Science teachers can teach computational thinking through distributed expertise

Tucker‐Raymond, Eli; Cassidy, Michael; Puttick, Gillian

doi:10.1016/j.compedu.2021.104284

Cited by 15 publications

(18 citation statements)

References 49 publications

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“…The teaching method constitutes one of the largest percentages in developing CT in mathematics education, i.e., 47%. There were four studies performed in ASEAN countries (Cheng et al, 2017;Fang et al, 2017;Matere et al, 2021;Shorey et al, 2021) and 12 studies conducted in non-ASEAN countries (Çoban & Korkmaz, 2021;Critten et al, 2021;Jeng et al, 2020;Marcelino et al, 2018;Mecca et al, 2021;Menolli & Neto, 2021;Mouza et al, 2017;Quitério Figueiredo, 2017;Relkin et al, 2021;Rich et al, 2021;Sung & Black, 2020;Tucker-Raymond et al, 2021). This systematic review explains various types of teaching methods used in developing CT in mathematics, such as instant communication (IM) teaching method (Cheng et al, 2017), innovative curriculum design relying on an Internet-of-Things (IoT) programming course (Jeng et al, 2020), project-based learning and problem-solving learning method (Menolli & Neto, 2021), BootUp's model teaching method (Rich et al, 2021), technological pedagogical content knowledge (TPACK) educational technology course (Mouza et al, 2017), pre-programming (CS0) course (Quitério Figueiredo, 2017), elearning course employing Moodle as a learning management system (Marcelino et al, 2018), designbased learning (Matere et al, 2021), blending learning flipped class (Fang et al, 2017), physical body movement practice (Sung & Black, 2020), coding as another language (CAL) curriculum (Relkin et al, 2021), guided play activities (Critten et al, 2021), online performance-based assessment (Çoban & Korkmaz, 2021) and procedural programming course (Mecca et al, 2021).…”

Section: Teaching Methodsmentioning

confidence: 99%

“…This systematic review explains various types of teaching methods used in developing CT in mathematics, such as instant communication (IM) teaching method (Cheng et al, 2017), innovative curriculum design relying on an Internet-of-Things (IoT) programming course (Jeng et al, 2020), project-based learning and problem-solving learning method (Menolli & Neto, 2021), BootUp's model teaching method (Rich et al, 2021), technological pedagogical content knowledge (TPACK) educational technology course (Mouza et al, 2017), pre-programming (CS0) course (Quitério Figueiredo, 2017), elearning course employing Moodle as a learning management system (Marcelino et al, 2018), designbased learning (Matere et al, 2021), blending learning flipped class (Fang et al, 2017), physical body movement practice (Sung & Black, 2020), coding as another language (CAL) curriculum (Relkin et al, 2021), guided play activities (Critten et al, 2021), online performance-based assessment (Çoban & Korkmaz, 2021) and procedural programming course (Mecca et al, 2021). This includes delegating responsibility to students, encouraging independent problem-solving among students, co-learning with students, fostering interdependence among students, offering a variety of additional resources (Tucker-Raymond et al, 2021) and digital making (DM) summer camp (Ng & Cui, 2021).…”

Section: Teaching Methodsmentioning

confidence: 99%

“…Relying on the literature review, researchers discovered that six types of tools were used in developing CT in mathematics education. The tools are teaching method (Cheng et al, 2017;Çoban & Korkmaz, 2021;Critten et al, 2021;Fang et al, 2017;Jeng et al, 2020;Marcelino et al, 2018;Matere et al, 2021;Menolli & Neto, 2021;Mouza et al, 2017;Quitério Figueiredo, 2017;Relkin et al, 2021;Rich et al, 2021;Sung & Black, 2020;Tucker-Raymond et al, 2021;Yildiz Durak, 2020), game-based learning (Agbo et al, 2021;Croff, 2017;Hooshyar et al, 2021;Menolli & Neto, 2021;Ng & Cui, 2021), coding programming (Critten et al, 2021;Deng et al, 2020;Fanchamps et al, 2019;Marcelino et al, 2018;Mecca et al, 2021;Ng & Cui, 2021;Özmutlu et al, 2021;Piedade et al, 2020;Quitério Figueiredo, 2017;Relkin et al, 2021;Rich et al, 2021;Ríos Félix et al, 2020;Shorey et al, 2021;Silva et al, 2021;Sun et al, 2021a;Sung & Black, 2020;Yi Wu & Sheng Su, 2021;Yildiz Durak, 2020), robotic activities (Critten et al, 2021;…”

Section: The Most Populated Tools Used To Develop Ct In Mathematics E...mentioning

confidence: 99%

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Computational thinking in mathematics education: A systematic review

