Teaching computational thinking using scenario-based learning tools

Educational Robotics (ER) is a tool that can favor teamwork, stimulate creativity, and the ability to solve students' problems through the process of assembling and programming robots. In this sense, ER has been used to improve educational practices. There are several initiatives that seek to stimulate Computational Thinking (CT) to stand out. Although ER being used to develop CT, there are still no guidelines based on scientific studies that support this use. The objective of this Research Full Paper is to identify: a) the main pedagogical approaches are applied in classes with ER focused on developing students' CT; b) the central difficulties are encountered in classes with ER focused on developing students' CT; c) the CT skills are presents in classes with ER focused on developing students' CT; d) the instruments are used to assess students' CT during classes with ER; e) the students' profile who participated in classes with ER focused o on developing students' CT; f) the educational robotics technologies are adopted in classes focused on developing students' CT. To achieve these goals, we planned and carried out systematic mapping of the literature. The results show that, through ER, CT is approached through the teaching of programming languages in a collaborative way in Early Childhood and High School. The most CT skills observed are related to programming, such as sequencing, algorithm, loop, generalization, decomposition, and debugging. The main instruments for assessing students are tests, and the most significant difficulty is the limitation of class hours. Finally, LEGO® is the most used technology in class.

Computational thinking (CT) is a critical set of 21st century skills that young learners must develop to be successful in contemporary work and life. As more K-12 schools begin to integrate CT into the curriculum, elementary preservice teachers (EPSTs) must be prepared to teach CT to their future students. Therefore, elementary teacher preparation programs should explore ways to incorporate CT into their coursework, ensuring that EPSTs graduate with the necessary knowledge and skills to teach CT. This study investigates the impacts of a three-week CT module implemented in a technology integration course in an elementary teacher preparation program on EPSTs’ CT value beliefs, self-efficacy, and teaching efficacy. The purpose of the module was to introduce EPSTs to basic concepts and big ideas in CT, support their application of CT in block-based coding environments, and promote connection building between CT and authentic real-world scenarios that balance different types of CT activities. Guided by the Interconnected Model of Professional Growth (IMPG), this study examined EPSTs’ learning as a dynamic process of change across the personal and external domains. The module was located in the external domain as a stimulus for learning, while the Teacher Beliefs about Coding and Computational Thinking (TBaCCT) instrument was used to assess changes in value beliefs, self-efficacy, and teaching efficacy within the personal domain. A convergent parallel mixed-methods design was used to analyze pre- and post-surveys administered to 50 EPSTs and semi-structured interviews with four of the EPSTs. The results showed that the beliefs of the CT value of EPSTs evolved and their self-efficacy and teaching efficacy of CT significantly improved after participating in the module, suggesting that the interaction between external learning experiences and internal belief systems - facilitated by reflection and action - contributed to early professional growth. Notably, EPSTs also developed a more accurate understanding of CT, recognized the benefits of both plugged and unplugged CT activities, and expressed a desire for continued CT learning and professional development. Although EPSTs expressed concerns with the scarcity of physical computing toys in schools, they demonstrated a commitment to incorporating CT activities into their teaching and exploring alternative funding avenues to acquire such resources. Practical implications include potential approaches for elementary teacher preparation programs to embed CT into existing curricula.

In recent years, different countries have implemented the teaching of computer programming from the first grade in schools, with the aim of incorporating computational thinking as a new way of thinking that is a necessary skill for scientific and technological development, a fundamental axis for development in the 21st century. Recent reviews of the state of the art have shown that this task is only being carried out by countries that have given space to technology and science, incorporating it at the elementary school level. However, developing countries do not yet consider the significance of the issue, so to date they have not taken the necessary steps in this direction. In addition, learning computer programming is fundamental for countries to join technological development, so that they can be creators of technology and not just users of it. The problem is that there is no didactic development for the teaching of programming, nor validated evaluation instruments to quantify the learning of computer programming. The objective of this research is to validate a programming test that evaluates technical skills in the Python programming language. The instrument proposed to be validated is a 90-item test, which after validation by experts was reduced to 70 items, and after the psychometric analysis that considered the calculation of reliability, difficulty and discrimination indexes, resulted in the proposed 45-item instrument, as a standard instrument for the evaluation of learning in the Python programming language. It should be noted that the validation methodology was carried out using the classical theory of psychometric analysis.

