Algorithmic Thinking Research Articles

Promote students’ computational thinking in rural areas: The moderating role of gender

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

TEACHING COMPUTATIONAL THINKING IN SAUDI ARABIAN SCHOOLS USING SMARTPHONE APPS

This study investigates the efficacy of smartphone applications in teaching computational thinking to students in Saudi Arabian primary and secondary schools. Computational thinking, encompassing problem-solving, computational logic, and algorithmic thinking, is an essential skill in the modern world. The research assesses various educational apps that support the development of computational thinking and examines their integration into the Saudi curriculum. This study incorporates qualitative and quantitative research techniques to provide an inclusive perspective. Participants included primary and secondary school students from various schools in Saudi Arabia. The research employed pre- and post-tests for quantitative analysis and interviews and questionnaires for qualitative data. Quantitative results include pre- and post-tests of the students in computational thinking with the aid of co-varying with Computational thinking, while interviews and questionnaires with teachers and students offer qualitative data. Best-selling educational applications for ScratchJr, Lightbot, Cargo-Bot, and Kodable are compared concerning how efficiently they introduce programming ideas and encourage rational thinking. Results have suggested that smartphone applications have a positive influence on students’ computational skills. An enhanced standard is also noticed regarding the students’ aspects that involve problem analysis, identification of pattern, and formation of algorithm. The use of applications such as ScratchJr and Lightbot is justified regarding an application’s capacity to improve students’ motivation and willingness to learn computational concepts. Teachers indicate that their students have shown more concern and engagement mainly when the teacher integrates these apps. Hence, based on the findings of the study, it is evident that incorporating Smartphone applications into the curriculum will lead to a reforming change in the teaching approaches towards efficiency as well as feasibility of the learners. These findings are valuable for the Saudi context for policy and teaching enhancement, signaling the need to provide more resources in digital tools and invest in the professional development of teachers to capture the virtues of advanced technology in improving learners’ performance.

Evaluating Usability and Accessibility of Visual Programming Tools for Novice Programmers—The Case of App Inventor, Scratch, and StarLogo

The current state of the labor market requires modern engineers to acquire programming skills at different levels of advancement and to apply them in multidisciplinary environments. Not all modern engineers will become fully fledged programmers, but sometimes the possibility to use low-code programming environments like LabView or MIT App Inventor will be sufficient. In order to give good foundations for adulthood, schools use elements of visual programming, e.g., Scratch or StarLogo, to enhance the critical and algorithmic thinking of future engineers. This article attempts to answer the question of whether anyone cares about following general accessibility and usability guidelines in the case of solutions like Scratch, App Inventor, and StarLogo. Another goal is to show a set of tools that is successful in such an assessment. The authors used Nielsen’s heuristics, followed by analyzing WAVE output and Web Content Accessibility Guidelines compliance. Especially, the last one provides insights usually omitted when evaluating low-code environments. It was found that Scratch and App Inventor are leading solutions in terms of look and feel, functionality, documentation, interface navigation, and memorization. The StarLogo interface, on the other hand, is less friendly in terms of aesthetics and functionality.

Game On: A Journey into Computational Thinking with Modern Board Games in Portuguese Primary Education

Recent studies highlight the potential of modern board games (MBGs) to foster computational thinking (CT) skills in students. This research explored the impact of integrating MBGs into a primary education classroom through an embedded concurrent mixed-methods approach, with a pre-experimental design in its quantitative aspect and content analysis in its qualitative dimension, with 20 fourth-grade students from a school in Portugal. The students participated in 10 game sessions, each lasting 50 min, and their CT skills were assessed using Bebras tasks in both the pre-test and post-test phases. Statistical analysis, including the Shapiro–Wilk test for normality and paired sample t-tests, revealed significant improvements in key CT areas, particularly abstraction, algorithmic thinking, and decomposition. Descriptive statistics were also calculated, and content analysis using Nvivo software was conducted on field notes, corroborating the quantitative data. The results suggest that MBGs can serve as a valuable educational tool for developing CT skills in young learners. This study not only highlights the effectiveness of MBGs but also emphasises the need for further research using more robust experimental designs to enhance CT development in educational settings.

