Gifted Students' Use of Computational Thinking Skills Approaching a Graph Problem: A Case Study

Wetzel, Sina; Milicic, Gregor; Ludwig, Matthias

doi:10.21125/edulearn.2020.1797

Cited by 9 publications

(4 citation statements)

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“…This finding is consistent with Hart's (1998) hypothesis that students' algorithmic problem-solving competence develops through constructing models of problem situations first. However, the results contrast with those of Wetzel et al (2020), who reported that 17-year-olds experienced difficulty in mathematizing a shortest-path problem because they tended to focus on unnecessary information from a given street map while constructing a vertex-edge graph. The findings of the present study extend the existing literature by illustrating the importance of mathematical modelling competence in making sense of discrete mathematical problems.…”

Section: Making Sense Of Assignment Problems (Research Question 1)contrasting

confidence: 76%

“…Hart hypothesizes that students' competence in algorithmic problem-solving develops through this instructional pattern and provides some anecdotal evidence that students' competence in using the graph colouring algorithm is developed through this approach (Hart, 2008;Hart & Martin, 2018). Indeed, Wetzel, Milicic, and Ludwig (2020) found that three high-achieving 17-year-olds were able to model a shortest path problem and develop an algorithm similar to Dijkstra's algorithm through a comparable instructional pattern.…”

Section: Review Of Related Literaturementioning

confidence: 99%

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Making Sense of Algorithms in Discrete Mathematics

Lehmann

2021

Int J of Sci and Math Educ

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Network analysis is a topic in secondary mathematics education of growing importance because it offers students an opportunity to understand how to model and solve many authentic technology and engineering problems. However, very little is known about how students make sense of the algorithms typically used in network analysis. In this study, I used the Hungarian algorithm to explore how students make sense of a network algorithm and how it can be used to solve assignment problems. I report the results of a design-based research project in which eight Year 12 students participated in a teaching experiment that spanned four 60-min lessons. A hypothetical learning trajectory was developed in which students were introduced to the steps of the Hungarian algorithm incrementally. The results suggest that students made sense of the intermediate steps of the algorithm, the results of those steps, and how the algorithm works to solve assignment problems. The difficulties that students encountered are also discussed.

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Section: Making Sense Of Assignment Problems (Research Question 1)contrasting

confidence: 76%

Section: Review Of Related Literaturementioning

confidence: 99%

Making Sense of Algorithms in Discrete Mathematics

Lehmann

2021

Int J of Sci and Math Educ

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“…As the defined GTs are based on learning processes with codes and algorithms, which are also the predominant tasks in programming education, we can thus also support the claim that CT is more than just coding [35], requiring more tasks and activities fostering CT skills apart from algorithmic thinking and debugging. In a case study in 2020, we also observed students with a strong interest in CS and highly evolved algorithmic thinking skills only demonstrating basic abstraction skills [11]. This could be a consequence of a strong focus on programming in K-12 computer science education, in which the other CT skills are mostly only moderately addressed, if at all.…”

Section: Resultsmentioning

confidence: 72%

“…In prior works, we have already expressed the desideratum to know more about how different core CT skills can be addressed and fostered; e.g., in a case study [11], we met students with highly evolved skills in the area of algorithmic thinking but very few abilities regarding the skill of abstraction. This mismatch led us to scrutinize how single skills can be purposefully targeted.…”

Section: Introductionmentioning

confidence: 99%

Generic Tasks for Algorithms

2020

Self Cite

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Due to its links to computer science (CS), teaching computational thinking (CT) often involves the handling of algorithms in activities, such as their implementation or analysis. Although there already exists a wide variety of different tasks for various learning environments in the area of computer science, there is less material available for CT. In this article, we propose so-called Generic Tasks for algorithms inspired by common programming tasks from CS education. Generic Tasks can be seen as a family of tasks with a common underlying structure, format, and aim, and can serve as best-practice examples. They thus bring many advantages, such as facilitating the process of creating new content and supporting asynchronous teaching formats. The Generic Tasks that we propose were evaluated by 14 experts in the field of Science, Technology, Engineering, and Mathematics (STEM) education. Apart from a general estimation in regard to the meaningfulness of the proposed tasks, the experts also rated which and how strongly six core CT skills are addressed by the tasks. We conclude that, even though the experts consider the tasks to be meaningful, not all CT-related skills can be specifically addressed. It is thus important to define additional tasks for CT that are detached from algorithms and programming.

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Mathematical modelling as a vehicle for eliciting algorithmic thinking

Lehmann

2023

Educ Stud Math

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Gifted Students' Use of Computational Thinking Skills Approaching a Graph Problem: A Case Study

Cited by 9 publications

References 0 publications

Making Sense of Algorithms in Discrete Mathematics

Making Sense of Algorithms in Discrete Mathematics

Generic Tasks for Algorithms

Mathematical modelling as a vehicle for eliciting algorithmic thinking

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