Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4-10, temperature = 300.15-330.15 K, the dose of adsorbent = 1-10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption-desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

Cognitive stimulation of executive functions through computational thinking.

The healthy development of cognitive functions, including executive functions, has been shown to depend mainly on the experiences and learning opportunities of people, especially during childhood. Over the past few years, researchers have been studying the impacts of diverse types of interventions on children's cognitive development in which computational thinking programs are a recent field. This pilot study evaluated the effect of computational thinking training based on the "Programming for Children" program on the executive functions of children aged 10 and 11 years: working memory, inhibition, and planning (N = 30). The results showed that children in the experimental group improved on tests of visuospatial working memory, cognitive inhibition, and sequential planning compared with the control group. However, tests of verbal working memory, memory strategy, and visual spatial planning did not show any observed changes. Although this was an exploratory study, and its findings should be interpreted cautiously due to the small sample size, the findings support the relevance and feasibility of conducting similar larger studies with larger samples.

Design, development, and evaluation of a virtual reality game-based application to support computational thinking.

Computational thinking (CT) has become an essential skill nowadays. For young students, CT competency is required to prepare them for future jobs. This competency can facilitate students' understanding of programming knowledge which has been a challenge for many novices pursuing a computer science degree. This study focuses on designing and implementing a virtual reality (VR) game-based application (iThinkSmart) to support CT knowledge. The study followed the design science research methodology to design, implement, and evaluate the first prototype of the VR application. An initial evaluation of the prototype was conducted with 47 computer science students from a Nigerian university who voluntarily participated in an experimental process. To determine what works and what needs to be improved in the iThinkSmart VR game-based application, two groups were randomly formed, consisting of the experimental (n = 21) and the control (n = 26) groups respectively. Our findings suggest that VR increases motivation and therefore increase students' CT skills, which contribute to knowledge regarding the affordances of VR in education and particularly provide evidence on the use of visualization of CT concepts to facilitate programming education. Furthermore, the study revealed that immersion, interaction, and engagement in a VR educational application can promote students' CT competency in higher education institutions (HEI). In addition, it was shown that students who played the iThinkSmart VR game-based application gained higher cognitive benefits, increased interest and attitude to learning CT concepts. Although further investigation is required in order to gain more insights into students learning process, this study made significant contributions in positioning CT in the HEI context and provides empirical evidence regarding the use of educational VR mini games to support students learning achievements.

Promoting learning transfer in science through a complexity approach and computational modeling.

This article concerns the synergy between science learning, understanding complexity, and computational thinking (CT), and their impact on near and far learning transfer. The potential relationship between computer-based model construction and knowledge transfer has yet to be explored. We studied middle school students who modeled systemic phenomena using the Much.Matter.in.Motion (MMM) platform. A distinct innovation of this work is the complexity-based visual epistemic structure underpinning the Much.Matter.in.Motion (MMM) platform, which guided students' modeling of complex systems. This epistemic structure suggests that a complex system can be described and modeled by defining entities and assigning them (1) properties, (2) actions, and (3) interactions with each other and with their environment. In this study, we investigated students' conceptual understanding of science, systems understanding, and CT. We also explored whether the complexity-based structure is transferable across different domains. The study employs a quasi-experimental, pretest-intervention-posttest-control comparison-group design, with 26 seventh-grade students in an experimental group, and 24 in a comparison group. Findings reveal that students who constructed computational models significantly improved their science conceptual knowledge, systems understanding, and CT. They also showed relatively high degrees of transfer-both near and far-with a medium effect size for the far transfer of learning. For the far-transfer items, their explanations included entities' properties and interactions at the micro level. Finally, we found that learning CT and learning how to think complexly contribute independently to learning transfer, and that conceptual understanding in science impacts transfer only through the micro-level behaviors of entities in the system. A central theoretical contribution of this work is to offer a method for promoting far transfer. This method suggests using visual epistemic scaffolds of the general thinking processes we would like to support, as shown in the complexity-based structure on the MMM interface, and incorporating these visual structures into the core problem-solving activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11251-023-09624-w.

