Computational Thinking In Students Research Articles

Computational Thinking Readiness Level of First-Year Students of Mathematics Education

The main objective of this study was to measure the level of computational thinking readiness in prospective first-year mathematics education students. In addition, this study also aims to identify factors that influence their level of readiness towards computational thinking. This research is qualitative and descriptive in nature. This study describes first-year mathematics education students' mathematical computational thinking ability based on the theory of mathematical computational thinking. This study was conducted on first-year mathematics education students in the academic year 2023/2024. There were 16 first-year mathematics education students, all of whom were taken as samples in this study, to obtain more in-depth information about the computational thinking ability of first-year mathematics education students for further research development. The instruments used to collect data on first-year mathematics education students' mathematical computational thinking ability are (1) a mathematical computational thinking ability test and (2) an interview. The data obtained were calculated using statistical tests, and the results will be explained in depth. The mean score of the first-year mathematics education student's computational thinking ability test was 59.68, indicating that students generally have a fairly good level of computational thinking ability.

Development of computational thinking of students – future teachers by means of solving problems on the processing of structured data

The problem and the purpose. Computational thinking is one of the categories that currently assesses the quality of specialist training, including that of a future teacher. The mentor of a digital school needs computational thinking skills for the successful implementation of work functions and for determining the trajectory of his personal development. The purpose of the study is to identify the possibilities of structured data processing tools for the development of computational thinking of future teachers. Research methods. Structured data is information organized in the form of tables and files of other formats. The research was conducted on the basis of Vyatka State University (Russian Federation). The experiment involved 330 students studying in the field of training: 44.03.05 Pedagogical studies (with two specialties). The use of structured data processing tools was purposefully carried out in the classes of the design discipline from the block “Digital technologies in education”. A set of techniques was used to determine the level of computational thinking such as “Geometric figures”, “Quantitative relations”, “Complex analogies”. For statistical processing of data, Pearson's χ2 (chi-square) criterion is used. Results. Interaction in the experimental group was implemented in stages: the study of basic tools; development of skills of graphical interpretation; the use of functions that allow analyzing and protecting the contents of structured data. The features of the proposed information interaction are: students with different educational paths are involved; the technology of reasoned discourse is applied at every stage. Statistically significant differences were revealed in the qualitative changes that occurred in the pedagogical system (χ2 = 7.024; p < 0.05). Conclusion. The tasks on the processing of structured data that maximize the development of computational thinking are formulated: conditional formatting; establishing links between the source data and the result; automatic recalculation of the result; filtering, sorting, etc. The difficulties that complicate the use of the proposed innovations are defined: how to state a task and to adapt it to the specifics of the field of training; how to organize a reasoned discourse.

Enhancing computational thinking in students with autism spectrum disorder and intellectual disabilities: a robot programming approach

Objectives Research examining computational thinking skills development via programming education using robotic teaching tools for students with Autism Spectrum Disorder and Intellectual Disabilities remains limited. This study aims to analyze the advancements in computational thinking skills among middle school students with Autism Spectrum Disorder and Intellectual Disabilities through participation in robot programming classes. Methods Problem-solving proficiency in computational thinking was assessed utilizing Brennan and Resnick’s three dimensions framework. A tailored teaching and learning approach was implemented for robot programming education, accounting for participants’ cognitive abilities and required levels of support. Four middle school students, comprising one with Autism Spectrum Disorder and three with Intellectual Disabilities, were enrolled. Results Significant enhancements were observed in all participants’ computational thinking skills across conceptual understanding, practical application, and perspective. Notably, all participants successfully resolved the final project problem at their individual proficiency levels. The acquisition of computational concepts was found to correlate positively with improved practical computational skills. Additionally, all participants demonstrated keen interest in and positive attitudes toward both robots and programming. Conclusions This study underscores the efficacy of robotic teaching tools in enhancing computational thinking skills through programming education. Additionally, it highlights considerations for enhancing students’ accessibility in future programming class design.

The Effect Of Discovery Learning In Mathematics Learning On Computational Thinking In Terms Of Self-Regulated Learning (Srl) In Student

Discovery learning is a learning process aimed at uncovering new knowledge through teaching and learning activities. This study investigates the effect of discovery learning in mathematics on computational thinking, considering students' self-regulated learning levels. Employing a quantitative approach with an experimental method, specifically the Posttest-Only Control Group Design, the research sampled VB class students as the experimental group and VC class students as the control group. Data were collected through computational thinking tests, focusing on decomposition, abstraction, algorithm design, and pattern recognition, and analyzed using a two-way ANOVA with a treatment by level 2 x 2 design. The findings reveal: (1) Computational thinking of students exposed to discovery learning differs from those taught with a scientific approach, (2) There is an interaction effect between learning models and self-regulated learning on computational thinking, (3) Students with high self-regulated learning exhibit higher computational thinking in mathematics through discovery learning compared to those taught with a scientific approach, (4) No significant difference exists in computational thinking outcomes between students with low self-regulated learning regardless of the teaching method. Consequently, discovery learning, combined with high self-regulated learning, leads to better computational thinking outcomes in mathematics.

