PERBEDAAN HASIL BELAJAR MODEL KONVENSIONAL DAN MODEL PEMBELAJARAN GIVING QUESTION GETTING ANSWER SISWA MATA PELAJARAN AKIDAH AKHLAK KELAS X DI SMA AN-NUR BULULAWANG

The phenomenon in learning Akidah Akhlak teachers still use conventional learning with a lecture method where the teacher is only as a source of information so that students do play an pasife role in learning. With a learning model that is less effective, many students seem to be lazy, do not concern teachers in learning, many talk to their deskmates, and even in the classroom. Research type carried out is quantitative comparison with the design of " nonequivalent control Group Pretest-Posttest " using experimental groups with a control group and experiment group. The samples in this study were 24 students of class X IBB 3 and IPS 2with a non-probability sampling technique with purposive sampling type. Data analysis used was a paired sample test with the help of SPSS version 25. Based on the results of the study by conducting a Paired Sample Test with the provision that the sig value < 0.05, a sig value (2-tailed) of 0.000. So it can be concluded that a significant difference between the learning outcomes of the conventional model and the learning outcomes of the Giving Question Getting Answer learning model for class X students of Akidah Akhlak subjects at An-Nur Bululawang High School for the 2021/2022 academic year.Kata Kunci: Konvensional, Giving Question Getting Answer (GQGA), Hasil Belajar, Akidah Akhlak

RePOR: Mimicking humans on refactoring tasks. Are we there yet?

Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are `poor' solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers' performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code