A systematic review of integrating computational thinking in early childhood education

The study examined the effect of teaching text-based programming with a physical programming tool on secondary school students’ computational thinking skills and self-efficacy perceptions. The study was conducted according to a sequential explanatory design as a mixed method research. The study group consisted of 85 secondary school students. Within the scope of the study, a physical programming tool called Micro:bit was used to teach Python programming for a period of 6 weeks. Data were collected using the Self-Efficacy Perception Scale for Computational Thinking Skill, Bebras: International Challenge on Informatics and Computational Thinking Tasks, tests focused on programming tool, concepts, and processes, and through semi-structured interview questioning. According to the findings obtained from pretests and posttests, a significant and positive difference was found in the students’ computational thinking skills and self-efficacy perceptions towards computational thinking skill. As a result of having received instruction in programming, the students satisfactorily learnt the required programming concepts and processes. Through learning Python programming with a physical programming tool, the students not only gained the skills required to write appropriate syntax, and to test and debug code, but they also learnt programming concepts such as variables, conditional expressions, loops, and functions.

Two studies were conducted to examine the use of grounded embodied pedagogy, construction of Imaginary Worlds (Study 1), and context of instructional materials (Study 2) for developing learners' Computational Thinking (CT) Skills and Concept knowledge during the construction of digital artifacts using Scratch, a block-based programming language. Utilizing a conceptual framework for grounded embodied pedagogy called Instructional Embodiment, learners physically enacted (Direct Embodiment) and mentally simulated (Imagined Embodiment) the actions and events as presented within pre-defined Scripts. Instructional Embodiment utilizes action, perception, and environment to create a dynamic, interactive teaching & learning scenario that builds upon previous research in embodied teaching and learning. The two studies described herein examined the effects of Instructional Embodiment, Imaginary World Construction, and Context on the development of specific Computational Thinking Concepts and Skills. In particular, certain CT Concepts, such as Conditionals, Variables, Thread Synchronization, Collision Detection, & Events, and CT Skills, such as abstraction and pattern recognition, were identified and measured within the learners' individual digital artifacts. Presence and/or frequency of these Concepts and Skills were used to determine the extent of Computational Thinking development. In Study 1, fifty-six sixth- and seventh-grade students participated in a fifteen-session curricular program during the academic school day. This study examined the type of instruction and continuity of Imaginary World Construction on the development of certain CT Skills and Concepts used in a visual novel created in Scratch. Main effects were found for learners who physically embodied the pre-defined instructional materials: embodying the pre-defined Scripts led to the learners using significantly more ‘speech’ Blocks in their projects and more Absolute Positioning Blocks for ‘motion’ than those who did not physically embody the same Scripts. Significant main effects were also found for continuity of Imaginary World Construction: learners who were instructed to continue the premise of the first digital artifact (Instructional Artifact) implemented significantly more computational structures in their second digital artifact (Unique Artifact) than those who were instructed to create a Unique Artifact with a premise of their own design. In Study 2, seventy-eight sixth- and seventh-grade students participated in a seventeen-session curricular program during the academic school day. This study examined the type of instruction and context of instructional materials on the development of CT Skills and Concepts during the construction of a video game using Scratch. Similar to Study 1, findings suggest that physically embodying the actions presented within the pre-defined instructional materials leads to greater implementation of many of these same structures during individual artifact construction. The study also showed that as the pre-defined Scripts become more complex (e.g. single-threaded to multi-threaded), the effect of physical embodiment on the development of CT Skills and complex CT Concept structures becomes less pronounced. Findings from this study also suggest that Context has a significant effect on identifying & implementing the CT Skill pattern recognition: learning CT Concepts from an Unfamiliar Context had a significant positive effect on the implementation of both Broadcast/Receive couplings and Conditional Logic & Operator patterns. In sum, the findings suggest that the type of instruction, the continuity of the Imaginary World being constructed, and the context of the instructional materials all play a significant role in the learners' ability to develop certain Computational Thinking Skills and Concept knowledge. The findings also suggest that a physically embodied approach to teaching abstract concepts that is grounded in an unfamiliar context is the most effective way to integrate a grounded embodied approach to pedagogy within a formal instructional setting.