로봇을 활용한 코딩교육이 지적장애 초등학생의 컴퓨팅 사고력에 미치는 효과

로봇을 활용한 코딩교육이 지적장애 초등학생의 컴퓨팅 사고력에 미치는 효과

Professional Development in Computational Thinking: A Systematic Literature Review

This paper presents a systematic literature review of professional development programs in computational thinking (CT). CT has emerged as an essential set of skills that everyone should develop to participate in a global society. However, there were no pre-service or in-service teacher programs to integrate CT into the K–12 classrooms until very recently. Thus, it is important to identify how educators and researchers address the challenges to prepare the next generation of students and what gaps persist in the current literature. We review existing work in this field from two perspectives: First, we analyze the learning outcomes, assessment methods, pedagogical approaches, and pedagogical tools used in the professional development programs in CT. Second, we examine how these programs assess the teachers’ knowledge and skills as outcomes. We used the technological pedagogical and content knowledge (TPACK) framework to characterize existing literature and identify possible gaps in the preparation of pre-service and in-service teachers in CT. Our results suggest that (1) existing evidence is limited to developed countries; (2) many studies are only focusing on teachers understanding the concepts but do not explore how the participants evaluate or create learning activities; (3) no studies look into classroom observations as part of the program, which limits our understanding to how these programs work; and (4) most programs use block-based programming languages as the tool to develop student CT. While block-based programming languages are used for introductory training programs, students are often expected to transfer their learning to more professional programming languages.

Fortune-Telling Finches: Linear Functions as Predictors

This lesson gives students experience with block coding Finch robots to move across plexiglass surfaces. It builds student computational thinking skills, mathematical self-efficacy, and interest in technology careers (McLurkin, et al., 2013; Martin, 2019; Kazi, 2023). Finch robots are small, physical robots that can be coded to move in a space. In this 8th grade math unit, conducted in the library, students identify relationships between variables while using block coding to make predictions and test hypotheses. This lesson extends students' knowledge of linear functions through observing patterns and using the guess and check strategy to complete a variety of challenges.

A two-tier computerized adaptive test to measure student computational thinking skills

A two-tier computerized adaptive test to measure student computational thinking skills

Students’ Computational Thinking Skills In Physics Learning: A Case study of Kinematic Concepts

Physics learning provides a context for future careers in fostering ability in high-end logic with the 21st learning goals. Applying computational thinking in schools is challenging and requires systemic transformation and teacher attention. This study aims to investigate the computational thinking of students in physics learning. This study used exploratory qualitative research. Data were gathered through observation, interviews, and portfolio documents. The data are analyzed through six stages: preparing and organizing, exploring, building descriptions, representing the findings, interpreting the results, and validating the accuracy. The result indicated four primary computational thinking skills: decomposition, abstraction, simulation, and evaluation. The computational thinking skills in physics learning can develop students’ understanding and implementation of physics concepts based on data, not just mathematical formulas. Computational thinking in physics learning gives students the opportunity and space to explore and develop their ideas and logical reasoning more deeply in problem-defining, solutions, and evaluation. Students use their logical reasoning to solve the problem precisely. This study is expected to be used as a basis and support for physics teachers to integrate computational thinking into their learning classroom.

Web Technologies in the Development of Computational Thinking of Students with Mental Disabilities

Computational thinking is an important and necessary part of a modern person’s thinking. It has been proven that the development of this way of thinking in students with mental disabilities allows them to navigate quickly in the modern world, identify problems and create complex solutions. Online schooling during the COVID-19 pandemic demonstrated the possibilities of the usage of web technologies in the education of children with mental disabilities. This study aims to evaluate the impact of web technologies on the development of computational thinking of students with mental disorders. The experiment involved 14 students aged 8-12 and 4 tutors. For 8 weeks children were trained in computational thinking and computer science. Assessment of computational thinking was performed with cCT-test by El-Hamamsi et al. before and after the experiment. After conducting computer science lessons using web technologies the respondents showed a higher level of computational thinking (M=15,7, SD=3,69), compared to the results of preliminary testing (M=5,93, SD=2,3). Web technologies can significantly increase the effectiveness of inclusive pedagogy, which establishes the importance of integrating web technologies into the teaching system in inclusive classes of general education schools.