Confirmatory factor analysis of a rubric for assessing algorithmic thinking on undergraduate students

Algorithmic thinking is a key element for individuals to be aligned with the computer era. Its study is important not only in the context of computer science but also in mathematics education and all STEAM contexts. However, despite its importance, a lack of research treating it as an independent construct and validating its operational definitions or rubrics to assess its development in university students through confirmatory factor analysis has been discovered. The aim of this paper is to conduct a construct validation through confirmatory factor analysis of a rubric for the algorithmic thinking construct, specifically to measure its level of development in university students. Confirmatory factor analysis is performed on a series of models based on an operational definition and a rubric previously presented in the literature. The psychometric properties of these models are evaluated, with most of them being discarded. Further research is still needed to expand and consolidate a useful operational definition and the corresponding rubric to assess algorithmic thinking in university students. However, the confirmatory factor analysis confirms the construct validity of the rubric, as it exhibits very good psychometric properties and leads to an operational definition of algorithmic thinking composed of four components: Problem analysis, algorithm construction, input case identification, and algorithm representation.

Enhancing Algorithmic Thinking Skills through Phygital Games: The Case of Space Codyssey

This paper presents an educational phygital game, Space Codyssey, aiming at cultivating the algorithmic thinking skills of young children. Space Codyssey is a phygital game, and its contains programming activities for the Sequence, Repetition, and Selection Structures, as well as Code Debugging activities (Debugging). The game utilizes Augmented Reality technology to present missions via QR cards. Additionally, digital storytelling is used as a means of assistance and teaching for preschool and early primary students. In the game, reading skills are not a prerequisite, and programming is accomplished using symbolic commands through a "Visual Programming Language." The participatory design methodology that was adopted, involving 28 educators from various specialties, reflects a holistic approach to the development of educational technologies. This interdisciplinary collaboration allowed for the integration of diverse pedagogical perspectives, ensuring that the game meets the multifaceted needs of modern education. The paper highlights the educational goals and learning outcomes for the designtated age group. It presents the scenario, the subject, the elements of the game, the rules, and the game mechanics. It also describes the implementation stages and the development of the application. Finally, it presents the results from a study, which involved 28 educators and aimed to improve the game before its use in educational settings.

College of education students’ perceptions of their computational thinking proficiency

This study aimed to investigate college of education students’ level of computational thinking proficiency and the differences in their level based on their demographic characteristics, i.e., gender, program, and age. The study used a descriptive research design in which 190 students in the College of Education completed a computational thinking questionnaire. The computational thinking scale consisted of five dimensions, i.e., creativity, algorithmic thinking, cooperativity, critical thinking, and problem-solving. The results showed that the level of computational thinking among students was diverse and fell within a moderate range. Gender-based analysis indicated a significant difference in only one dimension of computational thinking, i.e., algorithmic thinking, with females scoring lower than males. In addition, based on students’ academic program, significant variations were observed in algorithmic thinking and overall computational thinking levels, particularly between Bachelor and PhD programs, with PhD students scoring higher than Bachelor students. Additionally, the age-based analysis highlights significant differences, with older students consistently outperforming younger ones across various computational thinking dimensions. Based on the findings a set of recommendations was provided.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

Improving mathematical proof based on computational thinking components for prospective teachers in abstract algebra courses

Understanding and constructing mathematical proofs is fundamental for students in abstract algebra courses. The computational thinking approach can aid the process of compiling mathematical proofs. This study examined the impact of integrating computational thinking components in constructing mathematical proofs. The researcher employed a sequential explanatory approach to ascertain the enhancement of algebraic proof capability based on computational thinking through the Wilcoxon test. A total of 32 prospective teachers in mathematics education programs were provided with worksheets for seven meetings, which were combined with computational thinking stages. Quantitative data were collected from initial and subsequent test instruments. Moreover, three prospective teachers were examined through case studies to investigate their mathematical proof capability using computational thinking components, including decomposition, abstraction, pattern recognition, and algorithmic thinking. The study's findings indicated that CT intervention enhanced students' logical reasoning, proof-writing abilities, and overall engagement with abstract algebra concepts. The findings illustrate that integrating computational thinking into learning strategies can provide a framework for developing higher-order thinking skills, especially in proving, which are essential for studies in mathematics education programs.

Computational thinking with game design: An action research study with middle school students

AbstractMiddle school students often enter Computer Science (CS) classes without previous CS or Computational Thinking (CT) instruction. This study evaluated how Code.org’s block-based programming curriculum affects middle school students’ CT skills and attitudes toward CT and CS. Sixteen students participated in the study. This was a mixed methods action research study that used pre- and post-tests, surveys, artifacts, and interviews as data sources. Descriptive statistics, paired samples t-tests, and inductive thematic analysis were administered. Findings showed a statistically significant increase in participants’ algorithmic thinking, debugging, and pattern recognition skills but not in abstraction skills. Attitudes toward CT and CS improved but the difference was not statistically significant. Qualitative themes revealed benefits of game-based learning to promote CT skills, collaboration to promote successful error debugging, and enjoyment of programming resulting from a balance between structured guidance and creative freedom. Findings emphasize the importance of low-threshold and engaging strategies to introduce novice learners to CT and CS.

