This document summarizes a study on the effects of computational thinking-based software (SW) coding education on creative problem solving. The study derived a creative problem-solving model based on the nine main concepts of computational thinking and applied this model to SW coding education for an experimental group. Pre- and post-tests were administered to measure changes in students' affective and cognitive abilities. The results showed that computational thinking-based SW coding education positively influenced students' creative problem-solving skills.
This research aimed to determine the effectiveness of the issue, discussion,
establishment, and application (IDEA) learning model in embedding
mathematical concepts understanding. It is a quantitative study with a quasiexperimental
approach. This research was conducted at Malang Islamic
University with students majoring Mathematics education study program
and in their first academic year as the subjects. Subjects were divided into
two group, experimental and control and measured their result using posttest
only control group design. There are six instruments consisting six items
used to measure mathematical concepts understanding. The results
confirmed that H0 is rejected, while Ha is accepted proved by t count
(3.132)>ttable (1.674). It means there is a significant difference between the
of both groups (experimental and control). In addition, the results indicated
that the IDEA learning models is effective to assist students in understanding
mathematical concepts showed by the higher score of experimental groups
than control group. Three factors are involved in order to implement the
IDEA learning model effectively; namely: individual problem-solving
opportunities, active student involvement, and guidance and assistance (from
lecturer) on the IDEA learning model. This research is only limited to
preservice teacher, further research is required in order to implement this
model for mathematics learning at junior and senior high schools’ level.
Computer Science is an interesting subject which helps students to develop their problem solving and computational thinking skills. The problem solving and algorithmic nature of computer science also promotes students’ creativity and innovation. However, teaching of computer science is different from other science subjects and requires special pedagogical skills. This presentation covers these aspects.
CS0: A Project Based, Active Learning Coursedrboon
The recruitment and retention of students in early computer programming classes has been the focus of many Computer Science and Informatics programs. This paper describes an initiative underway at Indiana University South Bend to improve the retention rate in computer science and informatics. The approach described in this work is inspired by the SCALE-UP project, and describes the design and implementation of an instructor-guided, active learning environment which allows students to gradually acquire the necessary critical thinking, problem solving, and programming skills required for success in computer science and informatics.
THE IMPACT OF USING VISUAL PROGRAMMING ENVIRONMENT TOWARDS COLLEGE STUDENTS’ ...ijma
ABSTRACT
This study aimed to identify the impact of using a visual programming environment on college students’ achievement and understanding when learning computer programming. In this quasi-experimental study, 91 students were divided systematically into an experimental group (53 students) and a control group (38 students). The experimental group were exposed with a visual programming environment while the control group were using an ordinary text-based programming environment. Data was collected using pre-test and post-test, then analysed using paired t-test, independent sample t-test and thematic content analysis. A significant increase in the students’ achievement was recorded during the paired t-test for both groups. However, there is no significant difference in the students’ achievement between the groups. Surprisingly, the thematic analysis showed that students’ understanding in the experimental group were improved relatively better than in the control group. Thus, we conclude that visual programming environment have better impact to the students’ understanding.
Student’s critical thinking skills that are currently still low are important to develop because they affect the way students think in solving problems. The problem-based learning model with the inquiry method used to help improve student’s critical thinking skills. The iSpring learning media used can help students understand the material. The research results were obtained from the output of the paired sample t-test, a significance value < 0.05, so it was concluded that there was a difference between before applying the learning model and after applying the learning model. The results of the questionnaire responses to the media also resulted in positive responses from students.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
Development of a collaborative learning with creative problem solving process...Panita Wannapiroon Kmutnb
Sitthichai Laisema and Panita Wannapiroon, " Development of a Collaborative Learning with Creative Problem-Solving Process Model in Ubiquitous Learning Environment," International Journal of e-Education, e-Business, e-Management and e-Learning vo. 3, no. 2, pp. 102-106, 2013.
This research aimed to determine the effectiveness of the issue, discussion,
establishment, and application (IDEA) learning model in embedding
mathematical concepts understanding. It is a quantitative study with a quasiexperimental
approach. This research was conducted at Malang Islamic
University with students majoring Mathematics education study program
and in their first academic year as the subjects. Subjects were divided into
two group, experimental and control and measured their result using posttest
only control group design. There are six instruments consisting six items
used to measure mathematical concepts understanding. The results
confirmed that H0 is rejected, while Ha is accepted proved by t count
(3.132)>ttable (1.674). It means there is a significant difference between the
of both groups (experimental and control). In addition, the results indicated
that the IDEA learning models is effective to assist students in understanding
mathematical concepts showed by the higher score of experimental groups
than control group. Three factors are involved in order to implement the
IDEA learning model effectively; namely: individual problem-solving
opportunities, active student involvement, and guidance and assistance (from
lecturer) on the IDEA learning model. This research is only limited to
preservice teacher, further research is required in order to implement this
model for mathematics learning at junior and senior high schools’ level.
Computer Science is an interesting subject which helps students to develop their problem solving and computational thinking skills. The problem solving and algorithmic nature of computer science also promotes students’ creativity and innovation. However, teaching of computer science is different from other science subjects and requires special pedagogical skills. This presentation covers these aspects.
CS0: A Project Based, Active Learning Coursedrboon
The recruitment and retention of students in early computer programming classes has been the focus of many Computer Science and Informatics programs. This paper describes an initiative underway at Indiana University South Bend to improve the retention rate in computer science and informatics. The approach described in this work is inspired by the SCALE-UP project, and describes the design and implementation of an instructor-guided, active learning environment which allows students to gradually acquire the necessary critical thinking, problem solving, and programming skills required for success in computer science and informatics.
THE IMPACT OF USING VISUAL PROGRAMMING ENVIRONMENT TOWARDS COLLEGE STUDENTS’ ...ijma
ABSTRACT
This study aimed to identify the impact of using a visual programming environment on college students’ achievement and understanding when learning computer programming. In this quasi-experimental study, 91 students were divided systematically into an experimental group (53 students) and a control group (38 students). The experimental group were exposed with a visual programming environment while the control group were using an ordinary text-based programming environment. Data was collected using pre-test and post-test, then analysed using paired t-test, independent sample t-test and thematic content analysis. A significant increase in the students’ achievement was recorded during the paired t-test for both groups. However, there is no significant difference in the students’ achievement between the groups. Surprisingly, the thematic analysis showed that students’ understanding in the experimental group were improved relatively better than in the control group. Thus, we conclude that visual programming environment have better impact to the students’ understanding.