Computational thinking is recognized as a critical competency in contemporary education, preparing individuals to tackle complex challenges in a digitally pervasive world. In this quasi-experimental design study with pretest and post-test measures, the possibility of developing computational thinking from the field of didactics of mathematics in higher education students was investigated. This was performed via a problem-based learning (PBL) methodology using problem solving in the experimental group or, alternatively, focused on the analysis of solved problems in the control group. After the intervention, the control group experienced a statistically significant improvement in the scores obtained in the post-test measure. Thus, PBL and problem solving did not lead to an improvement in the students’ computational thinking, whereas the analysis of solved problems approach did. Therefore, the results suggested the potential benefits of this latter methodology for teaching computational thinking.

Across the world there has been a paradigm shift in school education, as many countries are incorporating Computer Science (CS), programming, and Thinking (CT), into their K-12 curriculums for the first time. In 2018 these subjects will be introduced to the New Zealand (NZ) Digital Technologies curriculum under the subject of Computational Thinking. Like the majority of countries who have introduced these topics, NZ faces challenges in preparing teachers, and in successfully implementing a curriculum which achieves its educational goals. To support this curriculum, we have been conducting studies in primary schools across NZ, trialing resources with teachers and students. Building on a 2014 exploratory study on CS and programming in intermediate school, this project investigates the existing assumptions around CT education in K-12. It aims to address the specific questions: How do we teach CT concepts and approaches to primary aged students? If they learn CT, does it have the expected positive impacts on students learning? And, what other positive and negative impacts does this have? Throughout 2015 and 2016 a study was conducted with 18 primary school teachers from across NZ, who used CS Unplugged and educational programming languages in their classrooms. Teachers submitted feedback on these classes through an online form, and participated in semi-structured interviews. The feedback responses and interview transcripts were analysed using a thematic approach, which revealed many positive impacts on students general learning, minimal negative impacts, and observations of CT development.

Software education has emerged as a hot topic around the world, with the goal to raise interests on computational thinking. However, assessments on the computational thinking have not been actively conducted thus far. According to a study by Lee (2019), which analyzed 138 papers on computational thinking published in Korean journals from 2015 to 2018, software education has been introduced and studies on computational thinking are being conducted, but studies on teaching methods that2 improve computational thinking are needed. In this study, we developed and applied a primary educational programming language(EPL) program focused on execution time analysis aimed at improving computational thinking. By using the “Bebras Challenge” as an assessment tool and SPSS as a statistical tool, educational effects were analyzed through the results of pre- and post-computational thinking assessments. The analysis outcomes showed that the EPL education focused on execution time analysis was effective in improving the computational thinking of elementary school students. Putting execution time analysis EPL into primary software education as an educational topic will be effective in improving computational thinking.

The graphics calculator (GC) is a hand-held technology. With it, students visualize and understand concepts in mathematics and science better. It also allows them to make real-world connections, binding together advanced graphing functionality and conceptual science, technology, engineering, arts and mathematics (STEAM). As they gain a deeper understanding of abstract concepts, they acquire creative and critical thinking and problem- solving skills they need to reach greater academic successes. In learning, GC plays several roles in increasing students’ computational thinking and procedural skills. Despite being a learning tool that satisfies science and mathematics curriculum needs, the calculator often has been used as an add-on to traditional teaching methodology rather than central and in more integrated ways. This study looks into the potential of developing computational thinking skills using GC technology. The data of the study was obtained from secondary sources through a systematic review method. The study showed that GC seems to be effective tools to practice STEAM education as well as to develop students’ computational thinking through analysing and decompose real problem as well as engaging in the abstraction process and developing an algorithm using coding. The findings provides justifications on the impact of GC in developing computational thinking skills thereby maximizing the pedagogical benefits to students.

