Computational Thinking Concepts Research Articles

Emotional impact and performance in serious games: investigating computational thinking in inclusive education

Serious games have the potential to improve teaching and learning processes by attracting, inspiring and motivating students’ interest. By using educational games, students can develop skills that encompass the computational thinking, enabling students to solve real problems. Given the importance of the development of computational thinking for all students, including those with disabilities, this article aims to evaluate the impact of the insertion of serious games on teaching and learning basic computational thinking concepts in inclusive education. We analyze and correlate the emotional and performance aspects, in order to emotional and performance aspects can contribute to better interactions among students in inclusive education and interaction barriers can impact negativelly on task performance and playability. For this purpose, we selected two serious games and recruited nineteen students with special needs to perform a set of tasks in each game. We measured the emotional and performance aspects through the Geneva Emotions and performance wheel. The results indicate a strong correlation between positive students’ emotions and good students’ performance, significantly impacting the accomplishment of tasks and playability of games.

Enhancing problem-based learning with computational thinking concepts for nursing students in virtual simulation context: Bridging knowledge and practice

Enhancing problem-based learning with computational thinking concepts for nursing students in virtual simulation context: Bridging knowledge and practice

Can robot-supported learning enhance computational thinking?—A meta-analysis

Computational thinking (CT) is crucial for students. Robot-supported learning has emerged as a popular approach for CT cultivation. To understand the effects of robot-supported CT cultivation, the current study conducted a meta-analysis to analyze studies from Web of Science, Google Scholar, and Science Direct databases (2012–2022) which screened using keywords ``computational thinking'' and ``robot''. After further screening, 26 peer-reviewed articles were selected. We synthesized the 33 effect sizes to assess the overall effectiveness of robot-supported learning on CT and its sub-dimensions: concepts, practices, and perspectives. Our findings revealed a medium effect of robot-supported learning on students' CT (g = 0.643), a large effect on CT concepts (g = 0.650), a medium effect on CT practices (g = 0.587), and a huge effect on CT perspectives (g = 1.419). We also explored the moderators (i.e., grade level, study duration, culture, learning strategy, and assessment tool) that might influence the effects of robot-supported CT cultivation. Moderator analyses indicated that short-term (less than four weeks) robot-supported learning had a larger effect (g = 0.901) on CT practice than long-term (more than eleven weeks) learning (g = 0.309). This meta-analysis indicates that educators could apply robot-supported learning to cultivate students' CT and concludes with related implications.

Computational Thinking in Secondary Mathematics Education with GeoGebra: Insights from an Intervention in Calculus Lessons

AbstractNowadays, mathematics teachers in K–12 strive to promote their students‘ mathematical knowledge and computational thinking (CT) skills. There is an increasing need for effective CT-embedded mathematics learning material and a better understanding of students’ views toward them. In this work, we present the results of a research study, which included the design of a six-lesson learning activity aimed at fostering 16- to 17-year-old secondary students’ CT skills in calculus lessons using the dynamic mathematics software GeoGebra. Our goal was to investigate how students experienced the CT-embedded calculus lessons with GeoGebra and what challenges they faced during their interaction with the learning material and software. We collected and analyzed data from students’ code in GeoGebra, workbooks, semi-structured interviews, and questionnaires. Our findings suggest that most students mastered using CT concepts in calculus activities to a satisfactory degree and could reason about their computational solutions using GeoGebra and the generated graphs. Students’ understanding of the mathematical content knowledge introduced was essential to complete the lesson series successfully and unnoticed gaps in prior knowledge emerged. Our study shows that students appreciate the CT-embedded calculus lessons and GeoGebra’s exploratory approach to mathematics problems when provided with appropriate support. We conclude that an integrated approach to mathematics education and CT is viable and can contribute not only to fostering CT but also to increasing interest in mathematics.

Deepening children’s STEM learning through making and creative writing

Current trends demonstrate that researchers and K-12 educators are moving towards integrating computational thinking (CT) concepts outside of the computer science (CS) classroom (Lee et al., 2020). However, one of the present challenges includes a lack of engaging learning content and instruction for translating computational concepts that non-CS instructors can bring into their classrooms. Our research team developed the Grove-Blockly platform, a website that employs block-based programming and physical computing elements with simulations to support CT learning and IoT practices. We designed a 5-day workshop with the National Writing Project for students to engage in STEAM learning through making and creative writing. By examining students’ learning through the processes and outcomes of making, coding, and creative writing, we aim to better understand how learning occurs at the intersections of making, writing, and computing. The findings show students’ positive learning outcomes of CT and physical computing from the workshop. Mainly, students gained a deeper understanding of IoT elements (e.g., sensors, actuators) through the process of making and creative writing. This work provides empirical evidence on how students learn CT and computational practices through making and creative writing. Finally, we discuss how the engagement of STEAM practices supports and empowers students’ learning of CT and physical computing through the creation of e-crafts by students.