Student’s critical thinking skills that are currently still low are important to develop because they affect the way students think in solving problems. The problem-based learning model with the inquiry method used to help improve student’s critical thinking skills. The iSpring learning media used can help students understand the material. The research results were obtained from the output of the paired sample t-test, a significance value < 0.05, so it was concluded that there was a difference between before applying the learning model and after applying the learning model. The results of the questionnaire responses to the media also resulted in positive responses from students.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
Development of a collaborative learning with creative problem solving process...Panita Wannapiroon Kmutnb
Sitthichai Laisema and Panita Wannapiroon, " Development of a Collaborative Learning with Creative Problem-Solving Process Model in Ubiquitous Learning Environment," International Journal of e-Education, e-Business, e-Management and e-Learning vo. 3, no. 2, pp. 102-106, 2013.
The document discusses interventions for teaching mathematics to at-risk non-specialist students from a cybernetics perspective. It analyzes factors that put students at risk for mathematics and discusses cognitive and pedagogical background theories. The document proposes designing interventions based on Kolb's experiential learning cycle and closed-loop lecturing models. It suggests implementing interventions using technology and continuous assessment and evaluating interventions using hybrid methods.
This document discusses motivational design and the ARCS model of motivation. It provides an overview of the ARCS model which stands for Attention, Relevance, Confidence, and Satisfaction. The document outlines the four categories of the ARCS model and the steps in the ARCS motivational design process. It also discusses applying the ARCS model and other motivational design principles to instructional design.
A Meta-Analysis Of Research Of Problem Solving Activities In Online DiscussionShannon Green
This document discusses a meta-analysis of research on problem solving activities in online discussions. Nine studies were selected that focused on problem solving in higher education and used online mediums. The studies used various learning activities like project scenarios, problem scenarios, case studies, and problem-solving tasks. The most common activities were scenarios and case studies. The analysis found that learning activities involved giving students scenarios or cases to discuss online to find solutions.
Students’ Perception about Fundamental Programming Course Teaching and Learningrahulmonikasharma
Programming learning has unique characteristics as it is a subject that requires skill and higher order thinking. Students come to class with a perception about the subject mostly obtained from their seniors including fear or perceived difficulty. Senior students have a perception about programming learning that was supported by their experience during the subject learning. Students’ views (+ / -) about the course could affect their performance. A qualitative survey was conducted with 93 third year students to obtain their views about the students’ point of views while learning programming and the recommendation for modifying the course. Obstacles identified by students could be tackled with the aid of technology enhanced learning (TEL) including tutoring system. This survey is done as a preliminary step in developing and incorporating technical solution to students’ problems. The findings were: Mostly, students are satisfied with the amount of time and effort they dedicated to the subject. While some mentioned that they would practice coding more and perform some projects beyond the course level. Majority of the students pointed out that they got useful advice from seniors about the subject learning. Less feedback was discouraging to students. About their suggested modification about the way the course setup, their overall responses approved the course design. There were minor comments about the proportions of the theoretical to practical components and the suitable amount of assignments.
Problem-based learning (PBL) has been widely applied as an alternative to improve learning outcomes, but it is still little studied in the context of the probability theory course. This study described how implementing the PBL model improves students’ problem-solving and critical thinking skills in probability theory course and evaluates its quality. This design research involved 58 undergraduate students and two probability theory course lecturers from two universities in Indonesia as participants. Data collection used observation to describe PBL implementation, then questionnaire and pretest-posttest to evaluate the quality of the model. This study produced a PBL model for the probability theory course which is implemented through five steps: i) Orienting students on problems; ii) Organizing students to study; iii) Assisting individual and group investigations; iv) Developing and presenting work or solutions; and v) Analyzing and evaluating problem solving processes. Lecturers and students consider that the implementation of PBL is practical. PBL implementation can also improve students’ problem-solving skills and critical thinking in the probability theory course. Thus, implementing PBL can be used as a solution to optimize learning outcomes in the probability theory course.
A learning program not only aims to make students understand and master what and how things happen, but also provide an understanding of why it happened. Thus, a lesson that emphasizes problem-solving becomes very important to teach. One form of learning that emphasizes the problem solving is to apply systematic approach to problem solving. This is a guide to perform an action that serves to assist a person in solving a problem. Problem solving steps based on systematic approach to problem solving consists of four stages, namely problem analysis, problem solving process planning, calculation operations, and checking answers and interpretation of results. This study is a classroom action research that aims to see the inceasing of student learning outcomes after applied systematic approach to problem solving. The subject of this research is 25 students of class VIIIA MTs Salafiyah Syafi'iyah Tebuireng Jombang. Instruments in this study is a matter of student learning outcomes on the material wake up space. This research was conducted in two cycles because in the second cycle has reached the indicator of success that students achieve the minimal clarity of at least 75%. The results showed that student learning outcomes in the first cycle reached 36% classical completeness, and in the second cycle of classical completeness of 84%. This shows that the application of systematic approach to problem solving can increase student learning outcomes.
3 D Project Based Learning Basics for the New Generation Science Standardsrekharajaseran
This presentation is a part of the workshop presented at Griffin RESA Drive-In STEM Conference on September 28, 2016. It provides an introduction to the basics of three dimensional project based learning for STEM Education and New Generation Science Standards.
LACLOD: LEARNING ANALYTICS FOR CHILDREN’S LOGIC DEVELOPMENTijma
Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly
learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA
PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics
(GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile
or tablet which is using Android. In this paper, the development of this learning analytic platform is
presented. For evaluation of this system, observation and survey have been used, to get the feedback from 2
teachers (female) and 3 children (2 female and 1 male). Based on the evaluation, it can be seen that there
are still rooms for improvement. Female children found it quit hard to understand the game but the male
children looked satisfy because he knew on how to navigate the app and he actively played the app by himself.
As for teachers, the acceptance to this kind of assessment is moderate, however they agree that this
application can better improve the children’s learning especially in logic development.
LACLOD: LEARNING ANALYTICS FOR CHILDREN’S LOGIC DEVELOPMENTijma
Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly
learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA
PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics
(GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile
or tablet which is using Android. In this paper, the development of this learning analytic platform is
presented. For evaluation of this system, observation and survey have been used, to get the feedback from 2
teachers (female) and 3 children (2 female and 1 male). Based on the evaluation, it can be seen that there
are still rooms for improvement. Female children found it quit hard to understand the game but the male
children looked satisfy because he knew on how to navigate the app and he actively played the app by himself.
As for teachers, the acceptance to this kind of assessment is moderate, however they agree that this
application can better improve the children’s learning especially in logic development.
Implementation of different tutoring system to enhance student learningijctet
This document discusses different tutoring systems that can be used to enhance student learning. It describes three types of tutoring: no tutoring (without feedback), computer tutoring using intelligent tutoring systems, and human tutoring using scaffolding techniques. Intelligent tutoring systems use domain models, teaching models, student models, and interfaces to provide individualized instruction. They track student performance and recommend additional work. Human tutoring employs scaffolding where teachers support students until they can solve complex problems independently. Scaffolding includes hard, static supports and soft, dynamic supports tailored for each student.
