Proxy-Based Programming: Making Programming More Accessible through Virtual, Physical, and Social Embodiment

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 a theory born in computer science that applies computational principles to solve a problem. It is a new concern in the PISA 2021 mathematical assessment to accommodate human resources needs that can compete in the 21st-century digitalization era. This study aims to develop a learning tool called student worksheet (LKPD) that integrates indicators of computational thinking skills into the material ratio. The method used is Research and Development (R&D), with the subject of research being 32 students in one Junior High School in Ciamis Regency. The data collection technique is done by observation, interview, and questionnaire given to material experts, media experts, and students. The data were analyzed quantitatively using descriptive product validity and practicality scores. The results showed that the assessment of material experts, including aspects of legality and morality, the feasibility of content, the feasibility of presentation, and aspects of computational thinking, received a level of validity of 99%, so it is included in the category of very valid. Likewise, the assessment of media experts covering anatomy, graphics, and use of language was declared very valid, with a validity rate of 82 %. The product's practicality gets a very practical category with an average score of 86% covering material aspects, use of language, interest, and aspects of computational thinking. The products that have been developed are considered valid and practical for use as educational resources for learning ratios and enhancing computational thinking ability.

Aim/Purpose: This paper discusses theoretical and curricular aspects of computational thinking in curriculum and challenges noticed on introducing recent ICT perspectives in Australian Schools. Background: It presents the way computational thinking is defined and understood in curriculum documents and a set of relatively new implementations that were de-signed nationally and in the New South Wales state. Methodology: This paper uses qualitative research methods such as content analysis and text analysis methods. Contribution This research analyzes some recent trends in introducing computational thinking and explore the was these reforms are described in the official documents. Findings: It was noticed that although the importance of computational thinking was highly emphasized, the documents cannot describe a consistent implementation of this set of educational policies, as at this time implementing computational thinking largely underperforming. Recommendations for Practitioners: It is recommended a more systemic way of designing policies and curriculum content for the integration of computational thinking in Australian schools. Future Research: Future research needs to explore reasons for delaying these reforms of introduc-ing computational thinking.

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 effects of scaffolded programming scripts on pre-service teachers’ computational thinking: Developing algorithmic thinking through programming robots

In 2018, the New York Hall of Science (NYSCI) undertook a collaboration with teachers at a local elementary school to embed computational thinking into their instructional practice. With origins in computer science, computational thinking has evolved into a process for solving problems with broader applications for learning science, technology, engineering, and mathematics (STEM), as well as other subjects. This chapter describes the collaboration, its purpose, the specific actions undertaken in partnership with teachers, and its impact. Key aspects of computational thinking, as defined in this initiative, include defining and breaking down a problem into simpler components, finding patterns within the components, removing unnecessary information, and developing step-by-step instructions for solving the original problem and addressing errors. The work summarized in this chapter demonstrates the potential of collaborations between museum educators and teachers to codesign strategies for implementing computational thinking skills. As reported in the chapter, the project helped teachers to feel more competent in integrating computational strategies throughout their practice, encouraged students to take ownership of their own learning, and improved students’ understanding of the content of and connections between different subjects.

Computational thinking (CT) is a critical skill needed for STEM professionals and educational interventions that emphasize CT are needed. In engineering, one potential pedagogical tool to build CT is modeling, an essential skill for engineering students where they apply their scientific knowledge to real-world problems involving planning, building, evaluating, and reflecting on created systems to simulate the real world. However, in-depth studies of how modeling is done in the class in relation to CT are limited. We used a case study methodology to evaluate a model-planning activity in a final-year undergraduate engineering classroom to elicit CT practices in students as they planned their modeling approach. Thematic analysis was used on student artifacts to triangulate and identify diverse ways that students used CT practices. We find that model-planning activities are useful for students to practice many aspects of CT, such as abstraction, algorithmic thinking, and generalization. We report implications for instructors wanting to implement model-planning activities into their classrooms.

Speculative adders divide addition into subgroups and execute them in parallel for higher execution speed and energy efficiency, but at the risk of generating incorrect results. In this paper, we propose a lightweight correlation-aware speculative addition (CASA) method, which exploits the correlation between input data and carry-in values observed in real-life benchmarks to improve the accuracy of speculative adders. Experimental results show that applying the CASA method leads to a significant reduction in error rate with only marginal overhead in timing, area, and power consumption.

