scholarly journals Self-Generated Figures in Sequence Processing

2021 ◽  
pp. 13-21
Author(s):  
David GINAT
Keyword(s):  
Problem Solving ◽  
Mental Model ◽  
Algorithmic Problem ◽  
Advanced Stage ◽  
Sequence Processing ◽  
Hidden Patterns ◽  
Better Than

A figure may convey an idea, an argument and even a proof, sometimes better than words. It may also elicit an idea, an argument and a proof. In problem solving, a figure may give a “feel” of a problem. A self-generated figure may help getting insight, or serve as a means for representing one’s inner associations, or mental model of the problem. This paper presents selfgenerated figures in algorithmic problem solving. Students of our IOI advanced stage demonstrated constructive utilization of self-generated figures in solving challenging sequence processing tasks. The figures elicited associations of hidden patterns, whose recognition yielded elegant and efficient algorithmic solutions. We advocate the application and examination of self-generated figures in algorithmic problem solving.

PENGGUNAAN MODEL KOOPERATIF TIPE TEAM GAMES TOURNAMENT UNTUK MENINGKATKAN KEMAMPUAN SISWA SEKOLAH DASAR

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhammad Noor
Keyword(s):  
Problem Solving ◽  
Sampling Technique ◽  
Control Group ◽  
Control Group Design ◽  
Team Games ◽  
Conventional Models ◽  
Grade Material ◽  
Normalized Gain ◽  
Cooperative Models ◽  
Better Than

The purpose of this study was to obtain empirical evidence about the use of cooperative models of Team Games Tournament to increase the ability of students on solving problems with the summation material fractions. To achieve these objectives, the research carried out in the form of an experiment by comparing the problem solving ability of students to the material sum of fractions through the use cooperative model of TGT and students who received conventional learning. The design is a pretest-posttest control group design. The sampling technique used is purposive sampling technique. The instrument used is to use tests that pretest and posttest. The data were analyzed quantitatively for the results of the pretest, posttest, and normalized gain value. Based on data analysis in this study we concluded that there are differences in problem solving ability of students to the material sum of fractions through the use of cooperative models of Team Games Tournament with students who studied with conventional models, and improved problem solving abilities of students in the material that follows the fractional summation cooperative learning of TGT better than students who take the conventional learning model. Therefore, the ability of solving problems of students at grade material fractions summation cooperative modeled of TGT has increased quite good.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

Algorithmic Puzzles

2011 ◽  
Author(s):  
Anany Levitin ◽  
Maria Levitin
Keyword(s):  
Problem Solving ◽  
Computer Science ◽  
Algorithm Design ◽  
Divide And Conquer ◽  
Algorithmic Problem ◽  
Design Strategies ◽  
Job Interviews ◽  
Skill Levels ◽  
Problem Solving Skills ◽  
Solution Methods

While many think of algorithms as specific to computer science, at its core algorithmic thinking is defined by the use of analytical logic to solve problems. This logic extends far beyond the realm of computer science and into the wide and entertaining world of puzzles. In Algorithmic Puzzles, Anany and Maria Levitin use many classic brainteasers as well as newer examples from job interviews with major corporations to show readers how to apply analytical thinking to solve puzzles requiring well-defined procedures. The book's unique collection of puzzles is supplemented with carefully developed tutorials on algorithm design strategies and analysis techniques intended to walk the reader step-by-step through the various approaches to algorithmic problem solving. Mastery of these strategies--exhaustive search, backtracking, and divide-and-conquer, among others--will aid the reader in solving not only the puzzles contained in this book, but also others encountered in interviews, puzzle collections, and throughout everyday life. Each of the 150 puzzles contains hints and solutions, along with commentary on the puzzle's origins and solution methods. The only book of its kind, Algorithmic Puzzles houses puzzles for all skill levels. Readers with only middle school mathematics will develop their algorithmic problem-solving skills through puzzles at the elementary level, while seasoned puzzle solvers will enjoy the challenge of thinking through more difficult puzzles.