Pengembangan Proyek Pemrograman Medıa Pembelajaran Interaktıf Untuk Tugas Pemecahan Masalah Pelatihan Berpıkır Komputasıonal Mahasiswa Dengan Scratch

Media is needed in learning to attract and make it easier for students to understand the material being taught. Interactive learning media for computational thinking material through Scratch programming as an example of a block-based programming project assignment by applying computational thinking concepts for PGSD student training has not been researched in Indonesia. The research carried out aims to develop an example of a final programming project for interactive media learning computational thinking for PGSD student programming training as a valid and practical problem solving task using the Scratch program as well as the level of competency of the programming project being created. The research carried out is of the research and development type using the ADDIE model. The research participants were 40 students from a private university in West Sumatra. Data was collected using observation, interview, artifact analysis, and questionnaire techniques. The research instrument was a questionnaire with a Likert scale filled in by material, media and research subject experts. Analysis of research data was carried out in a qualitative descriptive manner. From the development research carried out, it was found that the final project example of interactive media programming for computational thinking learning which was developed for PGSD students' computational thinking training as a problem solving task received a very good score with a score of 85.5% from the validation of material experts, media and students, 85. 2% for product practicality, and makes it easier for students to created the program because the difficulty is at intermediate level.

A metaphor-based robot programming approach to facilitating young children’s computational thinking and positive learning behaviors

In the artificial intelligence age, cultivating young children's computational thinking (CT) has sparked tremendous attention. Programmable robotics is a developmental-appropriate and screen-free means that provides young children with great opportunities to learn programming and develop CT. However, it is reported that young children might have difficulties learning abstract CT concepts. As a helpful pedagogical facilitator, metaphors can help turn abstract concepts into more concrete and clear concepts that learners are familiar with. Therefore, this research proposed a metaphor-based robot programming (MRP) approach and explored its impact on young children's CT and behavioral patterns. A total of 118 children aged 5–6 were recruited in this experiment with two conditions: the experimental group adopted the metaphor-based robot programming (MRP) approach while the control group used the conventional robot programming (CRP) approach. Results revealed that children who adopted the MRP approach outperformed children who adopted the CRP approach on CT. In addition, behavioral analysis indicated that the proposed MRP approach could facilitate children's superior learning performance and more positive learning behaviors, so as to help them achieve learning objectives. Accordingly, this study can provide insightful guidance and inspiration for future research on effective programming teaching and CT development for young children.

Using students’ cognitive, affective, and demographic characteristics to predict their understanding of computational thinking concepts: a machine learning-based approach

ABSTRACT Logistic regression models have traditionally been used to identify the factors contributing to students’ conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected cognitive, affective, and demographic data from 4,142 primary students as features to predict their understanding of computational thinking (CT) concepts. We identified and used five popularly used machine learning models. All five machine learning models outperformed the logistic regression model, with the extreme gradient boosting (XGBoost) model achieving the highest predictive accuracy. We used these features and the K-means algorithm of the unsupervised clustering technique to identify four optimal clusters of students. By comparing the representative students from each of the four clusters, selected by the t-distributed stochastic neighbour embedding algorithm, we found that each cluster had its characteristics. For example, one cluster consisted of students who outperformed students in the other three clusters in mathematics, scored highest in prior experience in programming, and used computers and the Internet most frequently. By identifying the characteristics of these clusters, pedagogical design, and resource support can be proposed to support students’ learning of CT concepts.

