Identifikasi Extranous Cognitive Load Siswa Dalam Mengembangkan Computational Thinking Skill Melalui Pembelajaran Jaring-Jaring Makanan Berbasis Snap!

Food webs learning using the Snap! is one of the learning strategies that are expected to help improve students' computational thinking. For students, this learning strategy were something new and can cause Extraneous Cognitive Load (ECL). The purpose of this study was to identify students' ECL in food web learning using the Snap! to develop computational thinking skills. The research method used in this study was a pre-experimental design with a modified research design from an iterative action design. The sampling technique was purposive sampling. The sample in this study consisted of 30 seventh grade students at SMPN 2 Bandung. The research instrument used in this study was a student mental effort questionnaire to measure ECL, field notes, and a computational thinking test. Based on the results of the study, students' ECL was relatively low and increased at each meeting, except for the second meeting. Students experience an increase in their computational thinking skills after participating in food web learning using the Snap! computational model. The results of the N-Gain analysis also show that the improvement of students' computational thinking is in the moderate category and is quite effective

PRECODING ACTIVITIES TO IMPROVE STUDENT'S COMPUTATIONAL THINKING SKILLS

Coding means telling the computer to do something. In order to achieve this, the coder must understand what the problem is and what the best solution is. One of the things needed is pre-coding skills, including computational thinking skills. This study aims to improve students' computational thinking skills as a pre-coding ability. This classroom action research was conducted at a public elementary school in Dauh Puri, Denpasar, involving 20 first grade students. Data were collected based on observations in working on activity sheets and the results of student work on activity sheets. The data that has been collected was analyzed by quantitative descriptive. The results of data analysis showed that the students' computational thinking ability increased from pre-cycle activities, cycle I and cycle II respectively from 47.70 % to 65.05% and increased again to 81.50%. This increase in scores indicates that students' computational thinking skills can be improved by using student age-appropriate activity sheets as the basis for coding skills without having to involve a computer

Can Pre-school Children Learn Programming and Coding Through Guided Play Activities? A Case Study in Computational Thinking

Guided play activities were developed so that coding clubs could promote computational thinking skills in preschool children. The clubs involved fifteen children aged between 2 and 4 years, including a group of children with communication difficulties. The children took part in an action-research scoping study over three coding clubs involving six 45–60-min sessions. The activities were developed to teach computational skills and, ultimately, concepts of programming and coding. The findings suggested that the children began to develop many of the skills necessary for programming and coding as well as computational thinking skills such as collaboration, logical thinking and debugging algorithms. However, they found programming specific algorithms into Bee-Bots complicated and they needed support from adults to direct the robots along routes on simple maps. Overall, the guided play activities could be used in nurseries and preschool establishments to teach early computational thinking skills

Pembelajaran Daring Menggunakan Simulasi PhET untuk Melatih Kemampuan Computational Thinking Peserta Didik

This study aims to improve the skill of student’s SMP Negeri 14 Makassar of computational thinking on online learning using PhET simulation. This study applied classroom action research (PTK).  Planning, action, observe and reflect in two cycles were employed. This study engaged grade IX students in the 2020/2021 academic year on Dynamic Electricity. The instrument that was used were observation and tests. The result showed that Computational Thinking using PhET simulation has achieved the target according to the indicators. The increasing from first cycle to second cycle was 53.84%. Therefore, it can be concluded that science studies using PhET simulation improved students' Computational Thinking.Penelitian ini bertujuan untuk meningkatkan kemampuan Computational Thinking peserta didik SMP Negeri 14 Makassar pada pembelajaran daring  menggunakan media  simulasi PhET. Penelitian ini merupakan penelitian tindakan kelas (PTK). Penelitian Tindakan Kelas dilaksanakan sebanyak 2 siklus yang pada masing-masing siklus terdapat tahapan perencanaan, pelaksanaan tindakan, observasi dan evaluasi tindakan, dan refleksi. Penelitian ini dilakukan terhadap peserta didik kelas IX tahun pelajaran 2020/2021 pada materi Listrik Dinamis. Pengambilan data penelitian dilakukan dengan menggunakan instrumenlembar observasi dan tes. Dari hasil analisis data penelitian, diperoleh peningkatan persentase ketuntasan secara klasikal dari siklus I ke siklus II sebesar 53,84%. Dapat disimpulkan bahwa pembelajaran IPA dengan menggunakan media simulasi PhET dapat meningkatkan kemampuan Computational Thinking peserta didik

DESCRIPTION OF THINKING PROCESS IN SOLVING MATHEMATICS PROBLEMS BASED ON BRANDSFORD AND STEIN’S STAGES REVIEWED FROM ADVERSITY QUOTIENT