The implementation of design thinking models on the entrepreneurship learning...Laurensia Claudia Pratomo
PENERAPAN MODEL PEMBELAJARAN DESIGN THINKING PADA MATA PELAJARAN KEWIRAUSAHAAN DI SEKOLAH KEJURUAN
Laurensia Claudia Pratomo, Siswandari, Dewi Kusuma Wardani
Postgraduate of Economic Education Department Universitas Sebelas Maret, Indonesia.
Email: laurensiaclaudiap@gmail.com
ABSTRAK
Penelitian ini bertujuan untuk mengetahui penerapan model pembelajaran design thinking pada mata pelajaran kewirausahaan terutama pada kompetensi media pemasaran. Metode eksperimen digunakan pada penelitian ini dengan 120 siswa sekolah kejuruan di Surakarta sebagai objek penelitian. Data diperoleh dengan membagikan kuesioner kepada para siswa. Data diolah menggunakan aplikasi SPSS Statistik 23. Hasil dari N-Gain memperlihatkan jika terjadi peningkatan kreativitas dan kesadaran berwirausaha siswa pada kelas eksperimen setelah menerima pembelajaran kewirausahaan dengan model design thinking Stanforf D School. Pada kategori kreativitas tinggi terjadi peningkatan sebanyak 7% sedangkan pada kategori kesadaran berwirausaha tinggi terjadi peningkatan sebanyak 10%.
Kata kunci: model pembelajaran; design thinking; pembelajaran kewirausahaan; sekolah kejuruan.
----
The Implementation of design thinking Models on the Entrepreneurship Learning in Vocational Schools
Laurensia Claudia Pratomo, Siswandari, Dewi Kusuma Wardani
Faculty of Teacher Training and Education of Sebelas Maret University, Indonesia
Email: laurensiaclaudiap@gmail.com
Abstract
This study aims to determine the implementation of the design thinking learning model in entrepreneurship education, especially in marketing media competencies. The experimental method was used in this study with 120 vocational school students in Surakarta as research objects. The data were obtained by distributing questionnaires to students. The data is processed using SPSS Statistics 23 application. The results of N-Gain shows that students' creativity and entrepreneurial alertness in the experimental class has increased after receiving entrepreneurship education with Stanford D School's design thinking model. In the category of high creativity has increased by 7% while in the category of high entrepreneurial alertness has increased by 10%.
Keywords— learning model, design thinking, entrepreneurship learning, vocational schools.
The document discusses instructional system design and the ADDIE model. It defines key concepts in instructional design like instruction, design, system, model, and instructional design. It also differentiates between various instructional design models like ADDIE, ASSURE, Dick and Carey, and rapid prototyping. The document then focuses on applying the ADDIE model to designing open and distance learning course materials, outlining the analysis, design, development, implementation, and evaluation phases.
This document outlines an activity to teach participants how to conduct action research to solve instructional problems in the classroom. It involves using videos and lectures to illustrate classroom situations and problems, then providing templates to guide analyzing the issues, collecting and interpreting relevant data, and developing potential solutions. The goals are to design an action research project focused on classroom instruction, apply statistical methods in Microsoft Excel, and recognize the value of action research for addressing school problems. Participants will individually construct an action research project meeting division and local requirements.
This research is aimed at developing a learning model that encourages the skills of analytical thinking in science. The method used is research and development. The result is the ICAE (Incubation, Collection of data, Analysis, and Evaluation) model that promotes analytical thinking skills. Results of normalized gain tests show that the gain score is 0.28, which indicates that the ICAE learning model positively affects students’ analytical thinking, even though still within the lower category. The ICAE model also promotes the skills of analytical thinking in science and it has gained positive response from students.
Assessing Problem-Solving Strategy Use By Engineering UndergraduatesHeather Strinden
This document summarizes a research study that assessed problem-solving strategies used by engineering undergraduates. The study examined which strategies students reported using, whether certain strategies clustered together, and if frequent strategy use correlated with better course performance. It also looked at how confidence, familiarity with course material, and interest affected performance. The researchers identified three types of strategies - Execution, Planning and Looking Back, and Low Confidence in Ability. Frequent use of strategies and higher confidence predicted better grades, while low confidence hindered performance. Strategy use differed between physics and thermodynamics courses.
A Nobel Approach On Educational Data Miningijircee
This document discusses educational data mining and its applications. It begins with introducing data mining and its goal of extracting useful information from large databases. Educational data mining is then discussed as using data mining techniques to understand how students learn. The objectives of educational data mining are outlined as supporting educational research, effective learning, prediction, and feedback. Common data mining techniques discussed include summarization, cluster analysis, classification and prediction, decision trees, and association. The document concludes with how these techniques can be applied in education for knowledge discovery and improving student success.
Application of Graph Theory in an Intelligent Tutoring System for Solving Mat...Valerie Felton
This document discusses applying graph theory to develop a model for solving mathematical word problems involving motion in an intelligent tutoring system. It first categorizes motion problems based on characteristics like number of vehicles and their direction of motion. It then proposes using a graph structure to represent the relationships between variables in motion problems. Finally, it outlines three main models for solving different categories of motion problems using graph theory techniques like backward and forward chaining.
This document summarizes a study that aimed to enhance creativity among university students using e-learning. It developed an e-learning system integrating Problem-Based Learning and Creative Problem Solving models. Students took a pre-test, received an 8-week intervention using the e-learning system, and took a post-test. Both quantitative and qualitative data were collected. Quantitative data came from pre- and post-tests using the Torrance Test of Creative Thinking. Qualitative data came from the e-learning discussion forum and open-ended questions about the system's effectiveness. The study aimed to produce a creativity learning framework for tertiary education students.
Essay On Teachers Day (2023) In English Short, Simple BestSandra Long
The document provides instructions for submitting a request to an online writing service called HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and select one. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied with the work. The service promises original, high-quality content and refunds for plagiarized work.
10 Best Printable Handwriting Paper Template PDF For Free At PrintableeSandra Long
The document discusses differences between schools in America and India. It notes that American schools have earlier start times, later end times, and are divided into elementary, middle, and high school sections. American schools also place more emphasis on extracurricular activities and sports compared to Indian schools. Key differences include dress codes, lunch times, qualifications for teachers, and approaches to learning that are more hands-on in America versus memorization-focused in India.
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The document discusses interventions for teaching mathematics to at-risk non-specialist students from a cybernetics perspective. It analyzes factors that put students at risk for mathematics and discusses cognitive and pedagogical background theories. The document proposes designing interventions based on Kolb's experiential learning cycle and closed-loop lecturing models. It suggests implementing interventions using technology and continuous assessment and evaluating interventions using hybrid methods.
This document discusses motivational design and the ARCS model of motivation. It provides an overview of the ARCS model which stands for Attention, Relevance, Confidence, and Satisfaction. The document outlines the four categories of the ARCS model and the steps in the ARCS motivational design process. It also discusses applying the ARCS model and other motivational design principles to instructional design.