Possibilities of using chatbots in the teaching of grammar in teacher training

Education and the process of teaching and learning is undergoing a major transformation today. 21st century skills increasingly emphasize the importance of collaboration, self-development and lifelong learning. In addition, new information and communication technologies are emerging and the potential for the use of artificial intelligence in education is growing. In teacher education, the teaching of semantics and grammar (vocabulary, morphology, syntax) in the Hungarian Language II course is combined with the development of algorithmic thinking in mathematics. This provides space for integration between the subjects and the theory of multiple intelligences. By rethinking traditional teaching, we gave the students the task of playing the role of a chatbot. This paper summarizes the description and results of this experimental research, as well as the complex support for students based on multiple intelligences.

Investigating the use of ChatGPT to solve a GeoGebra based mathematics+computational thinking task in a geometry topic

ChatGPT is a chatbot with potential educational benefits, particularly in enhancing computational thinking (CT) proficiencies such as programming, debugging, and algorithmic thinking for students. Despite its promise, there is limited research on how ChatGPT can specifically support the integration of CT into mathematics education using tools like GeoGebra. The researchers implemented plugged-computational thinking in mathematics (Math+CT) lessons by means of the utilization of GeoGebra, an application that requires students to input commands in order to generate mathematical objects. The present investigation employed an educational design research (EDR) methodology in which the researchers incorporate ChatGPT into our Math+CT lessons to assist students in accomplishing the task. We purposely selected the participants who are mainly postgraduate students and collected data from the participants’ conversation with ChatGPT and recorded their screens while interacting with ChatGPT and our Math+CT task. We analyzed the data through descriptive qualitative method on the participants’ prompts, the final codes and the number of iterations. The researchers examined how ChatGPT could be utilized to assist the participants in writing GeoGebra commands in terms of its benefits and limitations. ChatGPT assisted most participants in completing the task successfully, with only a basic need for proficiency in GeoGebra commands, mathematics, and critical thinking. However, it revealed that participants did not yet utilize an affective prompt to ChatGPT. Furthermore, ChatGPT has the potential to be utilized for differentiated instruction due to the fact that its responses to individual users vary significantly based on the input prompts. Limited understanding of basic GeoGebra commands, and mathematical concepts could hinder the participants from training ChatGPT or prevent them from arguing with ChatGPT. This study enhances the existing literature by illustrating that ChatGPT can facilitate critical CT aspects, including programming and debugging, in a mathematics education context. This suggests that AI tools such as ChatGPT can contribute to the development of independent problem-solving skills, provide tailored support based on the needs of individual students, and enhance personalized learning experiences. Additional research involving students in school is required in order to gain a deeper understanding of the integration of ChatGPT into Math+CT lessons.

Modeling students’ algorithmic thinking growth trajectories in different programming environments: an experimental test of the Matthew and compensatory hypothesis

In recent years, programming education has gained recognition at various educational levels due to its increasing importance. As the need for problem-solving skills becomes more vital, researchers have emphasized the significance of developing algorithmic thinking (AT) skills to help students in program development and error debugging. Despite the development of various text-based and block-based programming tools aimed at improving students’ AT, emerging evidence in the literature indicates insufficient AT skills among students. This study was conducted to understand the growth trajectory of students’ AT skills in different programming environments. The study utilized a multigroup experiment involving 240 programming students randomly assigned to three groups: a text-and-block-based group, a block-based-only group, and a text-based-only group. Students in the text-and-block-based group were exposed to Alice and Python; those in the block-based-only group were exposed to Alice; and those in the text-based-only group were exposed to Python. We found that participants’ growth trajectory in AT skills is linear, with a significant growth rate. Although between-person variability exists across groups, we observed a compensatory effect in the text-and-block-based and block-based-only groups. Additionally, we found significant differences in AT skills across the groups, with no evidence of a gender effect. Our findings suggest that combining text-based and block-based programming environments can lead to improved and sustained intra-individual problem-solving skills, particularly in the field of programming.