Primary school students' perceptions and developed artefacts and language from learning coding and computational thinking using the 3C model

AbstractBackgroundA resurgence in teaching coding in primary school classrooms has led to a pedagogical swing towards using physical computing and coding to develop students' use of algorithms, computational thinking, and problem‐solving skills. Two obstacles impede the optimal development of these objectives: the availability of a suitable pedagogy and an instructional sequencing model for primary school teachers to effectively present coding and computational thinking concepts and skills to students in alignment with their developmental stage.ObjectiveThis study aims to address both obstacles by introducing the 3C Model, a newly developed instructional sequence grounded in established pedagogies and designed to effectively teach coding and computational thinking skills to primary school students based on their developmental stage.MethodsThe qualitative study employed two data sources to triangulate findings, using: (1) semi‐structured interviews and thematic analysis to investigate 11 primary school students' perceptions of their learning experiences with the 3C Model, and (2) researcher observations along with reflections of the students' developed and demonstrated learning through the method of knowing‐in‐action, reflection‐in‐action, and reflection‐on‐action.Results and ConclusionsThe findings of this study fill a gap in the existing literature by demonstrating that the pedagogical and sequential approach embedded in the 3C Model not only enhanced students' engagement levels but also resulted in improved curriculum learning outcomes. The 3C Model provides teachers with a coherent and age‐appropriate instructional structure. It uses physical computing devices and digital coding platforms to introduce coding concepts, furthering the development of computational thinking skills in primary school students beyond mere procedural and rote learning.ImplicationsThe study holds important implications for practical applications, as it addresses an absence in the literature of an established pedagogy and instructional sequencing model for effectively teaching coding and computational thinking concepts and skills to primary school students. Drawing on established pedagogical and developmental learning theories, the 3C Model provides primary school teachers with an engaging, age‐appropriate instructional method that avoids decontextualised teaching and surface‐based learning. Instead, it encourages collaborative student work and contextualised learning, steering away from isolated and generic approaches.

The Impact of a Computing Curriculum Accessible to Students with ASD on the Development of Computing Artifacts

There has been no study examining the effectiveness of an accessible computing curriculum for students with autism spectrum disorder (ASD) on their learning of computational thinking concepts (CTCs), flow control, data representation, abstraction, user interactivity, synchronization, parallelism, and logic. This study aims to investigate the effects of an accessible computing curriculum for students with ASD on their learning of CTCs as measured by the scores of 312 computing artifacts developed by two groups of students with ASD. Conducted among 21 seventh-grade students with ASD (10 in the experimental group and 11 in the control), this study involved collecting data on the computing projects of these students over 24 instructional sessions. Group classification was considered the independent variable, and computing project scores were set as the dependent variables. The results showed that the original curriculum was statistically significantly more effective for students in learning logic than the accessible one when all seven CTCs were examined as a single construct. Both curriculums were statistically significantly effective in progressively improving students’ learning of data representation, abstraction, synchronization, parallelism, and all CTCs as a single construct when examining the gradual increase in their computing artifact scores over the 24 sessions. Both curriculums were statistically significantly effective in increasing the scores of synchronization and all CTCs as a single construct when the correlations between CTCs and sessions for individual groups were analyzed. The findings underscore that students with ASD can effectively learn computing skills through accessible or standard curriculums, provided that adjustments are made during delivery.

Teachers in the Loop: Integrating Computational Thinking and Mathematics to Build Early Place Value Understanding

With increasing attention on the potential overlap between computational thinking (CT) and mathematical reasoning, STEM education researchers seek to understand how integrating CT and mathematics can deepen student learning across disciplines. Although there are various professional development programs that introduce teachers to CT concepts and strategies for curriculum integration, limited research exists on how teachers might apply this knowledge to create math + CT activities for use with their students. Additionally, the majority of research on CT integration through programming has focused on upper elementary grades, leaving the early grades (K-2) relatively unexplored. This qualitative exploratory study aims to examine how teachers in a graduate STEM education program collaborated with university STEM faculty to explore and critique a set of integrated math + CT block-based programming activities designed to build place value conceptual understanding. In-service elementary teachers enrolled in an online graduate CT course for educators (n = 13) explored these activities as learners and drew on their experiences as classroom teachers to offer feedback for program redesign. A sequence of deductive pattern coding and inductive holistic coding of course transcripts, collaborative problem-solving slides, and individual teacher reflections provided insights into how teachers were able to establish connections between their mathematical knowledge related to teaching place value and their emerging understanding of CT concepts, such as abstraction, algorithms, decomposition, and debugging. Implications for the design of professional development for elementary teachers on integrating CT and mathematics are offered.