A Meta-Analysis Of Research Of Problem Solving Activities In Online DiscussionShannon Green
This document discusses a meta-analysis of research on problem solving activities in online discussions. Nine studies were selected that focused on problem solving in higher education and used online mediums. The studies used various learning activities like project scenarios, problem scenarios, case studies, and problem-solving tasks. The most common activities were scenarios and case studies. The analysis found that learning activities involved giving students scenarios or cases to discuss online to find solutions.
Students’ Perception about Fundamental Programming Course Teaching and Learningrahulmonikasharma
Programming learning has unique characteristics as it is a subject that requires skill and higher order thinking. Students come to class with a perception about the subject mostly obtained from their seniors including fear or perceived difficulty. Senior students have a perception about programming learning that was supported by their experience during the subject learning. Students’ views (+ / -) about the course could affect their performance. A qualitative survey was conducted with 93 third year students to obtain their views about the students’ point of views while learning programming and the recommendation for modifying the course. Obstacles identified by students could be tackled with the aid of technology enhanced learning (TEL) including tutoring system. This survey is done as a preliminary step in developing and incorporating technical solution to students’ problems. The findings were: Mostly, students are satisfied with the amount of time and effort they dedicated to the subject. While some mentioned that they would practice coding more and perform some projects beyond the course level. Majority of the students pointed out that they got useful advice from seniors about the subject learning. Less feedback was discouraging to students. About their suggested modification about the way the course setup, their overall responses approved the course design. There were minor comments about the proportions of the theoretical to practical components and the suitable amount of assignments.
Problem-based learning (PBL) has been widely applied as an alternative to improve learning outcomes, but it is still little studied in the context of the probability theory course. This study described how implementing the PBL model improves students’ problem-solving and critical thinking skills in probability theory course and evaluates its quality. This design research involved 58 undergraduate students and two probability theory course lecturers from two universities in Indonesia as participants. Data collection used observation to describe PBL implementation, then questionnaire and pretest-posttest to evaluate the quality of the model. This study produced a PBL model for the probability theory course which is implemented through five steps: i) Orienting students on problems; ii) Organizing students to study; iii) Assisting individual and group investigations; iv) Developing and presenting work or solutions; and v) Analyzing and evaluating problem solving processes. Lecturers and students consider that the implementation of PBL is practical. PBL implementation can also improve students’ problem-solving skills and critical thinking in the probability theory course. Thus, implementing PBL can be used as a solution to optimize learning outcomes in the probability theory course.
A learning program not only aims to make students understand and master what and how things happen, but also provide an understanding of why it happened. Thus, a lesson that emphasizes problem-solving becomes very important to teach. One form of learning that emphasizes the problem solving is to apply systematic approach to problem solving. This is a guide to perform an action that serves to assist a person in solving a problem. Problem solving steps based on systematic approach to problem solving consists of four stages, namely problem analysis, problem solving process planning, calculation operations, and checking answers and interpretation of results. This study is a classroom action research that aims to see the inceasing of student learning outcomes after applied systematic approach to problem solving. The subject of this research is 25 students of class VIIIA MTs Salafiyah Syafi'iyah Tebuireng Jombang. Instruments in this study is a matter of student learning outcomes on the material wake up space. This research was conducted in two cycles because in the second cycle has reached the indicator of success that students achieve the minimal clarity of at least 75%. The results showed that student learning outcomes in the first cycle reached 36% classical completeness, and in the second cycle of classical completeness of 84%. This shows that the application of systematic approach to problem solving can increase student learning outcomes.
3 D Project Based Learning Basics for the New Generation Science Standardsrekharajaseran
This presentation is a part of the workshop presented at Griffin RESA Drive-In STEM Conference on September 28, 2016. It provides an introduction to the basics of three dimensional project based learning for STEM Education and New Generation Science Standards.
LACLOD: LEARNING ANALYTICS FOR CHILDREN’S LOGIC DEVELOPMENTijma
Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly
learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA
PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics
(GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile
or tablet which is using Android. In this paper, the development of this learning analytic platform is
presented. For evaluation of this system, observation and survey have been used, to get the feedback from 2
teachers (female) and 3 children (2 female and 1 male). Based on the evaluation, it can be seen that there
are still rooms for improvement. Female children found it quit hard to understand the game but the male
children looked satisfy because he knew on how to navigate the app and he actively played the app by himself.
As for teachers, the acceptance to this kind of assessment is moderate, however they agree that this
application can better improve the children’s learning especially in logic development.
LACLOD: LEARNING ANALYTICS FOR CHILDREN’S LOGIC DEVELOPMENTijma
Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly
learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA
PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics
(GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile
or tablet which is using Android. In this paper, the development of this learning analytic platform is
presented. For evaluation of this system, observation and survey have been used, to get the feedback from 2
teachers (female) and 3 children (2 female and 1 male). Based on the evaluation, it can be seen that there
are still rooms for improvement. Female children found it quit hard to understand the game but the male
children looked satisfy because he knew on how to navigate the app and he actively played the app by himself.
As for teachers, the acceptance to this kind of assessment is moderate, however they agree that this
application can better improve the children’s learning especially in logic development.
Implementation of different tutoring system to enhance student learningijctet
This document discusses different tutoring systems that can be used to enhance student learning. It describes three types of tutoring: no tutoring (without feedback), computer tutoring using intelligent tutoring systems, and human tutoring using scaffolding techniques. Intelligent tutoring systems use domain models, teaching models, student models, and interfaces to provide individualized instruction. They track student performance and recommend additional work. Human tutoring employs scaffolding where teachers support students until they can solve complex problems independently. Scaffolding includes hard, static supports and soft, dynamic supports tailored for each student.
The implementation of design thinking models on the entrepreneurship learning...Laurensia Claudia Pratomo
PENERAPAN MODEL PEMBELAJARAN DESIGN THINKING PADA MATA PELAJARAN KEWIRAUSAHAAN DI SEKOLAH KEJURUAN
Laurensia Claudia Pratomo, Siswandari, Dewi Kusuma Wardani
Postgraduate of Economic Education Department Universitas Sebelas Maret, Indonesia.
Email: laurensiaclaudiap@gmail.com
ABSTRAK
Penelitian ini bertujuan untuk mengetahui penerapan model pembelajaran design thinking pada mata pelajaran kewirausahaan terutama pada kompetensi media pemasaran. Metode eksperimen digunakan pada penelitian ini dengan 120 siswa sekolah kejuruan di Surakarta sebagai objek penelitian. Data diperoleh dengan membagikan kuesioner kepada para siswa. Data diolah menggunakan aplikasi SPSS Statistik 23. Hasil dari N-Gain memperlihatkan jika terjadi peningkatan kreativitas dan kesadaran berwirausaha siswa pada kelas eksperimen setelah menerima pembelajaran kewirausahaan dengan model design thinking Stanforf D School. Pada kategori kreativitas tinggi terjadi peningkatan sebanyak 7% sedangkan pada kategori kesadaran berwirausaha tinggi terjadi peningkatan sebanyak 10%.