Subramaniam

Maat

Mahmud

2022

CJES

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As a research area, computational thinking (CT) has gotten increased attention in mathematics education in the last decade. A study identifying patterns in CT research would be essential in understanding the technique for developing CT in mathematics and guiding future research attempts. As a result, the goal of this systematic literature review is to look at the learning methods promoting CT in mathematics lessons. The Preferred Reporting Items for Systematic Review and Meta-Analyses standards were utilised to guarantee that this study was done systematically. The result shows that even though there are various types of learning tools that are most commonly used, the coding programming tool and robotic activities tool are the most user-friendly methods for encouraging CT in mathematics education. This literature review is intended to provide educators with a better understanding of learning tools in order to enhance CT, which may help transform education into something more creative and meaningful. Keywords: Computational thinking; education; learning tools; mathematics; systematic review

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Section: Teaching Methodsmentioning

confidence: 99%

Section: Teaching Methodsmentioning

confidence: 99%

Section: The Most Populated Tools Used To Develop Ct In Mathematics E...mentioning

confidence: 99%

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Computational thinking in mathematics education: A systematic review

Subramaniam

Maat

Mahmud

2022

CJES

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“…Equipping prospective teachers is not only limited to computational thinking skills but what is more important is growing self-confidence in computational thinking skills. Prospective teachers who are very close to computational thinking skills are prospective biology and physics teachers (Taub et al, 2015;Tucker-Raymond et al, 2021;McDonald et al, 2022). Niss (2018) and Syafril et al (2021) convey that the main goal in physics is problem-solving skills.…”

Section: Introductionmentioning

confidence: 99%

Prospective Science Teachers’ Self-Confidence in Computational Thinking Skills

Syafril

Rahayu

Ganefri

2022

JPII

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This study aims to analyze prospective science teachers’ self-confidence in computational thinking skills on three main points: (i) prospective science teachers’ self-confidence in computational thinking skills, (ii) differences in prospective science teachers’ self-confidence in computational thinking skills as per gender, and (iii) differences in prospective science teachers’ self- confidence in computational thinking skills as per expertise (Biology and Physics). A quantitative cross-sectional survey methodology was used as the research design. A total of 1023 prospective science teachers (biology and physics) were randomly selected as the research sample from the 1959 total population. Data were collected using a self-confidence questionnaire on computational thinking skills. The adaptation results were assessed first by five experts before being tested on 74 prospective science teachers from different universities. The results show that prospective science teachers’ self-confidence in computational thinking skills was generally high (Mean = 78.57). The Mann-Whitney U test found no difference in prospective science teachers’ self-confidence in computational thinking skills as per gender (Mean= 78.05, SD= 9.03 for male, Mean= 78.73, SD= 6.86 for female, with a value of F= 6.028, Z= -0.891, Sig= 0.373 0.05). The Independent Sample t-test also showed no difference in prospective science teachers’ self-confidence in computational thinking skills as per expertise. This study concludes that prospective science teachers have high self-confidence in computational thinking skills as crucial skills in the science teaching profession.

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“…These pedagogical experiences reflect on teachers' choices of pedagogical strategies and decisions on tools that will enhance learning experiences. Besides, studentcentered pedagogical strategies such as problem-solving, open-ended tasks, project-based learning, group work and collaboration, inquiry-based learning, challenge-based learning, blended learning, flexible learning, and peer-to-peer learning were commonly adopted by teachers (Bower et al, 2017;Tucker-Raymond et al, 2021). Teachers have maintained positive experiences and deeper learning experiences are usually a result of student-led learning experiences, which are based on the active and engaging learning processes, pedagogical strategies, and a conducive learning environment (Adler et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Developing a pedagogical evaluation framework for computational thinking supporting technologies and tools

2022

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Frameworks for the evaluation of technological instructional tools provide educators with criteria to assess the pedagogical suitability and effectiveness of those tools to address learners’ needs, support teachers’ understanding of learning progress, and recognize the levels of achievement and the learning outcomes of the students. This study applied secondary document analysis and case study to identify five pedagogical indicators for teaching and learning computational thinking, including technology, pedagogical approaches, assessment techniques, data aspect, and teacher professional development. Based on the pedagogical indicators, this study proposed a computational thinking pedagogical assessment framework (CT-PAF) aimed at supporting educators with a strategy to assess the different technological learning tools in terms of pedagogical impact and outcome. Furthermore, three case-study instructional tools for teaching CT in K-12 were analyzed for the initial assessment of CT-PAF. Scratch, Google Teachable Machine, and the iThinkSmart minigames were marched to the underpinning characteristics and attributes of CT-PAF to evaluate the framework across the instructional tools. The initial assessment of CT-PAF indicates that the framework is suitable for the intended purpose of evaluating technological instructional tools for pedagogical impact and outcome. A need for expanded assessment is, therefore, necessary to further ascertain the relevance of the framework in other cases.

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Science teachers can teach computational thinking through distributed expertise

Cited by 15 publications

References 49 publications

Computational thinking in mathematics education: A systematic review

Computational thinking in mathematics education: A systematic review

Prospective Science Teachers’ Self-Confidence in Computational Thinking Skills

Developing a pedagogical evaluation framework for computational thinking supporting technologies and tools

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