scholarly journals PEMBELAJARAN UNTUK MENGEMBANGKAN KEMAMPUAN PENALARAN INFERENSIAL STATISTIS

2018 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Laila Hayati
Keyword(s):  
Learning Environment ◽  
Statistical Learning ◽  
Learning Theory ◽  
Thinking Skills ◽  
Learning Model ◽  
Statistical Reasoning ◽  
Inferential Reasoning ◽  
Reasoning Abilities ◽  
Informal Inferential Reasoning ◽  
Definition Of

Abstrak: Artikel ini membahas tentang pembelajaran yang dapat digunakan untuk mengembangkan kemampuan penalaran inferensial statistis. Tujuan kognitif pembelajaran Statistika adalah untuk mengembangkan kemampuan literasi, penalaran, dan berpikir statistis. Salah satu tujuan dalam penalaran statistis adalah mengembangkan kemampuan penalaran inferensial. Model pembelajaran yang dapat digunakan untuk mengembangkan kemampuan penalaran inferensial statistis adalah model  Statistical Reasoning Learning Environment  (SRLE) yang didasarkan pada teori belajar konstruktivisme. Analisis didasarkan pada: 1) definisi inferensi statistis; 2) definisi penalaran inferensial informal dan formal; 3) kerangka kerja penalaran inferensial informal; 5) model pembelajaran SRLE; dan 6) penelitian terkait penalaran inferensial statistis.Abstract: This article deals with learning that can be used to develop inferential reasoning abilities statistically. The cognitive goal of statistical learning is to develop literacy, reasoning, and statistical thinking skills. One of the goals in statistical reasoning is to develop inferential reasoning abilities. The learning model that can be used to develop static inferential reasoning abilities is Statistical Reasoning Learning Environment (SRLE) model based on constructivism learning theory. The analysis is based on: 1) the definition of statistical inference; 2) the definition of informal and formal inferential reasoning; 3) informal inferential reasoning framework; 5) the learning model of SRLE; and 6) research related to inferential statistical reasoning.

ONLINE COLLABORATIVE LEARNING ELEMENTS TO PROPOSE AN ONLINE PROJECT BASED COLLABORATIVE LEARNING MODEL

2015 ◽  
Vol 77 (23) ◽  
Author(s):  
Sharifah Nadiyah Razali ◽  
Faaizah Shahbodin ◽  
Hanipah Hussin ◽  
Norasiken Bakar
Keyword(s):  
Collaborative Learning ◽  
Learning Environment ◽  
Learning Experience ◽  
Learning Task ◽  
Thinking Skills ◽  
Learning Model ◽  
Critical Thinking Skills ◽  
Design Factor ◽  
Online Collaborative Learning ◽  
Interaction Factor

Interest in collaboration is a natural outgrowth of the trend in education toward active learning. Many researchers have found that the advantages of collaborative learning; improves academic performance, promotes soft skills development (i.e., communications, collaboration, problem-solving and critical thinking skills), and increases satisfaction in the learning experience. Nevertheless, several studies have reported the complete opposite. In that respect, based on previous findings, three elements that are involved in the effectiveness of Online Collaborative Learning Environments are; Learning Environment, Learning Task, and Learning Interaction. This report proposes to determine the elements that can clarify all of the previously identified factors. Using the same approach as prior work, this study was conducted qualitatively; in the form of a document review. The outcome of this work suggests that (i) the learning interaction factor consists of learner-learner interaction and learner-teacher interaction elements, (ii) the elements of the learning design factor are content, process, evaluation, and time constraint, and (iii) usability, accessibility and stability are the ingredients of the learning environment factor. This study also proposes an Online Project-Based Collaborative Learning model. This model is currently only in a conceptual phase and requires significant development before it can be used to gather data. Therefore, in the next stage of this study, a prototype will be designed and developed; based on the proposed model.

