How does Rasch modeling reveal difficulty and suitability level the fraction test question?

This article explains how to analyze test items in arithmetic operation with fractions to obtain the items' level of difficulty and fitness. Data were collected by using multiple-choice questions given to 50 fourth-grade students of an elementary school in Tasikmalaya city. The answers were then analyzed using the Rasch model and Winsteps 3.75 application, a combination of standard deviation (SD) and logit mean values (Mean). The score data of each person and question were used to estimate the pure score in the logit scale, indicating the level of difficulty of the test items. The categories were difficult (logit value +1 SD); very difficult (0.0 logit +1 SD); easy (0.0 logit -1 SD); very easy (logit value –SD). Three criteria were used to determine the level of difficulty and fitness of the questions: the Outfit Z-Standard/ZSTD value; Outfit Mean Square/MNSQ; and Point Measure Correlation. It resulted in a collection of test items suitable for use with several levels of difficulties, namely, difficult, very difficult, easy, and very easy, from the previous items, which had difficult, medium, and easy categories. Rasch model can help categorize questions and students' ability levels.

Student’s mathematical connection ability through GeoGebra assisted project-based learning model

Several previous studies related to mathematical connection abilities and GeoGebra-assisted project-based learning models, but this research focuses on improving students' mathematical connection ability in the Integral Calculus course. This study examines the improvement of mathematical connection abilities through a project-based learning model assisted by GeoGebra. The research method used was a quasi-experimental design with a pretest-posttest nonequivalent multiple group design. The population is students of the Mathematics Education Study Program at the University in West Java, Indonesia. 1A and 1B students, which are used as samples. The technique of taking the research subject uses purposive sampling. The instrument consisted of a mathematical connection ability test with three essay questions. The test in this study used a pretest and posttest students' mathematical connection ability and the Group Embedded Figure Test (GEFT). The data analysis technique used an independent sample t-test and a two-way ANOVA test. The results showed that the improvement of students' mathematical connection ability who obtained the GeoGebra-assisted project-based learning model was better than students who obtained the project-based learning model. There is no interaction effect of learning models and cognitive styles on the achievement and improvement of students' mathematical connection abilities. The implication of this research is to provide significant changes in student learning habits in integral calculus courses to use technology and foster high self-regulated learning. This research has implications for universities implementing project-based learning models combined with other technology applications in other subjects.

Senior high school students’ understanding of mathematical inequality

Mathematics inequality is an essential concept that students should fully understand since it is required in mathematical modeling and linear programming. However, students tend to perceive the solution of the inequalities problem without considering what the solution of inequality means. This study aims to describe students’ mistakes variations in solving mathematical inequality. It is necessary since solving inequality is a necessity for students to solve everyday problems modeled in mathematics. Thirty-eight female and male students of 12th-grade who have studied inequalities are involved in this study. They are given three inequality problems which are designed to find out students’ mistakes related to the change of inequality sign, determine the solution, and involve absolute value. All student work documents were analyzed for errors and misconceptions that emerged and then categorized based on the type of error, namely errors in applying inequality rules, errors in algebraic operations, or errors in determining the solution set, then described. The result shows that there were some errors and misconceptions that students made caused by still bringing the concept of equality when solving the inequalities problem. It made them did not aware of the inequality sign. Students are still less thorough in operating algebra and do not understand the number line concept in solving inequalities. The other factor was giving “fast strategy” to the students without considering the students’ understanding.