Non-Verbal Rigidity, Creativity, and Problem Solving

1969 ◽  
Vol 29 (3) ◽  
pp. 715-718 ◽  
Author(s):  
Bernard S. Gorman ◽  
Stephen Breskin
Keyword(s):  
Problem Solving ◽  
Inductive Reasoning ◽  
Verbal Test ◽  
Better Than ◽  
Fluency Test

Rigidity vs flexibility has often been mentioned in discussions of creativity and problem solving. The present study investigated the relation of a non-verbal test of rigidity (Breskin Rigidity Test) to tests of semantic redefinition, associational fluency, inductive reasoning, and drawing completion. The performance of flexible Ss was significantly better than the performance of rigid Ss on all tests but the associational fluency test.

scholarly journals IMPLEMENTATION OF MODEL-ELICITING ACTIVITIES TO IMPROVE THE ABILITY OF MATHEMATICAL PROBLEM SOLVING

2018 ◽  
Vol 1 (3) ◽  
pp. 312 ◽  
Author(s):  
Rubaitun Rubaitun
Keyword(s):  
Problem Solving ◽  
Mathematical Problem ◽  
Mathematical Problem Solving ◽  
Homogeneity Test ◽  
Problem Solving Skills ◽  
Normality Test ◽  
Model Eliciting Activities ◽  
Class Viii ◽  
And Control ◽  
Better Than

This study aims to determine whether the improvement of students' mathematical problem solving skills that get the learning of Model-Eliciting Activities is better than students who get regular learning. Method in this research is experiment and research design pretest and postest in experiment and control class. The population in this study were all students of MTs Kota Cimahi. School samples were taken at random, and obtained by MTs Negeri Kota Cimahi. Then the sample is selected two class VIII at random class. The experimental class uses Model-Eliciting Activities, while the control class uses ordinary learning. The hypothesis in this research is the improvement of student solving abilities of MTs students in Cimahi whose learning using Model-Eliciting Activities is better than using ordinary learning. Research data obtained through the instrument of posttest mathematical problem solving ability. The posttest data is processed by normality test, homogeneity test, and two average difference test using SPSS (Statistical Product and Service Solution) software version 16.0 for Windows. The results showed that the improvement of problem solving ability of MTs students in Cimahi whose learning using Model-Eliciting Activities was better than those using ordinary learning.

scholarly journals Perbedaan Jigsaw II dan GI terhadap pemahaman konsep dan pemecahan masalah masalah pada kompetensi mendiagnosis permasalahan pengoperasian PC dan Peripheral ditinjau dari motivasi belajar

2013 ◽  
Vol 3 (2) ◽  
Author(s):  
Pipit Utami ◽  
Pardjono Pardjono
Keyword(s):  
Problem Solving ◽  
Cooperative Learning ◽  
Comparison Group ◽  
Learning Motivation ◽  
Type Ii ◽  
Concept Understanding ◽  
Group Design ◽  
Analysis Technique ◽  
Quasi Experimental ◽  
Better Than