Development of E-Module Based on Problem Based Learning Assisted with Scratch Applications to Improve Students Computational Thinking Skills

This study aims to develop a valid, practical, and effective problem-based learning e-module to improve the computational thinking skills of fifth-grade elementary school students. The research method used was Research and Development, using the ADDIE development model. The research instruments used were validation questionnaires, practicality questionnaires for students and teachers, and pretest and posttest question sheets to measure students' abilities. While the data analysis technique to test the validity and practicality using a Likert scale, while the product effectiveness test was carried out using the Pretest-Posttest Control Group Design and analyzed using SPSS. The results of the research showed that the validity results based on lesson plans obtained a value of 94.44% (very valid), material expert validators 90.48% (very valid), linguists 92.86% (very valid), and media experts 89.6% (very valid). The practical results can be seen from the student response of 90.42% or very practical, and the teacher's response of 93,52 % or very practical. At the same time, the effectiveness test from the results of the pretest and posttest obtained that count> table, namely 15,499 > 1,711 and Sig (2-tailed) 0,000 <0.05, so it can be concluded that there is an average difference in students' computational thinking abilities from the results of the pretest to the results of the posttest in the experimental class. Furthermore, the effectiveness test based on N-gain was obtained at 60.6203 or 60.6%, including the quite effective category. So, the development of PBL-based e-modules assisted by scratch applications to improve the computational thinking skills of Grade V elementary school students is declared valid, practical, and effective.

Enhancing student's computational thinking skills with student-generated questions strategy in a game-based learning platform

Computational thinking (CT), a way to think about solving problems, is recognized as a basic skill for the 21st century. Although game-based learning (GBL) has been shown to improve student's CT skills, students may fail to self-evaluate or debate as they play games, thus failing to develop higher-order thinking. Also not widely explored is how teachers are to practice the CT core competencies during the learning process to cultivate students' problem-solving thinking. As the adoption of student-generated questions (SGQ) is an instructional strategy to promote higher-order thinking, we develop a GBL platform that uses this strategy to enhance students' CT skills, learning motivation, and learning confidence. For this purpose, fifty-three primary school students were recruited for this study, and data were collected during four classes of experimental CT activities. This study shows that the experimental group using the SGQ strategy with a GBL platform exhibited significantly higher CT skills than the control group. In addition, with respect to learning motivation and learning confidence it is found that the experimental group also outperformed the control group. This confirms that integrating the SGQ strategy and CT core competencies fosters higher-order cognitive thinking in students in problem-solving through gamification of the learning environment, resulting in higher motivation and confidence to complete experimental activities.

Identifying computational thinking in students through project-based problem-solving activities

PurposeThis paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program.Design/methodology/approachThis case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment.FindingsResults show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process.Originality/valueThis study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.

Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic?

Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic?

Formative evaluation of immersive virtual reality expedition mini-games to facilitate computational thinking

Recently, virtual reality (VR) technology has shown great potential in advancing education with many pedagogical benefits for building the 21st-century teaching and learning experience. This study conducted a formative evaluation of an immersive VR expedition application with the aim of understanding users' learning processes and how the application facilitates higher education students' computational thinking skills. Six participants were randomly selected to conduct this evaluation. A mixed research approach consisting of quantitative and qualitative methods was employed. The study quantitatively analyzed users' scores from gameplay to understand how the intervention supported computational thinking skills. Participants were also interviewed to collect data after playing the mini-games to investigate users' experiences. The study showcases players' computational thinking competency, assessed automatically during gameplay. Further, this study used inductive content analysis to demonstrate users' reactions to prototyped VR mini-games. The qualitative findings suggest that users found the VR mini-games interactive and immersive, which provided an opportunity to foster learners' computational thinking skills. The quantitative analysis revealed that student's computational thinking competency can be enhanced through consistent playing of the mini-games. Moreover, the expedition aspect of the VR game stimulated learners' curiosity, which sustained their learning progress. Furthermore, users gained new knowledge and found the mini-games educative. Nevertheless, several aspects of the VR mini-games need improvements, according to users' perceptions. This study contributes to the knowledge in terms of the affordances of VR in education research and provides relevant insights that can shape future studies, for example, the recent hype of metaverse in education.

