Exploring Students Competencies to be Creative Problem Solvers With Computational Thinking Practices

This study investigates the relationship between two related computational thinking practices: data practices and computational problem-solving practices in acquiring related computational thinking practices during a first-year undergraduate engineering course. While computational thinking theory is still developing, empirical studies can help further understand how students demonstrate this knowledge and their progression in attaining the practices. Therefore, with this empirical study, the following questions are addressed. RQ1: What are the differences in students' computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? The use of a descriptive non-experimental design that aims to understand the correlation between practices related to data and computational problem-solving is presented. A machine learning technique is employed, utilizing historical data from introductory programming for a problem-solving course with more than 1000 first-year engineering students. After identifying groups of students defining different profiles, data from posterior performance in more advanced programming topics were descriptively analyzed. This study supports the characterization of four different student profiles demonstrating differences in their performance at the beginning of the semester. From these four profiles, two of them show a subsequent differential progression besides their similarity at the beginning of the semester. In this particular case, troubleshooting and debugging appear as a relevant competency when distinguishing these two learners' groups. These findings suggest that previous knowledge or exposure to different practices can result in different progressions of more complex computational practices, emphasizing the relevance of troubleshooting and debugging as a practice required for a successful and timely progression on the acquisition of other computational thinking practices.

Zoombinis is an award-winning digital game aligned with four computational thinking practices—problem decomposition, pattern recognition, abstraction, and algorithm design. The study manipulates two levels of scoring: Evidence identification (EI)—identifying outcomes from a single puzzle-level,—and evidence accumulation (EA)—summarizing observed outcomes across puzzles and levels. For three puzzles, machine learning detectors provide information about players’ computational thinking practices. For the remaining 45 puzzle-levels, only basic information (e.g. degree of player success) is available. These are combined with two EA algorithms: the average of detector outputs, and a Bayesian network. This study uses data from 716 students complete pre- and post-assessments. The correlation among the measures of computational thinking was positive yet modest, suggesting that the measures focus on different aspects of computational thinking.

The study examines the research–practice partnership (RPP) model aimed at co‐constructing and integrating computational thinking (CT) in culturally responsive (CR) ways within early childhood and elementary (PreK‐5) classrooms, focusing on teachers' RPP experiences. Recent research underlines the importance of integrating CT in early childhood and elementary education, demonstrating that culturally, linguistically, and developmentally responsive computational activities in PreK‐5 settings are key to such efforts. However, there is a gap in research regarding effective professional development models that would prepare early childhood teachers in terms of both disciplinary ideas and practices of CT, as well as attending to cultural, linguistic, and developmental differences in young children in asset‐based ways. Our research contributes to this body of knowledge by examining how RPP can be a model for teacher learning in which teachers, administrators, and researchers can co‐develop knowledge and confidence in integrating CT into the PreK‐5 teaching curriculum and practices in CR ways. The study outlines the collaborative development of processes and frameworks co‐constructed by the RPP educators, as well as teacher‐developed curricular materials and lesson plans integrating CT in CR ways. Findings include teachers' experience of these lessons and teacher insights about the support needed to incorporate CT and CR practices into their existing curricula. The study concludes by highlighting the potential challenges and opportunities inherent in such endeavors, thereby contributing to the broader discourse on supporting CT integration into early childhood classrooms in CR ways.

In this qualitative study, I investigated the form of interaction when learners co-constructed computational thinking practices in an informal setting. This study focuses on the roles of five youth, three adult mentors, and a facilitator during a summer camp and documents how learners interacted to exchange ideas, collaborate, and accommodate other perspectives. Participants used questions that promote deep thinking to engage in computational thinking practices during the interactions. Analysis of data shows that learners developed computational thinking practices including interpreting, modeling connections to the real-world, manipulating parameters, discovering ideas to explore models, prompting, conjecturing, and making predictions when working on agent-based modeling and writing codes in NetLogo. These practices are further grouped together as model interpretation and connection to the real world, parameter manipulation and discovery, prompting and exploring, and making predictions/conjectures and generalizing. This study also finds some community-based practices such as practical wisdom, trying something and adjusting, and the use of network of resources relevant during interaction to develop computational thinking practices.

The development of computational thinking skills is essential for information management, problem-solving, and understanding human behavior. Thus, the aim of the experience described here was to incorporate computational thinking practices to improve learning in a first Python programming course using programming tools such as PSeInt, CodingBat, and the turtle graphic library. A quasi-experimental methodological design was used in which the experimental and control groups are in different academic semesters. Exploratory mixed research was carried out. The control and experimental group consisted of 41 and 36 students, respectively. The results show that with the use of support programming tools, such as PSeInt, CodingBat, Python turtle graphic library, and the incorporation of computational thinking practices, the experimental group students obtained better learning results. It is concluded that student performance and motivation in university programming courses can be improved by using proper tools that help the understanding of programming concepts and the skills development related to computational thinking, such as abstraction and algorithmic thinking.