Penelitian ini bertujuan untuk mengetahui perbedaan pemahaman konsep dan pemecahan masalah pada materi KK3: (1) antara siswa yang diajar dengan pembelajaran kooperatif tipe Jigsaw II dan siswa dengan pembelajaran kooperatif tipe Group Investigation (GI) ketika motivasi belajar TKJ dikendalikan; dan (2) antara penggunaan tipe pembelajaran kooperatif (tipe Jigsaw II dan tipe GI) dengan tingkat motivasi belajar TKJ (tinggi dan rendah). Penelitian ini merupakan penelitian eksperimen semu dengan nonequivalent comparison-group design menggunakan dua kelas perlakuan sebagai variabel bebas yang diberikan pembelajaran kooperatif dengan dua tipe berbeda. Satu kelas menggunakan tipe Jigsaw II, sedangkan kelas yang lainnya diberikan tipe GI. Variabel motivasi belajar TKJ dijadikan sebagai pembagi kategori kelompok siswa yang memiliki motivasi belajar TKJ tinggi dan rendah serta sebagai kovarian. Teknik analisis data yang digunakan adalah analisis multivarian kovariat dan desain faktorial dengan progam SPSS 16. Artikel ini menunjukkan tujuan kedua, dengan hasil penelitian menunjukkan bahwa: (1) untuk pencapaian pemahaman konsep, pembelajaran kooperatif tipe Jigsaw II dan tipe GI baik diaplikasikan untuk siswa yang memiliki motivasi belajar TKJ tinggi dan rendah, akan tetapi untuk siswa yang memiliki motivasi belajar TKJ rendah lebih baik menggunakan tipe GI; dan (2) untuk pencapaian pemecahan masalah, pembelajaran kooperatif tipe Jigsaw II dan tipe GI baik diaplikasikan untuk siswa yang memiliki motivasi belajar TKJ tinggi, siswa yang memiliki motivasi belajar TKJ rendah maupun siswa yang memiliki motivasi belajar TKJ tinggi lebih baik menggunakan tipe GI. Kata  THE DIFFERENCES OF JIGSAW II AND GI ON THE CONCEPT UNDERSTANDING AND PROBLEM SOLVING IN COMPETENCE OF DIAGNOSING PROBLEMS WHEN OPERATE PC AND PERIPHERAL IN TERMS OF LEARNING MOTIVATIONAbstractThis research aims to describe the differences of concept understanding and problem solving on the KK3 material: (1) between students taught using the cooperative learning Jigsaw Type II and GI Type when TKJ learning motivation was controlled; and (2) between the use of cooperative learning (Jigsaw Type II and Group Investigation (GI) Type) with the levels of TKJ learning motivation (high and low). This research was quasi-experimental with the nonequivalent comparison-group design using two treatment classes as independent variables which were given cooperative learning with two different types. One class used the Jigsaw Type II while the other used the GI Type. The TKJ learning motivation was used as the divider category of students who have high and low TKJ learning motivation as well as covariant. The data analysis technique in this research was the multivariat analysis of covariate and factorial design using the SPSS 16 program. This article shows the second aim, and the results shows that: (1) for concept understanding achievement, cooperative learning Jigsaw Type II and GI Type are good to be applied for students who have high and low TKJ learning motivation, but for those who have low TKJ learning motivation, GI Type is better than Jigsaw Type II; and (2) for problem solving achievement, cooperative learning Jigsaw Type II and GI Type are good to be applied to students who have high TKJ learning motivation, where for those who have high and low TKJ learning motivation GI Type is better than Jigsaw Type II.

scholarly journals Kemampuan Berpikir Kritis dan Metakognisi Siswa dalam Menyelesaikan Masalah Matematika melalui Pendekatan Problem Solving

2017 ◽  
Vol 6 (2) ◽  
pp. 234 ◽  
Author(s):  
Muhammad Ikhsan ◽  
Said Munzir ◽  
Lia Fitria
Keyword(s):  
Critical Thinking ◽  
Problem Solving ◽  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Control Group ◽  
Control Group Design ◽  
Critical Thinking Ability ◽  
Group Design ◽  
Thinking Ability ◽  
Better Than

The aims of this study are to determine the improvement of critical thinking skills mathematical and metacognition of students who are taught with problem solving approach and the correlation between mathematical critical thinking and metacognition of students. This research is an experimental research with pretest-posttest control group design. The sample this research is the students of class VIII_2 and VIII_3 in SMP Negeri 1 Banda Aceh. Collecting data technique are test and nontest. Data were analyzed using t-test and correlation test. The result of the research shows 1) the critical thinking ability of the students who get the learning through problem solving approach is better than the students who get the conventional learning, 2) Metacognition of students who get the learning by using problem solving approach is better than the students who get the conventional learning, 3) a positive and significant relationship between students' metacognition and critical thinking skills.

Are Two Solutions Better than One? Effects of Multiple Solutions on Group Problem Solving

2021 ◽  
Vol 2021 (1) ◽  
pp. 12013
Author(s):  
Gbemi Abimbola ◽  
P Robert Duimering ◽  
Arielle Grinberg
Keyword(s):  
Problem Solving ◽  
Multiple Solutions ◽  
Group Problem ◽  
Group Problem Solving ◽  
Better Than
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