The Coding Revolution

Paul, Annie

doi:10.1038/scientificamerican0816-42

Cited by 11 publications

(7 citation statements)

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“…Teaching K-12 students to program has emerged as a crucial instructional goal for school teachers (Kalelioğlu, 2015;Kazimoglu, Kiernan, Bacon, and MacKinnon, 2012;Lye and Koh, 2014). And yet, preservice and in-service teacher education programs are still in need of a nationwide curriculum that supports integration of programming and other computational skills such as debugging at the K-12 level (Paul, 2016). Learning to program and debug program errors can be a daunting task for preservice teachers, especially if they are novice learners with limited to no background in computing.…”

Section: Debuggingmentioning

confidence: 99%

Early childhood preservice teachers’ debugging block-based programs: An eye tracking study

2020

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Learning computational skills such as programming and debugging is very important for K-12 students given the increasing need of workforce proficient in computing technologies. Programming is an intricate cognitive task that entails iteratively creating and revising programs to create an artifact. Central to programming is debugging, which consists of systematically identifying and fixing program errors. Given its central role, debugging should be explicitly taught to early childhood preservice teachers so they can support their future students' learning to program and debug errors. In this study, we propose using eye-tracking data and cued retrospective reporting to assess preservice teachers' cognitive strategies while debugging. Several eye-tracking studies have investigated learners' debugging strategies though the literature lacks studies (a) conducted with early childhood preservice teachers and (b) that focus on blockbased programming languages, such as Scratch. The present study addresses this gap in the literature. This study used mixed methods to triangulate quantitative findings from eye movement analysis and qualitative findings about employed debugging strategies into the creation of descriptive themes. Results showed that participants developed strategies such as simultaneous review of output and code, use of beacons to narrow down the area to be debugged, and eye fixation on output to form hypotheses. But most often, debugging was not informed by a hypothesis, which led to trial and error. Study limitations and directions for future research are discussed.

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Section: Debuggingmentioning

confidence: 99%

Early childhood preservice teachers’ debugging block-based programs: An eye tracking study

2020

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“…Environmental and remote sensing sciences need to leverage all technological advances, including programming (Vitolo et al, 2015). Programming has been deemed as an essential skill for the 21 st century, just as important as reading or writing (Paul, 2016;Vee, 2013). However, the lack of programming skills is hampering our ability to answer complicated questions related to mangrove ecosystems.…”

Section: Automation Requires Skillsmentioning

confidence: 99%

“…Automating image processing is on the rise and it can save 50-60% of the time spent on a project (Giri, 2016). The ability to sub-divide the acquisition, processing and analysis of remotely sensed data into smaller, simpler tasks that a computer can do should not require a software engineer (Paul, 2016;Wing, 2008). Scientists should be able to read and write code to make their work easier and faster.…”

Section: Automation Requires Skillsmentioning

confidence: 99%

“…Computational thinking and problem-solving skills go hand in hand, and many argue that programming gives researchers the ability to dissect a big problem into smaller, simpler pieces (Paul, 2016;Wing, 2008). Solving smaller problems is easier and faster than solving a single, more complex problem.…”

Section: Automation Requires Skillsmentioning

confidence: 99%

“…This is especially true for new and early career scientists who have grown with technology and are very comfortable with computers, internet and myriad software and hardware platforms. Researchers need not be expert programmers, but "virtual segregation" in research is a reality that should be overcome (Paul, 2016). With freely available ever-growing datasets, processing power and programming skills become a need rather than a luxury.…”

Section: The Way Forwardmentioning

confidence: 99%

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Monitoring mangrove forests: Are we taking full advantage of technology?

Younes

Joyce

Maier

2017

International Journal of Applied Earth Observation and Geoinfor

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Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research.

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Distributed Collaboration in Project-Based Learning and Its Assessment in Next-Generation Learning Environments

Hamilton¹,

Kallunki²

2020

Encyclopedia of Education and Information Technologies

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The Coding Revolution

Cited by 11 publications

References 0 publications

Early childhood preservice teachers’ debugging block-based programs: An eye tracking study

Early childhood preservice teachers’ debugging block-based programs: An eye tracking study

Monitoring mangrove forests: Are we taking full advantage of technology?

Distributed Collaboration in Project-Based Learning and Its Assessment in Next-Generation Learning Environments

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