Kata kunci: model pembelajaran; design thinking; pembelajaran kewirausahaan; sekolah kejuruan.
----
The Implementation of design thinking Models on the Entrepreneurship Learning in Vocational Schools
Laurensia Claudia Pratomo, Siswandari, Dewi Kusuma Wardani
Faculty of Teacher Training and Education of Sebelas Maret University, Indonesia
Email: laurensiaclaudiap@gmail.com
Abstract
This study aims to determine the implementation of the design thinking learning model in entrepreneurship education, especially in marketing media competencies. The experimental method was used in this study with 120 vocational school students in Surakarta as research objects. The data were obtained by distributing questionnaires to students. The data is processed using SPSS Statistics 23 application. The results of N-Gain shows that students' creativity and entrepreneurial alertness in the experimental class has increased after receiving entrepreneurship education with Stanford D School's design thinking model. In the category of high creativity has increased by 7% while in the category of high entrepreneurial alertness has increased by 10%.
Keywords— learning model, design thinking, entrepreneurship learning, vocational schools.
The document discusses instructional system design and the ADDIE model. It defines key concepts in instructional design like instruction, design, system, model, and instructional design. It also differentiates between various instructional design models like ADDIE, ASSURE, Dick and Carey, and rapid prototyping. The document then focuses on applying the ADDIE model to designing open and distance learning course materials, outlining the analysis, design, development, implementation, and evaluation phases.
This document outlines an activity to teach participants how to conduct action research to solve instructional problems in the classroom. It involves using videos and lectures to illustrate classroom situations and problems, then providing templates to guide analyzing the issues, collecting and interpreting relevant data, and developing potential solutions. The goals are to design an action research project focused on classroom instruction, apply statistical methods in Microsoft Excel, and recognize the value of action research for addressing school problems. Participants will individually construct an action research project meeting division and local requirements.
This research is aimed at developing a learning model that encourages the skills of analytical thinking in science. The method used is research and development. The result is the ICAE (Incubation, Collection of data, Analysis, and Evaluation) model that promotes analytical thinking skills. Results of normalized gain tests show that the gain score is 0.28, which indicates that the ICAE learning model positively affects students’ analytical thinking, even though still within the lower category. The ICAE model also promotes the skills of analytical thinking in science and it has gained positive response from students.
Assessing Problem-Solving Strategy Use By Engineering UndergraduatesHeather Strinden
This document summarizes a research study that assessed problem-solving strategies used by engineering undergraduates. The study examined which strategies students reported using, whether certain strategies clustered together, and if frequent strategy use correlated with better course performance. It also looked at how confidence, familiarity with course material, and interest affected performance. The researchers identified three types of strategies - Execution, Planning and Looking Back, and Low Confidence in Ability. Frequent use of strategies and higher confidence predicted better grades, while low confidence hindered performance. Strategy use differed between physics and thermodynamics courses.
A Nobel Approach On Educational Data Miningijircee
This document discusses educational data mining and its applications. It begins with introducing data mining and its goal of extracting useful information from large databases. Educational data mining is then discussed as using data mining techniques to understand how students learn. The objectives of educational data mining are outlined as supporting educational research, effective learning, prediction, and feedback. Common data mining techniques discussed include summarization, cluster analysis, classification and prediction, decision trees, and association. The document concludes with how these techniques can be applied in education for knowledge discovery and improving student success.
Application of Graph Theory in an Intelligent Tutoring System for Solving Mat...Valerie Felton
This document discusses applying graph theory to develop a model for solving mathematical word problems involving motion in an intelligent tutoring system. It first categorizes motion problems based on characteristics like number of vehicles and their direction of motion. It then proposes using a graph structure to represent the relationships between variables in motion problems. Finally, it outlines three main models for solving different categories of motion problems using graph theory techniques like backward and forward chaining.
This document summarizes a study that aimed to enhance creativity among university students using e-learning. It developed an e-learning system integrating Problem-Based Learning and Creative Problem Solving models. Students took a pre-test, received an 8-week intervention using the e-learning system, and took a post-test. Both quantitative and qualitative data were collected. Quantitative data came from pre- and post-tests using the Torrance Test of Creative Thinking. Qualitative data came from the e-learning discussion forum and open-ended questions about the system's effectiveness. The study aimed to produce a creativity learning framework for tertiary education students.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
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تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
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This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
2. A Study on the SW Coding Education and Creative Problem Solving
http://www.iaeme.com/IJARET/index.asp 327 editor@iaeme.com
is increasing to explain creative problem solving by establishing nine main concepts called
Computational Thinking. Computational Thinking provides an empirical methodology for the
process of solving problems in each step by applying the principles of computer science to
problem -solving.
Therefore, in this study, we will apply the model of Creative Problem-Solving, which has
been focused on creative talent education so far, to SW education. Through this, a more
empirical and systematic software education creative problem -solving model is proposed.
2. RELEATED BACKGROUND
2.1. Creative Problem Solving and Concept of Computational Thinking
Creative problem solving is a mental process of creating a Creative Problem-Solving method
in order to solve a problem. Creative problem solving means not getting any help and being
able to solve a problem independently [1].
The Creative Problem-Solving model is the most well-known model approaching creative
problem solving in a systematic way. Osborn initially proposed a 7 step Creative Problem-
Solving model in 1953 which further improved through several changes. Recently, Isaksen &
Treffiner(2005) improved to version 6.1TM
with 4 components(Understanding the Challenge,
Generating Ideas, Planning your Approach, Preparing for Action) and 8 steps(Constructing
Opportunities, Exploring Data, Framing Problems, Generating Ideas, Developing Solutions,
building Acceptance, Appraising tasks, Designing Process) and is being used in various
researches [2-3]. Computational Thinking was first used in 1996 by Seymour Papert and
became widely known by Wing [4-5]. Computational Thinking uses the basic concepts and
principals of computer science. Computational Thinking is defined as problem solving, system
design and the understanding of human deed Computational Thinking also includes abstract
thinking, recursive thinking, analytical thinking, procedural thinking and logical thinking. ISTE
defined the main concepts of Computational Thinking using the results of research earned from
Barr, Harrison, & Coney and is shown in the Tables 1 [6-7].