Development of algorithmic thinking skills in K-12 education: A comparative study of unplugged and digital assessment instruments

In the rapidly evolving landscape of digital competencies, the need for a robust and universal method to assess students’ algorithmic thinking (AT) skills has become increasingly pronounced. Algorithmic thinking refers to the ability to analyse a problem and develop a step-by-step process to solve it.This research investigates the efficacy of the Cross Array Task (CAT) as an assessment tool for AT skills within Switzerland’s compulsory education system. Originally conceptualised as an unplugged activity, where students performed the task without digital technologies (e.g., by using gestures on paper) and an administrator manually assessed them, the CAT evolved into a digital activity that runs on an iPad. The CAT’s digital transformation has automated the scoring of student responses and data collection, streamlining the assessment processes and facilitating efficient large-scale assessments. It has also enhanced scalability, making the CAT suitable for widespread use in educational settings. Furthermore, it provides immediate feedback to students and educators, supporting timely interventions and personalised learning experiences.Our study aims to comprehensively investigate algorithmic competencies in compulsory education, examining their variations and influencing factors. This research examines key variables, such as age, sex, educational environment and school characteristics (e.g., the level and grade of education), and regional factors (e.g., the canton of the school) in Switzerland, and characteristics related to the specific assessment tool, including the type of artefact used, the complexity of the algorithms generated, and the level of autonomy. Additionally, it seeks to analyse the effectiveness of the unplugged and digital approaches in assessing AT skills, specifically comparing the unplugged and virtual CAT versions, aiming to provide insights into their advantages and potential synergies.This investigation delineates the developmental progression of AT skills across compulsory education, emphasising the influence of age on algorithm development and problem-solving strategies. Furthermore, we reveal the impact of artefacts and the potential of digital tools to facilitate advanced AT skill development across diverse age groups. Finally, our investigation delves into the influence of school environments and sex disparities on AT performance, alongside the significant individual variability influenced by personal abilities and external circumstances.These findings underscore the importance of tailored educational interventions and equitable practices to accommodate diverse learning profiles and optimise student outcomes in AT across educational settings.

EDUCATION OF CODE, PROBLEM SOLVING, ALGORITHMICAL THINKING IN PRIMARY CLASS PUPILS

In this article, early learning classically includes reading, writing, and mathematics. The importance of addition in "counting" as a basic skill that every child should learn. The goal is development. Computational thinking does not come naturally and requires training and guidance. This study was conducted to determine the effects of coding education on elementary school pupils. It has a positive effect on elementary school pupils' attention, problem solving, and algorithmic thinking skills. In particular, the ideas of Turkish scientists about the concepts of coding education in primary grades should first be reflected in human thinking. The coding training used in this study contributed to the development of basic algorithmic skills in pupils, such as "when faced with any problem, they can easily write an algorithm for that problem or perform an algorithmic procedure when performing any task." happy In our country, the attention paid to elementary school textbooks and the impact of changes in them on the educational process is known to a significant extent. Topics in primary school textbooks are becoming more and more complex. As a result, the school assigns a more responsible task to primary school teachers.

Computational thinking with Scratch or App Inventor in primary education

<p>Computational thinking (CT) is increasingly encouraged in subjects such as technology and digitalisation, and robotics. The effect on the development of generic competencies is of interest. This study investigates the use and effect of Scratch and App Inventor on the development of skills and competencies associated with CP (self-efficacy, motivation, creativity, collaborative work, algorithmic thinking, communication and social interaction) in primary education. A systematic review of articles in English and Spanish was conducted using the PRISMA statement, in the Web of Science (WOS), Dialnet and SCOPUS databases, obtaining 425 papers, from which 10 were finally selected. The results showed that both Scratch and App Inventor favour the development of skills and competencies for learning in the context of primary education and that these are underused strategies that should be promoted from the first years of compulsory education.</p>

Supporting parents in facilitating computational thinking for young children through loose parts construction play

AbstractComputational thinking (CT) as an essential problem‐solving and thinking skill for all students has been the focus of much interest from education systems all over the world (Chen et al., 2023). Unplugged activities like constructing and play with loose parts provide a low‐cost solution to naturally weave in learning of CT skills for young children at home and formal learning environments. Such activities provide opportunities particularly for children from economically disadvantaged backgrounds to access learning of CT skills so they will not be left behind. This article describes the pilot study of a programme that introduced an open‐ended construction play activity using loose‐parts to parent–child dyads from economically disadvantaged backgrounds, with the aim to support parents in facilitating play interactions in ways to promote CT skills for their children using question cards. The cards contained questions that parents could use to foster CT skills like decomposition and algorithmic thinking in their play interactions. Focus group discussions were held before and after the play sessions and parents in the study reported more confidence in supporting their children in play and learning of 21st century skills of teamwork, collaboration and CT skills. The results demonstrate the potential of unplugged pedagogy for promoting CT skills for young children especially in the home environment through engagement with families, with implications for home–school collaborative learning environments and parent education. Recommendations for practice and research are provided accordingly.