Developing pre-service teachers' computational thinking: a systematic literature review.

Recently, computational thinking (CT) has gained importance in education systems worldwide, specifically the CT training of pre-service teachers. This study conducted a systematic literature analysis (2011-2021) of 38 works on pre-service teachers' CT based on Web of Science, Science Direct, and Google Scholar databases. The results were as follows: (1) Six training methods were found, (2) CT training effectively improved pre-service teachers' CT, (3) A positive relationship was found between pre-service teachers' CT ability and the five factors affecting the ability, (4) A mode of training to improve CT ability of pre-service teachers and the relationship between CT ability and teaching methods were considered. This study suggested ideas for designing training modules of CT ability and a reference for realizing the best training effect. Finally, future research trends and a general model of training were presented as references for researchers, instructors, and policy makers to promote the CT of pre-service teachers.

Bringing computational thinking to technology education classrooms: Hacking car activity for middle schools in the republic of korea.

The purpose of this study was to develop a program that incorporates computational thinking into technology education classrooms and to investigate its effect on students. Software (SW) education and physical computing education are frequently addressed topics in technology education, but education about computational thinking (CT) lacks interest and research. Therefore, it is necessary to further develop educational programs in technology. In this study, we developed a program integrating CT, which centered on technological problem-solving processes. The program comprised 12 total hours of hacking a remote control (RC) car using Micro:bit development tool. This study investigates the effects of the developed program with a single group pre- and post-test quasi-experimental design. Nineteen students participated in the study, completing survey instruments that measure CT competency and attitudes toward CT and technology, answering an open-ended questionnaire, and voluntarily took part in semi-structured interviews. The results showed that the technological problem-solving program positively affected participants' CT-related competencies. Moreover, we observed improvement in participants' attitudes toward technology due to the integration of CT into their technology education classes. This study provides a strong case for incorporating CT into technology education. It also suggests future research direction regarding the development of students' CT competencies in various technological problem-solving contexts.

Computational Thinking to Learn Environmental Sustainability: A Learning Progression.

Current environmental problems are the primary focus for environmental science students and researchers. Sustainable environmental solutions require interdisciplinary thought processes, which pose difficulty to both students and the public. Computational thinking is an emerging term emphasized by progressive science curricula. Computational thinking and environmental science are both interdisciplinary by nature. Learning about sustainable environmental solutions requires students to partake in computational thinking. These ideas lend toward an expansive learning progression that encourages scaffolded and differentiated student progress in both computational knowledge and environmental knowledge. The learning progression, which emerges from the conceptual framework, emphasizes the spheres of sustainability, research, education, and economic perspectives to support environmental science learning through computational thinking. Computational thinking emphasized by the computational components (input, integration, output, and feedback) support learning about environmental solutions within the learning progression. The learning progression promotes application and implications for educators, students, researchers, and environmental scientists.

Effects of Robotics Education on Young Children's Cognitive Development: a Pilot Study with Eye-Tracking.

The emerging field of robotics education (RE) is a new and rapidly growing subject area worldwide. It may provide a playful and novel learning environment for children to engage with all aspects of science, technology, engineering, and mathematics (STEM) learning. The purpose of this research is to examine how robotics learning activities may affect the cognitive abilities and cognitive processes of 6-8 years old children. The study adopted the mixed methods approach with a repeated measures design; three waves of data collection over 6 months, including quantitative data obtained from cognitive assessments and eye-tracking, and qualitative data from the interviews. A total of 31 children were recruited from an afterschool robotics program. To the best of our knowledge, this study is the first RE research that used a combination of eye-tracking, cognitive assessments, and interviews for examining the effect of RE on children. Using linear growth models, the results of cognitive assessments showed that children's visuospatial working memory as well as logical and abstract reasoning skills improved over time. The interview data were analyzed by a thematic analysis. The results revealed that children perceived RE activities as game play, which made children more engaged in their study; parents found their children to be more focused on activities comparing to six months ago. Additionally, the visualization of the eye-tracking data suggested that children became more focused on RE activities and got faster to process the information across six months in general, which echoed the findings in assessments and interviews. Our findings may help educators and policymakers better understand the benefits of RE for young children.