Scholars believe that computational thinking is one of the essential competencies of the 21st century and computer programming courses have been recognized as a potential means of fostering students’ computational thinking. In tradition instruction, PFCT (problem identification, flow definition, coding, and testing) is a commonly adopted procedure to guide students to learn and practice computer programming. However, without further guidance, students might focus on learning the syntax of computer programming language rather than the concept of solving problems. This study proposes a peer-assessment-supported PFCT (PA-PFCT) approach for boosting students’ computer programming knowledge and computational thinking awareness. A quasi-experiment was conducted on a computer programming course in a high school to evaluate its influence on students’ learning achievement, computational thinking awareness, learning motivation, and self-efficacy. An experimental group of 51 students learned with the proposed approach, while a control group of 49 students learned with the traditional PFCT (T-PFCT) approach. The experimental results show that the proposed approach significantly enhanced the students’ computational thinking awareness, learning motivation, and self-efficacy, while not having significant impacts on their computer programming knowledge test scores.

We present a curricular model for introducing data analytics concepts into an introductory computer science course for non-majors. This is accomplished through the design and implementation of hands-on laboratories projects using the Python programming language and associated tools. While introducing students to an important research area, we believe the use of these projects improves students' learning experiences, enabling them to apply and relate fundamental computational thinking concepts of algorithmic reasoning, data representation, and computational efficiency to data analytics problems. We present the curricular modules, as well as preliminary experiences using them.

A growing body of research is focusing on integrating artificial intelligence (AI) and computational thinking (CT) to enhance student learning outcomes. Many researchers have designed instructional activities to achieve various learning goals within this field. Despite the prevalence of studies focusing on instructional design and student learning outcomes, how instructional efforts result in the integration of AI and CT in students’ learning processes remains unclear. To address this research gap, we conducted a systematic literature review of empirical studies that have integrated AI and CT for student development. We collected 18 papers from four prominent research databases in the fields of education and AI technology: Web of Science, Scopus, IEEE, and ACM. We coded the collected studies, highlighting the students’ learning processes in terms of research methodology and context, learning tools and instructional design, student learning outcomes, and the interaction between AI and CT. The integration of AI and CT occurs in two ways: the integration of disciplinary knowledge and leveraging AI tools to learn CT. Specifically, we discovered that AI- and CT-related tools, projects, and problems facilitated student-centered instructional designs, resulting in productive AI and CT learning outcomes.

In this study, the authors constructed structural equation models in order to determine the relationship between students’ learning attitudes and their computational thinking perspectives and programming empowerment. The purpose is to understand students’ perceived competence to use computational thinking effectively, along with their computer programming learning attitude regarding the C++ programming language for one semester (2 hours per week, 36 total learning hours). A total of 495 students specializing in the medical field participated in the study. Structural equation models were constructed according to three adapted scales: the computer programming learning attitude scale, the computational thinking perspectives scale, and the programming empowerment scale. The computer programming learning attitude scale is based on three factors: willingness, negativity, and necessity. The computational thinking perspectives scale also considers three factors: the ability to express, the ability to connect, and the ability to question. The programming empowerment scale is composed of four factors: meaningfulness, impact, creative self-efficacy, and programming self-efficacy. The results showed that a positive learning attitude will positively affect computational thinking perspectives and programming empowerment. However, when students have a negativity attitude, feeling that they are being forced to learn the C++ programming language, their computational thinking perspectives and programming empowerment will be negatively affected. In order to promote students’ learning attitude, various teaching strategies, teaching curriculum design, and pedagogy design could be further explored.

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.