No patterns in pattern recognition: A systematic literature review

Dasgupta, Annwesa; Purzer, Şenay

doi:10.1109/fie.2016.7757676

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Whereas, from a science education perspective, there does not seem to be such a clear and sole favorite so far. Nevertheless, we were able to identify eight papers that explore possible candidates for meaningful science competencies that you could consider effective model competencies: Pattern recognition because it is considered one aspect of CT (Dasgupta and Purzer, 2016). Also, Miller (2019) takes a specific look at this mathematical competence and assumes a close connection between CT on the one hand and pattern recognition and further mathematical concepts on the other hand, too.…”

Section: On Focus: Concrete Skills and Competencesmentioning

confidence: 99%

Computational literacy in science education–A systematic review

Braun

Huwer

2022

Front. Educ.

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Computational literacy is one of the 21st century skills–students must therefore acquire the appropriate competencies in computer science to meet the demands of 21st century society. To achieve this, teachers must be adequately trained. Furthermore, this also means that introductory computer science education concepts must also be integrated into the training of (pre-service) science teachers and anchored in the curriculum. This is particularly important because many scientific professions or research can no longer be done without basic computer knowledge and computational literacy. The central approach to educating future teachers is the university course of study. This paper aims to systematically provide an insight into which scientifically relevant publications address this issue. The essential criterion in this review is the restriction on science education or more general science, technology, engineering, and mathematics (STEM) education in schools or universities. Equally important is the specification that computational literacy (informatics competencies, information literacy, computer literacy, or computational thinking) are purposefully taught or trained. Only publications in the Web of Science database were included for quality assurance. This paper investigates to what extent scientifically valid research or knowledge is available at this junction, i.e., computational literacy in science education. Thereby, we distinguish between different approaches, such as effect studies on individual aspects, isolated practical contributions with a pronounced trial-and-error character, or systematic or model-based considerations in quantitative and qualitative studies. The results show common aspects, prominent trends, and promising approaches. However, possibly existing (subject-) didactic concerns should be considered in more detail and the dual perspective role of the students as future teachers. In addition to learning basic computer skills, the ability to teach them is also essential so that the scientific education of the students can benefit from them to the required extent.

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Section: On Focus: Concrete Skills and Competencesmentioning

confidence: 99%

Computational literacy in science education–A systematic review

Braun

Huwer

2022

Front. Educ.

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“…One study [Dasgupta and Purzer 2016] followed a similar path, i.e. targeted specifically PR, but found only two studies.…”

Section: Related Workmentioning

confidence: 99%

Pattern Recognition in Computing Education: A Systematic Review

Junior

Silva

Cavalheiro

et al. 2022

Anais Do XXXIII Simpósio Brasileiro De Informática Na Educação (SBIE 2022)

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This paper presents a systematic literature review to investigate how pattern recognition has been approached in computing education. Pattern Recognition has long been an important concept among various areas, from cognitive psychology and neuroscience to machine learning and computer vision. Recently, it gained the attention of education, being associated with computational thinking. An old concept being revisited in a new context raises important questions on how it is being approached, how it is being assessed and if it is making use of previous contributions from the other fields that have been studying the concept. This work systematically reviews the literature to answer these questions on pattern recognition. It is found that, as other concepts related with computational thinking, it is rarely treated alone and when among others, few studies have pattern recognition as the main theme. The results also show that no standardized assessment method is used and contributions from other fields are barely mentioned.

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“…Generalization and evaluation were added from CSTA and ISTE and those skills of CT have been used later by several authors (Anderson, 2016, Selby, 2015, Csizmadia et al, 2015, Angeli et al, 2016, Dagienė and Sentence, 2016, Marcelino et al, 2016, Duncan et al, 2017and Dagienė et al, 2017. As some of the dimensions of CSTA and ISTE (2011) are common with Selby and Woollard (2013), several authors have con-sidered both ideas (Seiter and Foreman, 2013, Dasgupta and Purzer, 2016and Mouza et al, 2017.…”

Section: Identifying the Directions Of Ctmentioning

confidence: 99%

A Model for Developing Computational Thinking Skills

Palts¹,

Pedaste²

2020

Informatics in Education

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Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills:

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No patterns in pattern recognition: A systematic literature review

Cited by 7 publications

References 5 publications

Computational literacy in science education–A systematic review

Computational literacy in science education–A systematic review

Pattern Recognition in Computing Education: A Systematic Review

A Model for Developing Computational Thinking Skills

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