Assessing the Efficacy of an Accessible Computing Curriculum for Students with Autism Spectrum Disorders

There is a limited amount of research dedicated to designing and developing computing curricula specifically tailored for students with autism spectrum disorder (ASD), and thus far, no study has examined the effectiveness of an accessible computing curriculum designed specifically for students with ASD. The goal of this study is to evaluate the effectiveness of an accessible curriculum in improving the learning of computational thinking concepts (CTCs) such as sequences, loops, parallelism, conditionals, operators, and data, as well as the development of proficiency in computational thinking practices (CTPs) including experimenting and iterating, testing and debugging, reusing and remixing, and abstracting and modularizing. The study involved two groups, each comprising twenty-four students. One group received instruction using the accessible curriculum, while the other was taught with the original curriculum. Evaluation of students’ CTCs included the analysis of pretest and posttest scores for both groups, and their CTPs were assessed through artifact-based interview scores. The results indicated improvement in both groups concerning the learning of CTCs, with no significant difference between the two curricula. However, the accessible computing curriculum demonstrated significant enhancements in students’ proficiency in debugging and testing, iterating and experimenting, modularizing and abstracting, as well as remixing and reusing. The findings suggest the effectiveness of accessible computing curricula for students with ASD.

Ethnomathematical problems in the context of the ethnocultural content of mathematical education

Relevance. The relevance of this study lies in the fact that modelling the content of ethnomathematical problems determines the range of mathematical competencies and skills, including mathematical reasoning and problem-solving, the cycle of which comprises the corresponding ethnocultural processes. Modelling measures how effectively the state prepares students to use mathematics in all aspects of their personal, civic, and professional life so that they become rational, committed, and thinking members of modern society.Purpose. The purpose of this study is to investigate the modelling of ethnomathematical problems necessary for the successful establishment and development of mathematical competencies of students in grades 5�6.Methodology. These problems include the use of concepts, properties, procedures, and tools to describe, explain, and predict phenomena, which, in turn, help to draw informed conclusions and make informed decisions.Results. The role of ethnomathematical problems in a rapidly changing world driven by new technology and trends, where students in grades 5�6 are creative and actively involved in learning, can be of great importance and enable the children to make non-standard decisions on behalf of themselves or the society they live in. This puts at the forefront the capacity for mathematical thinking, which has always been part of the development of teaching competencies. Modern technological advances also require students to understand the concepts of computational thinking that are part of mathematical skills. The ability of modelling allows enables logical reasoning, as well as allows honestly and convincingly presenting arguments, which are becoming an increasingly important structure of ethnocultural practice in the modern world.Conclusions. The practical significance lies in the definition of mathematical means of folk pedagogy, which allows developing a methodology and technology of teaching to structure and model the content of ethnomathematical tasks.

How can unplugged approach facilitate novice students’ understanding of computational thinking? An exploratory study from a Nigerian university

Computational thinking (CT) has been relevant in teaching STEM courses, and educators are incorporating both plugged and unplugged CT activities in their modules to prepare new entrants for advanced knowledge. However, additional empirical data are needed to understand how an unplugged activities approach can lead to an improved understanding of CT concepts. Therefore, this study investigates the impact of unplugged activities on novice students' CT skills and unravels the characteristics of students' CT competency from performing unplugged activities. The experimental aspect of this study was conducted with a cohort of 210 students participating in an introductory programming course at a University in Nigeria. A mixed-method research approach and a quasi-experimental design were applied in this study to understand how the unplugged approach in teaching CT could help students improve their CT competency. A paired-sample t-test of familiarity score for each CT concept showed a significant increase in the score before and after the intervention. Furthermore, Latent Dirichlet Allocation (LDA) was used for topic modeling of the text describing students’ solutions. Topic modeling allows for more insights into the steps taken by the students to solve CT problems. Only 27.62 % of students' solution descriptions contained CT concepts, 56.90 % of the students solved the puzzle with an optimal score, and 72.41 % of students who applied CT concepts of problem abstraction and decomposition were able to solve the puzzle correctly. Indeed, CT unplugged activities significantly increased the number of students who gained CT competency. In addition, this study provides recommendation for computer science educators and highlight future direction.

