Computational Creativity Exercises: An Avenue for Promoting Learning in Computer Science

Peteranetz, Markeya S.; Flanigan, Abraham E.; Shell, Duane F.; Soh, Leen-Kiat

doi:10.1109/te.2017.2705152

Cited by 33 publications

(21 citation statements)

References 35 publications

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“…The study used a definition based on Barr and Stephenson's [2] of (a) problem decomposition, (b) algorithmic thinking, (c) abstraction, (d) data collection, analysis, and representation, (e) automation, and (f) simulation. [38] The study listed that computational thinking skills as abstraction and generalization, recognizing patterns, algorithmic design, problem decomposition, and evaluation of problem solutions [38].…”

Section: Studymentioning

confidence: 99%

“…As observed from Table 5, there were a host of different pedagogical methods used throughout the data set, however, many of the papers in the set did not fully explain the exact nature of the teaching environment, only discussing small pieces such as the nature of the activity or mentioning aspects of teaching such as lecturing. Some studies mentioned specific frameworks that were used such as ubiquitous learning [48], the computational thinking-based creative problem solving model [19], and the use of computational creativity exercises [38]. However, many papers did not have formal frameworks or explicit definitions of the teaching method and pedagogy used.…”

Section: Studymentioning

confidence: 99%

“…For example, many studies reported on student perceptions of their learning, attitudes toward the activity, or student interest based on an intervention that used computational thinking, at least in part [19,21,22,41]. In contrast, many studies focused on aspects of student learning, understanding, or performance as measured through course assessments [18,21,22,38,42,48].…”

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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: Studymentioning

confidence: 99%

Section: Studymentioning

confidence: 99%

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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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“…A current limitation of empirical studies in subjects such as computational thinking has been the difficulty in measuring and assessing the computational skills gained. Existing methods for assessing computational thinking are limited to taxonomies (Malyn-Smith & Lee, 2012;Weintrop et al, 2016) and computational thinking knowledge tests that focus heavily on programming knowledge (Peteranetz, Flanigan, Shell, & Soh, 2017;Shell & Soh, 2013). Shifting the focus of these assessments from near transfer of programming skills to broader, far transfer of computational problem-solving capabilities will be instrumental in assessing the learning of computational competencies.…”

Section: New Research Directionsmentioning

confidence: 99%

Toward computational apprenticeship: Bringing a constructivist agenda to computational pedagogy

Fennell

Lyon

Madamanchi

et al. 2020

J of Engineering Edu

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“…A current limitation of empirical studies of computational thinking has been difficulty in measuring and assessing computational thinking. Existing methods for assessing computational thinking are limited to taxonomies (Malyn-Smith & Lee, 2012;Weintrop et al, 2016), and computational thinking knowledge tests that focus heavily on programming knowledge (Peteranetz, Flanigan, Shell, & Soh, 2017;Shell & Soh, 2013). The development of standardized rubrics and tests that can assess specific components of computational thinking are critical for supporting empirical study of the effectiveness of the computational apprenticeship framework.…”

Section: New Research Directionsmentioning

confidence: 99%

Computational apprenticeship: Cognitive apprenticeship for the digital era

Fennell¹,

Lyon²,

Madamanchi³

et al. 2019

Preprint

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The conceptualization of Computational Thinking as a cross-cutting skill with relevance across disciplines has ushered in wide-ranging efforts to increase computational education in all facets of education. However, the majority of initiatives for integrated computing education have focused on K-12 settings, as has most education research around computational thinking. At the postsecondary level, computing education remains largely siloed within specific programming courses and has not been well-integrated throughout the STEM curriculum. Current instructional approaches often leave students poorly prepared to transfer their computing knowledge to solve new real-world problems. Additionally, there is limited education research into how best to develop computational thinking among postsecondary students. In fact, education research into computational thinking remains undertheorized and is often definitional in nature. Here, we integrate computational thinking with the educational psychology concept of adaptive expertise. Finally, we contextualize computational thinking within constructivist learning theories by introducing computational apprenticeship, an application of cognitive apprenticeship to computing. Computational apprenticeship provides a research and practice model for supporting the development of computational adaptive expertise.

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Computational Creativity Exercises: An Avenue for Promoting Learning in Computer Science

Cited by 33 publications

References 35 publications

Computational thinking in higher education: A review of the literature

Computational thinking in higher education: A review of the literature

Toward computational apprenticeship: Bringing a constructivist agenda to computational pedagogy

Computational apprenticeship: Cognitive apprenticeship for the digital era

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