ABSTRAKPenelitian ini memfokuskan pada analisis kemampuan representasi matematis mahasiswa dalam mata kuliah Geometri Transformasi berdasarkan latar belakang pendidikan menengah. Latar belakang pendidikan yang diamati dikelompokkan menjadi empat kelompok yaitu SMA-IPA, SMA-IPS, SMK, dan MA. Pendekatan yang digunakan yaitu pendekatan kuantitatif dengan metode expost facto dan desain kausal-komparatif. Sampel dalam penelitian ini adalah seluruh mahasiswa semester V tahun ajaran 2015-2016. Berdasarkan uji Kruskal-Walis H diperoleh nilai signifikansi untuk keempat kelompok adalah 0,168. Hal ini menunjukkan bahwa tidak terdapat perbedaan kemampuan representasi matematis berdasarkan mahasiswa pada mata kuliah Geometri Transformasi berdasarkan latar belakang pendidikan menengah.
Kata kunci: representasi matematis, latar Belakang pendidikan menengah.
ABSTRACT
This study focus on the student mathematical representation in Transformation Geometry
Penelitian ini memfokuskan pada analisis kemampuan representasi matematis mahasiswa dalam mata kuliah Geometri Transformasi berdasarkan latar belakang pendidikan menengah. Latar belakang pendidikan yang diamati dikelompokkan menjadi empat kelompok yaitu SMA-IPA, SMA-IPS, SMK, dan MA. Pendekatan yang digunakan yaitu pendekatan kuantitatif dengan metode expost facto dan desain kausal-komparatif. Sampel dalam penelitian ini adalah seluruh mahasiswa semester V tahun ajaran 2015-2016. Berdasarkan uji Kruskal-Walis H diperoleh nilai signifikansi untuk keempat kelompok adalah 0,168. Hal ini menunjukkan bahwa tidak terdapat perbedaan kemampuan representasi matematis berdasarkan mahasiswa pada mata kuliah Geometri Transformasi berdasarkan latar belakang pendidikan menengah. Kata kunci: representasi matematis, latar Belakang pendidikan menengah.
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 mixedmethods 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.
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.
Online activity increasing spreads with the power of technological development. Many studies reported the impact of online activities on decision making. From the statistical perspective, decision making is related to statistical inference. In this regard, it is interesting to propose a new method of statistical inference for online decisions. This method is built by the logarithm distribution of the likelihood function, which allows us to determine statistics using the normal statistical test approach iteratively. It means that the inference can be made in an online way every time new data arrive. Compared to classical methods (commonly, chi-squared), the advantage of this method is that it allows us to make decisions without storing large data. In particular, the novelty of this research is expressed in the algorithm, theorem, and corollary for the statistical inference procedure. In detail, this paper’s simulation discusses online statistical tests for multinomial cases and applies them to transportation data for item delivery, namely traffic density. Changes in traffic density resulted in changes to the strategy of item delivery. The goal is to obtain a minimum delivery time for the risk of losses.
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