CONCEPT-BASED LEARNING DAN KEMAMPUAN BERFIKIR KRITIS MATEMATIS

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Maifalinda Fatra ◽  
A Rizki ◽  
Tita Khalis Maryati
Keyword(s):  
Critical Thinking ◽  
Mathematics Learning ◽  
Sampling Technique ◽  
Thinking Skills ◽  
Learning Model ◽  
Critical Thinking Skills ◽  
Control Group ◽  
Simple Explanation ◽  
Critical Thinking Ability ◽  
Thinking Ability

Mathematical Critical thinking is one of  mathematical abilities that must be obtained by students. Critical thinking is one of the high order thinking processes that can make concepts in student knowledge.  Students with critical thinking in mathematics learning mostly do rational activities such as interpreting information based on a particular theoretical framework, linking theory with practice, making claims and justifying it, utilizing data in support of argumentation, making relations or relationships between various ideas, asking questions, evaluating knowledge, predict, describe something, analyze, synthesize, and categorize. This study aims to analyze the effect of the Concept-Based Learning Model on the critical thingking mathematical abilities.The purpose of this research is to analyze the effect of Concept-Based Learning Model toward mathematics critical thinking ability. The method used in this research is quasi experiment by Randomize Control Group Post Test Only Design with cluster random sampling technique. Indicators of mathematics critical thinking skills measured in this study include providing simple explanations, building basic skills, concluding, making more explanations, and deciding an action. The results showed that the mathematics critical thinking ability of students in the experimental class for the five indicators that being analyze was higher than the ability of students in the control class. A fairly high difference in the indicator showed in give a simple explanation and concluding. and it means that the application of Concept-Based Learning Model significantly influences the  abilities  of students' mathematics critical thinking.

KEEFEKTIFAN MODEL GUIDED DISCOVERY LEARNING UNTUK MENINGKATKAN KETERAMPILAN KETERAMPILAN BERPIKIR KRITIS

Edupedia ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 17-27
Author(s):  
Dian Noer Asyari
Keyword(s):  
Critical Thinking ◽  
Thinking Skills ◽  
Learning Model ◽  
Critical Thinking Skills ◽  
Discovery Learning ◽  
Learning Tools ◽  
Learning Situation ◽  
Guided Discovery

Discovery learning is a learning situation on wich the principle content of what is tobe learned is not give but must be independly discovered by student. The effectiveness of guided discovery learning model to improve students’ critical thinking skills uses 6 indicators, are: formulating problems, formulating hypotheses, analyzing date, providing alternatives, summing up, communicating and applying principle. The guided discovery learning model was included in the effective category in terms of: (a) Improvement (N-gain) of students’ critical thinking skills by 0.65 with moderate criteria and (b) Students respond positively to learning using guided discovery learning model and its learning tools on implementation.

PENGARUH MODEL PROBLEM BASED LEARNING (PBL) DAN GROUP INVESTIGATION (GI) DALAM PEMBELAJARAN SEJARAH DITINJAU DARI MOTIVASI BELAJAR DI SMA MARTAPURA

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Muhammad Dody Hermawan
Keyword(s):  
Critical Thinking ◽  
Interaction Effect ◽  
Problem Based Learning ◽  
Thinking Skills ◽  
Learning Model ◽  
Positive Influence ◽  
Critical Thinking Skills ◽  
Learning Motivation ◽  
Influence Model ◽  
Data Collection Technique

The purpose of this reaserch are (1) find differences influence model of Group Investigation (GI) and Problem Based Learning (PBL). (2) find differences influence learning motivation of students to critical thinking abilities of learners. (3) find the interaction effect between learning models and learning motivation of the critical thinking skills of learners. This study will be conducted in SMA Martapura, Banjar regency, South Kalimantan. Subjects of the study were students of class XI SMA Martapura academic year 2015/2016. This type of research to be carried out in this study is a quantitative study using experimental methods. Design of this research is 2 X 2 factorial design to data collection technique motivation questionnaire and tests critical thinking skills. The result, 1) There is a difference between the positive influence of the Model Group Investigation (GI) and Problem Based Learning (PBL). 2) There are differences positively influence the motivation of students to critical thinking ability of students in learning the history. 3) There are nointeractions influence student learning model and motivation for students' critical thinking skill There is no interaction effect Learning Model and the Motivation of students' critical thinking skills of students in Learning Historys. Keywords: PBL Models, GI Models, Learning Motivation

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

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