Playing coding games to learn computational thinking: What motivates students to use this tool at home?

Coding games are widely used to teach computational thinking (CT). Studies have broadly investigated the role of coding games in supporting CT learning in formal classroom contexts, but there has been limited exploration of their use in informal home-based settings. This study investigated the factors that motivated students to use a coding game called Coding Galaxy in a home-based setting. It explored the connections between the students' perceptions of and usage of the tool. An 11-day intervention was conducted at a primary school in Hong Kong with 104 participants. The students' perceptions of the game were collected via questionnaires and information on their use of the tool was extracted from log files. Results indicated that coding motivation and feeling of enjoyment were predictors of the actual use of the game, with coding motivation the dominant factor. Focus group interviews were also conducted to further explore the students' motivation to play the game. Through comparisons of active and inactive users, the qualitative findings supported the quantitative results, indicating that students who were more intrinsically motivated tended to be more active in using the game. The implications of the study for researchers and practitioners in CT education are discussed.

Educational Robotics: Development of computational thinking in collaborative online learning.

In the previous study the work experience on organization of teaching Robotics to secondary school students at school lessons and in study groups was introduced. This study which was conducted within 2019 and 2021 covered the period of distant learning caused by COVID-19 pandemic and even post-pandemic period, when a part of school students continued learning online. The study deals with the problem of developing school students' computational thinking in online learning. We consider computational thinking as a set of cognitive skills of solving educational and cognitive problems. The research questions raised were aimed at solving the problem of the influence of Educational Robotics on developing computational thinking. During the research we have found out that due to the adaptability of robots, Educational Robotics, the development of individual learning programs, and the arrangement of collaborative online learning are instruments and a solution to the problem of developing computational thinking. The main components of computational thinking, which were studied within those 3 years, are the following: algorithmic thinking, ability to program, and efficiency in team work. The influence of the learning strategy we chose enabled us to determine the level of computational thinking and its dependence on learning Robotics. We used statistical criteria in order to summarize the results of our research. The statistics provided suggests progress in the indicator tracked. Based on the experimental data received we approximated reliability (R2) and relevant exponential equation (trend lines). The research we carried out also has led to the general conclusion that Educational Robotics helps to create synergistic learning environment for stimulating students' motivation, collaboration, self-efficacy and creativity.

Predicting pre-service teachers' computational thinking skills using machine learning classifiers.

Computational thinking (CT) skills of pre-service teachers have been explored extensively, but the effectiveness of CT training has yielded mixed results in previous studies. Thus, it is necessary to identify patterns in the relationships between predictors of CT and CT skills to further support CT development. This study developed an online CT training environment as well as compared and contrasted the predictive capacity of four supervised machine learning algorithms in classifying the CT skills of pre-service teachers using log data and survey data. First, the results show that Decision Tree outperformed K-Nearest Neighbors, Logistic Regression, and Naive Bayes in predicting pre-service teachers' CT skills. Second, the participants' time spent on CT training, prior CT skills, and perceptions of difficulty regarding the learning content were the top three important predictors in this model.

The Neural Correlates of Computational Thinking: Collaboration of Distinct Cognitive Components Revealed by fMRI.

Recent technical advance attracts great attention to the promotion of programming skills, in particular, and computational thinking (CT), in general, as a new intellectual competency. However, the understanding of its cognitive substrates is limited. The present study used functional magnetic resonance imaging to examine the neural correlates of programming to understand the cognitive substrates of CT. Specifically, magnetic resonance imaging signals were collected while the participants were mentally solving programming problems, and we found that CT recruited distributed cortical regions, including the posterior parietal cortex, the medial frontal cortex, and the left lateral frontal cortex. These regions showed extensive univariate and multivariate resemblance with arithmetic, reasoning, and spatial cognition tasks. Based on the resemblance, clustering analyses revealed that cortical regions involved in CT can be divided into Reasoning, Calculation, Visuospatial, and Shared components. Further, connectivity increased during programming within the CT network constructed by these four components and decreased between the CT network and other cortical regions. In sum, our study revealed the cognitive components underlying CT and their neural correlates and further suggests that CT is not a simple sum of parallel cognitive processes, but a composite cognitive process integrating a set of intellectual abilities, particularly those in the science, technology, engineering, and math domains.

