Higher order thinking skill (HOTS) is one of the students’ abilities that should be developed through teaching and learning. Teachers’ knowledge about HOTS and its teaching and learning tactics is a key to successful education. The purpose of this research is to describe teachers’ knowledge about higher order thinking skills (HOTS). The research involves qualitative study with the phenomenological approach. The research participants are 27 mathematics teachers from state and private junior high schools across 7 provinces in Indonesia. The researcher collected data with a test followed by focus group discussion (FGD) and interviews. The analysis of data involved Bogdan & Biklen model and descriptive statistics for data from the test. The analysis of FGD, and test data intends to get information on 6 sub-themes; teachers’ knowledge about HOTS, importance of HOTS, teaching about HOTS to students, improving students’ HOTS, measuring and assessing HOTS, and teachers’ ability for solving HOTS-based problems. The results indicate that teachers’ knowledge about HOTS, their ability to improve students’ HOTS, solve HOTS-based problems, and measure students' HOTS is still low. There are facts, however, that teachers already understand the importance of HOTS and teaching it by using various innovative learning models. Keywords: HOTS, measurement and assessment, teachers’ knowledge, teaching and learning

This research aims at knowing the factors, both at students and school levels, related to the math learning achievement for students in Indonesia, Japan, and Algeria by using PISA 2015 data. The sample in this study consists of students from three countries that took part in PISA 2015. The three countries chosen are Indonesia, Japan, and Algeria, each respectively having as participants 5. 800, 6.411, and 4.460. The findings showed that the sense of belonging of students towards mathematics, the socio-economic status of their families, and the average of schools' social-economic status can predict significantly the students' math learning achievement for the Indonesia and Japan, while for the Algerian students the socio-economic status is statistically insignificant in predicting their learning performance. The outcomes of this analysis support the idea that the school attended plays a big role as far as mathematics learning achievement is concerned. So, it should be summed up that the affective characteristics (sense of belonging of students), family background (students' socioeconomic status), and the variable school-level (average socio-economic status of schools) can explain the big portion of variance among students as far as mathematics learning achievement is concerned.

Penelitian ini bertujuan untuk mendeksripsikan kualitas butir soal ujian akhir semester mata kuliah statistika ekonomi yang dikembangkan oleh Universitas Terbuka (UT) sebagai dasar dalam mengembangkan bank soal yang terkalibrasi menggunakan pendekatan Teori Respons Butir. Penelitian ini merupakan penelitian deskriptif kuantitatif. Sumber data penelitian ini adalah pola jawaban mahasiswa UT yang telah mengikuti ujian akhir semester (UAS) mata kuliah statistika ekonomi selama enam masa ujian, dengan ukuran sampel sebanyak 23334 mahasiswa. Hasil penelitian ini menunjukkan bahwa butir-butir soal ujian akhir semester mata kuliah statistika ekonomi yang dikembangkan UT: (1) terbukti valid secara konstruk, yakni hanya mengukur satu faktor dominan, yaitu kemampuan statistika ekonomi; (2) memiliki kehandalan yang baik dengan nilai koefisien reliabilitas empiris lebih dari 0,70 (koefisien reliabilitas empiris = 0,7335); (3) dari 140 butir soal yang dikalibrasi terdapat 108 butir soal (25 butir soal berkualitas baik atau tanpa revisi dan 83 butir soal berkualitas kurang baik atau perlu revisi) yang layak disimpan dalam bank soal, sedangkan 32 butir soal berkualitas tidak baik; dan (4) mampu memberikan informasi akurat terkait kemampuan statistika ekonomi mahasiswa pada level kemampuan yang tinggi (-1,3 sampai +4,0). Quality of statistical test bank items (Case study: Final exam instrument of statistics courses in Universitas Terbuka) AbstractThis study aims to determine the quality of final semester test items of economic statistics course that was developed by Universitas Terbuka (UT) as a basis for developing calibrated item banks using Item Response Theory. This research uses a quantitative descriptive approach. The researcher investigates the answer pattern of the final semester exam (UAS) in the economic statistics course during six periods of the final exams. The sample size in this study was 23334 students. The results of this study indicate that the final semester exam items of economic statistics courses developed by UT: (1) proved to construct valid, i.e. only measure one dominant factor, namely the ability of economic statistics; (2) has good reliability with empirical reliability coefficient values more than 0.70 (empirical reliability coefficient = 0.7335); (3) of the 140 items calibrated there are 108 items (25 items of good quality or without revision and 83 items of poor quality or need to be revised) that are worth keeping in the question bank, while 32 items of quality are not good; and (4) able to provide accurate information related to students' economic statistical abilities at a high level of ability (-1.3 to +4.0)

The goal of the research is to gain insights into the characteristics of the items in the mathematics national examination, the attributes on which the items were formulated and the result of a conceptual error diagnosis of the mathematics materials based on the result of the junior high school mathematics national examination. This is quantitative descriptive research. The data were collected from 3,079 grade-nine students of junior high schools who took the National Examination in the academic year of 2015/2016. The sample was established randomly based on the package code of the examination which is P0C5520 with 574 students as the examinees. Documentation method was applied in collecting the data. The result of the research shows that -upon the implementation of the classical test theory -there are 16 items in 'difficult' category, 24 in 'intermediate' category, and no items in 'easy' category. Furthermore, upon the implementtation of the item response theory, the result shows that 28 items are in 'good' category and 12 items are in 'poor' category. In addition, there are 50 attributes on which the Junior High School Mathematics National Examination test (package P0C520) is formulated. Four attributes are content attributes and the rest (46) are process skill attributes. The result of the diagnosis shows that there are 11 types of errors made by the students when trying to complete the content items. Most of the errors are conceptual errors related to the geometric materials especially in the submaterials of polyhedron, triangles, and quadrangles.

This study aims to determine the prediction model of the graduation status of prospective teacher students at IAIN Bone in terms of attributes, accuracy levels, and differences in the level of accuracy produced in the attributes of decision tree C4.5, Naïve Bayes, and k-NN data mining algorithms. This research uses a quantitative approach by adopting the Data Mining method. This research was conducted at IAIN Bone. The data collection process in this study used documentation techniques in the form of data on alumni of the Tarbiyah Faculty of IAIN Bone. The data analysis used was a descriptive analysis using decision tree C4.5, Naive Bayes, and k-NN data mining algorithms assisted by the RapidMiner application.  The results of this study show that (1) model prediction of the graduation status of prospective teacher students in IAIN Bone in terms of attributes generated in the Decision Tree C4.5 and Naïve Bayes data mining algorithms  consist of gender, age, Semester 1 IP, Semester 2 IP, Semester 3 IP, Semester 4 IP, and GPA, while the attributes produced in   k-NN data mining algorithm  consists of gender, regional origin, number of siblings, age, IP Semester 1, IP Semester 2, IP Semester 3, IP Semester 4, and GPA; (2) model prediction of graduation status of iain bone teacher candidate students in terms of the accuracy rate generated in the Decision Tree C4.5 data mining algorithm  of 93.90%, Naïve Bayes by 90.24%, and k-NN of 92.07%; and (3) there was no significant difference between the accuracy rate produced by decision tree's data mining algorithm.  C4.5 and Naïve Bayes (p-value = 1.00); Decision Tree C4.5 and k-NN (p-value = 1.00); as well as Naïve Bayes and k-NN (p-value = 1.00) in predicting the graduation status of iain bone teacher candidate students.