Analysis of Implementation Project-Based Learning Model of Teaching Integrated with Computer Programming in Improving Computational Thinking Skills in a Classical Mechanics Course

This research aims to explore student computational thinking skills in implementing a project-based learning model of teaching integrated with computer programming in classical mechanics course in projectile motion topic. The research design uses one group pretest and post-test design. The computational thinking skills have five indicators: abstraction, generalization, decomposition, algorithm, and debugging. The computational thinking indicator was analyzed from the result of Pretest and post-test scores and a comparison between manual solution and numerical solution from computer programming. The instruments used in this study were tasks, rubrics, and questionnaires. The result shows the average score of the Pretest is 53.05, and the post-test score is 80.22. The student computational thinking skills in algorithm and debugging in Pretest are 29.70% and 24.30% and 59.00%, and 54.00% in the post-test stage. This result indicates the implementation of PBL model of Teaching integrated with computer programming has a significant impact on student computational thinking skills

Effects of voice assistant creation using different learning approaches on performance of computational thinking

Designing artificial intelligence (AI) artefact learning has gone beyond command-line-based instruction, to include a low-barrier threshold with block-based programming. Such instructional design must not solely emphasise AI workings. Rather, it must offer students computational thinking (CT) practice to support their AI-related artefact creation while reducing their AI anxiety about future job replacement or sociotechnical blindness. In this study, this research explored an experiential learning approach to improve CT along with AI application capabilities when engaging undergraduate students in creating a voice assistant application (VA app). A total of 56 students participated in the study. The control group (CG) of 26 students used a conventional subject-based learning method, while the experimental group (EG) of 30 students adopted an experiential learning method. This study aimed to examine the differences in the learning achievement of CT and AI concept, as well as the perspectives of AI anxiety, and CT; in the meanwhile, this study analysed the students' learning behaviours using sequential behavioural analysis to discuss the learning process. Results showed that the CT ability of the EG was better than that of the CG, although no significant difference was found between the two groups’ AI concepts and anxiety. The behaviour analysis also revealed that the EG students were willing to ask more questions, and conducted their VA evaluation, whereas the CG students were inclined to focus on the input and output of knowledge, and replicated what the teacher presented. Suggestions and implications are given for future research.

Primary School Students’ Computational Thinking in Solving Mathematics Problems Based on Learning Style

Computational thinking is related to the ability to solve mathematical problems. This study aims to describe the computational thinking of primary school students with visual, auditory, and kinesthetic learning styles in solving mathematical problems as measured by indicators of abstraction, decomposition, algorithmic thinking, and generalization. This research is descriptive qualitative research. Data collection techniques using questionnaires, assignments, and interviews. Data analysis techniques using technical triangulation. The results showed that the computational thinking of students with visual and auditory learning styles fulfills all indicators of every aspect of computational thinking. Both identify important information by mentioning known and asked information, making mathematical models, solving problems by breaking them down into several parts, and solving them to get the right results. Both provide logical arguments regarding the method used, generalize the problem and apply the solution to similar problems. The difference lies in the mathematical form created. The mathematical form made by visual learning styles is more complete than auditory learning styles. This causes the settlement steps to also be different. Meanwhile, kinesthetic learning styles make mistakes when understanding the problem and making mathematical models, so the completion steps and the final results obtained are not appropriate. So, students need to get used to solving problems that can train their computational thinking skills so that students will get used to thinking systematically and logically

Student Computational Thinking in Solving Sequence Problems Based on Learning Styles

The purpose of this study is to describe students' computational thinking in solving sequence problems based on learning styles. This research approach is qualitative research with exploratory and descriptive research types. The subjects in this study were students majoring in Mathematics Tadris, Fakultas Tarbiyah dan Ilmu Keguruan UIN SATU Tulungagung semester 5. First, students' computational thinking in solving sequence problems based on assimilation and convergent learning styles, including incoherent, complete, and systematic. Second, students' computational thinking in solving sequence problems based on assimilation and accommodation learning styles, including coherent, complete, and systematic. Third, students' computational thinking in solving sequence problems is based on convergent learning styles and accommodation, including coherent, complete, and unsystematic. Fourth, students' computational thinking in solving sequence problems is based on divergent and convergent learning styles, including incoherent, incomplete, and unsystematic.

Nurturing Secondary School Student Computational Thinking Through Educational Robotics

Digital literacy is becoming a key factor in today’s digital revolution age. Computational Thinking (CT) is a new digital literacy that is gradually being introduced in the school curriculum due to its applicability in the daily problem-solving process. Educational Robotics (ER) has been increasingly used as a pedagogical tool to attract students to learn computer programming, and when integrated with CT, they can be used to develop high-order thinking skills. However, intertwining between CT and ER remains a challenge for teachers. This paper describes a method to expose secondary school children to CT concepts and skills through ER learning activities. The method integrates the four CT core concepts, which are problem analysis and algorithm; abstraction; pattern recognition; and decomposition, in a two days’ ER workshop implementation. The result of the study shows that the method of integrating CT with ER has the potential to nurture students’ CT and programming skills. This study shows a statistically significant increase in the students' understanding of the two CT concepts which are pattern recognition and decomposition concepts.