg 2022;Sanusi, Oyelere, and Omidiora 2022).…”

Section: Introductionmentioning

confidence: 99%

Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers

Xie,

Sarin,

Wolf

et al. 2024

AAAI

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High school teachers from many disciplines have growing interests in teaching about artificial intelligence (AI). This cross-disciplinary interest reflects the prevalence of AI tools across society, such as Generative AI tools built upon Large Language Models (LLM). However, high school classes are unique and complex environments, led by teachers with limited time and resources with priorities that vary by class and the students they serve. Therefore, developing curricula about AI for classes that span many disciplines (e.g. history, art, math) must involve centering the expertise of cross-disciplinary teachers. In this study, we conducted five collaborative curricular co-design sessions with eight teachers who taught high school humanities and STEM classes. We sought to understand how teachers considered AI when it was taught in art, math, and social studies contexts, as well as opportunities and challenges they identified with incorporating AI tools into their instruction. We found that teachers considered technical skills and ethical debates around AI, opportunities for "dual exploration" between AI and disciplinary learning, and limitations of AI tools as supporting engagement and reflection but also potentially distracting. We interpreted our findings relative to co-designing adaptable AI curricula to support teaching about and with AI across high school disciplines.

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“…This involves going beyond analytic operations to include considerations of context (Bhargava, 2019; Lee & Wilkerson, 2018). As visual representations of data proliferate in the media landscape (D'Ignazio, 2019), their role in functional scientific engagement increases, as does the interconnectedness between data and scientific literacy (Jiang et al, 2022; Stage et al, 2013). The public's ability to understand issues, form positions, and make choices relating to science hinges on the ability to seek, interpret, and reason through data.…”

Section: Introductionmentioning

confidence: 99%

What drives the public's use of data? The mediating role of trust in science and data literacy in functional scientific reasoning concerning COVID‐19

Tabak

Dubovi

2023

Science Education

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Is public engagement with science deliberative and evidence‐based? The public is often perceived as underprepared to use data and susceptible to partisan and emotional manipulation. Consequently, educational efforts focus on the ability to identify reliable information. We posit that effective engagement with science goes beyond this and hinges on data literacy. We leveraged the unique circumstances of COVID‐19, where diverse people inundated with pandemic‐related data representations in the media needed to make consequential decisions, to examine whether people use data and what factors affect such use. In a survey of a representative Israeli adult sample, participants reported their information habits and beliefs before and during COVID‐19. On being presented with graphs and datasets, they answered data literacy and COVID‐19‐related functional reasoning assessments (e.g., would you travel abroad?). Data literacy distinguished those who incorporated data from those who did not. Yet, participants incorporated moral, social, and economic considerations at all data literacy levels, suggesting that people may be deliberative even when they do not attend to quantified data. Moreover, participants' trust in science and data interpretation competence were key factors mediating the relationship between self‐efficacy in data interpretation skills and the incorporation of data in reasoning. The findings extend beyond COVID‐19 to a broader understanding of the factors influencing public engagement with quantitative representations. Rather than focusing solely on remediating data interpretation, we suggest that educative efforts work on multiple fronts and that cultivating trust in science is key to a broader, more deliberative engagement with science.

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Data science education across the disciplines: Underexamined opportunities for K‐12 innovation

Cited by 12 publications

References 26 publications

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

Trends in Educational Research for Sustainable Development in Postgraduate Education Programs at a University in Peru

Co-designing AI Education Curriculum with Cross-Disciplinary High School Teachers

What drives the public's use of data? The mediating role of trust in science and data literacy in functional scientific reasoning concerning COVID‐19

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