Computational thinking is a new concept that is hardly known by most ordinary Malaysians. On a positive note, in 2017, the Ministry of Education of Malaysia implemented a new curriculum by introducing two new secondary school subjects, namely Fundamental of Computer Science and Computer Science. These subjects contain five components of computational thinking called ‘techniques, which are taught to Form 1 and Form 4 students, respectively. Likewise, the Department of Teachers Education of Malaysia also introduced six components of computational thinking in its training programs. In contrast, there is a lack of attention to such a concept given to tertiary education. A preliminary survey was conducted in early 2019, which involved 50 students majoring in 21 educational programs. The findings showed 92% of the respondents had no knowledge regarding the computational thinking skill, signifying an urgent need to determine relevant components that characterize such a skill needed in educational programs, which according to Wing (2006) has numerous components. In this study, the researchers aimed to identify relevant components that characterize the computational thinking skill required for students in learning educational programs. An extensive review of documents published in leading online databases, such as IEEEXplore, Science Direct, and Web of Science, was carried out that yielded 116 articles. Further analysis reduced this number to 29 articles that were related to the characterization of the computational thinking skill with 66 components. Among these components, algorithm, abstraction, and decomposition were the top three components with the highest frequency of being cited in the selected articles, registering percentage points of 9.78%, 7.41%, and 5.35%, respectively. As such, the design and development of new instructional approaches for the teaching of educational programs should emphasize three components to help students develop strong computational thinking skills.

Interests in Maker Education have been increasing among K-12 educators. In this study, we focused on one of the contexts of Maker Education, digital fabrication activities, at a makerspace in Finland. We aimed to explore: 1) the potentials and the factors to develop twenty-first century skills and computational thinking practices through digital fabrication activities, and 2) challenges of utilizing digital fabrication in K-12 Maker Education. We examined perspectives of teachers and facilitators who have roles to support pupils in Maker Education. We presented three cases of school visits (3-5 days), where the pupils (7th-9th grades) created tangible artefacts with digital fabrication facilities at the makerspace. We collected data through participants’ observation, informal interviews and focus group interviews with teachers and facilitators. For data analysis, we employed theory-driven and data-driven approaches. The results showed that digital fabrication activities can provide learning opportunities for twenty-first century skills and computational thinking practices. The teachers and the facilitators discussed the six factors of digital fabrication activities which influenced pupils’ learning. However, the result also indicated the possibility that the teachers and the facilitators might not be familiar with the concepts of computational thinking. Also, different perspectives between the teachers and the facilitators towards the structure of the current activities surfaced. By identifying potentials and challenges of the current practices, the study has implications to advance Maker Education to be better integrated into K-12 school contexts.

The work is devoted to the consideration of improving the quality of teaching students the discipline “Numerical methods” through the development of the cognitive component of computational thinking based on blended learning. The article presents a methodology for the formation of computational thinking of mathematics students, based on the visualization of algorithmic design schemes and the activation of the cognitive independence of students. The characteristic of computational thinking is given, the content and structure of computational thinking are shown. It is argued that a student with such a mind is able to manifest himself in his professional field in the best possible way. The results of the application of the technique are described. To determine the level of development of the cognitive component of computational thinking, a diagnostic model has been developed based on measuring the content, operational and motivational components. It is shown that the proposed method of developing computational thinking of students, taking into account the individual characteristics of students’ thinking, meaningfully based on the theoretical and practical aspects of studying the discipline, increases the effectiveness of learning the course “Numerical methods”. The materials of the article are of practical value for teachers of mathematical disciplines who use information and telecommunication technologies in their professional activities.

This study compares computational thinking skills evidenced by two groups of students in two different secondary schools: one group per school was studying a qualification in Computer Science. The aim was to establish which elements of computational thinking were more prevalent in students studying Computer Science to a higher level. This in turn would evidence those elements likely to be present from their earlier computing education or through their complementary studies in Science or Mathematics, which all students also studied. Understanding this difference was important to identify any increased competence in computational thinking that was present in the Computer Science groups. Artefact-based interviews were carried out using questions and practical computing problems designed to elicit and demonstrate the students’ computational thinking skills based on the Brennan and Resnick (2016) model of computational concepts, practices and perspectives. Analysis of students’ responses showed surprisingly little difference between the computational thinking practices of the two groups in relation to abstraction, decomposition, evaluation, generalisation/reusing, logical reasoning and debugging/testing. The study concludes that general computational thinking skills can be developed either at a lower level of study or in cognate curriculum areas, leaving computer science as the rightful locus of computational thinking for automation.