Tables 1 Computational Thinking Main Concepts
Concept Definition
Data Collection
Problem understanding, analysis and collect data based on analysis to
solve the problem
Data Analysis
Carefully sorting and analyzing the data collected and data provided in the
problem
Data
Representation
Express data in problem using graphs, charts, words and images
Problem
Decomposition
Dividing and analyzing the problem to solve the problem
Abstraction Defining the main concepts to reduce the complexity of the problem
Algorithm and
Procedures
Expressing the steps required to solve the problem until now
Automation
Creating an algorithm of the solution procedure for a computing machine
to carry it out
Simulation Creating an experimental model to solve the problem
Parallelization Coming up with a common objective to solve a problem
3. Jungin Kwon
http://www.iaeme.com/IJARET/index.asp 328 editor@iaeme.com
2.2. Creative Problem-Solving Model based on Computational Thinking
The main concepts of Computational Thinking were divided into 3 stages, and then we applied
it in Creative Problem-Solving model. In the introduction stage for problem understanding,
Data Collection, Data Analysis and Data Representation concepts are mainly used. Lesson
development based on Problem Decomposition, Abstraction, Algorithm and Procedure effect
the speed of reaching fast learning goals. Using automation, simulation and Parallelization for
the solutions earned through lesson development to organize data help solve complex and
difficult problems. Figure 1 shows a model of such hypotheses organized together. Therefore,
we can establish a research model which shows the 9 main concepts of computational thinking
effect the improvement of creating problem solving [8].
Figure 1. Creative Problem-Solving Model based on Computational Thinking
3. METHODS AND RESULTS
3.1 Research Subject and Methods
In order to verify the experiment of the research model, a freshman from S University in Seoul
was selected as an experiment subject. In this experiment, a total of 434 people were randomly
divided into the experimental group and the control group.
Table 2
Group Target number of people
Experimental Group 218
Controlled Group 216
Total 434
In order to analyses the effectiveness of Computational Thinking-based SW Coding
education on creative problem solving, the experimental group and the controlled group were
divided. Prior to the experiment, to test the homogeneity of the experimental group and the
controlled group, a two-independent sample t-test was conducted based on the results of the
pre-test. As a result, the two groups were found to be homogeneous. In the experimental group,
training in which SW Coding based on Computational Thinking was applied to the Creative
Problem-Solving teaching-learning course was conducted 10 times out of 15 weeks, and
controlled group was applied the teaching-learning course plan for general SW coding.
Education was conducted to examine the differences between the two groups, and the degree
of improvement in the affective part and the improvement in the cognitive part were
investigated. Test papers were used for pre-test and post-test examination of the affective
domain of creative problem solving. This test paper was developed by the MI Research Team
4. A Study on the SW Coding Education and Creative Problem Solving
http://www.iaeme.com/IJARET/index.asp 329 editor@iaeme.com
at Seoul National University's Psychology Laboratory in 2004 based on the “Simple Creative
Problem-Solving Ability Test and Development Research (I)” (2001) studied by the Korea
Educational Development Institute [9]. The simple Creative Problem-Solving ability test
consists of self-confidence and independence, diffuse thinking, critical and logical thinking,
and motivational thinking. It is composed of 5 questions for each area and is composed of a 5-
point scale, and each area is quantified through total and average. The larger the number of
results, the higher the Creative Problem-Solving ability.
A test sheet was developed to test the cognitive domain of creative problem solving. This
test paper was developed by adjusting the problem-solving literacy area of the 2003 PISA open
questions and some of the 2006 open questions to match the level of the first year of university,
and some of the items of the logical thinking ability test paper (GALT) to match the level of
the first year of university [10]. After the test sheet was developed, the questions were revised
based on the review of education experts and field teachers, and pre-test and post-test were
conducted.
3.2. Experiment Verification Methods
3.2.1 Pre- Test
1) The affective test of creative problem solving
For the affective test of Creative Problem-Solving, the number of students who participated in
the experiment collected a total of 463 questionnaires, 238 in the experimental group and 225
in the control group. However, a total of 434 surveys were statistically analyzed, with 218
people in the experimental group and 216 people in the control group, excluding test papers
such as unfaithful responses or typographical errors. A two-independent sample t-test was
performed to compare the mean of the experimental group and the control group and to verify
the similarity between the two groups are shown in the Tables 2.
Table 2 Statistical Analysis of Experimental Group and Control Group (Definitive Area) _Pre-
Verification
Group
classification
Mean
Standard
deviation
Standard error
of the mean
Self-confidence
and
independence
Experimental
group
13.8440 3.48181 .23582
Controlled group 13.8935 3.32192 .22603
Divergent
thinking
Experimental
group
15.2248 3.45210 .23381
Controlled group 15.3611 3.37283 .22949
Critical and
logical thinking
Experimental
group
16.4266 2.95381 .20006
Controlled group 16.3333 3.29623 .22428
Motivational
factor
Experimental
group
17.0550 3.13104 .21206
Controlled group 17.0972 3.06439 .20851
Shown in the Tables 3. In the results of the independent sample t-test of the experimental
group and the control group, the significance probability is 0.318, 0.518, 0.286, and 0.935 (self-
confidence and independence, divergent thinking, critical/logical thinking, and motivational
factors), respectively, which is greater than the significance probability of 0.05. It can be said
that the variance of the two groups is the same. In the case of self-confidence and independence,
the t-value is -0.151, which is greater than -1.96, and the t-value has a significance probability
of 0.88, which is greater than 0.05, so it can be concluded that there is no difference in the mean
5. Jungin Kwon
http://www.iaeme.com/IJARET/index.asp 330 editor@iaeme.com
between the two groups. In the case of divergent thinking, it can be concluded that there is no
difference in the mean between the two groups because the t value is -0.416, which is greater
than -1.96, and the t value has a significance probability of 0.678, which is greater than 0.05.
In the case of critical and logical thinking, it can be concluded that there is no difference in the
mean between the two groups because the t-value is 0.311, which is less than 1.96, and the t-
value has a significance probability of 0.432, which is greater than 0.05. In the case of the
motivational factor, the t-value is -0.142, which is greater than -1.96, and the t-value has a
significance probability of 0.887, which is greater than 0.05, so it can be concluded that there
is no difference in the mean between the two groups.
Therefore, it is possible to conclude that the experimental group and the control group are
statistically homogeneous in the positive experimental verification.
