Education is one way to develop thinking skills. The aim of implementing education is to produce quality human resources. So that the development of science and technology can develop in a better direction as technology advances. Quality learning will encourage students to have better cognitive abilities. Experiential learning implementation is a learning process that prioritizes experience during the learning process. The purpose of this study was to determine the effects of experiential learning on students’ statistical reasoning abilities. The design of this study was one sample group with purposive sampling technique. The subjects of this study were students who contracted industrial statistics courses. Statistical test results prove that experiential learning provides a positive contribution to students’ statistical reasoning abilities.

This study aims to examine the ability of mathematical representation of industrial engineering students through cognitive apperticeship learning. This research used Quasi-Experimental method by using random group, so it involved two randomly selected group samples, ie control samples using expository learning and experimental samples using cognitive apperticeship learning. The research instrument used is the ability test of mathematical representation. This research was conducted on the material of industry statistics II with the number of control class students 22 students and the experimental class as many as 22 students. The results showed that the ability of mathematical representation of students who received cognitive apperticeship learning better than students who memproleh expository learning.

Statistical Reasoning is the ability to understand information in daily life based on statistical data. This study aims to determine the ability of student statistical reasoning based on 5 levels of statistical reasoning models. The research subjects are two classes of first semester students majoring in Communication Sciences. The data collected is in the form of statistical reasoning test result. The data are categorized into five namely Level 1 (Idiosyncratic reasoning), level 2 (Verbal reasoning), level 3 (Transitional reasoning), level 4 (Procedural reasoning) and level 5 (Integrated process reasoning). Next, look at the percentage of student statistical reasoning included in the level 1, level 2, level 3, level 4 and level 5 categories. The results of data analysis showed that students level 3 with statistical reasoning were far more numerous than students’ statistical reasoning abilities categorized as than on the other.

The purpose of this study was to test the effectiveness of mathematics learning using the 5E learning cycle model and the 7E learning cycle model; as well as distinguishing more effective learning between the learning cycle models 5E and 7E in terms of the self-efficacy of class VIII students in learning to build flat side space. This research is a quasi-experimental study with a pretest-posttest non-equivalent comparison-group design. The study population included all eighth grade students of state junior high schools in Yogyakarta City consisting of high and medium level schools. With stratified random sampling technique, 2 schools were selected as research samples. The research instrument used student self-efficacy questionnaires. To test the effectiveness of the LC 5E model and the LC 7E model, whether there is an interaction between the learning model and the school level, as well as whether there is a difference in student self-efficacy in high school and medium school, the two way ANOVA test is used. Furthermore, to compare the self-efficacy of students who use the LC 5E model and the LC 7E model in high school and high school level students are using an independent sample t test, while to test whether there is a difference in effectiveness between the LC 5E model and LC 7E model against self-efficacy students used the N-Gain effectiveness formula. Each analysis was carried out at a significance level of 5%. The results showed that: 1) there was no difference in self-efficacy between students whose learning used the LC 5E model and students whose learning used the LC 7E model; 2) the self-efficacy of students who use the LC 5E model is not higher compared to those who use the LC 7E model in high and medium level schools; 3) there is no interaction between the learning cycle model with the school level on learning to build flat side space; 4) there is no difference in self-efficacy between students in high schools and students in medium schools; and 5) there is no difference in effectiveness between LC 5E and LC 7E models on student self-efficacy.