The study is qualitative, which aims at describing the students’ thinking process in solving mathematics problems based on Brandsford and Stein steps reviewed from Adversity Quotient. The research subjects were students of MTSN 1 Makassar, Consisted of 2 climber type students, 2 camper type students, and 2 quitter type students. The research instrument, namely ARP (Adversity Response Profile) test and task based interview. Data were analyzed by conducting data reduction, data display and conclusion drawing. The results of the study reveal that the students’ thinking process in problem identification stage: the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In terms of determining objectives: the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In exploring students’ strategy : the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In Anticipating the result and students action : the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In observing and students’ learning : the climber type students tended to do conceptual thinking process, camper type students tended to do conceptual thinking process, and quitter type students tended to do computational thinking process. Keywords: Thinking Process, Problem Solving, Brandsford and Stein, Adversity Quotient (AQ

DESCRIPTION OF THINKING PROCESS IN SOLVING MATHEMATICS PROBLEMS BASED ON BRANDSFORD AND STEIN’S STAGES REVIEWED FROM ADVERSITY QUOTIENT

The study is qualitative, which aims at describing the students’ thinking process in solving mathematics problems based on Brandsford and Stein steps reviewed from Adversity Quotient. The research subjects were students of MTSN 1 Makassar, Consisted of 2 climber type students, 2 camper type students, and 2 quitter type students. The research instrument, namely ARP (Adversity Response Profile) test and task based interview. Data were analyzed by conducting data reduction, data display and conclusion drawing. The results of the study reveal that the students’ thinking process in problem identification stage: the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In terms of determining objectives: the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In exploring students’ strategy : the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In Anticipating the result and students action : the climber type students tended to do conceptual thinking process, camper type students tended to do semi-conceptual thinking process, and quitter type students tended to do computational thinking process. In observing and students’ learning : the climber type students tended to do conceptual thinking process, camper type students tended to do conceptual thinking process, and quitter type students tended to do computational thinking process. Keywords: Thinking Process, Problem Solving, Brandsford and Stein, Adversity Quotient (AQ

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

How does language inform our downstream thinking? In particular, how do humans make meaning from language -- and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose \textit{rational meaning construction}, a computational framework for language-informed thinking that combines neural models of language with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a \textit{probabilistic language of thought} (PLoT) -- a general-purpose symbolic substrate for probabilistic, generative world modeling. Our architecture integrates two powerful computational tools that have not previously come together: we model thinking with \textit{probabilistic programs}, an expressive representation for flexible commonsense reasoning; and we model meaning construction with \textit{large language models} (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language. We illustrate our framework in action through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning about agents and their plans. In each, we show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings, while Bayesian inference with the generated programs supports coherent and robust commonsense reasoning. We extend our framework to integrate cognitively-motivated symbolic modules to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves

A framework for creating educational virtual escape rooms to teach computational thinking

Dissertation (MCom (Informatics))--University of Pretoria, 2022.Due to the looming Fourth Industrial Revolution, massive changes in occupations are predicted that will require a new set of skills from the next generation. As a result, educational systems are struggling to equip students with the right skills to thrive in the future. The Institute for the Future identified Computational Thinking as one of the essential skills that will be critical for success in the future workplace. Although there is no clear definition for computational thinking, many researchers have come to accept Wing’s definition as an approach to solving problems, designing systems, and understanding human behavior by drawing on concepts fundamental to computer science. However, integrating computational thinking into the curriculum remains an educational challenge. Escape room games could potentially aid in the development of computational thinking skills because they immerse learners in a narrative-based, problem-solving scenario. Nicholson defines an escape room as a live-action adventure game in which players find themselves locked in a room, or series of rooms, from which they must escape within a limited amount of time. This research study aims to illustrate a virtual escape room for teaching of computational thinking, reflect on its usefulness as a teaching tool, offer guidance on where to make improvements, and present a framework that educators can use to create their own virtual escape rooms. This research followed a Design-Based Research methodology that consisted of three iterative cycles. During the cycles, participants were given a pre-test before the virtual escape room and a post-test after the virtual escape room. Although the findings do not show a significant difference between the pre-test and post-test results, participants indicated that the experience with the escape room increased their motivation to learn more about computational thinking. This paper recommends that virtual escape rooms be investigated further since they could provide significant insight for learners in computational thinkingInformaticsMCom (Informatics)Unrestricte

Robotics to develop computational thinking in early Childhood Education / Robótica para desarrollar el pensamiento computacional en Educación Infantil

The development of programming skills is currently promoting from an early school age, trying to get children to take an active and creative role in the use of technologies. The objective of this article is to verify the repercussion of educational robotics activities on kindergarten students in the acquisition of computational thinking and programming skills. The research design is quasi-experimental, with pre-test and post-test measures, using experimental and control groups. The sample consists of 131 students from the second cycle of early education (between 3 and 6 years old), all from the same Spanish school. Computational thinking is measured through three dimensions: sequences (algorithms), action-instruction correspondence and debugging. The intervention sessions, as well as the structure of the challenges that were used in the pre- and post-test evaluations, were designed based on the reference program of robotics studies called “TangibleK”. The intervention, carried out doing learning activities using educational robotics resources, presents positive results in relation to the computational thinking skills achieved. The differences between the pre-test and the post-test in the experimental and control groups are statistically significant, in that children engaged in robotics program achieves a greater advance in the three dimensions of computational competence through this method