BackgroundVarious errors in the procedure of specimen collection have been reported as the primary causes of pre‐analytical errors. The aim of this study was to monitor and assess the reasons and frequencies of rejected samples in China.MethodsA pre‐analytical external quality assessment (EQA) scheme involving six quality indicators (QIs) was conducted from 2017 to 2019. Rejection rate was calculated for each QI. The difference of the rejection rates over the time was checked by Chi‐square test. Furthermore, the 25th, 50th, and 75th percentiles of the results from total laboratories each year were calculated as optimum, desirable, and minimum level of performance specifications.ResultsIn total, 423 laboratories submitted data continuously for six EQA rounds. The overall rejection rates were 0.2042%, 0.1709%, 0.1942%, 0.1689%, 0.1593%, and 0.1491%, respectively. The most common error was sample hemolysed (0.0514%–0.0635%), and the least one was sample not received (0.0008%–0.0014%). A significant reduction in percentages was observed for all QIs. For biochemistry and immunology, hemolysis accounted for more than half of the rejection causes, while for hematology, the primary cause shifted from incorrect fill level to sample clotted. The quality specifications had improved over time, except for the optimum level.ConclusionThe significant reduction in error rates on sample rejection we observed suggested that laboratories should pay more attention to the standardized specimen collection. We also provide a benchmark for QIs performance specification to help laboratories increase awareness about the critical aspects in the need of improvement actions.

Ever since automatic speech recognition (ASR) was introduced as a means to improve the terminology lookup mechanism and reduce additional cognitive effort in performing a terminology query, many studies have been conducted to investigate the use of ASR-CAI tools in simultaneous interpretation (SI). However, few studies have implemented the process-oriented method in addition to the product-based method in analyzing how the use of ASR-CAI tools may affect the interpreting process. By using both product/process-oriented approaches, this paper set out to investigate the impact of ASR-CAI tool on interpreters’ overall performance. The results showed that the support of ASR-CAI tool led to a significant reduction in error rate and omissions as well as improved the quality of terminology rendition. The paper also discussed the impact of ASR-CAI tool on interpreters’ processing capacity and cognitive effort during SI. In addition, potential benefits and limitations of ASR-CAI tool were analyzed to provide a better understanding regarding the tool’s usability so that such tools can be better integrated into the interpreting process.

In this paper, an approach for illumination normalization based on DWT and DCT for face recognition under varying lighting conditions is presented. The wavelet transform decomposes the full image into one low-frequency and three high frequency sub bands. To compensate the illumination variations, it is required to suppress the low-frequency components. At the same time, the useful information from the low-frequency components has to be preserved for the purpose of discrimination between different face images. Therefore, in the presented approach, firstly, DWT is performed on the image and DCT Transform is employed on low-frequency sub band. Then Low-Frequency DCT (LFDCT) coefficients are modified to suppress the illumination variations. Finally, improved low-frequency band and rest three high frequency sub bands are combined using inverse DWT. Classification is performed using k Nearest Neighbor Classifier (k-NNC). To show the performance improvement of the given approach, experiments have been performed on Yale B database. Significant reduction in error rate has been achieved by the given approach of down scaling low-frequency DCT coefficients of low-frequency sub band of DWT.

Promoting computational thinking is a priority in CS education and other STEM and non-STEM disciplines. Our innovative, NSF-funded IC2Think project blends computational and creative thinking. In Spring 2013, we deployed Computational Creativity Exercises (CCE) designed to engage creative competencies (Surrounding, Capturing, Challenging and Broadening) in an introductory CSI course for engineering students. We compared this CCE implementation semester (80 students, 95% completing 3 or 4 CCEs) to the Fall 2013 semester of the same course (55 students) without CCEs. CCE implementation students had significantly higher scores on a CS concepts and skills knowledge test (F(1, 132) = 7.72, p < 01, partial Eta2 = .055; M=7.47 to M=6.13; 13 items) and significantly higher self-efficacy for applying CS knowledge in their field (F(1, 153) = 12.22, p < .01, partial Eta2 = .074; M=70.64 to M=61.47; 100-point scale). CCE implementation students had significantly higher study time (t(1, 136) = 2.08, p = .04; M=3.88 to M=3.29; 7-point scale) and significantly lower lack of regulation, which measures difficulties with studying (t(1, 136) = 2.82, p = .006; M=2.80 to M=3.21; 5-point scale). The addition of computational creativity exercises to CS courses may improve computational thinking and learning of CS knowledge and skills.