Computational Thinking and Educational Robotics Integrated into Project-Based Learning.

In the context of the science, technology, engineering, arts and mathematics disciplines in education, subjects tend to use contextualized activities or projects. Educational robotics and computational thinking both have the potential to become subjects in their own right, though not all educational programs yet offer these. Despite the use of technology and programming platforms being widespread, it is not common practice to integrate computational thinking and educational robotics into the official curriculum in secondary education. That is why this paper continues an initial project of integrating computational thinking and educational robotics into a secondary school in Barcelona, Spain. This study presents a project-based learning approach where the main focus is the development of skills related to science, technology, engineering, arts and mathematics and the acquisition of computational thinking knowledge in the second year of pupils' studies using a block-based programming environment. The study develops several sessions in the context of project-based learning, with students using the block-programming platform ScratchTM. During these sessions and in small-group workshops, students will expand their knowledge of computational thinking and develop 21st-century skills. We demonstrate the superior improvement of these concepts and skills compared to other educational methodologies.

Co-Learning Computational and Design Thinking Using Educational Robotics: A Case of Primary School Learners in Namibia.

In a two-day educational robotics workshop in a Namibian primary boarding school, learners with no programming skills managed to apply both computational and design thinking skills with the aid of educational robotics. Educational robotics has proved to be an area which enhances learning both computational thinking and design thinking. An educational robotics (ER) workshop focusing on Arduino robotics technologies was conducted with primary school learners at Nakayale Private Academy. Observation methods through watching, listening and video recordings were used to observe and analyze how the learners were interacting throughout the workshop. Based on the results, it was concluded that this approach could be applied in classrooms to enable the primary school learners apply computational and design thinking in preparation of becoming the producers and not only the consumers of the 4IR technologies.

Sparking the Interest of Girls in Computer Science via Chemical Experimentation and Robotics: The Qui-Bot H2O Case Study.

We report a new learning approach in science and technology through the Qui-Bot H2O project: a multidisciplinary and interdisciplinary project developed with the main objective of inclusively increasing interest in computer science engineering among children and young people, breaking stereotypes and invisible social and gender barriers. The project highlights the social aspect of robotics applied to chemistry, at early ages. We successfully tested the project activities on girls between 3 to 13 years old. After taking part in the project, the users rated their interest in science and technology to be higher than before. Data collected during experiences included background information on students, measurements of the project's impact and students' interest in it, and an evaluation of student satisfaction of this STEM activity. The Qui-Bot H2O project is supported by the actions of territorial public administrations towards gender equality and the contributions of humanistic and technological universities and entities which specialize in education and business.

iArm: Design an Educational Robotic Arm Kit for Inspiring Students' Computational Thinking.

Educational robotics is an effective carrier of information technology education, making its way into classrooms. However, the design of the educational robotic arm kit and the study on the effect of robotic arms on students' thinking literacy remain to be completed. In this paper, iArm, a 6-DOF robotic arm consisting of a drive chassis, an arm body, and end tools, is presented. Its auxiliary modules, including the vision module and conveyor belt, and the curriculum targeting students' computational thinking are also developed to refine the current educational robotic arm kit. Furthermore, to explore the effectiveness of the iArm kit, thirteen high school students participated in the semester-long curriculum, completed assigned projects, and filled out the pre-test and post-test scales. By formative and summative evaluation, the result shows that the iArm kit effectively enhanced students' computational thinking.

Algorithmic Explanations: an Unplugged Instructional Approach to Integrate Science and Computational Thinking.

