Teaching Structured Authoring and DITA Through Rhetorical and Computational Thinking

Evia, Carlos; Sharp, Matthew R.; Pérez-Quiñones, Manuel A.

doi:10.1109/tpc.2016.2516639

Cited by 28 publications

(15 citation statements)

References 15 publications

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“…In addition, actual metrics of learning and developing computational thinking are needed. This contrasts with how coursework can be structured through a lens of computational thinking, which was the use for computational thinking in many of the studies investigated in this review [9,19,22,48]. However, questions such as "how and when do students use computational thinking?"…”

Section: Implications For Educational Researchmentioning

confidence: 92%

“…Additionally, these studies were very different in terms of methodology. Evia et al [9] looked at student reflection surveys of a computational thinking structured intervention and gave student quotes based on the reflections. In contrast, Yuen and Robbins [56] took a grounded theory approach through open and axial coding of clinical interviews to develop descriptive categories of computational thinking skills.…”

Section: Methodsmentioning

confidence: 99%

“…From Table 4, it is apparent that many of the contexts in which computational thinking was operationalized focused on STEM fields such as mathematics, programming courses, computer science courses, or even biology courses. However, there are examples in the data set of non-STEM fields using computational thinking within their classroom, such as the use of computational thinking in a professional writing course [9].…”

Section: Classroom Learning Designmentioning

confidence: 99%

“…implementations of computational thinking curricula were in STEM courses, however, there were a handful of fields of study represented in the database. Multiple studies were focused on areas of study external to STEM; with studies looking at English and humanities students [9] as well as education students [19,21,42]. However, even in these non-STEM fields, the focus of the study was often on technical skills such as computer programming and educational technology, with the exception being a humanities course in which the authors were looking at the use of computational thinking to structure a professional writing course.…”

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confidence: 99%

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Computational thinking in higher education: A review of the literature

Lyon

Magana

2020

Comp Applic In Engineering

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Computational thinking is of growing interest to the science, technology, engineering, and mathematics (STEM) education research community. Calls from national agencies look to increase computation in STEM education. This review identifies key areas for future study by reviewing recent empirical studies that investigate computational thinking in teaching and learning contexts within higher education. Using a systematic process, this review identified four different databases for peer‐reviewed research articles using keywords. Results were evaluated against inclusion and exclusion criteria. Studies were analyzed for types of methods, target population, the role of computational thinking, pedagogical designs used, and significant findings of the study. This process resulted in a final set of 13 studies. The results indicate that computational thinking research in higher education is growing, yet there are opportunities for more research. The findings of this study highlight the need for more concrete definitions and implementations of computational thinking within higher education spaces.

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Section: Implications For Educational Researchmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Classroom Learning Designmentioning

confidence: 99%

mentioning

confidence: 99%

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Computational thinking in higher education: A review of the literature

Lyon

Magana

2020

Comp Applic In Engineering

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“…Technical communication researchers have long explored structured authoring practices and supporting technologies [7][8][9]. Technical communication students who have experience with structured authoring broadly and the DITA standard for XML have a significant advantage when they leave our programs.…”

Section: Micro-reflections In Markdown: Learning Dita By Letting It Gomentioning

confidence: 99%

Teaching Design through Micro-Assignments

Ross

Card

Gimse

2020

Proceedings of the 38th ACM International Conference on Design of Communication

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This paper considers three brief experience reports on teaching design in both academia and industry with an eye towards training future design professionals. We focus on production-based microassignments: small, low-stakes pieces of work that serve broader goals and encourage adaptive thinking, arguing that low-stakes projects assigned either in the classroom or in the workplace for training help future designers better prepare themselves for complex, multimodal workplaces and careers. The microprojects we consider here include examples from a Document Design classroom, teaching structured authoring in DITA, and the value of microtrainings in the workplace. CCS CONCEPTS • Social and professional topics; • Information systems; • Applied computing → Operations research;

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The use of engineering model‐building activities to elicit computational thinking: A design‐based research study

Lyon

Magana

2021

J of Engineering Edu

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Background Computation and computational thinking are of great interest to both engineering research and teaching communities. Effective learning environments are needed to incorporate computational thinking within the engineering disciplines. Design‐based research is uniquely positioned to address this need for designing effective learning environments. Purpose This design‐based research study characterizes the different ways in which students used computational thinking when building computational models. The design of the model‐building activities was grounded in productive failure and model‐eliciting activities. Design The design‐based research study implemented the computational modeling activities within an engineering capstone course. The research question was: What types of computational thinking outcomes emerge when engineering students build computational models? Thematic analysis was applied to individual student artifacts to identify key computational thinking outcomes that were elicited as a result of the intervention. Results Throughout the building of the model, students demonstrated the use of computational thinking outcomes, mainly abstraction, algorithmic thinking, evaluation, generalization, and decomposition. However, the diversity and density of use for each outcome were different and unique. Conclusions This study shows how building computational models, when guided by current educational theories, can allow for students to practice the use of computational thinking and for educators to incorporate these key practices into their engineering classrooms.

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Teaching Structured Authoring and DITA Through Rhetorical and Computational Thinking

Cited by 28 publications

References 15 publications

Computational thinking in higher education: A review of the literature

Computational thinking in higher education: A review of the literature

Teaching Design through Micro-Assignments

The use of engineering model‐building activities to elicit computational thinking: A design‐based research study

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