Processing of Experimental Data as Educational Method of Development of Algorithmic Thinking

Hubálovský, Štěpán

doi:10.1016/j.sbspro.2015.04.309

Cited by 4 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the algorithmic thinking category, student skill also significantly improved (P = 0.014; P < 0.05), the student improvement. This condition is the same as the previous research results that reveal the optimal educational method of developing students' algorithmic thinking is the system and multidisciplinary approach based on the identification of real systems, processes, and modelling (Hubalovsky, 2015).…”

Section: Data Analysis Of Student Responsesupporting

confidence: 64%

Development and evaluation of granular simulation for integrating computational thinking into computational physics courses

et al. 2021

View full text Add to dashboard Cite

Computational thinking (CT) is an essential skill in the twenty-first century. The computational physics course (CPC) is one subject that is designed to support students in the practice of CT. Many studies show that the worksheets could be a solution in a CPC as a scaffold to achieve the CT objectives both online and offline. The study aims to develop the worksheet and integrate it with CT in a computational physics course. This study applied the research and development (R & D) method with the ADDIE model approach. In the results, the evaluation test from the experts reached a very good interpretation score based on the learning media expert (96%), the teaching material expert (95%), and the pedagogy experts (92%). So that this media is declared feasible to be used in the CPC. Furthermore, after the experimental study of students who took the computational physics course (n = 31), the study showed that the modified course could significantly improve student skills regarding overall CT (p value <0.05). However, this research also found that cooperative learning as part of CT had no improvement (p value >0.05). The experiment was conducted amid the COVID 19 pandemic wherein the students could only study at home for the whole semester. These findings indicate that the pandemic has impacted the collaborative skills of students on the course.

show abstract

Section: Data Analysis Of Student Responsesupporting

confidence: 64%

Development and evaluation of granular simulation for integrating computational thinking into computational physics courses

et al. 2021

View full text Add to dashboard Cite

show abstract

“…An interesting systematisation of skills involved in algorithmic thinking include a) analysing given problems, b) specifying a problem precisely, c) finding the basic actions that are adequate to the given problem, d) constructing a correct algorithm to a given problem using the basic actions, e) thinking about all possible special and normal cases of a problem, and f) improving the efficiency of an algorithm [19]. Creativity is seen as important for algorithmic thinking [19], [21], needed to find solutions for the problems and to improve existing algorithms. Developing an algorithm also requires expressing it in some language [22].…”

Section: Computational Thinking and Algorithmic Thinkingmentioning

confidence: 99%

“…Teaching or supporting the development of algorithmic thinking is challenging and the subject of extensive discussions [1], [19], [21] and it is also important to find suitable support for students' selflearning. [23].…”

Section: Algorithmic Thinking In Early Childhood Educationmentioning

confidence: 99%

Algorithmic Thinking in Early Childhood Education: Opportunities and Supports in the Portuguese Context

Figueiredo¹,

Amante²,

Gomes³

et al. 2021

EDULEARN Proceedings

View full text Add to dashboard Cite

ALGO-LITTLE is an EU-funded project, with partners from Portugal, Italy, Turkey and Slovenia, searching for ways to integrate Algorithmic Thinking skills into preschool education for the purpose of preparing future code literates starting from an early age. Algorithmic Thinking stems from the concept of an algorithm, which refers to solving a problem by developing a set of steps taken in a sequence to achieve the desired outcome. The concept can be traced to the work of Papert and is connected to the seminal article by Wing that introduced Computational Thinking. Early Childhood Education algorithmic skills include abilities to learn and work according to the rules or models since children are capable of understanding, using, applying and developing simple algorithms. The paper analyses initiatives, studies, and projects that are connected to Algorithmic Thinking in Early Childhood Education in Portugal. In terms of curricular documents, it was found that code has been relevant for Portuguese Early Childhood Education (at least) since 1997. In 2016, the Portuguese Curricular Guidelines for Preschool Education (PCGPSE) were reviewed and this second version amplifies the mentions to technology. In terms of studies and projects, the "Kids Media Lab Project II" stood out as the only systematic initiative for promoting Computational Thinking in Preschool Education.

show abstract

“…The problem in question is known by a variety of names ( Table 2) [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Computer analysis [28][29][30][31], elimination of outliers [32,33], and confidence limits for the abscissa [22,34,35] have been subject to study.…”

Section: Independent Values Of Chemical Quantities Expressed In Si Unmentioning

confidence: 99%

Intersecting Straight Lines: Titrimetric Applications

Martín¹,

Martin²,

Asuero³

2017

Advances in Titration Techniques

View full text Add to dashboard Cite

Plotting two straight line graphs from the experimental data and determining the point of their intersection solve a number of problems in analytical chemistry (i.e., potentiometric and conductometric titrations, the composition of metal-chelate complexes and binding interactions as ligand-protein). The relation between conductometric titration and the volume of titrant added leads to segmented linear titration curves, the endpoint being defined by the intersection of the two straight line segments. The estimation of the statistical uncertainty of the end point of intersecting straight lines is a topic scarcely treated in detail in a textbook or specialized analytical monographs. For this reason, a detailed treatment with that purpose in mind is addressed in this book chapter. The theoretical basis of a variety of methods such as first-order propagation of variance (random error propagation law), Fieller's theorem and two approaches based on intersecting confidence bands are explained in detail. Several experimental systems described in the literature are the subject of study, with the aim of gaining knowledge and experience in the application of the possible methods of uncertainty estimation. Finally, the developed theory has been applied to the conductivity measurements in triplicate in the titration of a mixture of hydrochloric acid and acetic acid with potassium hydroxide.

show abstract

Processing of Experimental Data as Educational Method of Development of Algorithmic Thinking

Cited by 4 publications

References 2 publications

Development and evaluation of granular simulation for integrating computational thinking into computational physics courses

Development and evaluation of granular simulation for integrating computational thinking into computational physics courses

Algorithmic Thinking in Early Childhood Education: Opportunities and Supports in the Portuguese Context

Intersecting Straight Lines: Titrimetric Applications

Contact Info

Product

Resources

About