Objective: The benefits of computational thinking have become an increasingly acknowledged and popular subject of investigation among researchers. This research aims to gather detail and comprehensive information regarding the learning of computational thinking skills in mathematics at various educational levels through a systematic literature review approach. Methods: With a focus on education level, instructional media, mathematics content, and the components of computational thinking addressed by previous researchers, this paper applied the PRISMA Systematic Review Protocol to offer a comprehensive synthesis of sixteen empirical studies retrieved from the Scopus database on the implementation of computational thinking in mathematics education. Findings: Most research on fostering computational thinking in mathematics education is concentrated at the elementary and junior high school. To optimize the development of computational thinking in mathematics, teachers should be reminded of strategies to support students, particularly through activities involving simulations using various instructional media. Examples of such media include programming platforms, visualization tools, and interactive simulations and games. Number operations and geometry are the mathematical content most widely used for fostering computational thinking. Algorithmic thinking, a crucial component in fostering computational thinking among elementary school students, helps them develop a strong foundation for understanding higher mathematical concepts. Conclusion: A systematic review of computational thinking in learning mathematics at the various education level is conducted in this study. The chosen studies were systematically analyzed for the advancement of computational thinking in learning mathematics to provide new an insight information for educators and stakeholders. Keywords: computational thinking, mathematics, systematic literature review. DOI: http://dx.doi.org/10.23960/jpmipa/v25i1.pp34-52

The development of students’ computational thinking practices in elementary- and middle-school classes using the learning game, Zoombinis

The use of computer simulations in higher education has been widely used in various learning materials. Computational thinking ability is one of the abilities needed when using computer simulations during the educational process. This study aims to ascertain the effect of computational thinking on students’ computer simulation abilities in terms of learning outcomes. This study employed a one-group quasi-experimental design, utilizing pre-tests and post-tests for evaluation. Fifty students enrolled in a digital communication program participated in the research study. Computational thinking treatment is given during learning. Students are introduced to the four essential components of computational thinking throughout the educational process, such as algorithmic thinking, pattern recognition, abstraction, and decomposition. The computational thinking instrument's validity score is 0.8, and its reliability score is 0.84, according to the test results. The study's findings demonstrate that students' average N-Gain, is 0.749, with high criteria. The T-test results show that computational thinking influences student learning outcomes in digital communication. The research implications show that computational thinking helps enhance students' abilities for learning.

School systems globally have begun to integrate computational thinking (CT) practices into elementary teaching and learning standards to provide young students with a comfort for and foundational understanding of computer science. This integration requires educating teachers to develop and implement CT learning opportunities. Our work contributes to best practice by exploring the role language plays in elementary teachers' discussions and development of CT-infused science lessons. To do this, we captured and analyzed the language used by elementary teachers as they spoke and wrote about CT in a multi-week professional development experience during which the teachers created and delivered CT-infused lessons to their students. We determined that teachers exhibited differing CT conceptualizations, which we categorized from broad to narrow. These differences were not, however, observed in either their spoken or written language. While there were instances of teachers using language that mirrored how the facilitators discussed CT, this use did not align with how they conceptualized CT. Our work analyzing connections between CT language use and CT conceptualization suggests that in order to understand how teachers are conceptualizing CT, professional development designers and teacher educators should focus on creating opportunities for teachers to employ CT practices in context rather than relying only on language. We feel this will support a more precise and nuanced engagement with CT.

There are long-standing debates in the computing education research community around teaching computer science (CS) versus computational thinking (CT). At the same time, schools have begun to embrace CT integration, particularly at the elementary and middle school levels. This chapter applies a systems change and policy implementation perspective to the case of a small school district’s development of a CT-focused computing education initiative in order to examine this issue on the ground. It explores why CT was embraced by district leaders, highlighting motivations that were rooted in goals around both equitable participation in computing education as well as district mission alignment. It then highlights tensions that played out following the move toward CT that were linked to coordination with existing district initiatives, leadership team constitution, faculty buy-in around CT-related instructional shifts, and numerous limitations of the CT frame vis-à-vis the goals of computing education in the district. Our findings reflect how many of the very debates that exist in the “ivory tower” of the CS education research world were mirrored in the “schoolhouse” – the context of real-world implementation – and shed empirical light on how CT as a construct needs to be considered not only from a pedagogical perspective but from an institutional one as well.

Computational Thinking is a skill that plays a significant role in improving students' skills, achievements, and global competitiveness. This research aims to determine the Computational Thinking abilities of preservice physics teachers for each indicator by creating a simulation using the Scratch application. This research is a descriptive qualitative study. The respondents involved in the research were 10 pre-service physics teachers at Khairun University. The respondents are students who have taken Computer Programming courses. The saturated sampling technique was used to determine the respondents. The data collection technique for this research is a test. The instrument used is a simple project to simulate projectile motion. The results of this research show that all students were able to complete the abstraction and generalization components perfectly. Furthermore, in the decomposition and algorithmic thinking components, two students made incorrect commands in the Scratch application. However, after the interview, it was revealed that the error was not due to their lack of understanding of physics concepts, but rather due to the limitations of mathematical operations in the Scratch application. Importantly, several students explained that most of the Computational Thinking components had already been developed through their habit of solving physics phenomena cases. Therefore, it can also be concluded that Computational Thinking can train students to solve physics problems effectively.