Predicting Computational Thinking in Elementary Science Lessons Using a Multilevel Model Approach

Computational thinking (CT) is an essential problem-solving skill that students need to successfully live and work with developing technologies. There is an increasing call in the literature by researchers and policy leaders to integrate CT at the elementary level into core subjects to provide early and equitable access for all students. While some critics may claim the concepts and skills of CT are developmentally advanced for elementary age students, subjects such as science can provide real-world and relevant problems to which foundational CT components can be applied. By assessing how CT concepts and approaches integrate authentically into current science lessons, policymakers, and district leaders can be more intentional in supporting implementation efforts. This research used an exploratory survey design to examine the frequencies of CT concepts (decomposition, algorithms, abstraction, and pattern recognition) and approaches (tinkering, creating, debugging, perseverance, and collaboration) that exist in science in K–5 schools in a northeast state in the United States as reported by elementary science teachers (n = 259). Hierarchical linear modeling was used to analyze the influence of teacher and district factors on the amount of time CT concepts and approaches were integrated in the science lessons. Experience, grade level, confidence, and participation in a research–practice partnership were found to be significant predictors of CT. This study contributes to a better understanding of variables affecting CT teaching frequency that can be leveraged to impact reform efforts supporting CT integration in science.

Engaging elementary students in data science practices

Purpose This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approach This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning. Findings Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables. Originality/value Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

Peningkatan Soft Skill Melalui Program Pelatihan Bebras Challenge Untuk Meningkatkan Kemampuan Computational Thinking Siswa SMK

The purpose of this service activity is to improve the Computational Thinking (CT) skills of vocational students through the Bebras Challenge training program. Since its introduction by Wing in 2006, CT has become a major focus in science, technology, engineering and math (STEM) education. In the context of this service activity, some of the problems identified involved students' lack of proficiency in CT, ignorance of Bebras Challenge activities, and lack of promotion of the use of the internet in CT learning. This training was carried out by applying lecture methods, question and answer sessions, and exercises/practices. The results of the implementation of this service activity show that students can apply Computational Thinking to solve everyday problems. This service method proved effective in motivating students and teachers to actively use technology as a CT learning tool. In addition, this training helps students to better understand the types of challenges faced in Bebras and the concept of CT.

Experimenting with computational thinking for knowledge transfer in engineering robotics

AbstractBackgroundDespite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K‐12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to sustainable learning outcomes.ObjectivesThe current study examines how a series of Arduino‐based robotics learning activities capture the fuller essence of concepts related to CT.MethodsCollege students (n = 50) completed a series of six robotics learning activities. Think‐alouds, student reflections and performance scores were used to assess students' CT through a robotics challenge in virtual and physical learning environments.Results and ConclusionsStudents verbalized CT concepts related to algorithmic thinking much more than abstraction, problem decomposition and testing and debugging. Students exposed to active learning performed better in a virtual robotics challenge compared to their peers in a traditional‐oriented classroom. Students' scores on the physical robotics challenge increased as a function of the number of references they made to CT concepts during the think‐alouds. It is possible to design pedagogical experiences that tap into various dimensions of CT at incremental levels of complexity through a series of Arduino‐based robotics activities. With the integration of an online simulation, students can visualize and transfer their CT skills between a virtual and physical learning environment, thus leading to more sustainable learning outcomes.

Procesul de formare și dezvoltare a gândirii computaționale și de implementare a activităților STEM: punctul de vedere al cadrelor didactice preuniversitare

Studying the 126 teachers’ didactic cases from different parts of Moldova and different educational levels. The questionnaire reflects teaching experience, level of preparation for STEM projects and familiarity with the notion of computational thinking. The results of the questionnaire indicate the fact that most teachers implement STEM projects, nevertheless additional training in this area is needed. Teachers confirm the importance of computational thinking and consider it foremost to contribute to the development of a set of skills that students should possess. The conclusions offer suggestions for enhancing teacher training and implementing the concept of computational thinking in education.

Comparing the integration of programming and computational thinking into Danish and Swedish elementary mathematics curriculum resources

Computational thinking has become part of the mathematics curriculum in several countries. This has led recently available teaching resources to explicitly integrate computational thinking (CT). In this paper, we investigate and compare how curriculum resources developed in Denmark — digital teaching modules — and Sweden — printed mathematics textbooks — have incorporated CT in mathematics for grades 1–6 (age 7–12). Specifically, we identify and compare the CT and mathematical concepts, actions, and combinations in tasks within these resources. Our analysis reveals that Danish tasks are oriented toward CT concepts related to data, actions related to programming, and mathematical concepts within statistics. This is different from Swedish tasks, which are oriented toward CT concepts related to instructions and commands, actions related to following stepwise procedures, and mathematical concepts related to patterns. Moreover, what is most dominant in one country is almost or completely absent in the other. We conclude the paper by contrasting these two approaches with existing knowledge on computational thinking in school mathematics.