KARUNIA EKA LESTARI
@unsika.ac.id
Mathematics Education Departement
Universitas Singaperbangsa Karawang
EDUCATION
2016-07-06 to 2021-02-16 | Doctor of Mathematics/Ph.D (Doctor of Mathematics), Institut Teknologi Bnadung, Indonesia
2011-07-09 to 2013-07-09 | Master of Education/M.Pd (Mathematics Education), Universitas Pendidikan Indonesia, Indonesia
2007-07-04 to 2011-07-07 | Bachelor of Mathematics Education/S.Pd. (Mathematics Education), Universitas Pendidikan Indonesia, Indonesia
RESEARCH INTERESTS
Mathematics Education
Education Statistics
Education Research Methodology
Correspondence Analysis
Categorical Data
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ramlah*, Agung, Agung Prasetyo, Dewi Siti, Karunia Eka, and Mokhammad Ridwan
Eurasian Society of Educational Research
<p style="text-align: justify;">Digital puzzle worksheet (DPW) is innovative teaching material designed using open-source software such as Canva and Liveworksheets. Subsequently, puzzle games in the form of questions can improve problem-solving skills by engaging in metacognitive processes. This research used a case study method to describe the impact of applying the DPW to identify the metacognition levels of students through the assignment of contextual maths problems. The source of informants was third-grade elementary school students in West Java, Indonesia. Test instruments, observation sheets, and interviews were used, while data analysis adopted an iterative model. Furthermore, the method and time triangulation increased confidence in the resulting conclusions. The results showed that male students were at the metacognitive level of ‘strategic use’ and ‘aware use’ for females, based on the characteristics of the observed metacognitive level. The most prominent feature was identifying and determining problem-solving strategies with metacognitive awareness. The reaction of students to the DPW improved problem-solving abilities, expanded conceptual understanding, and enhanced digital technology competence. Therefore, this experience was applied when solving contextual mathematical problem assignments.</p>
Karunia Eka Lestari, Marsah Rahmawati Utami, and Mokhammad Ridwan Yudhanegara
AIP Publishing
Karunia Eka Lestari, , Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara, , and
Modestum Publishing Ltd
In statistical inference, adaptive reasoning is defined as logical thinking to determine what can be inferred from data or statistical results and whether the justifications led to valid conclusions. Accordingly, adaptive reasoning is a mathematical proficiency required in statistical inference. This study aims to discover the association between adaptive reasoning and the initial statistical competence of undergraduate students. For this purpose, we performed mixed-methods research conducted by sequential exploratory analysis. This study involved 66 participants selected from undergraduate students in the Statistical Inference course offered by the mathematics education department at one university in Indonesia. The qualitative result describes the characteristics of students' adaptive reasoning proficiency at each grade. The proportion of students from grade 1 to grade 4 is 4.55%, 21.21%, 48.48%, and 25.76%, respectively. The quantitative result based on the chi-squared statistics test shows a significant association between adaptive reasoning proficiency and initial statistical competence. The correspondence analysis solution depicts that a high level of statistical competence is strongly associated with a high grade of adaptive reasoning proficiency, and conversely. Generally, the results provide evidence that the mastery of initial statistical competence is an important aspect in developing students' adaptive reasoning proficiency. The study provides some recommendations that will benefit the lecturer to develop adaptive reasoning proficiency in the Statistical Inference courses.
Karunia E. Lestari, Udjianna S. Pasaribu, Sapto W. Indratno, and Hanni Garminia
Elsevier BV
K E Lestari, U S Pasaribu, and S W Indratno
IOP Publishing
Abstract The anaysis of the association between the variables of a three-way table requires a very different approach to the conventional correspondence analysis techniques. The conventional technique involves by collapsing three-way table into two-way table form. By doing so, no information about three-way interaction among variables. To included interaction among three variables at once, we consider higher-order singular value decomposition (HOSVD) Tucker3. Through the HOSVD Tucker3 decomposition, we can derive marginal of row, column, annd tube categories. Using those marginals, we can easily visualize dependence of the three categories. As a case study, Tucker3 is applied to household data which containing information on race, educational attainment and employment status.
M R Yudhanegara and K E Lestari
IOP Publishing
Abstract This paper present two-step cluster method to handle data sets in educational data mining. It can handle multi-dimentional metric data points especially in complex data sets. For a case study, we use tracer study data in order to assign a clustering of alumni based on the profile. The result of clustering will help the school to evaluate and improve the quality of its graduates.
K E Lestari, U S Pasaribu, S W Indratno, and H Garminia
IOP Publishing
Abstract In this paper, we confined our attention to compare two methods to obtain a graphical depiction of the association (dependency) between three categorical variables. We shall first describe how to recode a three-way contingency table by discussing the Burt matrix form of the data. This method is known as multiple correspondence analysis (MCA). Another method is to preserve a three-way contingency table form using Tucker3, it’s known as a three-way correspondence analysis (CA3). As a case study, we pay attention to analyze the association between race and gender in occupation field that may have contributes to differences in employment opportunity and the continuing increases in women’s educational attainment. The results show that CA3 is more simple in computation and provide the graphical depiction of three-way association simultaneously, while MCA’s plot can’t. Consider to the cumulative inertia on the two-dimensional plot, the percentage inertia of CA3’s plot is better than MCA’s plot.
K E Lestari, U S Pasaribu, S W Indratno, and H Garminia
IOP Publishing
The important thing in vehicle engineering is how well the car protected a driver and passenger during the crash. In this case, there are many variables that might be associated with the car protection system. The association of these variables can be analyzed by multiple correspondence analysis via Burt matrix. By performing an eigen-decomposition on the transformed Burt matrix, we can determine the correspondence plot that visualize the association between variables in a reduced dimension space. While some researchers’ consideration on quantifying this association and visually depicting it, we interest to notice the reliability of the plot in representing the association. In this paper, we shall only consider the reliability by describing circle confidence regions for each point in a low-dimensional correspondence plot. The application on crash car protection data sets is given.