Table 3 Independent sample t-test result (Definitive area) _Pre-validation
Levne`s test of
equal variance
T-test for equality of means
F
Significanc
e
probability
t
Degree of
freedom
Significance
probability
(both sides)
Mean
difference
Standard
error of
the
difference
95% confidence
interval of the
difference
Minimum Maximum
Self-
confide
nce and
indepen
dence
Equal
variance
is
assumed
.998 .318 -.151 432 .880 -.049 .3267 -.691 .592
No equal
variance
is
assumed
-.151 431.3 .880 -.049 .3266 -.691 .592
Diverge
nt
thinkin
g
Equal
variance
is
assumed
.418 .518 -.416 432 .678 -.136 .3276 -.780 .507
No equal
variance
is
assumed
-.416 431.9 .677 -.136 .3276 -.780 .507
Critical
and
logical
thinkin
g
Equal
variance
is
assumed
1.143 .286 .311 432 .756 .0932 .3003 -.497 .683
No equal
variance
is
assumed
.310 426.0 .756 .0932 .3005 -.497 .684
Motivat
ional
factor
Equal
variance
is
assumed
.007 .935 -.142 432 .887 -.042 .2974 -.626 .542
No euqal
variance
is
assumed
-.142 431.9 .887 -.042 .2974 -.626 .542
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2) Cognitive test of creative problem solving
Table 4 Statistical analysis of experimental and control groups (cognitive domain)_Pre-verification
Group classification Mean
Standard
deviation
Standard
error of the
mean
Cognitive test
score
Experimental group 51.88 19.383 1.313
Controlled group 53.03 18.849 1.283
To compare and evaluate the prior cognitive abilities of the experimental group and the
control group, a two-independent sample t-test was conducted to verify the similarity of the two
groups, similar to the analysis method of the affective domain. The results of the pre-cognition
verification of the two groups are shown in Table 4.
Shown in the Tables 5 as a result of the independent sample t-test of the experimental group
and the control group, the significance probability of the F value was 0.765, respectively, which
is greater than the significance probability of 0.05, so the variance of the groups can be said to
be the same. If the two groups have the same variance, the hypothesis is tested with the t-value
and the significance probability when equal variance is assumed, so the t-value is -0.627, which
is greater than -1.96, and the t-value is 0.531, which is greater than 0.05. It can be concluded
that there is no difference between them. Therefore, it is possible to conclude that the
experimental group and the controlled group are statistically homogeneous groups in cognitive
experiment verification.
Table 5. Results of independent sample t-test (cognitive domain) _Pre-test
Levne`s test of
equal variance
T-test for equality of means
F
Signifi
cance
proba
bility
t
Degree
of
freedo
m
Significan
ce
probabilit
y
(both
sides)
Mean
differen
ce
Standar
d error
of the
differen
ce
95% confidence
interval of the
difference
Minimu
m
Maximu
m
Cogniti
ve test
score
Equal
variance
is
assumed
.090 .765 -.627 432 .531 -1.152 1.836 -4.759 2.456
No equal
variance
is
assumed
-.628 431.8 .531 -1.152 1.835 -4.759 2.456
3.2.2. Experimental Treatment
Among the Creative Problem-Solving model, one of the systematic approaches to creative
problem solving, Treffinger, Isaksen & Dorval prepared a course plan according to the model
stage of Creative Problem-Solving v6.1 released in 2006. The Creative Problem-Solving v6.1
model consists of four components and eight specific steps: understanding the challenge,
generating ideas, preparing for action, and planning the approach. In this study, among the four
stages of the Creative Problem-Solving model, understanding challenges was applied as
understanding problems, generating ideas as designing solutions, and preparing for
implementation as the best choice. And the teaching-learning stage was organized according to
the stages of introduction, development, and arrangement in the teaching-learning process. In
order to Understanding Problem, it was possible to understand the learning problem through
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Data Collection, Data Analysis, and Data Representation. At this time, data collection, analysis,
and expression should be parallelized at the same time, and learners should be able to increase
the effect of understanding problems. In addition, for generating ideas for solving problems, a
teaching-learning process plan is constructed to go through the process of abstraction and
algorithms & procedures. Abstraction utilizes the learner's empirical knowledge to create a
solution for problem solving, and design it so that the created solution can be logically and
critically structured.
Finally, the teaching-learning process plan was designed to find the best solution through
computer-based simulation to select the best solution for problem solving.
3.2.3. Post- Test
1) The affective test of creative problem solving
For the affective test of creative problem solving, the experimental group and the controlled
group performed a post-test through the simple Creative Problem-Solving ability test paper,
which is the same test paper as the pre-test. shown in the Tables 6.
Table 6 Statistical analysis of experimental group and control group (definitive area) _Post
verification
Group
classification
Mean
Standard
deviation
Standard error
of the mean
Self-confidence
and independence
Experimental group 15.1697 2.88067 .19510
Controlled group 14.1019 3.18847 .21695
Divergent
thinking
Experimental group 15.7844 2.87598 .19479
Controlled group 15.5463 3.12495 .21263
Critical and
logical thinking
Experimental group 18.0734 2.34847 .15906
Controlled group 16.4769 3.11029 .21163
Motivational
factor
Experimental group 18.0046 2.33289 .15800
Controlled group 17.1759 2.96594 .20181
Table 7 Independent sample t-test result (Definitive area) _Post-validation
Levne`s test of
equal variance
T-test for equality of means
F
Significanc
e
probability
t
Degree
of
freedo
m
Significanc
e
probability
(both
sides)
Mean
differenc
e
Standard
error of
the
differenc
e
95% confidence
interval of the
difference
Minimu
m
Maximu
m
Self-
confidence
and
independe
nce
Equal
variance
is
assumed
2.293 .131 3.662 432 .000 1.067 .2916 .49467 1.64108
No equal
variance
is
assumed
3.660 426.8 .000 1.067 .2917 .49438 1.64136
Divergent
thinking
Equal
variance
is
assumed
.658 .418 .826 432 .409 .2381 .2882 -.32844 .80465
No equal
variance
is
assumed
.826 428.3 .409 .2381 .2883 -.32867 .80488
Critical
and logical
thinking
Equal
variance
is
assumed
9.208 .003 6.038 432 .000 1.596 .2644 1.07687 2.11622
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No equal
variance
is
assumed
6.031 400.0 .000 1.596 .2647 1.07609 2.11699
Motivation
al factor
Equal
variance
is
assumed
11.900 .001 3.237 432 .001 .828 .2560 .3254 1.33187
No euqal
variance
is
assumed
3.233 407.6 .001 .828 .2563 .3248 1.33250
Shown in the Tables 7 as a result of the independent sample t-test of the experimental group
and the control group, the significance probability of the F value for self-confidence and
independence was 0.131 each, which is greater than the significance probability of 0.05, so the
variance of the groups can be said to be the same.
