Characteristics of students who are diverse, such as different learning styles will also lead to the possibility of differences in students' ability to understand the learning material and every problem given, especially at the stage of understanding the problem given, because this stage is the most important step to determine the next problem-solving step. Therefore this study aims to reveal and illustrate how different characteristics of visual, audio and kinesthetic students in understanding problems. Data retrieval was done in class VIII of Arjasa 1 Junior High School with test methods, interviews, and questionnaires. Data were analyzed through stages of data reduction, analysis, and decision making. Furthermore, based on the data analysis that has been done, it can be concluded that there are differences in the characteristics of understanding problems for visual, auditory and kinesthetic students in terms of completeness and regularity of information writing, quantity of repetition of reading questions, marking important information and activity/movement habits done during the process of understanding the problem.
Mathematics is one of the important subjects in realizing the goals of education in Indonesia. However, in reality there are many students who have different problem solving abilities between one and the other and many students make mistakes when solving problems. The mistakes that are often carried out by students can also be influenced by differences in learning styles possessed. Therefore this article aims to describe differences in errors made by reflective-impulsive students in solving mathematical problems. This research is a qualitative descriptive that involving 2 research subjects with each subject having a different learning style. This research was conducted in class VII Pakusari Jember 1 Junior High School in the academic year of 2018/2019. The results of data analysis showed that differences in mistakes made by junior high school students with reflective-impulsive learning styles in solving mathematical problems were located at the stage of determining mathematical models and at the stage of completing mathematical models that had been made with the percentage of impulsive learning styles when compared to a subject that is reflective learning style.
This study aims to analyze the difficulties of students with special needs (mentally retarded, deaf, and blind children) in understanding mathematical concepts, which will be described by levels. The research uses descriptive method. Data collection technique used observations of students with special needs to see the difficulties in understanding mathematical concepts (dyscalculia), and interviews conducted with mathematics teachers to complement the results of the observations. The research sample is deaf students, blind students, and mentally retarded students in the Jember district. The data analysis technique in this study uses the flow model. The results of this study were that the level of achievement with good assessment categories was dominated by blind students, namely from 10 aspects of the assessment only weak in the section on grouping the shape dimensions, while the deaf achieved good assessments, namely 1) sorting objects based on the length and short size, 2) understanding the number of objects, 3) understanding numbers, and 4) writing and saying numbers. Meanwhile, mentally retarded students only have 1 good assessment aspect, namely understanding the length of objects. The conclusion is that dyscalculia is mostly suffered by mentally retarded children, deaf children suffered moderate level, and the best level of the subject is blind children.
SAE (Small Area Estimation) is often used by researchers, especially statisticians to estimate parameters of a subpopulation which has a small sample size. Empirical Best Linear Unbiased Prediction (EBLUP) is one of the indirect estimation methods in Small Area Estimation. The presence of outliers in the data can not guarantee that these methods yield precise predictions . Robust regression is one approach that is used in the model Small Area Estimation. Robust approach in estimating such a small area known as the Robust Small Area Estimation. Robust Small Area Estimation divided into several approaches. It calls Maximum Likelihood and MEstimation. From the result, Robust Small Area Estimation with M-Estimation has the smallest RMSE than others. The value is 1473.7 (with outliers) and 1279.6 (without outlier). In addition the research also indicated that REBLUP with M-Estimation more robust to outliers. It causes the RMSE value with EBLUP has five times to be large with only one outlier are included in the data analysis. As for the REBLUP method is relatively more stable RMSE results.
The purpose of this study was to determinate location of MBKM internship based on clusterring student's skills with K-Means. This is important to detect some of student’s skills which will become the output of the university that must be recorded early so that they were truly ready to compete. To analyze the skills of some students, the Mathematics Education study program at the University of Argopuro Jember conducted a survey of additional skills outside of lectures. This survey is carried out regularly every year as material for reporting on the development of students' skills and qualities. Thus the skills possessed by students can be monitored and evaluated whether in the future special skills were needed that must be given to students. The skills of students who become points in the survey include: 1)Foreign language skills, 2)IT Skills, 3)Public Speaking and Management Skills, 4)Analitical and Graphic Design Skills, 5)Microteaching Skills. We clustering 67 respondent, it is our student at Universitas Argopuro Jember in fiveth. Cause of outlier in 7 respondent, we just make clustering with K-means with 60 respondent. Based on K-Means clusterring we have 3 cluster .It shows that cluster 1 has 32 respondents, cluster 2 has 21 respondents and cluster 3 has 7 respondents. Abd also on the result and discussion, we knew that cluster 1 with 32 respondent has more skills, it were foreign language, public speaking skills and microteaching skills. So the internship locations that match these skills clusters such as LBB then Publication Offices such as Radio, Jawapos, etc. For second conclusion, we knew that cluster 2 with 21 respondent have more skills except for microteaching skills. Appropriate internship places for these students are in administrative offices, local government offices, etc.And for third conclusion, that cluster 3 with 7 respondent have more skills too except public speaking but they have a middle skills in microteaching. The office or internship location where we can suggest is Dinas Parawisata, Perbankan that needs good communication and good team work, etc
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.