From Theory Bias to Theory Dialogue

Kafai, Yasmin B.; Proctor, C. Patrick; Lui, Debora

doi:10.1145/3291279.3339400

Cited by 37 publications

(33 citation statements)

References 54 publications

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“…Despite differences between institutions, higher CS education shares a common author: the 1960s and 1970s academic computer scientists whose dominant, European scientific values [3] inspired a vision for computing that centered cognition and mathematics [18,29,38] to the exclusion and marginalization of identity and political vision [27,34,38,39,41]. Justice-centered approaches in higher CS education enable hosts (e.g.…”

Section: Reauthor Cs For More Just Futuresmentioning

confidence: 99%

CS Education for the Socially-Just Worlds We Need: The Case for Justice-Centered Approaches to CS in Higher Education

Lin

2021

Preprint

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Justice-centered approaches to equitable computer science (CS) education prioritize the development of students' CS disciplinary identities toward social justice rather than corporations, industry, empire, and militarism by emphasizing ethics, identity, and political vision. However, most research in justice-centered approaches to equitable CS education focus on K-12 learning environments. In this position paper, we problematize the lack of attention to justicecentered approaches to CS in higher education and then describe a justice-centered approach for undergraduate Data Structures and Algorithms that (1) critiques sociopolitical values of data structure and algorithm design and dominant computing epistemologies that approach social good without design justice; (2) centers students in culturally responsive-sustaining pedagogies to resist dominant computing culture and value Indigenous ways of living in nature; and (3) ensures the rightful presence of political struggles through reauthoring rights and problematizing the political power of computing. Through a case study of this Critical Comparative Data Structures and Algorithms pedagogy, we argue that justice-centered approaches to higher CS education can help students not only critique the ethical implications of nominally technical concepts, but also develop greater respect for diverse epistemologies, cultures, and narratives around computing that can help all of us realize the socially-just worlds we need. CCS CONCEPTS• Social and professional topics → Computing education.

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Section: Reauthor Cs For More Just Futuresmentioning

confidence: 99%

CS Education for the Socially-Just Worlds We Need: The Case for Justice-Centered Approaches to CS in Higher Education

Lin

2021

Preprint

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“…A critical lens has been adopted by researchers such as Blikstein (2013) to critique efforts that limit students' engagement on interdisciplinary learning tasks such as surface or basic learning of how to use technology tools and skills. Kafai et al (2019) support adopting frameworks that cross boundaries and focus on cognitive skills, social participation, critical-social justice approaches and on learning using computer technology. According to Blikstein (2013), educators should avoid “quick demonstration projects” that are aesthetically pleasing to the students but require little effort.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

STEAM education: student learning and transferable skills

Bertrand

Namukasa

2020

JRIT

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PurposeGlobally, interdisciplinary and transdisciplinary learning in schools has become an increasingly popular and growing area of interest for educational reform. This prompts discussions about Science, Technology, Engineering, Arts and Mathematics (STEAM), which is shifting educational paradigms toward art integration in science, technology, engineering and mathematics (STEM) subjects. Authentic tasks (i.e. real-world problems) address complex or multistep questions and offer opportunities to integrate disciplines across science and arts, such as in STEAM. The main purpose of this study is to better understand the STEAM instructional programs and student learning offered by nonprofit organizations and by publicly funded schools in Ontario, Canada.Design/methodology/approachThis study addresses the following research question: what interdisciplinary and transdisciplinary skills do students learn through different models of STEAM education in nonprofit and in-school contexts? We carried out a qualitative case study in which we conducted interviews, observations and data analysis of curriculum documents. A total of 103 participants (19 adults – director and instructors/teachers – and 84 students) participated in the study. The four STEAM programs comparatively taught both discipline specific and beyond discipline character-building skills. The skills taught included: critical thinking and problem solving; collaboration and communication; and creativity and innovation.FindingsThe main findings on student learning focused on students developing perseverance and adaptability, and them learning transferable skills.Originality/valueIn contrast to other research on STEAM, this study identifies both the enablers and the tensions. Also, we stress ongoing engagement with stakeholders (focus group), which has the potential to impact change in teaching and teacher development, as well as in related policies.

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“…First, the skills might not be disjoint, as an empirical validation is still pending. Second, some of the skills might not be as well known by many educators, as teaching CT is still a rather new idea that has been getting more and more attention and importance only recently [36]. Of course, another reason might be the proposed task design.…”

Section: Responses To Research Questionsmentioning

confidence: 99%

Generic Tasks for Algorithms

2020

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Due to its links to computer science (CS), teaching computational thinking (CT) often involves the handling of algorithms in activities, such as their implementation or analysis. Although there already exists a wide variety of different tasks for various learning environments in the area of computer science, there is less material available for CT. In this article, we propose so-called Generic Tasks for algorithms inspired by common programming tasks from CS education. Generic Tasks can be seen as a family of tasks with a common underlying structure, format, and aim, and can serve as best-practice examples. They thus bring many advantages, such as facilitating the process of creating new content and supporting asynchronous teaching formats. The Generic Tasks that we propose were evaluated by 14 experts in the field of Science, Technology, Engineering, and Mathematics (STEM) education. Apart from a general estimation in regard to the meaningfulness of the proposed tasks, the experts also rated which and how strongly six core CT skills are addressed by the tasks. We conclude that, even though the experts consider the tasks to be meaningful, not all CT-related skills can be specifically addressed. It is thus important to define additional tasks for CT that are detached from algorithms and programming.

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From Theory Bias to Theory Dialogue

Cited by 37 publications

References 54 publications

CS Education for the Socially-Just Worlds We Need: The Case for Justice-Centered Approaches to CS in Higher Education

CS Education for the Socially-Just Worlds We Need: The Case for Justice-Centered Approaches to CS in Higher Education

STEAM education: student learning and transferable skills

Generic Tasks for Algorithms

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