Therefore, since the variance of the two groups is the same for self-confidence and
independence, the t-value and the significance probability are hypothesized when equal
variance is assumed. At this time, since the t-value is 3.662, which is greater than 1.96, and the
significance probability of the t-value is 0.000, which is less than 0.05, it can be concluded that
there is a difference between the two groups. Since the significance probability of the F value
for diffuse thinking is 0.418 each, which is greater than the significance probability of 0.05, the
variance of the groups can be said to be the same. Therefore, in diffusive thinking, since the
variance of the two groups is the same, the hypothesis test is performed on the t value and the
significance probability as a case where equal variance is assumed. Since the t-value at this time
is 0.826, which is less than 1.96 and the significance probability of the t-value is 0.409, which
is greater than 0.05, we can conclude that there is no difference between the two groups. Since
the significance probability of the F value for critical and logical thinking is 0.003 each, which
is less than the significance probability of 0.05, it can be said that the group's variance is not
the same. Therefore, in critical and logical thinking, since the variances of the two groups are
not the same, the t-value and the significance probability are hypothesized when equal variance
is not assumed. Since the t-value at this time is 6.031, which is greater than 1.96, and the
significance probability of the t-value is 0.000, which is less than 0.05, it can be concluded that
there is a difference between the two groups. Since the significance probability of the F value
for the motivational factor is 0.001, which is less than the significance probability of 0.05, it
can be said that the variance of the groups is not the same. Therefore, since the variance of the
two groups is not the same as the motive factor, the t value and the significance probability are
hypothesized when equal variance is not assumed. Since the t-value at this time is 3.233, which
is greater than 1.96, and the significance probability of the t-value is 0.001, which is less than
0.05, it can be concluded that there is a difference between the two groups. Therefore, the
experimental group and the controlled group can conclude that self-confidence and
independence, critical and logical thinking, and motivational factors are statistically
heterogeneous groups in the positive post-experimental verification. Divergent thinking
appeared as a homogeneous group.
2) Cognitive test of creative problem solving
The post-cognitive test for creative problem solving in the experimental group and the
controlled group is similar to when the preliminary test sheet was formed, and the problem-
solving literacy area of the 2003 PISA open questions and some of the 2006 open questions
were adjusted to suit the level of the first year of university, Some of the items in (GALT) were
used, but they were not duplicated with those used in the pre-test. After the test sheet was
developed, the questions were revised based on the review of education experts and field
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teachers, and post-tests were conducted as in the pre-test. The results of the post-test by the
cognitive test paper are shown in Table 8.
As a result of the post-test, the average score of the experimental group and the controlled
group was 59.01 and 55.21, respectively, which improved the average score in both groups
compared to the pre-test. An independent sample t-test was conducted to verify whether the
difference between the means of the two groups was a statistically significant difference
Table 8. Statistical analysis of experimental and control groups (cognitive domain) _Post-verification
Group classification Mean
Standard
deviation
Standard
error of the
mean
Cognitive test score
Experimental group 59.01 16.588 1.124
Controlled group 55.21 19.489 1.326
Shown in the Tables 9 as a result of the independent sample t-test of the experimental group
and the controlled group, the F-value significance probability is 0.012 each, which is less than
the significance probability of 0.05, so it can be said that the group's variance is not the same.
If the variances of the two groups are not equal, the hypothesis test is performed with the t value
and the significance probability as the case where equal variance is not assumed.
Since the t-value at this time is 2.189, which is greater than 1.96, and the significance
probability of the t-value is also 0.029, which is less than 0.05, it can be concluded that there is
a difference between the two groups. Therefore, the experimental group and the controlled
group can be concluded as statistically heterogeneous groups in cognitive experimental
verification.
Table 9. Results of independent sample t-test (cognitive domain) _Post-test
Levne`s test of
equal variance
T-test for equality of means
F
Significance
probability
t
Degree
of
freedom
Significance
probability
(both sides)
Mean
difference
Standard
error of
the
difference
95% confidence
interval of the
difference
Minimum Maximum
Cognitive
test score
Equal
variance
is
assumed
6.382 .012 2.191 432 .029 3.805 1.737 .392 7.219
No
equal
variance
is
assumed
2.189 420.0 .029 3.805 1.738 .389 7.222
3.3. Results of Experimental Verification
3.3.1. Affective Verification Result of Creative Problem Solving
Shown in the Tables 10 as a result of pre-tests and post-tests for the affective tests of the
experimental group and the control group, the items of self-confidence and independence,
critical and logical thinking, and motivational factors were measured for the effectiveness of
the experiment. But there is no measurement of effectiveness in divergent thinking.
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Table 10. Comparison of pre- and post-tests between the experimental group and the control group
(definitive domain)
Group classification N
Mean
Pre-test Post-test
Self-confidence and
independence
Experimental group 218 13.8440 15.1697
Controlled group 216 13.8935 14.1019
Divergent thinking
Experimental group 218 15.2248 15.7844
Controlled group 216 15.3611 15.5463
Critical and logical
thinking
Experimental group 218 16.4266 18.0734
Controlled group 216 16.3333 16.4769
Motivational factor
Experimental group 218 17.0550 18.0046
Controlled group 216 17.0972 17.1759
In other words, it was concluded that the SW coding class applying the Creative Problem-
Solving model based on Computational Thinking can influence creative problem solving such
as self-confidence and independence, critical and logical thinking, and motivational factors.
3.3.2. Cognitive Verification Result of Creative Problem Solving
In the experimental group, we took a SW coding class that applied the Creative Problem-
Solving model based on Computational Thinking. In the controlled group, a SW coding class
that applied the Creative Problem-Solving model was conducted. And, shown in the Tables 11
as a result of comparing the pretest - posttest for the cognitive tests of the experimental group
and the controlled group, the effectiveness of the cognitive domain of the experimental group
was measured, although incomplete.
Table 11. Comparison of pre- and post-tests between the experimental group and the control group
(cognitive domain)
Group classification N
Mean
Pre-test Post-test
Cognitive test score
Experimental group 218 51.88 59.01
Controlled group 216 53.03 55.21
In other words, it was concluded that SW coding education applying the Creative Problem-
Solving model based on Computational Thinking can affect the cognitive domain of creative
problem solving.
4. RESULT
A study result was drawn that suggests that SW Coding education based on Computational
Thinking can affect creative problem solving. The stage of creative problem solving is largely
the process of understanding the problem, generating ideas for solving the understood problem,
and selecting among the best solution based on designed problem-solving process. The main
concepts of Computational Thinking are applied to each stage of creative problem solving and
used as follows.
First, to understand the problem, it provides learners and instructors with the process of
collecting data, analyzing the collected data, and expressing the analyzed data. Then, presents
practical actions on what learners and instructors should do to understand the problem. At this
time, each step is not separated, and understanding of the problem brings more effective results
when work is done at the same time.
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Second, if you understand the problem, you must design a solution process to solve the
problem based on the understanding. At this time, in order to design the solution process, the
process of problem solving should be designed using empirical knowledge or common sense
that the learner or instructor already knows. Based on this, a solution process should be designed
through repetitive convergence of diffuse and divergent thinking, and the designed solution
process should be procedural as a single process.
Third, among the methods of solving problems through a procedural process, the best
method is adopted. At this time, the fact that experimentation is impossible in reality or
experimentation is helpful in selecting the best solution for problem solving by using
simulation.
In this study, in order to investigate the influence of software coding education based on
Computational Thinking on creative problem solving, an empirical method was proposed for
learners and instructors to creatively solve problems at each stage. It is expected to be able to
provide more empirical experiences and opportunities for divergent and diffuse thinking to
instructors and learners, as well as increase the efficiency of problem solving.
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