Computational thinking in higher education: A review of the literature

Lyon, Joseph A.; Magana, Alejandra J.

doi:10.1002/cae.22295

Cited by 71 publications

(54 citation statements)

References 51 publications

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“…The study focused on elementary teachers to teach computer science using computational thinking by integrating CT into science education. The growing nature of computational thinking in science, technology, engineering and mathematics (STEM) was reviewed (Lyon & Magana, 2020). Based on various parameters such as types of methods, target population and the role of computational thinking, a set of 13 studies were reviewed and the results showed that there are huge opportunities for CT research in higher education.…”

Section: Computational Thinking (Ct) Pedagogymentioning

confidence: 99%

Development and Evaluation of Fishbone-Based Advanced Computational Thinking (FACT) Pedagogy: A Teacher-Student Collaborative Learning Environment in Engineering and Science Education

Gopinath¹,

Santhi²

2020

Higher Education for the Future

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In this article, Fishbone-based advanced computational thinking (FACT) pedagogy is proposed by fusing fishbone pedagogy and computational thinking pedagogy for enhancing teaching-learning process while teaching engineering and science courses, for engineering and science students respectively. The proposed FACT pedagogy has been implemented using the concept of X-ray machine in biomedical instrumentation course and biomolecules, in biochemistry course. Using fishbone approach, various components of X-ray machine in biomedical course and the components of biomolecules in biochemistry course are visually explained as ribs and riblets of a fishbone diagram, without coining the keywords X-ray and biomolecules in an engineering institution and science institution respectively. Finally, the targeted concept is arrived and explained. Similarly, the same concepts of X-ray and biomolecules are coined among students and they are asked to divide or decompose the concepts into sub-concepts separately. To implement and evaluate the proposed pedagogy, an engineering institution and a science institution have been selected and evaluation results have been published in this article. In this pedagogical approach, the same complex concept is taught as a backward thinking by the teacher using fishbone pedagogy and forward thinking by the students using computational thinking pedagogy. This combined approach helps students to understand any complex concept in science courses. Also, it helps the teachers to easily convey and embed the same among the student community while teaching science courses.

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Section: Computational Thinking (Ct) Pedagogymentioning

confidence: 99%

Development and Evaluation of Fishbone-Based Advanced Computational Thinking (FACT) Pedagogy: A Teacher-Student Collaborative Learning Environment in Engineering and Science Education

Gopinath¹,

Santhi²

2020

Higher Education for the Future

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“…Computational thinking is often described as the process of defining a problem and associated solutions such that either a human or machine (or both) can execute the proposed solutions (Wing, 2006). While no commonly accepted definition for computational thinking exists, the definitions used across the literature emphasize abstraction and automation (Lyon and Magana, 2020). Computational thinking and, more broadly, computational knowledge, are often developed within computer science (CS) courses as well as in courses that teach programming in disciplines beyond computer sciences, with the educational literature drawing distinction between the two.…”

Section: Introductionmentioning

confidence: 99%

Best Management Practices for Teaching Hydrologic Coding in Physical, Hybrid, and Virtual Classrooms

Kelleher

Gannon²,

Jones³

et al. 2022

Front. Water

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As the field of hydrologic sciences continues to advance, there is an increasing need to develop a workforce with tools to curate, manage, and analyze large datasets. As such, undergraduate and graduate curricula are beginning to regularly incorporate scientific programing in the classroom. However, there are several key challenges to successfully incorporating scientific programming into a hydrology course or curriculum, such as meeting disciplinary outcomes alongside teaching students to code, equity issues with access to computing power, and effective classroom management. While these challenges were exacerbated by the global pandemic, shifting to online and hybrid learning formats provided an opportunity to explore and re-evaluate the way we facilitated our hydrology courses and integrated coding exercises and learning. In this article, we reflect on these experiences in three very different hydrology courses (e.g., courses housed in geoscience/engineering, environmental science, and biology programs) with an eye toward identifying successes and opportunities for improvement. We explore this by presenting ten best management practices (BMPs), representing a series of recommendations we have for teaching a virtual, hybrid, or in-person hydrology course that incorporates coding. While all recommendations provided can be applied to many programming languages, the focus of the paper (given the expertise of the authors) is on R. Our BMPs focus on technological facilitation, managing the virtual classroom, and instructional resources, with lessons learned that are applicable to in-person instruction. We also summarize the ways that the authors of this article integrate coding into our coursework to serve as a framework for prepping new courses or those revising existing hydrologic coursework. Above all, we hope these series of recommendations will evolve as hydrology courses continue to emphasize computational skills alongside disciplinary learning.

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“…CT has been included in various educational settings, ranging from early childhood and preschool education to university levels ( Grover and Pea, 2013 ; Lyon and Magana, 2020 ; Fagerlund et al, 2021 ). The inclusion of CT notions in formal education has taken many forms, which include its integration in both computer science courses and its embedding in different disciplines, such as math ( Weintrop et al, 2016 ; Hickmott et al, 2018 ), science ( Sneider et al, 2014 ; Swanson et al, 2019 ), and art ( Bell and Bell, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Educational Robotics Intervention to Foster Computational Thinking in Preschoolers: Effects of Children’s Task Engagement

et al. 2022

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Computational thinking (CT) is a broadly used term in education to refer to the cognitive processes underlying the application of computer science concepts and strategies of problem-solving. Recent literature has pointed out the value of children acquiring computational thinking skills (i.e., understanding and applying concepts, such as conditionals, iteration, or generalization), especially while learning STEM subjects. Robotics has been used as a tool to introduce computational thinking and STEM knowledge to children. As physical objects, robots have been proposed as developmentally appropriate for the early childhood setting, promoting motivation and allowing young learners to represent abstract ideas in a concrete setting. This study presents a novel educational robotics (ER) intervention using RoboTito, a robot programmable through tangible elements in its environment designed for kindergarteners. We used a quasi-experimental design with an active control group. In addition, we conducted a structured observation of the filmed material of the sessions to gather data on children’s attention and motivation throughout the activities. Fifty-one children (male = 33; mean age = 66 months, SD = 5.49 months) attending level 5 (kindergarten) at a Uruguayan public school participated in the study. Children in our experimental condition participated in an intervention programming RoboTito using tangible elements, while children in our control condition played with the robot through sensory-motor activities using a remote control and did not engage in programming. Motivational and attentional factors were assessed through video-recorded sessions of the ER activities. Four trained observers blind to the experimental conditions participated in the coding. Children’s interactions were assessed in four categories: task engagement, distractibility, oral participation, and objective fulfillment. Our results suggest children’s task engagement mediated their gains in CT after the intervention; post-hoc Tukey contrasts revealed non-significant pre-test to post-test gains for the control and low engagement groups, and significant for the high engagement group. Overall, we conclude task engagement played a central role in children’s learning gains and our robotics intervention was successful in promoting CT for engaged children. We discuss the practical implications of our results for early childhood education and developmentally appropriate ER targeted for young learners.

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Computational thinking in higher education: A review of the literature

Cited by 71 publications

References 51 publications

Development and Evaluation of Fishbone-Based Advanced Computational Thinking (FACT) Pedagogy: A Teacher-Student Collaborative Learning Environment in Engineering and Science Education

Development and Evaluation of Fishbone-Based Advanced Computational Thinking (FACT) Pedagogy: A Teacher-Student Collaborative Learning Environment in Engineering and Science Education

Best Management Practices for Teaching Hydrologic Coding in Physical, Hybrid, and Virtual Classrooms

Educational Robotics Intervention to Foster Computational Thinking in Preschoolers: Effects of Children’s Task Engagement

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