The Algorithmic Philosophy — A Synthetic and Social Philosophy

This paper as the introduction part of the forthcoming namesake book introduces how the Algorithmic Thinking Theory, a theory of human minds, can be used to solve many philosophical puzzles and therefore form a “grand synthesis” or unification of existing various philosophies. Innate discrete thinking tools process information or data from the external world serially, selectively, repetitively, roundaboutly, and economically, leading to consequences such as “mental distortion”, knowledge solidification, and combinatorial explosion, thereby forming a dynamic, pluralistic, embracive, and expansive knowledge system where “Being” and “opinions”, ontology and epistemology, rationality and irrationality, and so on are all synthesized. Thoughtful entities exist, move, change, combine, interact, and multiply to make social phenomena as the “independent third party” between human and the world, which implies both the differences and compatibility between natural and social sciences. Democracy, freedom, market, institutions, and organizational power are explained logically consistently like never before. All major philosophical branches, schools, and scholars are included in this concise panorama that stems just from this formula: thinking = computation = (Instruction + information) × speed × time. This highly original approach was inspired by computer science and customized just for the making of principles of humanities and social sciences.

The Analyze Comparative of Physics Computational Thinking Skill (CTs) in Experiment Laboratory

Objective: This study aimed to analyze students' response to the use of computational thinking from the perspective of computational tools and to analyze the influence of gender on students' computational thinking skills. Method: Research design using a comparative approach with data collection techniques involved a survey using a Likert scale questionnaire comprising 25 items, covering five dimensions of computational thinking skills: abstraction, decomposition, algorithm thinking, evaluation, and generalization. The study subjects involved five classes: physics, physics education, geography, mining engineering, and vocational-technical education, focusing on students' ability to analyze data using JASP and IBM SPSS. The data analyze methods included: (1). Comparative Analyze; (2). Correlation analyzes (Spearman); (3). Chi-square test. Finding: The results showed that the computational thinking skills of students from various classes varied, with significant correlations between the skill dimensions. Physics and Physics Education stood out with exemplary achievements, while Geography and Mining Engineering also showed good progress. The vocational-technical education program displayed nearly perfect correlations in all aspects of computational thinking skills. Meanwhile, from the gender aspect, gender significantly influenced computational thinking skills (Sig<0.00). The analyze highlighted the differences in computational thinking skills between classes and the significant influence of gender. Implication: This emphasized the importance of developing computational thinking skills in higher education and the need for inclusive approaches to enhance computational excellence among students. The implications of this study give valuable insights for improving the teaching of computational thinking in physics education. Steps that might be addressed include identifying and enhancing weak components, such as abstraction and generalization, and using particular tactics to increase students' knowledge.

Incorporating Computational Thinking into Education: From Teacher Training to Student Mastery

The concept of computational thinking originated in the 1950s and 1960s as “algorithmic thinking” (Denning, 2009), which involves using a systematic and precise sequence of steps to solve problems, potentially using computers to automate the process. Today, 'computational thinking' is defined as is a solving problem process that involves formulating problems and solutions in a way that can be represented and solved through a series of computational steps, designing systems and understanding human behaviour, based on fundamental computer concepts. It includes processes such as abstraction and decomposition when dealing with complex tasks or designing extended systems (Wing, 2006). From an educational point of view, the challenge is to identify the cognitive skills that are expected of a person with this type of thinking and their ability to apply them in practice. Computational thinking encompasses a set of thought processes that originate from computer science, but are applicable in various fields. However, it is often wrongly perceived as “technological thinking”, which implies a mindset aimed exclusively at the effective use of technologies. The contribution points out that the lack of a clear definition hinders the integration of this concept into teacher training and broader educational contexts. If understood correctly, computational thinking could significantly improve understanding of the procedural aspects of knowledge, which are very useful for teaching. In this sense, the contribution also describes an exploratory research on the opinions of primary school teachers in central Italy aimed at understanding the perceived benefits that the incorporation of computational thinking could bring in a teaching context.