IntroductionScience, Technology, Engineering, and Mathematics (STEM) and STEAM (with A for Arts) have evolved to symbolize the renewal of science education. STEAM education offers a number of benefits, such as improved problem analysis and solving skills, as well as the development of creative abilities. Many researchers reiterate the importance of STEAM‐related activities and programs, especially the integration of maker education. Despite much interest in STEAM, it is often challenging for many educators to incorporate integrated activities into their teaching practice. This paper deals with the value of STEAM integration in a methodological sense, with a focus on the maker's laboratory and physical computing, as well as the application of design thinking and computational thinking approaches.Motivation and ObjectivesThe goal of this study is to develop a comprehensive conceptual framework for integrated STEM curricula focusing on the following research questions: (a) how to improve daily activities of STEM education by combining the activities of different laboratories and using a design thinking approach? and (b) how to combine FabLab activities and physical computing related to teaching different aspects of computational thinking in the context of STEM?Research Methodology and MethodsAs a research methodology, we implement a mixed methods strategy to combine theoretical study and empirical research based on a synthesized literature survey and the process of iterative model development based on an observational case study. We conduct a detailed case study of two applications of integrated activities based on FabLab and physical computing integration, and illustrate how design thinking can guide teachers to open up for interdisciplinary, creative, and project‐based opportunities.Results and FindingsThe paper provides a conceptual framework for STEM integration activities and step‐by‐step guidelines on how design thinking methods could could interact in practice. The implications of the results may be useful for educators seeking recommendations for the integration process, which enable educators to design hands‐on activities and incorporate integrated aspect of students' STEAM learning into teaching practice. In conclusions, the authors suggest that as interdisciplinary crossroads, design thinking provides a natural bridge between subjects, and fits especially to integrate activities of the maker's labs and physical computing, focusing on the integration of computational thinking and computational making approaches within STEM education environment. The absence of a statistical evaluation, which is positioned as a further research step, may be mentioned as a limitation of the study.

In this paper, we propose that paper circuitry provides a productive space for exploring aspects of computational thinking, an increasingly critical 21st century skills for all students. We argue that the creation and operation of paper circuits involve learning about computational concepts such as rule-based constraints, operations, and defined states. Moreover, paper circuitry materials are low cost, provide a low threshold to entry, and draw upon the familiarity that already exists with respect to paper as a hands-on and interactive medium. Paper circuitry thus provides multiple points of entry for students who are unfamiliar with computational thinking ideas while also supporting creative, artistic and crafting activities. It also provides an important alternative to the typically steep learning curve associated with learning a programming language. We define paper circuitry and associated technologies, show how they afford key dimensions of computational thinking, and present examples of paper circuit projects created by students.

The purpose of this study was to explore the nine components of computational thinking (CT) practices and their operational definitions from the view of science education and to develop a CT practice framework that is going to be used as a planning and assessing tool for CT practice, as it is required for students to equip with in order to become creative problem solvers in 21st century. We employed this framework into the earlier developed STEAM programs to see how it was valid and reliable. We first reviewed theoretical articles about CT from computer science and technology education field. We then proposed 9 components of CT as defined in technology education but modified operational definitions in each component from the perspective of science education. This preliminary CTPF (computational thinking practice framework) from the viewpoint of science education consisting of 9 components including data collection, data analysis, data representation, decomposing, abstraction, algorithm and procedures, automation, simulation, and parallelization. We discussed each component with operational definition to check if those components were useful in and applicable for science programs. We employed this CTPF into two different topics of STEAM programs to see if those components were observable with operational definitions. The profile of CT components within the selected STEAM programs for this study showed one sequential spectrum covering from data collection to simulation as the grade level went higher. The first three data related CT components were dominating at elementary level, all components of CT except parallelization were found at middle school level, and finally more frequencies in every component of CT except parallelization were also found at high school level than middle school level. On the basis of the result of CT usage in STEAM programs, we included ‘generalization’ in CTPF of science education instead of ‘parallelization’ which was not found. The implication about teacher education was made based on the CTPF in terms of science education.