Patrony: A mobile application for pattern recognition learning

Barrón-Estrada, María Lucia; Zataraín-Cabada, Ramón; Romero-Polo, Jorge Abraham; Monroy, Julieta Noguez

doi:10.1007/s10639-021-10636-7

Cited by 17 publications

(21 citation statements)

References 18 publications

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“…In our analysis, we identified four main subject areas in the 55 studies ( Cui, 2020;Psycharis & Kallia, 2017;Shumway et al, 2021;Wang et al, 2022), concepts (Cui & Ng, 2021;Rodríguez-Martínez et al, 2020;Sáez-L opez et al, 2019), habits of mind (Pei et al, 2018), and specific thinking skills (Barr on-Estrada et al, 2022;Città et al, 2019;Kim et al, 2021;Lockwood, 2022;Lockwood & De Chenne, 2020;Nogueira et al, 2022;Palmér, 2017;Sung & Black, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The studies focusing on the transfer effects of CT in mathematics investigated various skills and knowledge, including general mathematical competence (García‐Perales & Palomares‐Ruiz, 2020; Sung et al, 2017), problem‐solving skills (Fanchamps et al, 2021; Kaufmann & Stenseth, 2020; Miller, 2019; Muñoz et al, 2020; Ng & Cui, 2020; Psycharis & Kallia, 2017; Shumway et al, 2021; Wang et al, 2022), concepts (Cui & Ng, 2021; Rodríguez‐Martínez et al, 2020; Sáez‐López et al, 2019), habits of mind (Pei et al, 2018), and specific thinking skills (Barrón‐Estrada et al, 2022; Città et al, 2019; Kim et al, 2021; Lockwood, 2022; Lockwood & De Chenne, 2020; Nogueira et al, 2022; Palmér, 2017; Sung & Black, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Factors such as activating existing knowledge (n = 18) and applying knowledge in the real world (n = 26) were also reported in the studies. In the interventions that activated students' existing knowledge, as reported in 12 studies, some helped students identify this knowledge (e.g., Bortz et al, 2020;Città et al, 2019;Lockwood, 2022;Lockwood & De Chenne, 2020) and others offered guided reflection before introducing new content (e.g., Barr on-Estrada et al, 2022;Peel & Friedrichsen, 2018;Petrie, 2021;Sengupta et al, 2013;Sung & Black, 2020). Activating knowledge is necessary for new learning as, from a constructivist perspective, new knowledge construction can occur through integration with existing mental Kaufmann & Stenseth, 2020;Kim et al, 2021;Merino-Armero et al, 2022;Ng & Cui, 2020;Peel et al, 2019;Sen et al, 2021;Wang et al, 2022;Yin et al, 2022).…”

Section: Rq3: What Are the Elements Common To Ct Interventions That E...mentioning

confidence: 99%

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The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

Lai

Wong

2022

Computer Assisted Learning

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Background: Computational thinking (CT) is regarded as an essential 21st-century skill, and attempts have been made to integrate it into other subjects. Instructional approaches to CT development and assessment in the field of computer science have attracted global attention, but the influence of CT skills on other subject areas is under-researched.Objective: Our goal is to investigate the transfer effects of CT in different subject areas and examine the educational characteristics of CT intervention approaches that promote the transfer of learning.Method: We carefully selected and reviewed 55 empirical studies from leading bibliographic databases and examined the transfer of CT using a meta-analysis and a qualitative synthesis. Results and Conclusions:We identified and summarized these effects in the fields of mathematics, science, engineering and the humanities. A meta-analysis of these studies identified a generally significant effect of the transfer of CT skills to other subject areas. We also explored the characteristics of CT interventions that aid the transfer of learning by qualitatively assessing the identified studies. The results of the review offer a holistic view of the trends in CT transfer research that can be used as a reference for both researchers and instructors. K E Y W O R D S computational thinking, meta-analysis, qualitative synthesis, transfer of learning 1 | INTRODUCTION Computational thinking (CT) has become a key motivator for bringing computer science back into K-12 schools (Tikva & Tambouris, 2021).CT is initially defined as a set of cognitive problem-solving skills originating from computer science (Wing, 2006), and gradually the notion of CT has been developed into a new cross-disciplinary literacy, which can be a vehicle for personal expression and can connect with other literacy practices (Kafai & Proctor, 2021). Grover and Pea (2013)

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Rq3: What Are the Elements Common To Ct Interventions That E...mentioning

confidence: 99%

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The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

Lai

Wong

2022

Computer Assisted Learning

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“…. The first study reports the development and experimentation of Patrony, a software application to promote the learning of CT, especially PR [Barrón-Estrada et al 2022]. The software presents sequences of simple images and offers the user multiple choices for completing/continuing the sequence.…”

Section: Resultsmentioning

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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“…Additionally, due to the utilization of a live webinar to validate the subaxial cervical spine injury classification, participating members were given limited time to classify each injury. This may have led some members who process images at a slower rate, have less experience, or are not fluent in the English language to struggle with completing the validation in a timely fashion, which could have artificially suppressed the reliability and reproducibility of the classification [16][17][18][19]. However, given the substantial to excellent reliability and reproducibility of the classification system on a global level, this was likely of limited significance.…”

Section: Discussionmentioning

confidence: 99%

An international validation of the AO spine subaxial injury classification system

Karamian

Schroeder²,

Lambrechts³

et al. 2022

Eur Spine J

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Purpose To validate the AO Spine Subaxial Injury Classification System with participants of various experience levels, subspecialties, and geographic regions. Methods A live webinar was organized in 2020 for validation of the AO Spine Subaxial Injury Classification System. The validation consisted of 41 unique subaxial cervical spine injuries with associated computed tomography scans and key images. Intraobserver reproducibility and interobserver reliability of the AO Spine Subaxial Injury Classification System were calculated for injury morphology, injury subtype, and facet injury. The reliability and reproducibility of the classification system were categorized as slight (ƙ = 0–0.20), fair (ƙ = 0.21–0.40), moderate (ƙ = 0.41–0.60), substantial (ƙ = 0.61–0.80), or excellent (ƙ = > 0.80) as determined by the Landis and Koch classification. Results A total of 203 AO Spine members participated in the AO Spine Subaxial Injury Classification System validation. The percent of participants accurately classifying each injury was over 90% for fracture morphology and fracture subtype on both assessments. The interobserver reliability for fracture morphology was excellent (ƙ = 0.87), while fracture subtype (ƙ = 0.80) and facet injury were substantial (ƙ = 0.74). The intraobserver reproducibility for fracture morphology and subtype were excellent (ƙ = 0.85, 0.88, respectively), while reproducibility for facet injuries was substantial (ƙ = 0.76). Conclusion The AO Spine Subaxial Injury Classification System demonstrated excellent interobserver reliability and intraobserver reproducibility for fracture morphology, substantial reliability and reproducibility for facet injuries, and excellent reproducibility with substantial reliability for injury subtype.

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Patrony: A mobile application for pattern recognition learning

Cited by 17 publications

References 18 publications

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

The transfer effects of computational thinking: A systematic review with meta‐analysis and qualitative synthesis

Pattern Recognition in Computing Education: A Systematic Review

An international validation of the AO spine subaxial injury classification system

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