Computing has become essential in modern-day problem-solving, making computational literacy necessary for practicing scientists and engineers. However, K-12 science education has not reflected this computational shift. Integrating computational thinking (CT) into core science courses is an avenue that can build computational literacies in all students. Integrating CT and science involves using computational tools and methods (including programming) to understand scientific phenomena and solve science-based problems. Integrating CT and science is gaining traction, but widespread implementation is still quite limited. Several barriers have limited the integration and implementation of CT in K-12 science education. Most teachers lack experience with computer science, computing, programming, and CT and therefore are ill-prepared to integrate CT into science courses, leading to low self-efficacy and low confidence in integrating CT. This theoretical paper introduces a novel instructional approach for integrating disciplinary science education with CT using unplugged (computer-free) activities. We have grounded our approach in common computational thinking in STEM frameworks but translate this work into an accessible pedagogical strategy. We begin with an overview and critique of current approaches that integrate CT and science. Next, we introduce the Computational Thinking through Algorithmic Explanations (CT-AE) instructional approach. We then explain how CT-AE is informed by constructionist writing-to-learn science theory. Based on a pilot implementation with student learning outcomes, we discuss connections to existing literature and future directions.

The Influence of Online STEM Education Camps on Students' Self-Efficacy, Computational Thinking, and Task Value.

As a result of COVID-19, various forms of education and teaching are moving online. However, the notion of an online STEM camp is still in its beginnings, and there is little relevant research and experience in this context. At the beginning of April 2021, the research team launched an online STEM charity camp with the theme of "Shen Nong Tastes Herbs." Participants included 113 third- and fourth-grade primary school students ranging from 8 to 12 years of age from four schools in Karamay, Xinjiang Uygur Autonomous Region with weak educational capabilities. The camp lasted for 3 days and included 7 activities, while remote teaching was accomplished through Dingtalk. Pre- and post-test questionnaires and interviews were used to explore the impact of this camp on students. We found that online STEM camps could improve students' self-efficacy, computational thinking, and task value, and there is a significant improvement in the self-efficacy (p = 0.000) and task value (p = 0.001) dimensions. In addition, students with high self-efficacy had higher scores in the other two dimensions. Finally, we summarized the experiences and gains of students and teachers and proposed suggestions for developing online camps based on this experience. [Table: see text]. Supplementary Information: The online version contains supplementary material available at 10.1007/s10956-022-09967-y.

Development and evaluation of granular simulation for integrating computational thinking into computational physics courses.

Computational thinking (CT) is an essential skill in the twenty-first century. The computational physics course (CPC) is one subject that is designed to support students in the practice of CT. Many studies show that the worksheets could be a solution in a CPC as a scaffold to achieve the CT objectives both online and offline. The study aims to develop the worksheet and integrate it with CT in a computational physics course. This study applied the research and development (R & D) method with the ADDIE model approach. In the results, the evaluation test from the experts reached a very good interpretation score based on the learning media expert (96%), the teaching material expert (95%), and the pedagogy experts (92%). So that this media is declared feasible to be used in the CPC. Furthermore, after the experimental study of students who took the computational physics course (n = 31), the study showed that the modified course could significantly improve student skills regarding overall CT (p value <0.05). However, this research also found that cooperative learning as part of CT had no improvement (p value >0.05). The experiment was conducted amid the COVID 19 pandemic wherein the students could only study at home for the whole semester. These findings indicate that the pandemic has impacted the collaborative skills of students on the course.

Patrony: A mobile application for pattern recognition learning.

Pattern recognition is an important skill of Computational Thinking and is one of the most important competences for solving a problem that involves finding similarities or patterns in small problems to solve more complex ones. In this work, we present the mobile application software Patrony. The main contribution of this work is to promote the learning of Computational Thinking, especially pattern recognition, in specific sectors of education in Mexico through the simple use of a software application. To evaluate the effectiveness of the mobile application, tests were carried out in two elementary schools with a total of 43 students, which were divided into 2 groups: a control group and an experimental group. The results of the tests showed that the learning gain (M = 6.50 in postest compared to M = 4.94 on pretest) of the students who used our mobile application produces a significant difference with respect to students who learned using a traditional method of classroom teaching. The results also infer that computational thinking applications can be used as effective learning tools within some important Mathematics topics in public and private schools in Mexico.